Manufacturing Technology (ME461) Lecture11

8/12/2019 Manufacturing Technology (ME461) Lecture11

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Manufacturing Technology

(ME461)

Instructor: Shantanu Bhattacharya


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Review of Previous Lecture

Determination of cutting parameters,

Velocity, Tool life etc.

Minimum cost model

Maximum Production Rate model

Numerical example.


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The principal process planning

approaches

The principal approaches to process planning are the manual experience

based method and the computer aided process planning method.

The manual experience-based planning method:

The manual experience based methods have the same steps for manually

generating a process plan as described earlier. However, it is a timeconsuming and inconsistent approach.

The feasibility of process planning is dependent on many upstream factors

such as design and the availability of machine tools.

Also, a process plan has a great influence on many downstream

manufacturing activities such as scheduling and machine tool allocation. Therefore, to develop a process plan, process planners must have sufficient

knowledge and experience. It may take a relatively longer time and is

usually expensive to develop the skill of planners.


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Computer aided process planning

The primary purpose of a process planning activity is to translate a design

of any product into manufacturing process details. This prima facie suggests a feed forward system in which the design

information is directly processed by a CAPP system to layout a

manufacturing plan. However, this does not rhyme very well with the

concurrent engineering philosophy where design is an integral part in all

steps of a manufacturing unit. So, somehow we have to integrate the CAPP system into the inter-

organizational flow.

1. For example if we change the design we should be able to fall back on a

CAPP system to generate cost estimates of these design changes.

2. Similarly, if there is a breakdown the CAPP should be able to suggest an

alternative plan so that the most economical situation can be adopted.

The two major methods that are used in computer aided process

planning: the variant CAPP and the generative CAPP method.


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Framework for a CAPP system


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The variant CAPP method

In the variant process planning approach, a process plan for a new

part is created by recalling, identifying, and retrieving an existingplan for a similar partmaking necessary modifications for the newpart.

Quite often, process plans are developed for parts representing afamily of parts. Such parts are called master parts.

The similarities in design attributes and manufacturing methodsare exploited for the purpose of formation of part families.

A number of methods have been developed for part familyformation using coding and classification systems of grouptechnology (GT), similarity coefficient based algorithms, and

mathematical programming models.


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4-steps to variant process planning Define the coding scheme: Adopt existing coding or

classification schemes to label partsfor the purpose of

classification.

Group the parts into part families: Group the parts into part

families using the coding schemeselected earlier based on

commonality of part features.

Develop a standard process plan:Develop a standard processplan for each part family based on the common features of the

part types. This process plan can be used for every part type

within the family with suitable modifications.

Retrieve and modify the standard plan: When a new partenters the system, it is assigned to a part family based on the

coding and classification scheme.Then the corresponding

standard process plan is retrieved and modified to

accommodate the unique features of the new part.


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Some commonly used variant

approaches

One of the most widely used systems iscomputer aided process planning, developedby Mcdonnell-Douglass Automation company

under the direction of CAM-I (Computer AidedManufacturing International).

The other popular variant is the MIPLAN,developed by OIR (Organization for industrialresearch) and General Electric Company(Hurzeel, 1976).


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The generative CAPP method In the generative approach, process plans are

generated by means of decision logic, formulas,

technology algorithms, and geometry based

data to perform uniquely the many processing

decisions for converting a part from raw

material to a finished state.

There are two major components of a generative

process planning system.

1. A geometry based coding scheme.

2. Process knowledge in the form of decision logic

and data.


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Geometry based coding scheme

The objective of a geometry based coding scheme is todefine all geometric features for all process relatedsurfaces togetherwith feature dimensions, locations,tolerances and the surface finish desired on the

features. The level of detail is much greater in a generative

system than a variant system.

For example, such details as rough and finished states

of the partsand process capability of machine toolstotransform these parts to the desired states areprovided.


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Process Knowledge in the form of decision logic

and data Basically, the matching of part geometry requirements with the

manufacturing capabilities is accomplished in this phase using

process knowledgein the form of a decision logic and data.

All steps related to process planning is automatically carried out.

1. Examples include process selection, machine tools, tools, jigs

and fixture, materials, inspection equipments and sequencing of

operations.

2. Setup and machining times are calculated.3. Operation instruction sheets are generated to help the

operators run the machines in the case of manual operations.

4. If the machines are numerically controlled, the NC codes are

automatically generated.


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Manufacturing knowledge is the backbone of process planning

is not a one time activity but a recurring dynamic phenomena.

Furthermore the sources of manufacturing knowledge are many

and diverse, such as the experience of manufacturing personnel;

handbooks; suppliers of major machine tools, tools, jigs or

fixtures, materialsand inspection equipments and customers.

To use this wide spectrum of knowledge ranging from qualitative

and narrative to quanitative, it is necessary to develop a good

knowledge structure to help provide a common denominator for

understanding manufacturing information, ensuring its clarity

and providing a framework for future modifications. Tools

available for this purpose are flowcharts, decision trees,

decision tables etc.

Basis of Process Knowledge method


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Decision Tables Decision tables provide a convenient way to document

manufacturing knowledge.

They are principle elements of all decision table-based processplanning systems.

The elementsof a decision table are conditions, actions, and rules.

They are organized in the form of an allocation matrix as shown in

the table belowwhere the conditions state the goal we want to

achieve and the actions state the operations we have to perform.

The rules formed by entry values according to the experience of

experts, establish the relation between conditions and actions.


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Decision Tables Entries can be either Boolean-type values (true, false and do

not care) or continuous values. Tables below show this.

The decision making mechanismworks as follows:

For a particular set of condition entries, look at its

corresponding rule, and from that rule determine the actions.

For example, if the condition is to

drill a hole, then from the rules we

look for the rule that can be applied,

and from that rule we get the

solution.


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S l i


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Solution

From the set of conditions given in the problem, it is

easy to see from the table that the rule 3 is suitable forthis situation. The action, therefore, is obviously turretlathe; that is, the operation is performed on a TurretLathe.

Similarly, the solution is engine lathe. From the conditions given in the problem, we find that

rule 2 is most suitable. Therefore, the recommendedactions are to finish parts on an engine lathe and

subsequently on a center-less grinding machine toachieve the desired specifications.


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Knowledge based process planning

A knowledge based system refers to a computer

program that can store knowledge of a particular

domain and use that knowledge to solve problems

from that domain in an intelligent way.

In a knowledge based process planning system, we use

a computer to simulate the decision process of ahuman expert.

Usually, human process planners develop process

planningbased on their experience, knowledge and

inference.

A computer to some extent can be used to perform

these functions and develop a process plan.


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Knowledge based process planning

Two major problems need to be addressed in such a system: The

knowledge representation and the inference mechanism.

The knowledge representation is a scheme by which a real world

problem can be representedin such a way that the computer can

manipulate the information.

For example to define a part, we need to define whether there isa hole in it. Given that there is a hole, we next have to define the

attributes of the hole, such as the type of hole, length, and the

diameter.

The reason for this is that the computer is not able to read the

design directly from the blueprint. The inference mechanism is a

wayin which the computer finds a solution.

One approach is based on IF-THENstructured knowledge like IF

there is a hole THEN a drill may be used.


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Knowledge based system: EXPLAN

The knowledge based system: EXPLAN is a

subproject of the research project on the factory of

the future, as part of the European Research

Initiative called EUREKA.

To build a comprehensive system, it is important thata proper model is established.

This system models the process planning world by

three approaches: work-piece geometry, machining

and planning.


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Work-piece geometry model In a work-piece geometry model, a part is described in terms of basic

processing elements (BEs).

Usually a processing element can be produced by a simple operation.

In this way we have establishedthe connectionbetween the operation

and the geometry. See figure below:


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Machining Model The machining model encompasses various types of planning knowledge,

such as operations and the sequence of operations needed to remove

the part of the material. It represents the process plan schematicallyand includes knowledge of

how each sequence of operations, such as setup and clamping is

performed. See Figure.


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Planning Model

The planning model is a portion where the

clamping positions and other factors are takeninto account for the purpose of reducing cost.

For example allocating different set of BEs to a

certain setup may affect the manufacturing cost. Usually the goal is to maximize the work content

of each individual setup, which helps to achieve

the ultimate objective of reducing the total cost

of the product.


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The EXPLAN ModelThree basic components of the system are knowledge base,

dialogueand inference engine.

The dialoguecomponent connects the users and the system anddefines how the users can use inference engine.

Also, the link with the external data files like Materials

Requirement Planning (MRP) and CAD database is included in this

component.

l


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Feature Recognition In Computer Aided Process Planning Capp systems usually serve as a link between CAD and CAM.

However, it is only a partial link, because most of the existingCAD/drafting

systems do not provide part feature information, which is essential data for

CAPP. CAPP systems do not understand the three dimensional geometry of the

designed parts for CAD systems in terms of their engineering meaning related

to manufacturing and assembly.

This is commonly referred to as the feature recognition problem.

For example the object shown in the solid geometry tree given below

represents a block primitive and a cylinder primitive combined by a Boolean

operator .

The shape and dimension of the object are

uniquely definedby this scheme.

However, some useful higher level information

such as whether the hole is a blind hole or a

through hole is not provided.

This kind of information, defined in terms of

feature, is essential to process planning.

Feature recognition in computer aided process


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Feature recognition in computer aided process

planning

From an engineering point of view, features areregarded as generic shapesof objects with whichengineers associate certain attributes and knowledgeuseful in reasoning about or describing the products.

A generic part feature recognition system should beable to:

1. Extract design information of a part drawn from aCAD database.

2. Identify all surface of the part.

3. Recognize, reason about, and interpret thesesurfaces in terms of part features.

P t f t iti h


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Part feature recognition approaches A number of approaches to part feature recognition for

rotational as well as prismatic parts have been developed suchas syntactic pattern recognition, geometry decomposition,

expert system rule logic, graph based and set rhetoric out ofwhich Graph based approach will be explored here.

Graph based approach:

This usually consists of three basic steps:

STEP1: Generating graph based representation of the object to berecognized.

STEP2: Defining part features.

STEP3: Matching features in the graph representation.

1. In the first step, an object is represented by graph. This step is necessary because the

data extracted from the database are usually in the form of boundary representationand are not directly usable for feature recognition.

2. In order to recognize a feature, the information regarding the type of face adjacency

and relationships between the sets of faces should be expressed explicitly.

3. To facilitate the recognition process the AAG (attributed adjacency graph) method is

used.

fi i i f A ib d Adj G h


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Definition of Attributed Adjacency Graph

An AAG can be defined as a graph G = (N,A,T), where N is the

set of nodes, A is the set of arcs, and T is the set of attributes

to arcs in A such that:

For every facef in F, there exists a unique node nin N.

For every edge ein E, there exists a unique arc ain A,

connecting nodes nito ni corresponding to face fiand face fj

which share the common edge e.

Every arc a inA is assigned an attribute t, where:

t = 0 if the faces sharing the edge form a concave angle [inside

edge]

t= 1 if the faces sharing the edge form a convex angle [ ourside

edge]


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The AAG is represented in the computer in the

form of a matrix, which is defined as follows:

Definition of Attributed Adjacency Graph

Definition of part features


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Definition of part features

To achieve feature recognition, we first need todefine the feature precisely- that is , what shape

we think is a feature.

Generally, we can define any shape as a feature;

however, only those that have manufacturing

meanings should be defined.

Six commonly used features in manufacturing

are the step, slot, three side pocket, four sidepocket, pocket (or blind hole), and through hole.


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Defining

features

Matching the feature


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Matching the feature After steps 1 and 2, the problem of recognizing machining featuresin part

changes to the problem of recognizing AAG subgraphs that represent the

features in the complete AAG graph representing the part.

The problem of searching of subgraphs in a larger graphs is a subgraphisomorphism problem and is computationally exhaustive and as such there

are no polynomial algorithms to do this.

An algorithm that can be an alternative to isomorphism can be based on

the following observation: A face that is adjacent to all its neighboring faces

with convex angle (270 deg.) does not form part of a feature.

This observation is used as a basis of separating the original graph into

subgraphs that could correspond to features.

The separation is done by deleting some nodes of the graph. The delete

node rule is as follows:If (all incident arcs of a node have attribute 1)

Then (delete this node (and all the incident arcs at the node) from AAG)

Because an AAG is represented in the form of a matrix in the program, the

delete node rule actually deletes rows and columns that represent the

nodes in the matrix.

Problem


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Problem It is easy for the human to see that the part shown below has

a slot and a pocket feature. In this example, however, we

simulate the computer to apply the feature recognition

algorithm discussed earlier. We therefore, want to solve the

following:

(a)Develop the AAG of the object.

(b) Give the matrix representation of the AAG.

(c)Recognize the features in this object.

Solution


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Solution(a)The AAG is first developed as shown below

For the purpose of inputting the

AAG graph into the computer,

we have to convert the graph

into the matrix form. The matrix

representation of AAG is given asfollows:

S l ti


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Solution

Manufacturing Technology (ME461) Lecture11

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Transcript of Manufacturing Technology (ME461) Lecture11