Generation of Alternative Process Plans for Automated...

International Journal of Engineering Research ISSN:2319-6890)(online),2347-5013(print)

Volume No.3 Issue No: Special 1, pp: 164-173 22nd

March 2014

NCETMESD@2014 64

Generation of Alternative Process Plans for Automated Machine Shop Using

Genetic Algorithm Approach

M.khaja gulam hussain, B. Chinna Ankanna

1,2Asst. Professor in School of Mechanical Engineering, RGMCET, Nandyal.

Corresponding Email: [email protected], [email protected]

Abstract— A methodology has been developed to

generate process plans using a Genetic Algorithm (GA)

approach based on the pre defined precedence relations of

manufacturing features. This introduces significant

improvements to the utilization of the manufacturing

facilities through the reduction in schedule conflicts,

reduction of flow-time and work-in-process, increase use of

production resources. GA is capable of performing multi-

objective optimization based on minimum production cost or

minimum processing time. Based on the objective selected,

optimal solutions were obtained using the GA.

Keywords— Computer Aided Process Planning, Computer

Aided Design ,Computer Aided Manufacturing Computer

Integrated Manufacturing, Group Technology ,Matrix

Laboratory, Excel Spread Sheet

I. Introduction

In manufacturing, the goal is to produce components

that meet the design specifications. The design specification

ensures the functionality aspect. Computer-aided process

planning (CAPP) is a bridge between design and manufacturing

in a Computer-Integrated manufacturing (CIM) environment.

Process planning systems are ordered sequences of task, able to

transform raw material into a final part under economical,

qualitative and competitive conditions. Process planning is a

production organization activity that transforms a product design

into a set of instructions to manufacture a machined part

economically and competitively. The information provided in

design includes dimensional specification (geometric shape and

its feature) and technical specification (tolerance, surface finish

etc.) and sequence of operations.

The major process planning activities are interpretation

of product design data, selection of machining processes,

determination of datum surfaces and fixtures, sequencing the

operations, determination of part tolerances, determination of the

cutting parameters, allocation of required manufacturing

resources such as machine tools, cutting tools, jigs and fixtures,

estimation of production times and generation of NC data.

Hence, CAPP has a strong impact on manufacturability

conditions, product quality and production cost.

New approaches tend to integrate CAD, CAPP and CAM

systems together, by using feature-based technology. Recently, a

new trend toward integration is to utilize adaptive, dynamic and

distribution process planning implies that production instructions

for machining a part are generated dynamically and adaptively in

accordance with changeable shop floor status. In other words,

process plan information is generated in real-time in the shop

floor based on current information of shop stages.

Alting and Zhang (1989); Hoda and elmaraghy (1993)

and Kiritsis overviewed on process planning (2001). Altinget. Al,

(1989) Computer aided process planning (CAPP) recognized as

playing key role in Computer Integrated Manufacturing (CIM).

Delbressine, et.al. (1993) have developed a design methodology,

which considers manufacturing restrictions in the geometric

design phase Machining operations have been sequenced in an

effort to minimize the total machining and tooling cost by Lin

and Wang (1993) and Koulomas. Goldberg (1989), Usher and

Bowden (1996) were mentioned that the genetic algorithm with

different coding schemes to reduce the size of the solution space.

Kusiak and Finke (1984) were developed a model for the

selection of a set of process plans, with minimum cost of

removing the material and minimal number of machine tools.

Bennet and Gupta (1969, 1970) synthesized tolerances by

minimizing the sum of the residual tolerance after stack up in

each design constraint. Similar approaches were used by Ostwald

and Huang (1977), Bjorke (1978), and Wade (1983).

Szadkowski (1971) used a graphical representation of

the feasible machining processes at each stage of the process

plan, and used Bellman’s optimum principle to determine the

economical path.

Process planning translates design information into the

process steps and instructions to efficiently and effectively

manufacture products [Crow, 1992]. Process planning can be

divided into macro and micro level production planning [Ham,

1988; Yao, 2003]. The macro level planning is to determine

the setups and process sequences and the micro level

planning is to determine the process details. Computer-aided

process planning (CAPP) has been studied intensively for years

[Zhang, 1999].

II . Manufacturing Process Planning Database.

Process planning system database

The data base of process planning system related to the

information of machines tools, cutting tools, accessories,

machining parameters data, raw materials data and various

manufacturing cost analysis data etc. That related information

data base shown in the following tables.

Table 1: Machines tools Database

mailto:[email protected]



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NCETMESD@2014 Page 165

Table 2: Cutting Tools Database

Table 3:Different Cost Index

Table .4: Raw material database

Table .5: Machining parameters data base for turning



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Table 6: Machining parameters for planning/shaping/slotting

Table 7: Machining parameters for drilling/reaming

Table 8: Tolerance table Machining Operations



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Tolerance Range of Machining Operations(±mm)

Operation Range of Nominal Dimensions (mm)

0-14.9 15-24.9 25-37.9 38-69.9 70-114.9 115-199.9 200-344.9 345-533

Drilling

0.050 ~

0.127

0.064 ~

0.152

0.076 ~

0.203

0.102 ~

0.254

0.127

~ 0.381

0.152 ~

0.381

0.203 ~

0.503

0.254 ~

0.635

Milling

0.030 ~

0.127

0.038 ~

0.152

0.050 ~

0.203

0.064 ~

0.254

0.076

~ 0.305

0.102 ~

0.381

0.127 ~

0.508

0.152 ~

0.635

Turning Boring Planing

0.020 ~

0.127

0.025 ~

0.152

0.030 ~

0.203

0.038 ~

0.254

0.050 ~

0.305

0.054 ~

0.381

0.076 ~

0.508

0.102 ~

0.635

Reaming

0.008 ~

0.050

0.010 ~

0.064

0.013 ~

0.076

0.015 ~

0.102

0.020 ~

0.127

0.025 ~

0.152

0.030 ~

0.203

0.040 ~

0.254

Broaching

0.005 ~

0.020

0.006 ~

0.025

0.008 ~

0.030

0.010 ~

0.038

0.013 ~

0.050

0.015 ~

0.064

0.020 ~

0.076

0.025 ~

0.102

Grinding Diamond Turning

0.004 ~

0.013

0.004 ~

0.015

0.005 ~

0.020

0.006 ~

0.025

0.008 ~

0.030

0.010 ~

0.038

0.013 ~

0.050

0.015 ~

0.064

Lapping and

Honing

0.004 ~

0.008

0.004 ~

0.010

0.005 ~

0.013

0.006 ~

0.015

0.008

~

0.020

0.010 ~

0.025

0.013 ~

0.030

0.015 ~

0.040

III. Optimization techniques for CAPP Geometric programming is a useful method that can be used

for solving nonlinear problems subject to nonlinear

constraints, especially if the objective function to be

optimized is a polynomial with fractional and negative

exponents, while the constraints may be incorporated in the

solution techniques. It is more powerful than other

mathematical optimization techniques when the problem is

restricted by one or two constraints. However if the degree of

difficulty increases the formulated problem might be more

complicated than the original problem. Geometric

programming can only handle continuous variables.

. Flow chart of process plans using GA

The process plan process, in this input are importing

by using GUI and data base read by using EXCEL SHEET,

output best plans, final plane time and cost are exporting in

this data importing and read, output exporting in the different

stages different values are importing and exporting that can

be show in the flow chart below .

Fig 1: Genetic algorithm flow chart

Optimization Process Plans using Genetic Algorithm

The data read from precedence relations is used for

generation of required number of plans which satisfies

precedence relations. The precedence relations should give as

specified. In present work, the precedence relations divided

into individual conditions which the sequence has to satisfy.

For example, the precedence relations for the following

network diagram

Precedence relations: From To table:



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Table..1: From To table for part

Fig 2: Precedence relations for part

Genetic Algorithm program flow chart

IV. Case study - 1 All dimensions are in mm

Raw Material Type: Alloy Steel,Raw Material Shape:

Prismatic (200mm x 100m x50mm), UnitCost:2000Rs/Kg

The precedence relations for the part shown above are shown

below. These precedence relations generated according to

some standard rules. However, the user is allowed to choose

the precedence relations according to requirements and

available resources. The relations shown below are just for

example by considering standard principles.

The precedence relations are then divided into individual

conditions since it is too difficult to deal with network

diagram directly.

After entering data into GUI, the user has to click the push

button with string name ―SUBMIT‖ shown in pink color. The

call back function of SUBMIT will automatically executes the

FROM TO

1 4

1 5

1 6

1 8

1 9

4 2

4 3

8 7



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program and prints the computed results in EXCEL SHEETS

as shown below.

By clicking on SUBMIT, the program starts to execute and

the following results will be exported to excel sheets as shown

below. The first Table5.3 shows the GA best plans generated

according to strategy and the second one Table 5.4 shows the

best alternative plans according to weightage value.

However, the best alternative plans according to time or cost

can be shown since we have already sorted the generated

plans according to cost and time. The user may give zero

weightage to one criterion in order to get the best alternative

plan according to the remaining criteria.

Results And Conclusions

From the present work, it is evident that genetic algorithm is

applicable to solve the problems where, the number of

constraints are more. From the results of the case studies

shown in previous chapter, it is concluded that, Genetic

algorithm can give optimum solution with reasonable time

and acceptable accuracy as compared to other AI techniques.

Conclusion

GA is capable of performing multi-objective optimization

based on minimum production cost or minimum processing

time. Based on the objective selected, optimal solutions can

be obtained using the GA. In this work, the developed

program in MATLAB has the flexibility to choose the

operations sequences based on minimum machining time.

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17-39.



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Operation Information Table:

Table5.1: Operations information table for part-1

L-Length, W-Width, H-Height, d-dia and D-Depth

F ID Feature Operations

Dimensions with Tolerance In mm

Dimensions Tolerance Total

F1 Surface Milling L-200, W-100, H-50 0.076

F2 Surface Milling L-200, W-100 0.076

F3 Pocket Vertical milling L-90, H-10 0.190 L=90.190, H=10.190

F4 Pocket Vertical milling L-10, W-40, H-10 0.190 L=10.190, W=40.190,H=10.190

F5 Step Shaping L-50,W-100,H-30 0.008

F6 Pocket Vertical milling L-20,W-20,H-10 0.190 L=20.190, W=20.190,H=10.19

F7 Hole Drilling d-10,Depth-10 0.127 D=10.127, Depth=10.127

F8 Pocket Vertical milling L-20,W-20,H-10 0.190 L=20.190, W=20.190, H=10.190

F9 Hole Drilling dia-10,Depth-10 0.127 D=10.127, Depth=10.127

F10 Pocket Vertical milling L-100,W-80,H-20 0.190 L=100.190, W=80.190, H=20.19

F11 Through hole Drilling d-10,Depth-50 0.127




F15 Surface Milling L-200,W-100,H-2 0.076



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Precedence Relations:

Table.5.2: From To table for part-1

Fig 5.2: precedence relations for part-1

Fig .3: Input for part 1

FROM TO

15 1

15 2

1 3

2 5

2 10

2 11

2 12

2 13

5 6

5 8

6 7

8 9



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Table5.3. best plan for part with tolerance

Table 5.4 5-plans of part

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