1

CURRENT TRENDS IN COMPUTER AIDED PROCESS PLANNING

Momin Zia1, Prof.Ashok Patole

2

1. SEM II, M.E (CAD/CAM and Robotics),PIIT,New Panvel.

2. Assistant Professor,Department of Mechanical Engineeering, PIIT,New Panvel.

Email- 1: mominzia80@yahoo.com

2. ashok.patole12000@gmail.com

ABSTRACT:

During the recent years, Computer Aided Process Planning (CAPP) evolved as one of

the most important engineering tools in industries. The current trends in CAPP tend to

eliminate human involvement between design and process planning. The paper

discusses the basics of CAPP and presents a comprehensive overview of the current

trends in CAPP, classifying them into several categories according to their focus. It also

discusses the flow chart of process planning in traditional CAD environment and

workflow in simulation based process planning, taking die design as an example.

Keywords: CAPP, CAD, MIPLAN, MICLASS.

1. INTRODUCTION

Process Planning is concerned with determining the sequence of individual machining

operations needed to produce a given part or product [1].This has traditionally been carried

out as tasks with a very high manual and clerical content. It is the task of industrial engineers

to write these process plans for new part design to be produced by the shop. The process

planning is very much dependent on the experience and judgment of the planner.

Accordingly, there are differences among operation sequences developed by various

planners. In one case, a total of 42 routings were developed for various sizes of a relatively

simple part [1].There are various other difficulties in traditional process planning procedure.

New machine tools in the factory render old routings less than optimal. These difficulties

have been overcome by employing Computer Aided Process Planning which is a result of

attempts made to capture the logic, judgment, and experiences required for this important

function and incorporate them into a computer program. Investigation shows that an efficient

CAPP system can result in a total reduction of the manufacturing cost by 30% and

manufacturing cycle time by 50% [2].

2. BASIC CAPP SYSTEMS

Two alternative approaches to CAPP have been developed [1]. These are

1. Variant Systems (Retrieval-Type Process Planning Systems)

2. Generative Process Planning systems

2

Figure1. Information Flow in Variant Process Planning System

2.1. Variant Systems (Retrieval-Type Process Planning Systems)

It follows the principle that similar parts require similar plans. Therefore, the process requires

a human operator to classify a part, input part information, retrieve a similar process plan

from a database (which contains the previous process plans), and edit the plan to produce a

new variation of the pre-existing process plan. Planning for a new part involves retrieving of

an existing plan and modification. In some variant systems parts are grouped into a number of

part families, characterized by similarities in manufacturing methods and thus related to

group technology. In comparison to manual process planning, the variant approach is highly

advantageous in increasing the information management capabilities. Consequently,

complicated activities and decisions require less time and labor. Also procedures can be

standardized by incorporating a planner’s manufacturing knowledge and structuring it to a

company’s specific needs. Therefore, variant systems can organize and store completed plans

and manufacturing knowledge from which process plans can be quickly evaluated. However,

there are difficulties in maintaining consistency in editing practices and adequately inability

to accommodate various combinations of geometry, size, precision, material, quality and

shop loading. The biggest disadvantage is that the quality of process plan still depends on the

knowledge background of a process planner. MIPLAN is one of Variant Process Planning

System used to generate Rout Sheet.

2.2 Generative Process Planning (GPP)

Generates process plans utilizing decision logic, formulae, manufacturing rules, geometry

based data to determine the processes required to convert the raw materials into finished

parts. It develops new plan for each part based on input about the part’s features and

3

attributes. Due to the complexity of this approach a generative CAPP system is more difficult

to design and implement than a system based on the variant approach. But a generative CAPP

system does not require the aid of a human planner, and can produce plans not belonging to

an existing part family. It stores the rules of manufacturing and the equipment capabilities in

a computer system. The generative approach is complex and a generative system is difficult

to develop. In comparison, the variant systems are better developed and mature than

generative systems; they are suitable for planning processes in mass or large production

volumes. For planning discrete processes of manufacturing products of great diversity,

generative systems are much more suitable than variant systems. However true generative

systems are still to come although the earlier optimistic speculation made by researchers.

Most CAPP systems in use now are either variant systems or semi-generative systems (with

some planning functions developed with variant approach, others with generative approach).

Proper combination of the two approaches can make an efficient CAPP system. First the

system will check whether the process planning is possible for a new part by variant

approach. If variant system is unable to identify the part to be of a previous group or family it

will use generative technique for process planning. So both the variant and generative process

planning approaches need further development. GENPLAN is one of the Generative Process

Planning System used to generate Rout Sheet.

3. SOME NEW APPROACHES

In the last two decades huge research work is performed in different research areas in CAPP.

These works can be categorized by the types of part involved in these works, like prismatic

part, cylindrical parts, and sheet metal, foundry and assembly systems. Besides this broad

classification, research works can also be categorized on the basis of geometric modeling

techniques. Some new ideas are presented here briefly.

Feature-based and Solid Model based Process Planning

Solid Model-based process planning uses solid modeling package to design a 3D part. In

feature-based process planning systems a part is designed with design oriented manufacturing

feature or a feature extraction/feature recognition system is used to identify part feature and

their attributes from the CAD file.

Nasser, El-Gayar and others [2] presented a prototype solid model based automated process

planning system for integrating CAD and CAM system. In this system a three dimensional

(3D) finished part is built by using a solid modeling package. The primitive (Cylinder, cone,

block, wedge, sphere and torus) is used to define the removal volume. This system consists of

three major sections: CAD interface, production knowledge, and process planning. The CAD

interface includes the finished part drawing. The finished part is built by using the AutoCAD

Advanced Modeling Extension (AME) module in a PC. With the AME module, the user can

create complex 3D parts and assemblies by using Boolean operations to combine simple

shapes. The production knowledge is placed before the process planning procedure, where it

accommodates the essential knowledge. It contains information about the machine tools,

tools, materials in stock, cutting parameters, and so forth. The process plan is then generated

based on recognized solid primitives and production rules.

In the prototype Feature Based Automated Process Planning (FBAPP) system features are

recognized from the removable volume point of view rather than from the design part point

of view. The entire process in FBAPP is naturally closer to the thinking of a human process

planner. A feature based approach for cylindrical surface machining process is developed by

Yong et. al [2]. The process involves the following steps: (1) Recognizing form features from

the part geometry, (2) converting the form feature into machining volumes (negative features)

4

suitable for turning and milling machining (3) combine them into alternative machining

volumes (4) associating machining process classes to each machining volume, and (5)

generate precedence relations between these volumes. Then the output is used by a process

planning system where process sequences are determined and the assignment to multiple

spindles and turrets is made.

For machining various types of pockets efficiently, it is necessary to decompose bulky

features of sculpture pockets into thin features. Joo, Cho, and Yun[2] designed a feature

based process planning for sculptured pocket machining. First the bulky feature of sculptured

shape of pocket is segmented into several thin layers and the temporal precedence of the

segmented features is constructed; then variable cutting condition is applied to each smaller

feature. They found that, if the sculpture shape of pocket is segmented horizontally and

vertically and apply variable cutting condition to each feature machining becomes easier.

Interactive and feature blackboard based CAPP is a new approach that complies with the

traditional process planning [2]. Human process planner gets familiar with the system very

quickly. Plans can be manually edited or completed by knowledge base systems. The

architecture of Black board system can be seen as a number of people sitting in front of a

blackboard. These people are independent specialists, working together to solve a problem,

using the blackboard for developing the solution. Problem solving begins when the problem

and initial data are written on the blackboard, looking for an opportunity to apply their

expertise to develop the solution. When a specialist finds sufficient information to make a

contribution, he records the information on the blackboard, solves a part of the problem and

makes new information available for other experts. This process continues until the problem

has been solved.

Currently many researches are conducted for the application of object-oriented approach to

different research problems [2]. Object oriented process planning is a logical means for

representing the real world components within a manufacturing system. The developer

identifies a set of system objects from the problem domain and expresses the operation of the

system as an interaction between these objects. The behavior of an object is defined by what

an object is capable of doing. The use of Object Oriented Design or Object Oriented

Programming for developing of a process planning system provides the tool for addressing

the complexity of process planning and the capability to incrementally and functionality as

the system matures, thereby permitting the developer creating a complete manufacturing

planning system. Object oriented systems are more flexible in terms of making changes and

handling the evolution of the system over time. This technique is an efficient means for the

representation of the planning knowledge and a means of organizing and encapsulating the

functionality of the system with the data it manipulates. This modularity results in a design

that can be extended to include additional functionality and address other processes. The

design also expands on traditional piece part planning by extending the part model to support

planning for end products composed of multiple parts and subassemblies.

4. LINKS BETWEEN CAD AND CAPP

Creating link between CAD and CAPP is one of the most difficult tasks in concurrent design

and manufacturing. Without proper interface between CAD and CAPP it is impossible to

generate a process plan, which will need least amount of time and cost. Feature recognition or

feature extraction is the key to achieve this objective. In mechanical assembly or machining

processes a feature is, usually, defined as a set of constituent faces. The geometric

information related to a feature is obviously subset of object. In addition to the geometric

information, some non-geometric information associated with a feature is also essential for

process planning. Feature extraction is categorized into three classes.

5

a) Graph/pattern matching,

b) Knowledge based system and

c) Geometric decomposition.

In graph/pattern matching search technique is used to find the primitives like faces and edges

in a part design. From these primitives the graph of the geometric shapes is created. Then the

graph is used to identify the features of the part. In the knowledge based feature extraction

technique expert system rules and techniques are used to extract features from the 3D solid

model using the internal boundary representation of the designed part.

Geometric decomposition includes cell decomposition, convex hull and constructive solid

geometry tree rearrangement. Yong [2] shows how decomposition technique can be used for

feature recognition. In his work central Form Feature Decomposition (FFD) is obtained from

its boundary geometry by applying convex decomposition, called Alternate Sum of Volumes

with Partitioning (ASVP) which uses convex hull and set difference operations. This feature

recognition method has important advantages to support automated process planning. Fore

mostly, interacting features are properly recognized. In addition, outside-in hierarchical

relations, face dependency, and accessibility information of features are obtained. The

extreme ability-based, outside-in, geometric hierarchy of the boundary faces of a part is

intrinsically important for both material removal oriented part manufacturing process and

additive processes such as deposition and assembly operations.

Kakino [2] was the first to develop a part description method on the basis of fundamental

concept of converting the drawing information into computer-oriented information for the

data structure. The part shape was described by using algebraic construction rules and

operation rules in set theory performed on the volumetric element formed by the revolving or

parallel movement of the reference surface. Based on Kakino’s work Jakubowski used a

syntactic manner to describe 2D profile information on 2D machined parts. He applied

extended context-free grammars to describe machined parts families and gave a detailed

explanation of techniques for parts construction. Chof [2] also outlined the use of syntactic

pattern recognition in identifying elementary machine surfaces for process planning in

machining centers.

5. PROCESS PLANING AND DIE DESIGN IN CONVENTIONAL CAD

ENIRONMENT

Stamping industry applies CAD techniques both in the process planning and die design

already for many years [3]. However, in a ‘traditional” CAD environment, these are

practically stand-alone solutions, i.e. for example a knowledge based process planning

solution is applied for the determination of the necessary types of forming processes, even in

some cases, the forming sequences can be determined in this way together with the

appropriate process parameters, too. After determining the process sequences and process

parameters, the forming dies are designed using sophisticated CAD systems, however, still

we do not have any evidence whether the designed tools will provide the components with

the prescribed properties. Therefore, before it goes to the production line, usually a time- and

cost consuming try-out phase follows, as it is shown in Figure2.

If the try-out is successful, i.e. the die produces parts with no stamping defects, it will be sent

to the stamping plant for production. On the other hand, if splitting or wrinkling occur during

the tryout, the die set needs to be reworked. It means that we have to return first to rework the

die construction by changing the critical die parameters (e.g. die radii, drawing gap, etc.). If it

6

does not solve the problem, a new die design, or a new process planning is required. Some

cases, we have to go back even to the product design stage to modify the product parameters.

The more we go back the higher the development and design costs are. Occasionally, the die

set is scraped and a perfectly new product-, process- and die design is needed. As a result, die

manufacturing time is increased as well as the cost of die making.

Figure2.Flow chart of process planning and die design in traditional CAD Environment.

6. SIMULATION BASED PROCESS PLANNING AND DIE DESIGN

Due to the global competition – and this is particularly valid for the automotive industry –

there is an overall demand to improve the efficiency in both the process planning and in the

die design phase, as well as to reduce the time and product development costs and to shorten

the lead times. It requires the efficient use of simulation techniques from the earliest stage of

product development, to give feedback from each step to make the necessary corrections and

improvement when it takes the least cost [3]. This principle is illustrated in the schematic

flow chart of simulation based process planning and die design as shown in Figure3.

With this approach, stamping defects may be minimized and even eliminated before the real

die construction stage. If any correction or redesign is needed, it can be done immediately,

with a very short feedback time, thus it leads to a much smoother die try-out, if necessary at

all and to significantly shorter lead times with less development costs. However, even with

this approach, there are some further shortfalls in the die design process, since most of the

simulation programs do not provide die construction in sufficient details, which can be easily

used in most of the CAD systems to complete the die design task. This shortage may be

overcome by integrating the CAD and FEM systems through a special interface model which

can provide a smooth, continuous and reliable data exchange between the two important parts

of design process.

Figure 3.Flow Chart of Process Planning and Die Design in Simulation Based System

6. CONCLUSION

Many CAPP systems have so far been developed and commercialized. New systems adopt

many advanced techniques and approaches such as feature-based modeling, object oriented

7

programming, effective graphical user interfaces and technological databases. But the

implementation of CAPP systems in industry lags behind the rate of development of new

systems and introduction of new ideas in the field [2].

Though tremendous effort has been made in developing CAPP systems, the effectiveness of

these systems is not fully satisfactory. CAPP as the main element in the integration of design

and production has not kept pace with the development of CAD and CAM. This situation has

made process planning a bottleneck in the manufacturing process. In spite of the benefit

promised by the various developed CAPP systems, their adaptation by industry is painfully

slow. Design of a part is generally done in the CAD environment. So it is necessary to create

link between CAD and CAPP where a two-way interaction will exist between design and

process planning. It is no longer sufficient to ensure an effective flow of information from

design to process planning to provide the data and knowledge necessary for creating an

effective process plan. It is also becoming increasingly essential to feedback information

from process planning to assist the designer at an early stage in assigning various design

features not only from functional point of view but also regarding manufacturability because

a large percentage of product cost is committed once its features, materials, tolerance and

surface quality parameters have been selected at the design stage. Dynamic process planning

which is one of the key areas for research and development, will integrate design and

manufacturing and reduce the total product development time by facilitating two-way

interaction between design and process planning.

7. REFERENCES

1. Mikell P.Groover,Emory W.Zimmers,Jr. “CAD/CAM”,1984.

2. Nafis Ahmad,Dr.A.F.M.Anwarul Haque,Dr.A.A.Hasin. “Current Trend in Computer

Aided Process Planning”, Proceedings of the 7th Annual Paper Meet, Paper No:10, Pages 81-

92, 2001.

3. M.Tisza. “Recent Achievements in Computer Aided Process Planning and Numerical

Modelling of Sheet Metal Forming Processes”,AMME,Volume24, Issue 1, 2007.

4. Chris McMohan, Jimmie Browne, “CAD/CAM”,Second Edition,Pearson Education

Ltd.,2006.

5. Ibrahim Zeid, “CAD/CAM, Theory and Practice”, Tata McGraw Hill Publishing Company

Ltd, 1998.

8

A GENERIC FRAMEWORK AND WORKCELL OF AGILE

MANUFACTURING

A.L.Godase a, A.S.Patole

b

a II sem (ME-CAD CAM and Robotics -PIIT New Panvel) bAssistant Professor (Department of Mechanical Engineering) PIIT New Panvel

a b Mumbai University

E- mail Address a atul_godase@yahoo.co.in , b ashok.patole12000@gmail.com

ABSTRACT: This review paper outlines

the concept of Agile Manufacturing. A

definition is provided along with detailed

description of basic concepts. A number of

key issues and key elements in this new

area are also explained with the help of

Case Study of an aerospace Industry.

Design of Work cell for agile

manufacturing is discussed.

Keywords- Agile Manufacturing,

Flexibility, Latest trends in Manufacturing

Industries, Productivity Lean, Customer

satisfaction

INTRODUCTION

Agility is defined in dictionaries as quick

moving, nimble and active. This is clearly

not the same as flexibility which implies

adaptability and versatility. Agility and

flexibility are therefore different things.

Leanness (as in lean manufacturing) is also a

different concept to agility. Sometimes the

terms lean and agile are used

interchangeably, but this is not appropriate.

The term lean is used because lean

manufacturing is concerned with doing

everything with less. In other words, the

excess of wasteful activities, unnecessary

inventory, long lead times, etc are cut away

through the application of just-in-time

manufacturing, concurrent engineering,

overhead cost reduction, improved supplier

and customer relationships, total quality

management, etc.

Thus agility is not the same as

flexibility, leanness or CIM. Understanding

this point is very important. But if agility is

none of these things, then what is it? This is a

good question, and not one easily answered.

Yet most of us would recognize agility if we

saw it.

For example, we would not say that a

Sumo wrestler was agile. Nor would we

think that 50 Sumo wrestlers, tied together by

a complex web of chains and ropes, all

pulling in different directions, as agile. It’s

quite the contrary. We would see them as

lumbering, slow and unresponsive. However,

we would all recognize a ballet dancer as

agile. We would also think of a stage full of

ballet dancers as agile, because what binds

them together is something quite different.

This analogy between Sumo wrestlers and

ballet dancers is very relevant to

understanding the property of agility. Many

of our corporations, to varying degrees,

resemble Sumo wrestlers, tied together, but

all pulling in different directions. If we want

to develop agile properties, we need to

understand what causes agility and what

hinders agility. Only when we have

developed this understanding can we begin to

think about designing an agile enterprise.

For, when we have such an understanding of

the causes of agility, we can start to audit our

current situation, and identify what needs to

be changed.

1.0 WHAT IS AGILE MANUFACTURING?

It is the capability of surviving and

prospering in a competitive environment of

continuous and unpredictable change by

9

reacting quickly and effectively to changing

markets, driven by customer-defined

products and services.

Agile manufacturing is a method for

manufacturing which combine our

organization, people and technology into an

integrated and coordinated whole.

1.1 WHY DO WE NEED TO BE AGILE?

Global Competition is intensifying.

Mass markets are fragmenting into niche

markets. Cooperation among companies

is becoming necessary, including

companies who are in direct competition

with each other. Customers are

expecting: Low volume products, High

quality products, Custom products, Very

short product life-cycles, development

time, and production lead times are

required. Customers want to be treated as

individuals

Real world example:

The Industry: Japanese car makers

The goal: To produce the three day car,

(three days from customer order for a

customized car to dealer delivery)

1.2 SCOPE OF AGILE

MANUFACTURING

Manufacturing industry is on the

verge of a major paradigm shift. This

shift is likely to take us away from mass

production, way beyond lean

manufacturing, into a world of Agile

Manufacturing.

Agile Manufacturing, however, is a

relatively new term, one which was first

introduced with the publication of the

Iacocca Institute report 21st Century.

Manufacturing Enterprise Strategy, 1991.

Furthermore, at this point in time,

Agile Manufacturing is not well

understood and the conceptual aspects are

still being defined. However, there is a

tendency to view Agile Manufacturing as

another program of the month, and to use

the term Agile Manufacturing as just

another way of describing lean

production or CIM.Agile Manufacturing

is something that many of our

corporations have yet to fully

comprehend, never mind implement.

Agile Manufacturing is likely to be the

way business will be conducted in the

next century. It is not yet a reality. Our

challenge is to make it a reality, first by

more fully defining the conceptual

aspects, and secondly by venturing into

the frontier of implementation

2.0 SOME KEY ISSUES IN AGILE

MANUFACTURING

1) The "I am a Horse" Syndrome

There is an old saying that hanging a

sign on a cow that says "I am a horse"

does not make it a horse. There is a real

danger that Agile Manufacturing will fall

prey to the unfortunate tendency in

manufacturing circles to follow fashion

and to re-label everything with a new

fashionable label.

The dangers in this are twofold.

First, it will give Agile Manufacturing a

bad reputation. Second, instead of getting

to grips with the profound implications

and issues raised by Agile

Manufacturing, management will only

acquire a superficial understanding,

which leaves them vulnerable to those

competitors that take Agile

Manufacturing seriously. Of course this

is good news for the competitors!

2) The Existing Culture of

Manufacturing

One of the important things that is

likely to hold us back from making a

quantum leap forward and exploring this

new frontier of Agile Manufacturing, is

the baggage of our traditions,

conventions and our accepted values and

beliefs. A key success factor is, without

any doubt, the ability to master both the

soft and hard issues in change

management.

However, if we are to achieve agility

in our manufacturing enterprises, we

should first try to fully understand the

nature of our existing cultures, values,

10

and traditions. We need to achieve this

understanding, because we need to begin

to recognize and come to terms with the

fact that much of what we have taken for

granted, probably no longer applies in the

world of Agile Manufacturing.

Achieving this understanding is the

first step in facing up to the pain of

consigning our existing culture to the

garbage can of historically redundant

ideas.

3) Understanding Agility

Agility is defined in dictionaries as

quick moving, nimble and active. This is

clearly not the same as flexibility which

implies adaptability and versatility.

Agility and flexibility are therefore

different things.

Leanness (as in lean manufacturing)

is also a different concept to agility.

Sometimes the terms lean and agile are

used interchangeably, but this is not

appropriate. The term lean is used

because lean manufacturing is concerned

with doing everything with less. In other

words, the excess of wasteful activities,

unnecessary inventory, long lead times,

etc are cut away through the application

of just-in-time manufacturing, concurrent

engineering, overhead cost reduction,

improved supplier and customer

relationships, total quality management,

etc.

We can also consider CIM in the

same light. When we link computers

across applications, across functions and

across enterprises we do not achieve

agility. We might achieve a necessary

condition for agility, that is, rapid

communications and the exchange and

reuse use of data, but we do not achieve

agility.

2.1 KEY ELEMENTS OF AGILITY

Enriching the customer,

Co-operating to enhance competitiveness,

Mastering change and uncertainty,

Leveraging people and information

3.0 CASE STUDY 1:

GEC-MARCONI AEROSPACE LTD

(UK)

GECMAe, is a part of the multi-

national group General Electric Company

Occupying a 25-acre site with 351,000 ft2

of factory and office space, employs

about 700 people, and has an annual

turnover in excess of £ 80 million.

GECMAe is an international market

leader in the design and production of a

wide range of critical systems needed to

maximize the performance, integrity and

safety of the current and next generation

aircraft and air/land systems.

It has been active for over 50 years

supplying

Systems for civil and military aircraft

and for A

Review of the product life cycles

revealed that some of the GECMAe

products are within the mature stage and

on the order books for the next 10 years!

GECMAe, however, does not rely on the

safety and security of these orders and

remain stagnant. In addition to investing

extensively in the skills of its people,

GECMAe takes full advantage of the

knowledge and experience available

within the GEC group of companies.

FIG 1 PRODUCT GROUPS

Project plans are regularly reviewed by

external specialists

Furthermore, major investment in the

latest high speed machining centers

provides unattended running and

improved quality, while reducing

processing times by up to 60% such that

11

some of the more complex products can

now be machined in ‘one hit’.

3.1 DATA COLLECTION AND

ANALYSIS

The following data have been

collected by interviewing members of the

GECMAe Change Team using the

Agility Audit Questionnaire.

Using a predetermined scale (see

Tables 1–4), the scores have been

calculated and summarized in each table

as a percentage of the total maximum

possible score, reflecting an actual and a

suggested agility index.

The results are given in Tables 1–4,

where the areas that require improving

with respect to agility are denoted by

(improvement) arrows.

3.2 RECOMMENDATIONS

It is observed that ‘late delivery’ is a

crucial problem area that needs

improving. A move towards a group

(cellular) technology layout would

improve these characteristics

dramatically

On the other hand, it should be noted

that, group technology has a little less

product flexibility than process layout,

but this does not affect the products that

are already well established.

Process layout could still be kept,

dedicated to the new products that require

more flexibility, and hence, facilitate

agility in the area of new build

production.

12

13

FIG 2 GENERIC FRAMEWORK OF AGILE MANUFACTURING

14

5.0 CASE STUDY 2: DESIGN OF AN

AGILE MANUFACTURING WORK

CELL FOR LIGHT MECHANICAL

APPLICATIONS.

Agile manufacturing is the ability

to accomplish rapid changeover between

the manufacture of different assemblies

utilizing essentially the same work cell.

The agile work cell developed at CWRU

(Case Western Reserve University)

consists of a flexible automation system,

multiple Adept robots. An important

feature of the work cell is the central

conveyor system

It is responsible for transferring

partially completed assemblies between

the robots and for carrying finished units

to an unloading robot The robots are

mounted on pedestals near the conveyor

system. Pallets with specialized parts

fixtures are used to carry assemblies

throughout the system, after which the

finished assemblies are removed from

the pallet by the unloading robot.

Finally, a safety cage encloses the entire

work cell, serving to protect the operator

as well as providing a structure for

mounting overhead cameras.

FIG 3 AGILE MANUFACTURING WORKCELL FOR LIGHT MECHANICAL APPLICATIONS

6.0 CONCLUSIONS

In the longer term, if we want to catch

up with and survive in competitive world,

manufacturing by present trends is not the

answer. What we need to do, is something

may well be Agile Manufacturing.

Case study for an aerospace Industry

reveals that even this is a renowned

company and doing well with 10 years of

orders booked, still agility index needs to

be improved at larger scales.

Design of Work cell for agile

manufacturing discussed can be effectively

implemented to improve productivity.

REFERENCES

1. Paul T. KIDD, Agile

manufacturing key Issues, Chesire

Henbury Consulting company

2. A. Gunasekaran, E. Tirtiroglu,

V.Wolstencroft, 2001, An

investigation into the application of

agile manufacturing in an

aerospace company, Technovation

22(2002) 405-415.

3. Roger D. Quinn, Greg C. Causey,

1996, Design of an Agile

Manufacturing Work cell for Light

Mechanical Applications, IEEE

International conference on

Robotics and Automation.

4. http://en.wikipedia.org/wiki/Agile_

manufacturing

5. Mikell. P.Groover, Automation, production Systems and CIM, PEARSON Education.

15

Lean Six Sigma applications in manufacturing and

non-manufacturing sectors Swati Chougule

#, Ashok Patole

*

#IInd semester (ME – CAD CAM and Robotics - PIIT, New Panvel) Mumbai University

*Assistant Professor (Department Mechanical Engineering, PIIT, New Panvel) Mumbai University

Email Address #ssgadave@gmail.com, *ashok.patole12000@gmail.com

ABSTRACT- The common goals of Six

Sigma and lean production are

improvement of process capability and

elimination of waste. Six Sigma and lean

production should be viewed as

complements to each other rather than as

equivalents of or replacements for each

other. The purpose of this paper is to

study the application of Lean Six Sigma,

an integrated model of Six Sigma and lean

production named “DMAIC” which

represents a logical, sequential structure

for driving process improvement in

different fields. It could be applied in both

manufacturing processes and non-

manufacturing processes that are willing

to implement it.

Four main areas focussed in this

paper are, basic concepts of Six Sigma and

lean production, identification of the basic

model structure and implementation steps,

study the toolsets in each of the

implementation phase of this model, and

case studies in the field of Education

System, Public or Government Sector &

Service Industry.

The integrated model will provide

benefits to enterprises, such as to find

optimal process, to create work

standardization, to reduce variation, and

eliminate wastes. According to these

effects above, we deeply believe that it

indeed can enhance product quality by

combining the concepts and methodologies

of Six Sigma and lean production. Finally,

it can satisfy customer’s requirements and

gain more competitive advantages.

Keywords- Lean Production, Six Sigma,

Standard Deviation, Integrated Model,

DMAIC, Lean Six Sigma

I. INTRODUCTION

Many Enterprises are confused about

the relationship between Six Sigma and

lean production, whether they are

independent or not. In fact, many

manufacturers had already used the

concept of lean production to eliminate

waste in the past; recently, lots of

companies lead in Six Sigma to reduce

variation and enhance quality. For most

companies, Six Sigma and lean

production attack problems in different

ways. Enterprises treat each approach as

different and unique, and divide these

two systems into different improvement

teams in practice. Enterprises put efforts

on those high-value added activities and

competitive domain. However, they have

to clearly understand that it is a crucial

issue to enhance competitive advantage

by strengthening product quality and

enriching customer loyalty. Because the

characteristics of most industries are

belonging to high cost operation and

emphasizing quality, we have to look

after both sides of low cost and high

quality. For these reasons, it is necessary

to integrate the systems of Six Sigma and

lean production.

Six Sigma and lean systems are

closely related. A lean culture provides

the ideal foundation for the rapid and

successful implementation of Six Sigma

quality disciplines. And the metrics of

Six Sigma lead to the application of the

discipline of lean production when it is

most appropriate. Furthermore, the

techniques and procedures of Six Sigma

should be used to reduce defects in the

16

processes, which can be a very important

prerequisite for a lean production project

to be successful. The two approaches

should be viewed as complements to each

other rather than as equivalents of or

replacements for each other. The

combination of these two approaches

represents a formidable opponent to

variation in that it includes both re-layout

of the processes and a focus on specific

variations.

The main objective of this paper

is to attempt to study the effect of Lean

Six Sigma implementation in different

types of fields like education system,

public/government sector, and service

sector. The results of this research might

be a reference material to help an

organization diagnose performance gaps

correctly and determine the right

continuous improvement strategy for

their processes.

II. LITERATURE REVIEW

A. Introduction to Six Sigma

Six Sigma is the major focus of

many companies for its powerful

breakthrough performance demonstrated

in GE, Motorola etc. recently. Six Sigma

can help companies to reduce cost,

increase profits, keep current customers

and create new customers. In brief, Six

Sigma is a methodology to reduce the

variation of every process and their

interfaces to achieve a very high quality

level. Six Sigma originated as a set of

practices designed to improve

manufacturing processes and eliminate

defects, but its application was

subsequently extended to other types of

business processes as well.

The philosophy of Six Sigma

recognizes that there is a direct

correlation between the number of

product defects, wasted operating costs,

and the level of customer satisfaction.

The graph below (Fig.1) shows that, there

is an acceptable point of imperfection and

any quality improvement made beyond

that point is more expensive than the

expected cost savings of fixing the

imperfection.

Fig. 1 Impact of Quality Level on Cost

Ten famous rules of Six Sigma

1.View performance from the

customer’s perspective

2.Understand the process

3.Make decisions based on data and

analysis

4.Focus on the most important issues

5.Use statistical tools

6.Pay attention to variation

7.Use standard methodologies

8.Select projects for financial impact

9.Establish project governance structure

10. Enlist senior management support

Statistical Standard Deviation (σ)

It is a numerical value in the units

of observed values that measures the

spreading tendency of the data. A large

standard deviation shows greater

variability of data than does a small

standard deviation. In the case

where X takes random values from a

finite data set x1, x2, …, xN, with each

value having the same probability, the

standard deviation is

or, using summation notation,

17

E.g.

Consider a population consisting of the

following eight values: 2, 4, 4, 4, 5, 5, 7,

and 9.

These eight data points have the mean (average) of 5:

To calculate the population standard

deviation, first compute the difference of

each data point from the mean and square the result of each:

Next compute the average of these

values, and take the square root:

This quantity is the population

standard deviation; it is equal to the

square root of the variance.

Six Sigma Statistics

In statistical theory, Six sigma is

an ideal target value, and expressed as:

6σ. It means when the process or product

we observed under a normal distribution,

the probability of a specific attribute

value shifts from the mean about positive

or negative six standard deviation would

be 0.002 part per million (ppm).

Motorola Company found a phenomenon

that the process mean would shift around

the centre point of specifications in a

long-term processing, and the shifting

range would be about positive or negative

1.5 standard deviations from the centre

point of specifications. Hence, Motorola

Company modified the statistical

meaning of six sigma. The definition can

allow the sample mean shifts from the

centre of the population, and the observed

process or product would out lie the six

sigma limits only 3.4 times per million

operations under the original

specifications. In addition, the sigma

performance can also be expressed by

“Defect per Million Operations

(DPMO)”. Bill Smith first formulated the

particulars of the methodology

at Motorola in 1986.

Fig. 2 Normal Distribution Curve

As you can see from Fig.2, +/-6

deviations (6 sigma) contains 99.9999%

of all values. It can never reach 100%

though. This means that there will always be room for improvement.

Because of the properties of the

normal distribution, values lying that far

away from the mean are extremely

unlikely. Even if the mean were to move

right or left by 1.5σ at some point in the

future (1.5 sigma shift), there is still a

good safety cushion. This is why Six

Sigma aims to have processes where the

mean is at least 6σ away from the nearest

specification limit.

18

Fig. 3 Normal Distribution Curve for Different

Values of µ and σ

Upper and lower control limits

(UCL, LCL) are set according to the

following formula:

UCL=CL+3σ

LCL = CL - 3σ

Where, σ is the standard deviation

of Xt.

Fig. 4 Normal Variation of Values

A variation of the process is

measured in Std. Dev, (Sigma) from the

Mean. The normal variation, defined as

process width, is +/-3 Sigma about the

mean.

Approximately 2700 parts per

million parts/steps will fall outside the

normal variation of +/- 3 Sigma. This, by

itself, does not appear disconcerting.

However, when we build a product

containing 1200 parts/steps, we can

expect 3.24 defects per unit (1200 x

.0027), on average. This would result in a

rolled yield of less than 4%, which means

fewer than 4 units out of every 100 would

go through the entire manufacturing

process without a defect.

For a product to be built virtually

defect-free, it must be designed to accept

characteristics which are significantly

more than +/- 3 sigma away from the

mean.

A design which can accept Twice

The Normal Variation of the process, or

+/- 6 sigma, can be expected to have no

more than 3.4 parts per million defective

for each characteristic, even if the process

mean were to shift by as much as +/- 1.5

sigma.

In the same case of a product

containing 1200 parts/steps, we would

now expect only 0.0041 defects per unit

(1200 x 0.0000034). This would mean

that 996 units out of 1000 would go

through the entire manufacturing process

without a defect. To quantify this,

Capability Index (Cp) is used.

A design specification width of

+/- 6 Sigma and a process width of +/- 3

Sigma yields a Cp of 12/6 = 2. However,

the process mean can shift. When the

process mean is shifted with respect

design mean, the Capability Index is

adjusted with a factor k, and becomes

Cpk.

Cpk = Cp(1-k).

The k factor for +/-6 Sigma

design with a 1.5 Sigma process shift:

1.5/(12/2) or 1.5/6 = 0.25

and the

Cpk = 2(1- 0.25)=1.5

DPMO and Sigma Sigma

Following tables I, II, III shows the

sigma values and their corresponding

DPMO values.

TABLE I

SHORT TERM CAPABILITY

19

TABLE II

LONG TERM CAPABILITY

TABLE III

DPMO AND SIX SIGMA

Six Sigma means the world

leading quality level. More and more

companies understand to use Six Sigma

to improve the process quality so as to

achieve the business dramatic

performance. This is because Six Sigma

requires the quantitative measurements

and analyses of the core business

processes as well as suppliers’ involved processes.

Originally, Six Sigma methodology

is applied to manufacturing industries.

However, the applications of Six Sigma are

no longer be limited in manufacturing

processes today. Keim (2001) demonstrated

Six Sigma is very suitable to improve the

service performance by two real cases. Paul

(2001) pointed that the recent trends in Six

Sigma are: emphasis on cycle time reduction,

smaller business deployment, and integration

with other initiatives.

As the Six Sigma market grows, so

does the availability of organizations to assist

in deployment and integration. This

availability of technical expertise allows

smaller businesses realistically consider Six

Sigma deployment with minimal economic

investment. Besides, due to the central

concern of Six Sigma is to pursue the

customer satisfaction and business

performance; we can view Six Sigma a main

structure while integrating with other

initiatives. As for the integrating initiatives

such as Lean Production System, Total

Quality Management or Quality Costs etc.

depend on the different requirements of each

company.

Common Six Sigma traits include:

1. A process of improving quality by

gathering data, understanding and controlling

variation, and improving predictability of a

school’s business processes.

2. A formalized Define, Measure, Analyze,

Improve, Control (DMAIC) process that is

the blueprint for Six Sigma improvements.

(The DMAIC process will be described in

greater detail later in this paper.)

3. A strong emphasis on value. Six Sigma

projects focus on high return areas where the

greatest benefits can be gained.

4. Internal cultural change, beginning with

support from administrators and champions.

B. Introduction to Lean Production System

“To get the right things to the right place

at the right time, the first time, while

minimizing waste and being open to

change”

— Taiichi Ohno, Toyota

Production System

Ten famous rules of Lean Production:-

1. Eliminate waste

2. Minimize inventory

3. Maximize flow

4. Pull production from customer demand

5. Meet customer requirements

6. Do it right the first time

7. Empower workers

8. Design for rapid changeover

9. Partner with suppliers

10. Create a culture of continual

improvement.

20

Lean Production System (also called

Toyota Production System) is the world

famous production system developed and

practiced by Toyota mobile company for a

long time. It is based on two concepts: “Just-

In-Time” and “Jidohka”. [Jidoka, Japanese

term, to mean "quality-at-the-source” or

"autonomation"] Both are based on varying

thinking to improve the business process,

enhance quality, production and competitive

position. This kind of production system is

very flexible to the dynamic change of

market demands, and Lean Production

System is established by many small group

improvement activities to eliminate all kinds

of wastes in the business.

An important literature written by

Spear and Bowen (1999) published in

Harvard Business Review pointed that, the

Toyota Production System and the scientific

method that underpins it were not imposed

on Toyota – they were not even chosen

consciously. The system grew naturally out

of the workings of the company over five

decades. As a result, it has never been written

down, and Toyota’s workers often are not

able to articulate it. That’s why it’s so hard

for outsiders to grasp. In the article, Spear

and Bowen attempted to lay out how

Toyota’s system works. They tried to make

explicit what is implicit. Finally, they

described four principles – three rules of

design, which show how Toyota sets up all

its operations as experiments, and one rule of

improvement, which describes how Toyota

teaches the scientific method to workers at

every level of the organization. It is these

rules –and not the specific practices and tools

that people observe during their plant visits –

that in their opinion form the essence of

Toyota’s system. Hence the two authors

called the rules as the DNA of the Toyota

Production System.

Lean Flow experts have found that

the greatest success can be achieved by

methodically seeking out inefficiencies and

replacing them with “leaner”, more

streamlined processes.

Sources of waste commonly plaguing

most business processes include:

1. Waste of worker movement

(unneeded steps)

2. Waste of making defective

products

3. Waste of overproduction

4. Waste in transportation

5. Waste of processing

6. Waste of time (idle)

7. Waste of stock on hand

Lean Flow is achieved by:

1. Analyzing the steps of a process and

determining which steps add value and which

do not.

2. Calculating the costs associated with

removing non-value-added steps and

comparing those costs versus expected

benefits.

3. Determining the resources required to

support value-added steps while eliminating

non-value added steps.

4. Taking action.

21

Fig. 5 Tools of Lean Production and Six Sigma

C. Characteristics of Methodologies

Characteristics of Six Sigma:

Top-down implementation

Project-focused

Reliance on experts

Use of statistical tools

Rigorous methodology

Emphasis on analysis and financial

results

Characteristics of Lean:

Operational, “shop-floor-focused”

Limited range of application

compared to Six Sigma

Less rigorous methodology compared

to Six Sigma

Challenging when transferring

concepts from production

environment to a service environment.

D. Lean and Six Sigma—Areas of Focus

Neither Lean nor Six Sigma alone will

help an organization achieve the greatest

possible returns.

III. LEAN SIX SIGMA

Lean Six Sigma achieves quality without

waste.There is no standard definition of Lean

Six Sigma (LSS). Commonly understood to be

the combination of Lean and Six Sigma tools to

reduce waste, improve flow, eliminate errors,

increase customer focus, and decrease

variability.

Organizations have the opportunity to

achieve operational excellence by combining

the top-down, data-driven, rigorous, analytical

aspects of Six Sigma with the bottom up,

operational, less analytical aspects of Lean

through Lean Six Sigma integration.

22

Fig. 6 Evolution of Lean Six Sigma

Most Six Sigma projects require the

application of Lean concepts and tools (e.g.,

cycle time reduction). Neither lean nor six

sigma can by themselves fulfill the operational

improvement demands lean and six sigma are

required to meet the customer expectations the

successful implementation of lean will enhance

the performance of six sigma and vice versa.

Six Sigma will eliminate defects but it

will not address the question of how to optimize

process flow Lean principles exclude the

advanced statistical tools often required to

achieve the process capabilities needed to be

truly ‘lean’. Each approach can result in

dramatic improvement, while utilizing both

methods simultaneously holds the promise of

being able to address all types of process

problems with the most appropriate toolkit. It

incorporates the conceptual strengths of each

approach, not just the tools.

Operating by itself, Lean Flow focuses

on using the minimum amount of resources

(people, materials, and capital) to produce

solutions and deliver them on time to customers.

The process, however, does not have the

discipline to deliver results predictably. That is,

in some cases, Lean Flow implementation

involves a non-formalized investigation into an

organization’s workflow followed by immediate

re-arrangement of processes. While this

approach produces change quickly, it cannot be

relied upon to consistently yield desired results.

On the other end of the spectrum, Lean Flow

implementation can involve extremely thorough

data collection and analysis that take years

before any change occurs. This approach often

yields desired results, but takes too long to get

there.

Meanwhile, Six Sigma, operating

independently, aims to improve quality by

enhancing knowledge generating processes. In

many cases, this leads to slow, deliberate,

change-intolerant practices. To combat these

challenges, organizations have found that by

“nesting” the Lean Flow methodology within

the Six Sigma methodology, a synergy is

attained that provides results much greater than

if each of the approaches was implemented

individually.

When Lean is added to Six Sigma, slow

processes are challenged and replaced with

more streamlined workflows. Additionally, the

data gathered during Lean Flow implementation

helps identify the highest impact Six Sigma

opportunities. When Six Sigma is added to

Lean, a much-needed structure is provided that

makes it easier to consistently and predictably

achieve optimum flow. The two methodologies

work so well together, that a new, integrated,

Lean Six Sigma approach, with its own unique

characteristics, has been defined and

23

incorporated by several leading organizations,

including Xerox Corporation.

Lean Six Sigma is the application of

lean techniques to increase speed and reduce

waste, while employing Six Sigma processes to

improve quality and focus on the Voice of the

Customer. Lean Six Sigma means doing things

right the first time, only doing the things that

generate value, and doing it all quickly and

efficiently.

DMAIC Model

Some integration models are introduced

to provide some guidance about how to properly

integrate and apply the best of both systems

(Jiang, et al., 2001). In this paper, we will use

the best combination of lean and Six Sigma

techniques to create a robust solution.

As illustrated in following Figure 7, this

model is named “DMAIC” which represents a

logical, sequential structure for driving process

improvement. We use the roadmap (Diagnose

and Define – Measure – Analyze – Improve –

Control) to provide a disciplined methodology

and a robust solution for firms that are willing

to conduct both Six Sigma and lean production

systems. There are some combined techniques

to form a complete set of improvement

framework in each of the five-infrastructure

shell.

In Fig.7 the blue ones (◎) represent lean

concept tools, the red ones (■) stand for six

Sigma concept techniques and the black ones

(@) symbolize concepts or tools exist in both

Six Sigma and lean production. In addition, the

roadmap is a continuous improvement process

circle that is the pursuit for operational

excellence.

Diagnose and Define Phase

The main purpose of diagnose and

define phase is to discover the causes of quality

deficiencies or investigate the symptoms of the

process. The projects should be initiated by

(Basu, 2001; Pyzdek, 2000; Snee and Hoerl,

2003; Martens, 2001):

◎ 7 Wastes Identification

◎ Flow Process

◎ Voice of Customers (VOC)

◎ Interview and Survey

� Project Charter

� Estimated Financial Impact

Measure Phase

In this phase it is important to quickly

understand what the inputs and outputs are of a

process. In the measurement phase,

improvement teams typically use tools such as

(Nave, 2002; George, 2002):

◎ Value Stream Mapping

◎ Motion and Time Study

� Process Mapping

� Measurement System Analysis （MSA）

� Capability Study

� Cause and Effect（C&E）Matrix

24

Fig. 7 DMAIC Model for Lean Six Sigma

Analyze Phase

In analysis phase, we use both lean and

Six Sigma techniques to analyze the process.

Some lean tools are feasible and powerful for

improvement personnel in this phase. Principal

tools used in the analyze phase include (Sahin,

2000; Burton, 2001):

◎ TAKT Time / Cycle Time Analysis

◎ Spaghetti Diagram

◎ Multi-Cycle Analysis

� Failure Mode Effects Analysis (FMEA)

� Control Charts

� Multi-Vari Study

� Screening Experiment

Improve Phase

After the collected data is analyzed and

conclusions are reached, improvements must be

implemented so that the overall process is

enhanced. Based on the literature review, tools

of this phase include (Moore, 2001; Sahin,

2000; Burton, 2001):

◎ Cell Design

◎ Visual Management

◎ Group Technology

◎ Line Balancing

◎ Single Minute Exchange of Die (SMED)

� Design of Experiments (DOE)/Quality

Engineering (Q.E.)

� SPC (Statistical Process Control)

Control Phase

This phase is designed to help the

improvement teams confirm the results and

make the gains lasting. The main purpose of

control phase is to document the changes and

new methods, and maintain an organized, clean,

and high performance process. We can optimize

the control plan by applying the following tools

(Lathin and Mitchell, 2001; George, 2002;

Burton, 2001):

◎ 5S

◎ Poka-Yoke (Mistake Proofing)

◎ Task Tracking

◎Checklists

◎Knowledge Management

◎Hand-off Training

� Control Plan

◎SOP (Standard Operating Procedure)

25

Lean Six Sigma Principles

1. Specify value in the eyes of the customer

2. Identify the value stream and eliminate waste /

variation.

3. Make value flow smoothly at the pull of the

customer.

4. Involve, align and empower employees.

5. Continuously improve knowledge in pursuit of

perfection.

Benefits of Lean Six Sigma

1. Achieve total customer satisfaction and

improved operational effectiveness and efficiency

-Remove wasteful/non-value added activities

-Decrease defects and cycle time, and increase

first pass yields

2. Improve communication and teamwork through

a common set of tools and techniques (a

disciplined, repeatable methodology)

3. Develop leaders in breakthrough technologies

to meet stretch goals of producing better products

and services delivered faster and at lower cost

Case Studies

Lean Six Sigma in Higher Education

This white paper:

• Provide the account and theories behind Lean

Flow and Six Sigma methodologies.

• Clarify the synergy attained by integrating Lean

Flow and Six Sigma into a consolidated approach.

• Validate how Lean Six Sigma can be utilized to

improve the ways higher education institutions

manage documents—and the information they

contain.

One key area where higher education

institutions seek to improve efficiency is by

implementing electronic document and digital

image repository to simplify and streamline

document-intensive business processes, such as

enrolment.

Imaging and document repository solutions

include scanning, organizing, and storing back

files and incoming documents so they are readily

available and instantly accessible to people who

need them most.

Lean Six Sigma-based DMAIC approach

Define

This is the phase where the current state,

problem statement, and desired future state are

determined and documented via the Project

Charter. Schools look to improve the ways

documents are created, stored, accessed, and

shared so they may accelerate and enhance

work processes, share information more

conveniently, and collaborate more effectively.

E.g.

• Paper-based work processes are slow,

expensive, and cumbersome, which challenges

the ability to support admissions.

• Compliance with government mandates like the

Patriot Act and Immigration and Naturalization

Services (INS Audit) is difficult.

• The ability to provide relevant and timely

information to alumni inhibits the ability to keep

them committed.

• To share paper-based information, workers must

make a copy and manually mail, overnight, and/or

fax the document.

Measure

The Measure phase is where Xerox

gathers quantitative and qualitative data to get a

clear view of the current state. This serves as a

baseline to evaluate potential solutions.

E.g.

• Amount of storage space being used and how

much is available

• Number of mail, phone, and fax requests

• Number of steps in a process

• Number of copies being made

• Number of approvals required

• Amount of time required to process a request

• Number of errors requiring re-work

• Level of user satisfaction

• Most common cause of defects

• Amount of duplication of effort

26

Analyze

In the Analyze phase, Xerox studies the

information gathered in the Measure phase,

pinpoints bottlenecks, and identifies

improvement opportunities where non-value-

added tasks can be removed.

E.g.

• Cost reduction by storing information online or

digitally instead on paper

• Savings gained by eliminating long-distance fax

charges and postal and courier expenses for

distributed campuses.

• Improvements in staff productivity and

satisfaction by “digitizing” document search and

retrieval methods.

Improve

The Improve phase is when

recommended solutions are implemented. A

project plan is developed and put into action,

beginning with a pilot program and culminating

in full scale, enterprise-wide deployment.

E.g. Common imaging and repository solutions

implemented in the Improve phase include

scanning services, Web-based document access,

and workflow solutions for task tracking and

automation.

Control

Once a solution is implemented, the next

step is to place the necessary “controls” to

assure improvements are maintained long-term.

E.g.

• Improved enrolment by responding quicker to

inquiries through process efficiency gains

• Satisfied students because of convenient self

service access and open lines of communication

with staff.

• Productive faculty, staff, and administrators due

to faster access to mission-critical information,

simpler collaboration with fewer paper-based,

labour-intensive tasks, and redundant effort.

• Secure solutions that ensure only authorized

personnel have access to confidential information.

• Solutions that, even in the event of a disaster,

ensure business continuity—because colleges and

universities can never shut down.

• Potential to capture records around a life-long

learner—application to grave—so they can be

mined for alumni contributions.

Lean Six Sigma in the Public Sector

This white paper:

• Provide the histories and theories behind Lean

Flow and Six Sigma methodologies.

• Explain the synergy attained by integrating Lean

Flow and Six Sigma into a single approach.

• Demonstrate how Lean Six Sigma can be

utilized to improve the ways state and local

governments manage documents—and the

information they contain.

One key area where state and local

governments seek to improve efficiency is by

implementing digital imaging and repository

solutions to simplify and streamline document-

intensive business processes.

Lean Six Sigma-based DMAIC approach

Define

This is the phase where the current state,

problem statement, and desired future state are

determined and documented via the Project

Charter. State and local governments look to

improve the ways documents are created,

stored, accessed, and shared so they may

accelerate and enhance work processes, share

information more conveniently, and collaborate

more effectively. As the project progresses and

more information is collected in future phases,

the problem statement developed in the Define

phase is refined.

E.g.

• It is difficult for government workers to access

or share information that resides only on paper.

• Paper documents are easily misfiled or

misplaced.

• Paper-based work processes are slow,

expensive, and cumbersome.

27

• Compliance with the Freedom of Information

Act is difficult.

Measure

The Measure phase is where Xerox

gathers quantitative and qualitative data to get a

clear view of the current state. This serves as a

baseline to evaluate potential solutions.

E.g.

• Amount of storage space being used and how

much is available

• Number of mail, phone, and fax requests

• Number of steps in a process

• Number of copies being made

• Number of approvals required

• Amount of time required to process a request

• Number of errors requiring re-work

• Level of user satisfaction

• Most common cause of defects

• Amount of duplication of effort

Analyze

In the Analyze phase, Xerox studies the

information gathered in the Measure phase,

pinpoints bottlenecks, and identifies

improvement opportunities where non-value-

added tasks can be removed.

E.g.

• Cost reduction by storing information online or

digitally instead of on paper

• Savings gained by eliminating long-distance fax

charges and postal and courier expenses for

distributed campuses.

• Improvements in staff productivity and

satisfaction by “digitizing” document search and

retrieval methods.

Improve

The Improve phase is when

recommended solutions are implemented. A

project plan is developed and put into action,

beginning with a pilot program and culminating

in full scale, enterprise-wide deployment.

E.g. Common imaging and repository solutions

implemented in the Improve phase include

scanning services, Web-based document access,

and workflow solutions for task tracking and

automation.

Control

Once a solution is implemented, the next

step is to place the necessary “controls” to

assure improvements are maintained long-term.

E.g.

• Satisfied constituents because of convenient

self-serve public records access and open lines of

communication with government officials

• Productive government agency workers due to

faster access to mission-critical information and

simpler collaboration with fewer paper-based,

labour-intensive tasks and redundant effort.

• Secure solutions that ensure only authorized

personnel have access to confidential information.

• Reduced costs—a primary objective in the

public sector.

• Solutions that, even in the event of a disaster,

ensure business continuity—because the

government can never shut down.

Lean Six Sigma in the Service Industry

Regarding industry characteristics,

service industry is quite different from

manufacturing industry. Even though there are

more wastes and improvement opportunities,

the application of Six Sigma, Lean Production

System or their integration in service industry is

quite few either in the literature/practice.

28

Fig. 8 Structure of Implementing LS3

Four Characteristics of Service Industry

Recently, due to the economic and

international trading environmental change, the

structures of many companies are also changed.

The growth of service industries rapidly chases

the growth of manufacturing industries.

Especially for the current situation in Taiwan,

many factories are moving to mainland China.

Hence, the needs for service industries to fill in

the space of economic activities become very

huge. That’s why service industries play an

important role in the economic development

recently.

This research concludes the four

characteristics of service industries based on the

literatures written by Kotler (1997), Regan

(1963) and Zeithmal, Parasur & Berry (1985) as

follows:

1. Intangibility: It means that services

can be consumed and perceived, but they cannot

easy to be objective measured like the

manufactured products. That’s why there is

usually a perception gap between the service

provider and consumer.

2. Variability: It means that services are

delivered by people, so the service quality may

change depending on different time, people and

consumer perception. That is, the variability of

services.

3. Perish ability: Unlike the tangible

manufactured products, services cannot be

inventoried. They are delivered simultaneously

while the demands from consumers appear.

Once the demands disappear, the services

perish.

4. Inseparability: Since the delivery and

consumption of services almost be done

simultaneously. Hence the interactions between

servers and consumers play an important role on

the evaluation of service quality. Consumers

evaluate the service quality on the moment of

consuming the service. That is, the

inseparability of services.

29

This research proposes an integration

model of Six Sigma and Lean Production

System for service industry called as “Lean Six

Sigma for Service (LS3)”. It balances the

viewpoints of internal and external customers,

and gives consideration to the Lean speed as

well as Six Sigma high quality. Also, this

research tries to contribute to the enhancement

of management technology.

The LS3 operating model proposed by

this research shown as in Fig.8.. Moreover, the

tools of LS3 are also shown as Figure 9.

Fig. 8 Tools of Implementing LS3

Conclusion

Government and private sector

organizations have much in common like,

Pressure to improve service and products,

expectations to control or cut costs, on-time

delivery is paramount, large organization

behaviour, etc.

Moreover, regarding the industry

characteristics, service industry is quite

different from manufacturing industry. Even

though there are more wastes and

improvement opportunities, the application of

Six Sigma, Lean Production System or their

integration in service industry is quite few

neither in literatures nor practice.

The significant benefits of the

DMAIC model are its implementation

roadmap, combination of techniques in every

step and philosophy of management, as well

as its development of the quality initiative in

the process of continuous quality

improvement. The integration model

presented here provides a roadmap to the

real-world application of Six Sigma and lean

production methodologies in industrial

circles, and it may be applied to any

30

industrial circumstance to improve process,

product, and service quality.

Lean Six Sigma can be successful in

higher education, government and private

sector organizations as well as service

industry.

REFERENCES

[1] Ross Raifsnider, Dave Kurt “Lean Six

Sigma In Higher Education”, White Paper,

Sept 2004, Xerox Global Services Inc.

[2] Kent Snyder, Newton Peters “Lean Six

Sigma In The Public Sector”, White Paper,

Sept 2004, Xerox Global Services Inc.

[3] Jui-Chin Jiang, Ming-Li Shiu and Hsin-Ju

Cheng “Integration Of Six Sigma And Lean

Production System For Service Industry”,

Proceedings of Fifth Asia Pacific Industrial

Engineering and Management Systems

Conference 2004

[4] http://www.barringer1.com/jan98prb.htm

[5] http://www.gifted.uconn.edu/siegle/research/

Normal/instructornotes.html

[6] http://en.wikipedia.org/wiki/Six_Sigma

31

Performance Evaluation of Compression Ignition Engine with Various

Blends of Petroleum Diesel and Bio-diesel

Sandeep M. Joshi1 and Aneesh C. Gangal

2

1Department of Mechanical Engineering, Pillai’s Institute of information Technology, Engineering, media

Studies and Research, New Panvel – 410206, Maharashtra, India.

*Author for Correspondence (smjoshi16@yahoo.co.in)

2Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai -

400076, Maharashtra, India.

ABSTRACT: Almost 90% of the world’s energy demands are being fulfilled through

ever depleting fossil fuels. This fulfillment though is not so environment friendly. The

world needs an alternate fulfillment arrangement. Bio-diesel, one of the renewable

energy sources is poised to be a better such alternative to check fossil fuel consumption

and address the environmental issues. This is an attempt to harness the potential of bio-

diesel as an alternative fuel. In this experimental set-up, a 4-stroke, vertical, air cooled,

single cylinder, compression ignition (CI) engine, coupled to an electric generator has

been put into tests on various blends of petroleum diesel and bio-diesel, at different load

conditions. The performance has been evaluated basis thermal efficiency, break specific

fuel consumption (BSFC) and emissions at exhaust. Fuel blend, B20 (20% bio-

diesel+80% petroleum diesel) has been observed to be the better performing of the lot

tested.

Keywords: CI Engine, Bio-diesel blend, Efficiency, BSFC

1. Introduction

In India, 80% of the industries' fuel demands have been met through imports. Dependability

of Indian industries is inevitable hence. This necessitates an in-house alternative to meet the

ever-growing energy demands. The feasible alternative found if addresses the environmental

issues as well, could be a boon to the Indian industry. The most preferential industry to start

with could be the transportation sector which happens to be the single largest consumer of

petroleum-diesel.

Bio-diesel, as a fuel in a neat or fuel blend form with petroleum diesel is poised to be a better

such alternative to meet the energy demands in a complete or a partial manner respectively.

Bio-diesel in a neat or fuel blend form would help minimize the dependability on foreign oil

supplies. Since produced in-house, Bio-Diesel generates employment and also triggers uplift

of the nation's micro economy. The carbon dioxide produced on combustion of bio-diesel,

can be utilized to channelize the photosynthesis of the bio-diesel producing oil-seed plants,

implies virtually zero emissions at exhaust.

Researchers across the globe have reported their experimental results on bio-diesel fired

engines. Kalligeros et al. tested an engine with pure marine diesel fuel and tested also with

blends of petroleum diesel with two different types of bio-diesels, in proportions ranging up

32

to 50%. Their findings suggest, both the types of bio-diesels appeared to have performed at

equal level, irrespective of the raw material for production. Their findings also suggests the

fuel blend, has improved the particulate matter, unburned hydrocarbons, nitrogen oxide and

carbon monoxide emissions [1]. In one of the reviews, Lapuerta et al. have reported the

increase in the BSFC with the proportionate increase of Bio-diesel in the fuel blend. This

holds good in most of the studies conducted owing to the reduction in calorific value of the

fuel blend [2]. Leevijit and Prateep Chaikul have stated that higher blend content of bio-

diesel results in a little higher brake specific fuel consumption, a slightly lower brake thermal

efficiency, a slightly lower exhaust gas temperature and a significantly lower amount of

black smoke [3]. In a review Murugesan et al. have reported the direct use of methyl ester, a

bio-diesel (B100) in diesel engines without any modifications for a short term at a slightly

lower performance level compared to that of petroleum-diesel. Also brake thermal efficiency

for bio-diesel slightly increases at B20 fuel blend [4]. Syed et al. in their review have

inferred that the engine performance was slightly inferior when using a fuel blend of

vegetable oil (a bio-diesel) and petroleum diesel, with the highly viscous oil causing injector

choking and contamination of the lubricating oil. The tests with refined oil blends have

registered a significant improvement in the performance [5]. In a review Graboski and

McCormick have stated that, the use of bio-diesel in either neat or blend form has no effect

on the energy based engine fuel economy. The lubricating ability of bio-diesel fuel is

superior to that of conventional petroleum diesel [6].

In this paper, we have presented a comparative study on various fuel blends of bio-diesel and

petroleum-diesel with the performance analysis based on thermal efficiency and BSFC.

2. Experimental

The experimental set-up is C.I. engine test rig which consists of a 4-stroke, vertical, air

cooled, high speed diesel engine coupled with an electric generator. Thermocouples are

installed at various points to measure the salient temperatures. A calorimeter is employed to

measure the amount of heat carried away by the exhaust gases. Also a volumetric type fuel

flow meter is engaged to monitor the fuel flow rate. The C.I. engine at the test rig is a

Kirloskar make with a 6KW rating having a bore diameter of 80mm and the stroke length of

110mm. The engine runs at 1500 RPM at a compression ratio of 16:1. Test set-up used is at

the IC Engines' Laboratory of Mechanical Engineering Department of Pillai’s Institute of

information Technology, Engineering, Media Studies and Research, New Panvel – 410206,

Maharashtra, India.

The experimental set-up employs commercially available petroleum diesel & locally

procured bio-diesel made up of palm extracts. The calorific value of the petroleum-diesel

used was 43000kJ/kg and that of the procured bio-diesel was 39500kJ/kg. Flash point of bio-

diesel was 120°C and it has a specific gravity of 0.88 with the ester content of 98.5%.

Fuel variants used for the experimentation purpose are a neat petroleum-diesel, various fuel

blends of petroleum-diesel with bio-diesel and a neat bio-diesel fuel. A system referred here

with a prefix “B” followed by a numeral, depicts the proportion of bio-diesel in percentage

with petroleum diesel in the fuel blend; e.g. a fuel blend with a 20% of bio-diesel and 80% of

33

petroleum diesel is termed as B20. This implies the neat bio-diesel as a fuel is termed as

B100 and a neat petroleum-diesel as a fuel is termed as B0. Not only B0 and B100 but also

B20, B40, B50, B75 & B90 fuel blend variants have been tested at the rig.

Fuel blends were synthesized in a 1000ml measuring flask on compounding the required

proportions of bio-diesel and petroleum-diesel; e.g. to synthesize a B40 fuel blend variant;

the neat bio-diesel ad-measuring 400ml and the neat petroleum-diesel ad-measuring 600ml

are compounded into the measuring flask.

Every fuel blend is tested, thrice each for the brake loads of 1, 2, 3 4, 5 and 6KW. The

average of the three such tests for the respective brake load condition has been reported in

this paper.

A total 162 such tests were carried out. To begin with the test, all the thermocouples were

monitored for the ambient temperatures, water flow to the calorimeter was set initially to 3

litres per minute. The engine test rig was triggered with hand cranking at a zero load

condition. The engine is gradually loaded to all the pre-set load conditions. The respective

load conditions were monitored for the time required to attain the 25ml of fuel consumption

at the constant engine speed of 1500 RPM. Water temperatures at the inlet and outlet of the

calorimeter were recorded. Similarly the respective temperatures of exhaust gases at the

outlet of the engine and at the inlet and outlet of calorimeter were recorded. The ambient

temperature was also noted down. Heat balance for the every load condition and each of the

fuel blends was established and thermal efficiency and Brake Specific Fuel Consumption

(BSFC) were calculated.

3. Results and Discussion

Figure 1 Thermal efficiency for all Blends and Load Conditions

34

The Bio-diesel has a lower calorific value than that of petroleum-diesel. It is clear hence,

with the increase in bio-diesel proportion in the fuel blend, the overall heating capacity of the

fuel blend decreases. One can infer from Figure 1, with the increase in load, the thermal

efficiency increases and is maxima for the rated or full load condition. Consider a specific

load condition of 1KW, the thermal efficiency is found to be around 10% for all the fuel

blends, with the maximum of 10.34% for B20 and minimum of 9.94% for B100. The trend

continues and holds good for higher load conditions as well, with the thermal efficiency

values slightly varying around a certain value with the maximum lying at B20 & minimum

lying at B100 fuel blend. The bio-diesel rich fuel blends tend to exhibit lower thermal

efficiency at all load conditions except for B20. It is implicit; with the increase in the

proportion of bio-diesel in the fuel blend, the fuel flow rate tends to increase, leading to the

lower thermal efficiency. However the fuel flow rate gain is not substantial up to B20,

naturally this fuel blend bears special characteristics and hence of our interest.

It is quite clear from Figure 2, the BSFC of the engine is quite high at part load conditions

than at the full load condition of 6KW. This is in well agreement with the thermal efficiency

which increases with the increase in the load. At the part load conditions i.e. 3KW and

below, it is observed that the BSFC for B20 is significantly lower compared to that of other

fuel blends. For the load conditions of 4KW and above, the neat petroleum-diesel or B0

exhibits relatively better performance than all other fuel blends except to lean bio-diesel fuel

blends, B10 and B20, which are not too far beyond.

All in all, it is possible to replace the neat petroleum diesel with any of its fuel blend variants

with the neat bio-diesel, without significantly affecting the performance.

Figure – 2 BSFC for all Blends and Load Conditions

35

4. Conclusions

Bio-diesel and petroleum-diesel fuel blends were synthesized and tested at a CI engine test

rig. Although the heating capacity of the fuel blend is lesser compared to neat petroleum-

diesel, it is observed that thermal efficiency of the engine improves for certain fuel blends.

The fuel blend B20 is found to be the better alternative basis thermal efficiency concerns.

Also for the lean fuel blends at part load conditions, even the BSFC is found to be lesser than

that of petroleum-diesel. Although with the increase in load, the BSFC increases for rich

fuel blend owing to the decrease in the calorific value of the fuel.

On a broader scale we can conclude with the sure possibility of bio-diesel replacing the

petroleum-diesel either completely or partially without any modifications in the existing CI

engine, with hardly any of a compromise on the performance of it.

5. Acknowledgments

Authors are grateful to Dr. K. M. Vasudevan Pillai, CEO, Mahatma Education Society, for

funding the project.

6. References

S. Kalligeros, F. Zannikos, S. Stournas, E. Lois, G. Anastopoulos, Ch. Teas, F.

Sakellaropoulos, Biomass and Bioenergy, Vol. 24, (2003), 141 – 149

M Lapuerta, Octavio Armas, J Fernandez, Progress in Energy and Combustion Science,

Vol. 34, (2008), 198–223

Leevijit T, Prateep Chaikul G, Fuel (2010), doi:10.1016/j.fuel.2010.10.013

A. Murugesan, C. Umarani, R. Subramanian, N. Nedunchezhian, Renewable and

Sustainable Energy Reviews, Vol. 13, (2009), 653–662

Syed Ameer Basha, K. Raja Gopal, S. Jebaraj, Renewable and Sustainable Energy

Reviews, Vol. 13, (2009), 1628–1634

Michael S. Graboski and Robert L. McCormick, Prog. Energy Combust. Sci. Vol. 24,

(1998), 125-164

This paper has been presented in the International Conference on Renewable Energy, 2011 January 17-21, 2011 at the University of Rajasthan, Jaipur, India.

36

Simplified Production of Large Prototypes using Visible Slicing

Onkar S. Sahasrabudhe1, K.P. Karunakaran

2

1Department of Mechanical Engineering, Pillai’s Institute of information Technology, Engineering, media

Studies and Research, New Panvel – 410206, Maharashtra, India.

2Department of Mechanical Engineering, Indian Institute of Technology Bombay, India.

ABSTRACT: Rapid Prototyping (RP) is a totally automatic generative manufacturing

technique based on a “divide-and-conquer” strategy called ‘slicing’. Simple slicing used

on 2.5-axis kinematics of the existing RP machines is responsible for the staircase error.

Although thinner slices will have less error, the slice thickness has practical limits.

Visible Slicing overcomes these limitations. A few visible slices exactly represent the

object. Each visible slice can be realized using a 3- axis kinematics machine from two

opposite directions. Visible slicing is implemented on Segmented Object Manufacturing

(SOM) machine under development. SOM can produce soft large prototypes faster and

cheaper with accuracy comparable to that of CNC machining.

Keywords: Rapid Prototyping, CNC machining, Visibility.

1. Introduction

CNC machining, a subtractive manufacturing method, is the most accurate process capable of

producing objects out of any material. However, it requires human intervention for generating

the cutter paths. The difficulty in developing foolproof CAPP systems for subtractive

manufacturing led to the development of additive or generative manufacturing methods

popularly known as Rapid prototyping (RP). Essentially RP is a CNC machine with an

embedded CAPP system for generative manufacturing. Total automation in RP is achieved

through a “divide and conquer” strategy called slicing. While slicing simplifies a 3D

manufacturing problem into several 2D manufacturing problems that could be automated, it

is the slicing that also introduces a staircase effect; the resulting stair step errors limit

severely the accuracy of the rapid prototypes (Figure 1). In other words, to achieve total

automation by limiting the motions to 2.5-axis kinematics, existing RP processes compromise

on accuracy. The accuracy can be improved by choosing very thin slices but that would

increase the time for producing the prototype thereby enhancing the cost prohibitively.

Furthermore, the surface finish of the rapid prototypes can hardly match that of the CNC

machined parts as the minimum layer thickness has practical limits. Therefore, ways and

means to increase the slice thickness without sacrificing accuracy have been explored by

many researchers.

The slices of all commercially available RP machines are of uniform thickness and have their

edge surfaces vertical, i.e., both the bottom and top contours of the slice are the same. This

type of slicing is called uniform slicing of 0th order edge surface [1]. As the number of slices

is very high in these RP machines, researchers have been exploring various ways to reduce it.

This led to the proposals for adaptive slicing by several researchers. Adaptive slicing results

in less number of slices than uniform slicing for the same accuracy. In adaptive slicing, the

slice thickness at any location depends on the local geometry, particularly, normal and

curvature. Furthermore, in addition to 0th order edge surfaces, researchers have considered

37

the use of 1st order, 2nd order or even higher order edge surfaces as illustrated in Figure 2;

the 1st order edge will be a ruled surface; the 2nd order edge will be a quadratic surface and

so on. The prismatic surfaces of the slices with 0th order edge can be realized with 2.5 axis

kinematics; Single axis in conjunction with a mask will also do as in the case of Solid Ground

Curing (SGC) and micro photolithography machines [2, 3]. The ruled surfaces can be

realized using end milling, wire EDM or laser machining which may require up to 5 axes. For

a given accuracy required, higher the order of edge surface, less is the number of slices.

Hybrid Layered Manufacturing (HLM), Solvent Welding Freeform Fabrication Technique

(SWIFT) and Thick-Layered Manufacturing (TLM) are some efforts in these directions [1, 4,

and 5]. However, these methods use the traditional “generative or additive approach” of RP

and hence (i) they inherently produce only approximations of the objects, (ii) the reduction in

the number of slices is not substantial and (iii) they suffer from severe implementation

difficulties in realizing the higher order slices. Therefore, manufacturing objects in thicker

slices without sacrificing accuracy on simple machines has been the dream of researchers for

quite sometime.

(a) CAD model (b) Physical prototype with stair steps

Figure 1 Staircase Effect in RP

The first attempt towards this goal was in SDM process [6]. SDM makes use of two

deposition heads, one each for depositing model material and a suitable support material. The

slices of the object are obtained by splitting it wherever its normal just becomes horizontal,

i.e., wherever its Z component changes its sign. To that extent, SDM also uses visibility

considerations for slicing. In any slice, the normals of the object may be upward or

downward. In all regions of the slice where the normals are downward, support is required

there and hence the support head deposits material filling those regions. Since any such

deposition is only near-net, machining is used to finish it. This is followed by the deposition

of model material and again finishing it using machining. Thus each slice is built by

deposition and machining of support and model materials alternately until the entire slab of

the slice is complete. Essentially, the previous region(s) deposited and machined act as mold

cavities to hold the subsequent depositions. In SDM, slicing and the subsequent process

planning to determine (i) the various regions for any layer, (ii) the order in which these

regions are to be deposited and (iii) the tool path for deposition and machining of each of

these regions, are all too involved.

38

Figure 2 Various Slicing Methods

The research group of K. Lee has proposed a Hybrid RP (HRP) process which also aims at

building objects with minimum number of slices [7]. They first identify and separate

machinable features and suppress them. The resulting geometry is only sliced for HRP. Each

slice which is quite thick is built through the near-net material deposition and net-shape

machining. Although this process claims to produce objects with minimum number of slices,

it requires fair amount of user input to determine the machinable features and the levels at

which slicing is to be done.

Similar segmentation approaches can be observed in a few other applications. “100

day engine project” carried out by Ford is one such example [8]. In order to reduce the engine

development time, they split the engine casting into slices of appropriate thicknesses

manually; these slices were machined and then joined by brazing. Another example is Space

Puzzle Molding process from Protoform of Germany which can automatically design the

injection molding dies of very complex objects in pieces that constitute the die halves and

inserts [9, 10]. These pieces fit together in a special frame like a 3D jigsaw puzzle. Molds are

manually assembled and disassembled during each shot. Chen and Rosen also have proposed

a method of automatically obtaining the injection mold in pieces from the CAD model of the

plastic object [11, 12]. Karunakaran et al. have developed a software program called

OptiLOM which eliminates grid cutting and decubing operations in LOM-RP [13]. In order

to extract the LOM prototype from inside a box, OptiLOM splits the material inside and

surrounding the object into the minimum number of extractable pieces; when the combined

STL file of all these pieces and the object are made in LOM machine, there will be no grid

cutting and decubing. The stock halves and the plugs calculated by OptiLOM essentially are

the mold halves and inserts. While the above three works, viz., the work of Chen and Rosen,

Space Puzzle Molding and OptiLOM, aim at obtaining the molds of an object, albeit in

pieces, visible slicing proposed here aims at splitting the object itself into segments each of

which satisfy certain manufacturability criteria, viz., cutter access to the entire surface of the

segment either from top or bottom. Interestingly, Dongwoo and K. Lee too have addressed

the problem of splitting an object such as a stamping die into pieces machinable from two

opposite directions [14]. However, they aim at splitting the object into a minimum number of

39

such machinable pieces so that they can be machined individually and then glued together;

the pair of machining directions corresponding to each piece could be different in their

method.

(a) Visible slices

(b) Visible slices

(c) Visible slices

(d) Visible slices

(e) Visible slices and hori. Levels

Figure 3 Illustration of Visible Slicing

The literature review in slicing reveals several technology gaps. The existing slicing method,

viz., uniform slicing of 0th order edge, used in popular RP machines gives rise to staircase

effect which in turn is responsible for approximation in the prototype geometry, poor surface

finish, large number slices and high cost. Emerging RP machines that make use of higher

order adaptive slicing continue to follow the traditional generative approach. Hence the

prototypes are still approximate albeit better than their predecessors. They use higher axis

kinematics which is too expensive and fool proof CAPP for subtractive manufacturing

required for these systems is still not available. Emerging RP machines that make use of

hybrid approaches, viz., combination of additive and subtractive processes, suffer from

40

severe implementation difficulties in realizing the slices. There has been a longstanding need

to develop a process that will use thick slices that conform exactly to the object. These need

not have parallel top and bottom planes. In other words, what is required is splitting the

object into segments wherein the segmentation is based on manufacturing considerations

without sacrificing accuracy. These slices can be realized using a 3-axis kinematics. The final

implementation of Visible Slicing may be a hybrid machine.

2. Visible Slicing

In the conventional slicing strategies, the slice thickness and the part accuracy are closely

related. As against this, visibility is used as the criteria for determining the slice thickness in

the proposed Visible Slicing. The object is split into visible slices, also known as segments.

The intersection of any vertical ray with the visible slice will be always a pair of points.

When the faces encountered by the ray happen to be vertical, one gets a line segment as

intersection in which case the end points of this line segment can be treated as the pair of

intersection points. This characteristic of visible slice ensures its machinability by a vertical

cutter from two opposite directions. Figure 3 illustrates the concept of visible slicing for an

object shown in Figure 3a.

Settings required in CNC machining for the same object: (a) bounding box of the object in the first

setting; (b) the blank at the end of the first setting; (c) the blank at the end of the second setting; and

(d) the blank at the end of the third setting.

An object need not have a unique set of visible slices and hence some more variants are

possible as shown in Figures 3b-e. Figures 3b & c are the two possible sets of visible slices.

The raw material used for realizing these visible slices will be equal but Figure 3d will

require the least amount of raw material. Therefore, after obtaining the visible slices, a post-

processing is done to transfer materials among these visible slices so as to minimize the total

raw material requirement.

41

The number of visible slices can be correlated with the number of setups required in CNC

machining to produce the object. Figure 4a shows the blank of this object in 1st setting.

Figure 4b shows the blank at the end of 1st setting. After reversing the object, the remaining

surfaces are machined except the eye-end hole (Figure 4c). Machining this hole requires a

separate setting as shown in Figure 4d. It is also possible to machine it in just two settings

shown in Figures 4c & d. Therefore, CNC machining, which is purely a subtractive process,

requires two to three settings to make this piece from a blank. The same object can be made

in just two visible slices (Figure 4d), each requiring machining from top as well as bottom.

Algorithm for visible slicing: (a) examples of visible and invisible faces; (b) prism obtained by

extruding the face upwards; (c) invisible patch Ip obtained by recursively collecting the invisible faces;

(d) solid Si obtained by extruding the invisible patch Ip until the bottom of the bounding box of the

manifold solid Sorg; (e) segment resulting from (Sorg − Si); and (f) segment resulting from (Sorg ∩ Si).

If the slicing is accurate enough, the horizontal surfaces of the object can be obtained during

the slicing operation itself whereas the non-horizontal surfaces will require machining in scan

milling. Therefore, after obtaining the set of visible slices that have the least heights, the

authors prefer to split them further if any of the slices have large horizontal surfaces.

Accordingly, the preferred set of slices for this object will be the one shown in Figure 3e.

This is obtained from Figure 3d by splitting the bottom slice at its horizontal surface.

42

Algorithm for Visible Slicing

A face of the solid will be called invisible face if (i) its normal is upward and (ii) it is

shadowed by its other faces; otherwise, it will be called a visible face. These are illustrated in

Figure 5a. A contiguous set of invisible faces is called invisible patch. The segments of the

object will be identified in a top-down manner in this algorithm. Let S be the set of visible

slices or segments. Algorithm 1 converts the object O into the set of visible slices S. It

produces visible slices but they could be more in number with the possibility of combining

some of the segments into one segment without affecting the visibility. This post-processing

is done by Algorithm 2.

Algorithm 1: Algorithm for determining the V-slices

Initialize S with O.

For each member of S, say Si,

{

status = Segment (Si, Ssegments);

If status = true, then continue as Si is

already a V-slice;

Remove Si from S and add its segments

Ssegments at the end of S;

}

Algorithm 2: Algorithm for the post-processing step to combine

V-slices wherever possible

For each member of S, say Si,

{

For each member of S, say Sj,

{

Continue if i = j;

Continue if Si and Sj do not overlap along z-direction;

Snew = SiUSj;

status = Segment (Snew, Ssegments);

If status = true, // This means that

Snew is a V-slice

{

43

Replace Si by Snew;

Remove Sj from S;

}

}

Function 1, viz., Segment takes a manifold solid S org as input. If S org is already a visible

slice, it returns “status = true”; otherwise, it returns “status = false” and also calculates the

segments S segments of the original solid S org. Note that S segments will be an array of

manifold solids but these may or may not be visible slices.

Function 1. Function to split the given solid Sorg into its segments Ssegments

Status Segment (Sorg, Ssegments)

{

Status = false; // initially assume that Sorg is not a V-slice.

Step 1. Identifying the first invisible face

For every face Fi of the input manifold solid Sorg,

{

Let Fi be the projection of Fi on the top of the bounding box of Sorg. Make an extruded solid P

between Fi and Fi (See Fig. 5(b)).

For every face Fj of Sorg,

{

If (i = j), Continue;

If (Fj is below Fi), Continue;

If Fj intersects P, break this loop since Fj is the first invisible face;

}

if (j > number of faces of Sorg),

{

Status = true; // Declare that the input solid as a V-slice.

Return from this function since the object is already a V-slice;

}

}

Step 2. Recursively growing the first invisible face Fj into an invisible patch Ip.

Initialize the invisible patch Ip with Fj;

44

while (true)

{

For each of the three neighboring faces of Fj, say Fi,

{

For every face Fk of Sorg,

{

If Fk is not same as Fi, continue;

If Fk lies outside the X and Y extents of Fi, continue;

If Fk is below Fi, continue;

If the projections of Fi and Fk in

XY-plane intersect

{

add face Fi to the invisible patch, Ip;

Set Fj = Fi;

}

}

}

If none of the three Fi is added to Ip, break the while loop as construction of the invisible

patch Ip is complete (see Fig. 5(c));

}

Step 3. Obtaining the segments Ssegments from the invisible patch

Make a solid S1 by extruding Ip until the bottom of the bounding box of Sorg (see Fig. 5(d))

Calculate (Sorg − S1) and (Sorg ∩ S1). These are shown in Figs. 5(e) and 5(f). These two solids

are two segments of Sorg.

If these are non-manifold solids, split them into manifold solids. All these manifold solids

will be returned as Ssegments. Note that all the elements of S need not be V-slices. Note also

that S1 and (Sorg ∩ S1) are the same in the illustration of Figs. 5(d) and 5(f); however, this may

not always be the case.

3. Illustrative Example

Gear lever housing, a fairly complex object shown in Figure 6a, was taken for illustrating the

principle of visible slicing. The visible slicing program of the authors was able to split this

object into 4 visible slices or segments. These segments are shown in exploded view in

Figure 6b. These visible slices were built using FDM 1650 RP machine; they could have been

made using a 3-axis CNC machine as well. These four physical segments are shown in

45

Figures 6c-f. The final physical object shown in Figure 6g was obtained by gluing these

segments.

The machine being built by the authors called Segmented Object Manufacturing (SOM) will

be able to produce this object automatically as explained in the previous section [15]. The

authors have developed the software for automatically generating the cutter path for

machining the visible slices using a single ball nose end mill. However, it is desirable to

develop software that would make use of ball, bull and flat end mills of different diameters

intelligently. Furthermore, more fine-tuning of the post-processing part of visible slicing

algorithm is desirable to transfer material among layers to minimize height.

(a) Gear lever housing to be built

(b) Exploded view of the visible slices or

segments

(c) 1st visible slice made on FDM 1650 RP

machine

(d) 2nd visible slice made on FDM 1650 RP

machine

(e) 3rd visible slice made on FDM 1650 RP

machine

(f) 4th visible slice made on FDM 1650 RP machine

46

(g) Visible slice assembled into gear lever housing

Figure 6 Illustration of the Manufacture of a Gear Lever Housing Using SOM Principle

6. Conclusions

Existing RP machines produce 3D objects by assembling their 2D approximations called

slices. Hundreds of thin slices constitute the object so as to make it reasonably accurate. On

the contrary, visible Slicing splits the object into a few exact chunks called visible slices or

segments which are automatically machinable from two opposite directions on a 3-axis

machine. This novel slicing method is implemented in a new RP process under development

called Segmented Object manufacturing (SOM). SOM will be useful for making soft large

prototypes automatically, accurately, quickly and economically. Particularly it will be useful

for manufacturing patterns of Evaporative Pattern Casting (EPC). The principle of SOM can

be used for manufacturing even hard objects using CNC milling semi-automatically; blocks

of the required thickness can be machined on two opposite faces to get the visible slices

which can be joined using fastening, adhesive bonding or brazing depending on the

application requirements. It is interesting to note that SOM and a few other RP processes

(like SDM, HLM and TLM) that aim at manufacturing objects in thick layers heavily depend

on machining. In other words, the conventional wisdom of RP being an additive or generative

process may no longer hold good.

References

1. Karunakaran, K.P., Shanmuganathan, P.V., Jadhav, S.J., Bhadauria, P. and Pandey, A.

(2000): “Rapid Prototyping of Metallic Parts and Moulds”, J. of Materials Processing

Technology, Vol. 105, pp. 371- 381.

2. Chua Chee Kai and Leong Kah Fai (1997): Rapid Prototyping: Principles and

Applications in Manufacturing, John Wiley & Sons.

3. M.Farsari & others (2000): “A novel high-accuracy microstereolithography method

employing an adaptive electro-optic mask”, Journal of Materials Processing Technology,

107, 167-172.

4. Taylor, J.B., Cormier, D.R., Joshi, S. and Venkataraman, V. (2001): “Contoured Edge

Slice Generation in Rapid Prototyping via 5-Axis Machining “, Robotics & CIM, Vol. 17, pp.

13-18.

47

5. Broek, J.J., Horváth, I., Smit, B., Lennings, A.F., Rusák, Z. and Vergeest, J.S.M. (2002):

“Freeform Thick Layer Object Manufacturing Technology for Large-Sized Physical Models”,

Automation in Construction, Vol. 11, pp. 335-347.

6. Krishnan Ramaswamy (1997): ”Process Planning for Shape Deposition Manufacturing”,

Ph.D. Dissertation, Department of Mechanical Engineering, Stanford University.

7. Junghoon Hur, Kunwoo Lee, Zhu-hu, Jongwon Kim (2002,): “Hybrid Rapid Prototyping

System Using Machining and Deposition”, Computer Aided Design, Vol 34, pp. 741-754.

8. http://rapid.lpt.fi/archives/rp-ml-1997/0366.html (2004): Email Communication of Prof.

Ian Gibson to RPML group.

9. www.protoform.com (2004): Web site of Protoform, Germany.

10. http://www.enimco.com/puzzle.html (2004)

11. Chen, Y., Rosen, D.W. (2003): “A Reverse Glue Approach to Automated Construction of

Multi-Piece Molds”, Journal of Computing and Information Science in Engineering, Vol. 3,

No. 3, pp. 219-230.

12. Chen, Y., Rosen, D.W. (2001): “A Region Based Approach to Automate Design of Multi-

Piece Molds with Applications to Rapid Tooling”, Proceedings of ASME Design Engineering

Technical Conference, September 9-12, Pittsburgh, Pennsylvania.

13. Karunakaran, K.P., Shivmurthy Dibbi, P. Vivekananda Shanmuganathan, Srinivasarao

Kakaraparti and D.Sathyanarayana Raju (2002): "Efficient Stock Cutting in Laminated

Manufacturing", Computer-Aided Design, Vol 34, No. 4, pp. 281-298.

14. Dongwoo Ki and Kunwoo Lee (2002): “Part Decomposition for Die Pattern Making”,

Journal Material processing Technology, Vol. 130-131, pp. 599-607.

15. K.P. Karunakaran, Saurabh Agrawal, Pankaj D. Vengurlekar, Onkar S. Sahasrabudhe,

Vishal Pushpa and Ronald H. Ely (2005): “Segmented Object Manufacturing”, IIE Journal of

Design and Manufacture, Vol. 37, No. 4, pp. 291-302.

This paper is a combined research work of Department of Mechanical Engineering, IIT Bombay and

Department of Mechanical Engineering, Methodist University, USA.

48

-Photgraphs by Avinash Katkar (T.E.Mech)

49

The Nobel Prize

Alfred Nobel left a legacy promoting peace and achievement, yet he made his fortune from a

weapon of war. The son of an arms manufacturer, Nobel was a chemical Engineer with an

interest in explosives. He discovered how to stabilize nitroglycerine named it “dynamite” and

patented it in 1867. Nobel left around 31million Swedish Kronor (valued US $ 240 million

today) in his will to establish a fund rewarding “those who, during the preceding year…have

conferred the greatest benefit on mankind.”

For more than 100 years, Nobel Prizes have had the power to transform scientists into

celebrities, writers and peace activists into legends. Here are few things you would like to

know about Nobel Prizes.

There are 5 Nobel Prize categories: Peace, Literature, Chemistry, Physics and Medicine/

Physiology. An Economic Sciences Prize-the Sveriges Riksbank Prize in the memory of

Alfred Nobel- has also been awarded since 1969.

817 people (40 women) and 23 organizations have been awarded prizes, with 4

individuals getting it twice.

Australian Lawrence Bragg, just 25 years old and the youngest winner, shared the

Physics Prize with his father, William, in 1915.

The Oldest winner was Leonid Hurwicz. He was 90 in 2007 when he got the

Economic Sciences Prize.

Robert Koch, who discovered the TB bacillus, received 55 nominations over 4 years

before he won a prize in 1905.

Two persons declined the Prize: Jean-Paul Sartre (Literature, 1964) and Le Duc Tho

(Peace, 1973).

87 affiliates of UK’s University of Cambridge have won a Nobel Prize- more than any

other institution.

Each prize is now worth 10 million Swedish kronor ($1.45 million), divided equally if a prize

is shared.

Indians in the list;

Ronald Ross, the 1902 Medicine Prize winner for his work on malaria, was born in

Almora (now in Uttarakhand) and did much of his work in India.

Rudyard Kipling, born in Bombay in 1865, got the 1907 Literature Prize.

Rabrindranath Tagore, 1913 Literature Prize. For “his profoundly sen sitive, fresh and

beautiful verse…”said the Nobel citation.

C.V.Raman got the Physics Prize in 1930 for his work on the scattering of light and

for the discovery of the effect named after him.

HarGobind Khorana, geneticist, born in Raipur (British India, now in Pakistan’s

Punjab) shared the 1968 Medicine Prize. He was a US citizen.

Mother Teresa, of Albanian ethnicity, an Indian citizen, won the Peace Prize in 1979.

S.Chandrsekhar, born in Lahore, then British India, got the Physics Prize in 1983 for

his study of stars. He was a US citizen.

Amartya Sen won the 1998 Economics Prize for his contributions to Welfare

economics.

Venkatraman Ramakrishnan, co-winner of the 2009 Chemistry Prize, is a US citizen

who was born in Chidambaram, Tamil Nadu.

- By Ketan Patil (T.E.Mech)

50

My Lovely Angel

Life was full of ups and downs, someone had stole her own view crown,

There was no one she had to blame; every yes was just a shame.

She was running lonely in the crowd; no fellow one was keen to bound,

The voice in her didn't speak for years, maybe lost it somewhere in the tears.

Her mind was full of chaos and things; it needed someone to flap her wings,

Then came this lovely heart and soul, I guess from heaven to make her bold.

He taught her how to be free and strong, gave all his love so that she can run it all along,

And gave her life's beautiful lessons, with all this care she was never blessed.

It made her smile whenever she did, and same was with her when he did,

I asked him once why you came here, he said “in search of LIFE with whom I can share”.

He said his heart was broken & needed care, and when THEY met it mended and repaired,

The timing was perfect for him and her, GOD bless that person who searched the lock for that

key.

Lets come to start his love had no comparison, it made her wise and gave her life a reason,

She flourished a plenty, he knows it all, “Oh my God! Can love do that all?”

The two roads that met are now one; these moments are special that can never be earned,

Stealing it all and keeping it safe, it's the only thing from her that no one can take.

Now there is one fear in her good heart imprisoned, she thinks it may happen someday,

sometime with reason,

If at all someday her Angel will leave, do you think there’s any chance that his love can

live????

-By Yugandhara Sonkusare (S.E.Mech)

- Photograph by Jayesh Raorane (S.E.Mech)

51

Tunnel boring machine

Tunnel boring machines (TBM) excavate tunnels with a circular cross section through a

variety of rock strata. They can be used to bore through hard rock or sand and almost

anything in between. Tunnel diameters can range from a meter (done with micro-TBMs) to

15 metres. The two biggest were built in 2005 to dig two tunnels for the same urban project

in Madrid (Spain). Dulcinea and Tizona, as they were called, have diameters of 15 metres.

Tunnel boring machines are used as an alternative to drilling and blasting (D&B) methods. A

TBM has the advantages of not disturbing surrounding soil and producing a smooth tunnel

wall. This significantly reduces the cost of lining the tunnel, and makes them suitable to use

in built-up areas. The key disadvantage is cost. TBMs are expensive to construct, difficult to

transport and require significant infrastructure.

Sketch of fluid shield TBM. Note that the cutting wheel is flooded by a bentonite suspension (light

brown). Bentonite pressure is controlled by a pressurized air reservoir (light blue). An erector grabs

the segments to build concrete rings.

Description

A tunnel boring machine (TBM) typically consists of one or two shields (large metal

cylinders) and trailing support mechanisms. At the front end of the shield a rotating cutting

wheel is located. Behind the cutting wheel there is a chamber where, depending on the type

of the TBM, the excavated soil is either mixed with slurry (so-called slurry TBM) or left as-

is. The choice for a certain type of TBM depends on the soil conditions. Systems for removal

of the soil (or the soil mixed with slurry) are also present.

52

Behind the chamber there is a set of hydraulic jacks supported by the finished part of the

tunnel which push the TBM forward. The action here is very much like an earthworm. The

rear section of the TBM is braced against the tunnel walls and used to push the TBM head

forward. At maximum extension the TBM head is then braced against the tunnel walls and

the TBM rear is dragged forward. Behind the shield, inside the finished part of the tunnel,

several support mechanisms which are part of the TBM can be found: dirt removal, slurry

pipelines if applicable, control rooms, rails for transport of the pre-cast segments, etc. The

cutting wheel will typically rotate at 1 to 10 rpm (depending on size and stratum), cutting the

rock face into chips or excavating soil (muck). Depending on the type of TBM, the muck will

fall onto a conveyor belt system and be carried out of the tunnel, or be mixed with slurry and

pumped back to the tunnel entrance. Depending on rock strata and tunnel requirements, the

tunnel may be cased, lined, or left unlined. This may be done by bringing in pre-cast concrete

sections that are jacked into place as the TBM moves forward, by assembling concrete forms,

or in some hard rock strata, leaving the tunnel unlined and relying on the surrounding rock to

handle and distribute the load.

The English Channel Construction-

Digging the tunnel took 13,000 workers over seven years, with tunneling operations

conducted simultaneously from both ends. The prime contractor for the construction was the

Anglo-French TransManche Link (TML), a consortium of ten construction companies and

five banks of the two countries. Engineers used large tunnel boring machines (TBMs).

In all, eleven TBMs were used on the Channel tunnel:

three French TBMs driving from Sangatte to under the Channel,

one French TBM driving the service tunnel from Sangatte cofferdam to the French

portal,

one French TBM driving one running tunnel from Sangatte cofferdam to the

French portal, then the other running tunnel from the French portal back to

Sangatte cofferdam,

three British TBMs driving from Shakespeare Cliff to the British portal,

three British TBMs driving from Shakespeare Cliff to under the Channel

The Channel Tunnel is 50.450 km (31.35 miles) long, of which 37.9 km (23.55 miles) are

undersea. The average depth is 45.7 m (150 ft) underneath the seabed, and the deepest is

60 m (197 ft). It opened for business in late 1994, offering three principal services: a shuttle

for vehicles, Eurostar passenger service linking London primarily with Paris and Brussels,

and freight trains.

53

In 2005, Euro tunnel carried 2,047,166 cars, 1,308,786 trucks and 77,267 coaches on its

shuttle trains. Rail freight carried through the Channel Tunnel in 2005 was 1.6 million tonnes.

Due to higher access charges, this dropped to 1.2 million tonnes by 2007.

The Passengers travel through the Channel Tunnel increased by 15% in 2004 and 2.4% in

2005 up to 7.45 million. In 2006 passenger numbers were 7.86 million. Travel has further

increased with the opening of High Speed 1 to London. In 2007, Eurostar carried 8.26 million

passengers between London, Paris and Brussels.

A journey through the tunnel lasts about 20 minutes; from start to end, a shuttle train journey

totals about 35 minutes, including traveling a large loop to turn the train around. Eurostar

trains travel considerably slower than their top speed while going through the tunnel

(approximately 160 km/h [100 mph]), rather than their maximum of 300 km/h (186 mph) to

fit in with the shuttle trains and avoid problems with heat generated in the tunnels by

compression of the air in front of the train.

At completion, it was estimated that the whole project cost around £10 billion, representing a

cost overrun of 80%. The tunnel has been operating at a significant loss, and shares of the

stock that funded the project lost 90% of their value between 1989 and 1998. The company

announced a loss of £1.33 billion in 2003 and £570 million in 2004, and has been in constant

negotiations with its creditors. Eurotunnel cites a lack of use of the infrastructure, an inability

to attract business because of high access charges, too much debt which causes a heavy

interest payment burden and a low volume of both passenger and freight traffic 38% and

24%, respectively, of that which was forecast.

Sketch of an earth pressure balanced TBM. Note that drilled material passes a screw conveyor before

it drops on a conveyor belt to be carried away. Pushing cylinders (yellow) press the TBM forward

against the concrete segmental lining. The concrete segments are transported via trains.

- By Sayooj Pillai (T.E.Mech)

54

The Rangoli Life

And so we talked all night about the rest of our lives

Where we're going to be when we turn 25,

I keep thinking times will never change,

Keep on thinking things will always be the same,

But when we leave this year, we won't be coming back,

No more hanging out 'cause we're on a different track

And if you got something that you need to say

You better say it right now 'cause you don't have another day

Cause we're moving on and we can't slow down

These memories are playing like a film without sound

And I keep thinking of that night in June

I didn't know much of love, but it came too soon

And there was me and you all

All together we had a ball.

We'd get so excited; we'd get so scared,

Laughing at ourselves thinking life's not fair.

And this is how it feels as we go on

We remember all the times we had together,

And as our lives change come whatever,

We will still be Friends forever,

So what if we get the big jobs

And we make the big money

When we look back now

Will our jokes still be funny?

Will we still remember everything we learned in school?

Still be trying to break every single rule?

Will we think about tomorrow like we think about now?

Can we survive it out there, can we make it somehow?

I guess I thought that this would never end

And suddenly it's like we're women and men

Will the past be a shadow that will follow us 'round?

Will these memories fade when I leave this town?

I keep; I keep thinking that it's not goodbye

Keep on thinking it's a time to fly.....

-By Lalit Mehta (B.E.Mech)

55

- By Shweta Karampudi (B.E.Mech)

56

From candle seller to CEO of `440-cr biz

He used to sell decorative candles to newly-wed couples along the roadside in

Chandigarh. “I was never interested in studies, and I always wanted to do something of my

own.” says Naresh Gulati,who is now the owner of ` 440-crore Oceanic Consultants Australia

Group(OCA Group).

From selling candles to wholesale cloth trading to cosmetics-wholesale and teaching

at Aptech Computers to running a computer centre, the 39 year old tried his hands at many

things before homing in on overseas education consultancy business.

The journey has not been easy for Mr.Gulati who flunked in class 10 and performed

miserably in college. But he is now a guest lecturer on entrepreneurship in leading Australian

universities. Armed with a diploma in electronic data processing, Mr Gulati went to RMIT,

Melbourne, in 1995 for a post-graduate course in information systems. However, destiny had

scripted a different chapter for him.

“When I reached there, I realized that I had been duped. I was promised a job in

Melbourne by my immigration consultant, and that would have helped me clear the loan that

I took for going overseas”, recalls Mr.Gulati. For the next 6 months, Mr.Gulati came in touch

with several students who had met the same fate. And this made him think about a fantastic

business opportunity-immigration consultancy business.

Mr.Gulati came back to Chandigarh in 1996 and started Oceanic Consultants.

“Chandigarh had over 110 such agencies at that time, and I was discouraged by many not to

venture into this business”, says Mr.Gulati. “There was a time when I had to choose between

two options-paying the rent or using that money for advertising. I chose the latter and the risk

paid off”.

In three years, Oceanic Consultants had opened branches in Ludhiana, Patiala,

Jalandhar and Amritsar. However the franchise model was not sustainable as quality was

getting affected and people were not interested in investing money. Moreover, established

players such as Study Overseas and IBP Education created a dent in whatever little marketing

Oceanic did.

Oceanic Consultants then zeroed in on company owned office model. And the

decision paid off. Oceanic now has 20 offices across India and will take the count to 60 by

2013.

“We opened our Australia office a decade back and the UK office last year. By the

end of this year, we will be present in US and Canada. Punjab offices have now started to

become profitable, while others will soon follow suit”, says Mr.Gulati. He saw another

opportunity in printing and distribution segments of universities. “In 2005, we developed a

new technology enabling online orders of prospectus printing, posting and tracking from

India to anywhere in the world. This outsourcing facility has helped universities save 25-65%

of their profits even when our investment in starting BPO intelligence was A$1000”, adds

Mr.Gulati.

57

In five years, BPO Intelligence is the leading company in Australia with 29 of the 39

universities using its services. Seven of the eight universities in New Zealand and 8 clients in

UK also use these services. The next year another idea on software solutions for the

education industry lead to formation of Object Next Software with an investment of A$5

million. In 2007, after a corporate restructuring, the OCA Group became the parent company

of Oceanic Consultants, BPO Intelligence and Object Next based out of Australia. The

companies have been winning accolades from Australian business Awards every year since

2008.

This year, Oceanic Consultants won the Australian Business Award for best enterprise

in personal services industry. While Object Next won the award for best new product, BPO

Intelligence won awards in two categories-product value and product excellence.

The Fairfax Media Group’s Business Review Weekly ranked BPO Intelligence as the

12th

fastest growing company in Australia this year, up from 93rd in 2008. Today, it

contributes more than 30-40% of the group’s total revenue of A$20 million. To make

Oceanic Consultants meaner and leaner, Mr Gulati brought in PriceWaterhouseCoopers last

year to do a performance management of the entire system, and the same time added a private

network connecting all its offices across different countries. Mr Gulati feels India will fuel

the growth in overseas education even when the Indian government is rooting for foreign

universities to come and set up shop.

“The demand for quality education and a global qualification is high in India. We plan

to capitalize on this demand and become a global player, enabling admissions from any place

to any place in the world. We are investing heavily into technology, which would allow us to

hold global webinars providing virtual access to everyone” adds Mr Gulati.

-By Gaurav Mendon (S.E.Mech)

EAGLES IN A STORM

Did you know that an eagle knows when a storm is approaching long before it breaks?

The eagle will fly to some high spot and wait for the winds to come. When the storm hits, it

sets its wings so that the wind will pick it up and lift it above the storm. While the storm rages

below, the eagle is soaring above it.

The eagle does not escape the storm. It simply uses the storm to lift it higher. It rises on the

winds that bring the storm.

When the storms of life come upon us - and all of us will experience them - we can rise above

them by setting our minds and our belief toward God. The storms do not have to overcome

us. We can allow God's power to lift us above them.

God enables us to ride the winds of the storm that bring sickness, tragedy, failure and

disappointment in our lives. We can soar above the storm.

Remember, it is not the burdens of life that weigh us down but it is how we handle them.

58

-Photograph by Sandeep Jana (T.E.Mech)

59

- Photographs by Aditya Shenoy (S.E.Mech)

60

How the Wright Brothers changed the world

On Dec. 17, 1903, The Wright Brothers' Flyer was the first powered airplane to execute

controlled and sustained flight.

CREDIT: NASA

It was an event that lasted just 12 seconds and made it into only four newspapers the next

morning. The pioneering, 120-foot flight over Kitty Hawk, North Carolina, may have gone

off with little fanfare that day in 1903, but it would soon have enormous implications that

wrapped, very literally, around the world.

Brothers Orville and Wilbur Wright did not invent flight, but they became the Internet of

their era with their invention of the first manned, powered, heavier-than-air and (to some

degree) controlled-flight aircraft, bringing people and ideas together like never before. In just

a few decades, the basics of their science and engineering became instrumental in warfare,

put globalization on the map and man on the moon.

Wilbur an enthusiast, not a crank

The birth of aeronautical science originated not from the laboratory of a respected university,

but the back room of a bicycle shop in Dayton, Ohio.

Interest in aeronautics had exploded during the 19th century, as the technical how-to finally

began to catch up with humanity's centuries-old interest in flight. Several scientists tested

gliders throughout the 1800s, filling data tables about lift and drag, but no gliders ran on

power other than that provided by the wind. A steam-powered airship built by Henri Giffard

flew successfully in 1852, marking what many now call the advent of powered flight.

In 1899, Wilbur wrote this letter to the Smithsonian Institution, requesting copies of all the

past research done:

61

"Dear Sirs:

I am an enthusiast, but not a crank in the sense that I have some pet theories as to the proper

construction of a flying machine. I wish to avail myself of all that is already known and then

if possible add my mite to help on the future worker who will attain final success.”

Paying the bills with sales from their bike store, Wilbur (the visionary) and Orville (the

engineer) set to work on a flying machine. The brothers started by building kites based on the

flight mechanics of birds they had observed and moved onto manned gliders.

Four years after Wilbur's humble letter, the Wrights were ready to test an aircraft powered by

an engine and propeller.

On December 17, 1903, Orville climbed into the primitive cockpit and lifted the "Flyer," as it

was called, from the level ground of Kitty Hawk into the air and flew for 12 seconds before

landing with a thud 120 feet away. The brothers made four flights that day, the last one

soaring 852 feet and lasting almost one minute, launching the world into the aviation age for

good.

From Kitty Hawk to outer space

When news about their feat at Kitty Hawk reached the newswires, the Wright brothers

became instant celebrities. The scientific reaction was swift, too, with competitive inventors

attempting their own flying machines in cornfields around the world.

It was the U.S. government that encouraged the first mass manufacturing of the airplane,

which it saw as a potentially powerful weapon and reconnaissance vehicle. When World War

I broke out in 1914, there was a new battlefield for the first time in millennia. Airplane

technology sped up dramatically during the war and was a pillar of the wartime economy. By

the 1930s, the U.S. had four airlines delivering millions of passengers, limited mostly to the

upper class, to points across the country and the Atlantic Ocean and, by the end of the decade,

the Pacific. With the dawn of commercial air service, the world opened up in a new way,

allowing people to visit places they'd only read about in books. Aviation greatly affected the

outcome of World War II, too, and war equally affected aviation. Airplanes carried

paratroopers across the English Channel, dropped the first atomic bomb and, by the end of

the war, its manufacturing had helped put the United States at the forefront of all the world's

postwar economies, where it remained until the 1970s.

There was nowhere to go but up. The birth of the jet age in the 1950s, man's first steps on the

moon, even Richard Branson's just-announced commercial space tourist plan, all have their

scientific roots in the field of Kitty Hawk.

In less than 100 years, the Wright's shaky craft had turned into a vehicle fit to explore outer

space.

-By Aniket Ranade (T.E.Mech)

62

Alexander Fleming

His name was Fleming, and he was a poor Scottish farmer. One day, while trying to

eke out a living for his family, he heard a cry for help coming from a nearby bog. He dropped

his tools and ran to the bog. There, mired to his waist in black muck, was a terrified boy,

screaming and struggling to free himself. Farmer Fleming saved the lad from what could

have been a slow and terrifying death.

The next day, a fancy carriage pulled up to the Scotsman's sparse surroundings. An

elegantly dressed nobleman stepped out and introduced himself as the father of the boy

Farmer Fleming had saved.

"I want to repay you," said the nobleman. "You saved my son's life."

"No, I can't accept payment for what I did," the Scottish farmer replied, waving off the offer.

At that moment, the farmer's own son came to the door of the family hovel.

"Is that your son?" the nobleman asked. "Yes", the farmer replied proudly.

"I'll make you a deal. Let me take him and give him a good education.

If the lad is anything like his father, he'll grow to a man you can be proud of."

And that he did. In time, Farmer Fleming's son graduated from St. Mary's Hospital

Medical School in London, and went on to become known throughout the world as the noted

Sir Alexander Fleming, the discoverer of Penicillin.

Years afterward, the nobleman's son was stricken with pneumonia.

What saved him? Penicillin!

The name of the nobleman, Lord Randolph Churchill!

His son's name, Sir Winston Churchill!

-By Mayur Patil (T.E.Mech)

63

- Photograph by Akhil Naraynan (T.E.Mech)

Facebook— Did you know?

Facebook has over 550 million members and is expected to grow to one billion by August

2012.

Over 145 million users are in the USA, followed by Indonesia (31.7m), the UK (28.9m),

Turkey (24m), France (20.4m) and India (16.5m). China where Facebook is generally

blocked has some 92,500 members.

Every minute: Facebook gets over 5 lakh comments, 3.8 lakh comments are liked, 2.3 lakh

messages are sent, 1.36 lakh photos are added, and nearly one lakh friendships are approved.

Facebook can be used in more than 75 languages.

At the rate of about 10,000 per day, some two million other websites are integrated with

Facebook.

64

Love & Time

Once upon a time, there was an island where all the feelings lived: Happiness, Sadness,

Knowledge, and all of the others, including Love. One day it was announced to the feelings

that the island would sink, so all constructed boats and left, except for Love.

Love was the only one who stayed. Love wanted to hold out until the last possible moment.

When the island had almost sunk, Love decided to ask for help.

Richness was passing by Love in a grand boat. Love said, "Richness, can you take me with

you?" Richness answered, "No, I can't. There is a lot of gold and silver in my boat. There is

no place here for you."

Love decided to ask Vanity who was also passing by in a beautiful vessel. "Vanity, please

help me!"

"I can't help you, Love. You are all wet and might damage my boat," Vanity answered.

Sadness was close by so Love asked, "Sadness, let me go with you."

"Oh . . . Love, I am so sad that I need to be by myself!"

Happiness passed by Love, too, but she was so happy that she did not even hear when Love

called her.

Suddenly, there was a voice, "Come, Love, I will take you." It was an elder. So blessed and

overjoyed, Love even forgot to ask the elder where they were going. When they arrived at dry

land, the elder went her own way. Realizing how much was owed the elder, Love asked

Knowledge, another elder, "Who helped me?"

"It was Time," Knowledge answered.

"Time?" asked Love. "But why did Time help me?"

Knowledge smiled with deep wisdom and answered, "Because only Time is capable of

understanding how valuable Love is."

-By Suja Pillai (S.E.Mech)

65

Arun Nadar (B.E. Mech)

The Mahindra Autoquotient was the 1st automotive quiz held in India and it was open

only for engineering students only. The 1st round was held at IIT-Mumbai and he won from

Mumbai. After that the regional finals for the western zone was held at the NDTV-studio in

Mumbai. In the western zone the others teams were from Ahemdabad, Bhopal and Pune in

which he won. After the zonal it was time for the national finals in which he was 4th. In that

1st placed were Banglore, 2

nd Pilani and 3

rd Rourkela.

He made our college very proud placing it on 4th

place among the 700 Engineering

colleges that had participated from all over India and the total number of contestants were

around 4,500.

The regional and national finals were broadcasted on NDTV-Profit and were hosted

by one of the premiere auto-journalist of our country, Siddharth Patankar.

66

Rohan Crasto (T.E.Mech), Shashanka Kshetrapalasharma (B.E.Mech)

The 10th

ISHRAE Intercollegiate Engineering Quiz Finals 2011, Mumbai was held on 10th

February, 2011 in the central quadrangle of Sardar Patel College of Engineering campus. The

quiz committee comprising Professor Dr. Roshini Easow, V Krishnan, Nitin Naik, D Krishna

Kumar, Vikram Murthy and the Quiz Master, B. Gautham Baliga, put together a quiz contest

which was a combination of KBC and IPL. Alric Ferns (‘Al’ for short), the radio jockey of

107.1FM, was the host for the evening which was attended by a packed and delirious

audience of over 400 students and faculty members.

The format of the quiz was as follows: Each of the six shortlisted teams got to the

high table, one at a time, and faced a total of 10balls (questions). Each correctly answered

question resulted in a run. As the team accumulated runs, they progressed in their levels.

Starting with 3runs for Gully Level, 6runs & 8 runs got the teams to Maidan and Ranji Level

respectively. With a perfect 10, Test Level was achieved. Giving a wrong answer or not

answering, resulted in the team going back one level, much like the snake and ladder game.

The level attained by the team at the end of 10 balls determined the level of the team and the

prize money earned. Starting from Gully Level, the prize money was progressively: Rs3000/-

, Rs6000/-, Rs12000/-, and Rs25000/-.

The IQL teams from all of the 10 colleges with Student chapters were appropriately

named: S P Lions, V. J Victors, Pillai Panthers, B.A.T.U Blasters, Somaiya Samrats, Datta

Meghe Dashers, Vidya Peeth Veeras, Rodrigues Rockers, Tilak Tigers, and Vardhini

Warriors.

A written elimination reduced the teams to 6 consisting of SP Lions, BATU Blasters,

Vardhini Warriors, Datta Dashers, Pillai Panthers and Rodrigues Rockers.

WINNERS:

The winners were the ‘Pillai Panthers’ team of Rohan Crasto and Shashanka

Kshetrapalasharma of our college with runners-up ‘Rodrigues Rockers’ team of Kaustubh

Pande and Shailesh Tripathi of Fr.C Rodrigues Institute of Technology, Vashi. Both teams

67

represented ISHRAE Mumbai chapter in the All-India Quiz Competition at ACREX 2011 in

New Delhi during 24th and 25th February 2011.

A large number of ISHRAE members including Viresh Ruhal, president - ISHRAE

Mumbai, M. P. Agarwal, president - ASHRAE Mumbai, and V. Krishnan, national president

elect, were present to encourage the students. The enjoyable event was put together by the

ISHRAE Mumbai team including the office staff.

- By Rajeshwari Hegde (B.E.Mech)

68

- By Shweta Karampudi (B.E.Mech)

69

70

The Society for the Promotion of Indian Classical Music And Culture Amongst Youth,

often known by its initials (SPIC MACAY), promotes Indian classical music, Indian

classical dance, and other aspects Indian culture. It is a movement with chapters in over 300

towns and cities all over the world. SPIC MACAY was established by Dr. Kiran Seth in 1977

at IIT Delhi.

It seeks to foster the exchange of traditional Indian values and to generate awareness of the

cultural traditions and heritage of India. In order to achieve its goals, SPICMACAY

organizes concerts, lectures, demonstrations, informal discussions, and seminars. These are

hosted by local chapters of the organization.

The inaugural chapter of SPIC MACAY was started in our college this year. It was held from

31st January to 8

th February 2011. It consisted of performances from various prominent artists

in the field of classical music.

It began with the screening of Satyajit Ray’s movies on the first two days.

The Yakshagana troupe performed Duryodhana Vadh from Mahabharata which was so

frolicsome to watch. It was on 2nd

of February.

On 3rd

February there was a performance by Dr.N.Rajam. She demonstrated the different

subtle variations on violin. Then there was an interactive session cum performance by Arati

Ankalikar-Tikekar on 4th

February. She even invited a student from the crowd to assist her on

Tabla. Students were simply ecstatic after her performance.

There was a workshop on Dhrupad singing from 7th

to 10th February by Ustad Zia Fariduddin

Dagar. On the final day we had performances from Ustad Fariduddin Dagar & Pandit

Pushparaj Koshti.

The event generated a great interest amongst students towards the Indian classical music.

71

72

Some pictures from the E.F.l. 2010-2011

73

Prizes

We are thankful to all the students who have helped us in making ‘The

Mechzine’ September 2010 issue by providing the literary material, sketches &

paintings. We got a lot of material some of which we have used in this edition.

The following are the names of students who have won cash prizes,

decided by the jury members, in different categories of the magazine.

Poems: Swapnil Bhatkar (T.E.Mech)

Sketches: 1.Shweta Karampudi (B.E.Mech) 2. Aniket Ranade (T.E.Mech)

Photographs: 1. Anup Patil (T.E.Mech) 2. Akhil Narayanan (T.E.Mech)

Articles: 1. Technical- Kushal Shamdasani (B.E. Mech) 2. Non-Technical- Santosh Naik (B.E. Mech)

During the last issue of The Mechzine, September 2010, one of the names of the members

from the magazine committee, Aniket Ranade wasn’t printed. The mistake is deeply

regretted.