CAPP , JIT, FMS

CAD/CAM MODULE IV AM/JA

1 Department of Mechanical Engineering, AJCE

Module IV

Automated process planning: Process planning, general methodology of group technology, code

structures of variant and generative process planning methods,

A1 in process planning, process planning software.

DISCLAIMER

These notes are not the ultimate ‘look-up’ for Model and University exams. Students are advised to read the

references mentioned at the end thoroughly for the exams

Process Planning

Process planning is concerned with the preparation of route sheets that list the sequence of operations

and work centers require to produce the product and its components. Products and their components

are designed to perform certain specific functions. Every product has some design specifications

which ensure its functionality aspects. The task of manufacturing is to produce components such that

they meet design specifications. Process planning acts as a bridge between design and manufacturing

by translating design specifications into manufacturing process details. It refers to a set of instructions

that are used to make a component or a part so that the design specifications are met, therefore it is

major determinant of manufacturing cost and profitability of products. Process planning answers the

questions regarding required information and activities involved in transforming raw materials into a

finished product. The process starts with the selection of raw material and ends with the completion of

part. The development of process plans involves mainly a set of following activities;

Analysis of part requirements

Selection of raw work piece

Selection of manufacturing operations and their sequences

Selection of machine tools

Selection of tools, tool holding devices, work holding devices and inspection equipments

Selection of manufacturing conditions i.e. cutting speed, feed and depth of cut.

Manufacturing firms try to automate the task of process planning using CAPP systems due to many

limitations of manual process planning.

Approaches to Process Planing

(1) The manual experience-based planning method

(2) Computer Aided Process Planning (Automated Process Planing)

The manual experience-based planning method:

The manual experience-based process planning is most widely used. It is mainly based on a

manufacturing engineer's experience and knowledge of production facilities, equipment, their

capabilities, processes, and tooling. The major problem with this approach is that it is time consuming

and developed plans may not be consistent and optimum. The feasibility of developed process plan is

dependent on many factors such as availability of machine tools, scheduling and machine allocation

etc. Computer aided process planning is developed to overcome this problems to some extent.



These includes:

– Tied to personal experience

– and knowledge of planner of production facilities, equipment, their Capabilities, process and

tooling. This results in inconsistent plans.

– Manual process planning is time consuming and slow.

– Slow in responding to changes in product design and production.

Computer Aided Process Planning (Automated Process Planning)

The primary purpose of process planning is to translate the design requirements into manufacturing

process details. This suggests a system in which design information is processed by the process

planning system to generate manufacturing process details. CAPP integrates and optimizes system

performance into the inter-organizational flow. For example, when one changes the design, it must be

able to fall back on CAPP module to generate manufacturing process and cost estimates for these

design changes. Similarly, in case of machine breakdown on the shop floor, CAPP must generate the

alternative actions so that most economical solution can be adopted in the given situation. A typical

CAPP frame-work is shown in figure below.

CAPP is the application of computer to assist the human process planer in the process planning

function. In its lowest form it will reduce the time and effort required to prepare process plans and

provide more consistent process plan. In its most advanced state, it will provide the automated

interface between CAD and CAM and in the process achieve the complete integration with in

CAD/CAM.

Advantages Over Manual Experience-based Process Planning



The uses of computers in process plan have following advantages over manual experience-based

process planning :

(i) It can systematically produce accurate and consistent process plans.

(ii) It leads to the reduction of cost and lead times of process plan.

(iii) Skill requirement of process planer are reduced to develop feasible process plan.

(iv) Interfacing of software for cost, manufacturing lead time estimation, and work standards can easily be done.

(v) Leads to the increased productivity of process planar.

With the emergence of CIM as predominate thrust area in discrete part industries process planning has

received significant attention, because it is the link between CAD and CAM. Hence, computer aided

process planning (CAPP) has become a necessary and vital objective of CIM system.

Steps Involved in CAPP Now-a-days, rapid progress is being made in the automation of actual production process and also the

product design element. However, the interface between design and production presents the greatest

difficulty in accomplishing integration. CAPP has the potential to achieve this integration. In general,

a complete CAPP system has following steps :

(i) Design input

(ii) Material selection

(iii) Process selection

(iv) Process sequencing

(v) Machine and tool selection

(vi) Intermediate surface determination

(vii) Fixture selection

(viii) Machining parameter selection

(ix) Cost/time estimation

(x) Plan preparation

(xi) Mc tape image generation.

(i) Identify the machinable volumes called pockets by taking the difference of blank size and the finished

component size. For each of the pocket attach the necessary technological details relevant for

manufacturing. The blank size if not given directly can be identified as the largest volume that completely

encloses the finished component.

(ii) Do a preliminary sorting of the pockets in order of levels that clearly indicate the likely se -

quence in the final process plan.

(iii) Examine the pockets for any possibility of combining so that the machining operations could be reduced.

(iv) Select the machine tool that can be used for each of the identified pockets. Minimise the total number of machine tools required. This may have to be modified recursively based on the operation sequence selected.

(v) Identify the process sequence required for the machining of each of the po cket based on the

technological requirements. Help may be obtained in the shape of canned sequences based on the

technological parameters. Any possible alternate plans can also be identified at this stage.

(vi) For each of the pocket and the operation decided, select the cutting tool required.

(vii) Obtain the optimum cutting process parameters (speed, feed, number of passes and depth of cut)



for each of the pocket, tool and the operation combination identified in the earlier steps.

(viii) Sort the operations on the basis of machine tool and cutting tool. Sequence the operations on the

basis of machine tool and cutting tool by making use of the heuristic rules for the purpose.

(ix) Evaluate the machining time and idle time involved in the production of the component. Select the

final process plan based on the lowest cost or machining time.

(x) Present the final results in any suitable form such as

Process sheet (alpha numeric)

Process pictures

Machining simulation steps CLDATA or CNC part program

CAPP MODEL

CAPP System Architecture

CAD System Preprocessor

Input CAPP

Knowledg

e

Planning Rules

Output

CAPP Model

Postprocessor

Production Planning

& Scheduling

CAD System

Machining Selection Module

Constraint

Creation Module

Preprocessor

Machining Knowledge Base

Process Plan Generation

Module

Constraint Application Module

Manufacturing Knowledge

Base



APPROACHES TO COMPUTER-AIDED PROCESS PLANNING

In recent days, several computer-aided process planning systems are available for use for a variety of

manufacturing operation.

These systems can broadly be clarified into two categories : (i) Variant computer aided process planning method.

(ii) Generative computer aided process planning method.

The details of these are explained in next subsections.

The variant CAPP method

In variant CAPP approach, a process plan for a new part is created by recalling, identifying and

retrieving an existing plan for a similar part and making necessary modifications for the new part.

Sometimes, the process plans are developed for parts representing a fmily of parts called 'master

parts'. The similarities in design attributes and manufacturing methods are exploited for the purpose of

formation of part families. A number of methods have been developed for part family formation using

coding and classification schemes of group technology (GT), similarity-coefficient based algorithms

and mathematical programming models.

The variant process planning approach can be realized as a four step process;

1. Definition of coding scheme

2. Grouping parts into part families

3. Development of a standard process plan

4. Retrieval and modification of standard process plan

A number of variant process planning schemes have been developed and are in use. One of the most

widely used CAPP system is CAM-I developed by McDonnell-Douglas Automation Company. This

system can be used to generate process plan for rotational, prismatic and sheet-metal parts.

– This has evolved out of the traditional manual process planning method. A process plan for a new

part is created by identifying and retrieving an existing plan for a similar part, followed by the

necessary modifications to adapt it to the new part.

– It is based on GT principles, i.e., part classification and coding. These coding allow the CAPP

system to select a baseline process plan for the part family and accomplish about 90% of the planning

work. The planner adds the remaining 10% of the planning by modifying the baseline plan.

– If the code of the part does not match with the codes stored in the database, a new process plan must

be generated manually and then entered into database to create a new baseline process plan for future

use.

Advantages and limitations of Variant CAPP

– Investment in hardware and software is not much.

– The system offers a shorter development time and lower manpower consumption to develop process

plan.

– The system is very reliable and reasonable in real production environments for small and medium

size companies.

– Quality of process plan depends on knowledge and background of process planner.



The general steps for data retrieval modification are as follows :

Establishing the Coding Scheme A variant system usually begins with building a classification and coding scheme. Because,

classification and coding provide a relatively easy way to identify similarity among existing and new

parts. Today, several classification and coding systems are commercially available. In some extreme

cases, a new coding scheme may be developed. If variant CAPP is preferred than it is useful for a

company to look into several commercially available coding and classification systems (e.g.

DCLASS, JD-CAPP etc.). Now, it is compared with companies before developing their own coding

and classification system. Because using an existing system can save tremendous development time

and manpower.

(i) Form the Part Families by Grouping Parts

The whole idea of GT lies into group numerous parts into a manageable number of part families. One

of the key issues in forming part families is that all parts in the same family should have common and

easily identifiable machined features. As a standard process plan are attached with each part family,

thereby reducing the total number of standard process plans.

(ii) Develop Standard Process Plans



After formation of part families, standard process plan is developed for each part families based on

common part features. The standard plan should be as simple as possible but detailed enough to

distinguish it from other.

(iii) Retrieve and Modify the Standard Plans for New Parts

Step1 to step 3 are often referred as preparatory work. Each time when a new part enters the systems,

it is designed and coded based on its feature, using the coding and classification scheme, and than

assigned to a part family. The part should be similar to its fellow parts in the same family. Also,

family’s standard plan should represent the basic set of processes that the part has to go through. In

order to generate detailed process routes and operation sheets to this part, the standard plan is

retrieved from the data

Advantages and Disadvantages of Variant CAPP Following advantages are associated with variant process planning approach:

(i) Processing and evaluation of complicated activities and managerial issues are done in an efficient

manner. Hence lead to the reduction of time and labour requirement.

(ii) Structuring manufacturing knowledge of the process plans to company’s needs through standardized procedures.

(iii) Reduced development and hardware cost and shorter development time. This is an essential issue

for small and medium scale companies, where product variety is not so high and process planner are

interested in establishing their own process planning research activities.

Disadvantages of Variant Process Planning Approach Following disadvantages are associated with variant process planning approach

(i) It is difficult to maintain consistency during editing.

(ii) Proper accommodation of various combinations of attributes such as material, geometry, size,

precision, quality, alternate processing sequence and machine loading among many other factors are

difficult.

(iii) The quality of the final process plan largely depends on the knowledge and experience of process

planner. The dependency on process planner is one of the major shortcomings of variant process

planning.

The generative CAPP method

In generative process planning, process plans are generated by means of decision logic, formulas,

technology algorithms, and geometry based data to perform uniquely processing decisions. Main aim

is to convert a part from raw material to finished state. Hence, generative process plan may be defined

as a system that synthesizes process information in order to create a process plan for a new component

automatically.

Generative process plan mainly consists of two major components :

(i) Geometry based coding scheme.

(ii) Proportional knowledge in the form of decision logic and data.

• Process plans are generated by means of decision logics, formulas, algorithms, and geometry based

data that are built or fed as input to the system.

• Format of input

– Text input (interactive)

– Graphical input (from CAD models)



• First key: to develop decision rules appropriate for the part to be processed. These rules are specified

using decision trees, logical statements, such as if-then-else, or artificial intelligence approaches with

object oriented programming.

• Second key: Finding out the data related to part to drive the planning. Simple forms of generative

CAPP systems may be driven by GT codes.

A pure generative system can produce a complete process plan from part classification and

other design data which does not require any further modification or manual interaction.

• In generating such plans, initial state of the part (stock) must be defined in order to reach the final

state i.e., finished part.

• Forward or backward planning can be done.

• Forward and backward planning apparently appear to be similar but they effect programming

significantly. The requirement and the results in of a setup in forward planning are the results and

requirements , respectively, of the set up in backward planning.

• Forward planning suffers from conditioning problems; the results of a setup affects the next set up.

• In backward planning, conditioning problems are eliminated because setups are selected to satisfy

the initial requirements only.

• The generative CAPP has all the advantages of variant CAPP however it has an additional advantage

that it is fully automatic and a up-to-date process plan is generated at each time.

• It requires major revisions if a new equipment or processing capabilities became available.

• The development of the system in the beginning is a difficult.

Geometry-based Coding Scheme All the geometric features for all process such as related surfaces, feature dimension, locations, on the

features are defined by geometry based coding scheme. The level of detail is much greater in

generative system than a variant system.

For example, various details such as rough and finished state of the part are provided to transform into

desired state.

Proportional Knowledge in the Form of Decision Logic and Data Process knowledge in the form of decision logic and data are used for matching of part geometry

requirement with the manufacturing capabilities. All the methods mentioned above is performed

automatically.

Operation instruction sets are automatically generated to help the operators to run the machines in

case of manual operation. NC codes are automatically generated, when numerically controlled

machines are used



Manufacturing knowledge plays a vital role in process planning. The process of acquisition and

documentation of manufacturing knowledge is a recurring dynamic phenomenon. In addition, there

are various sources of manufacturing knowledge such experience of manufacturing personnel,

handbooks, supplier of machine tools, tools, jigs and fixtures materials, inspection equipment and

customers etc. Hence, in order to understand manufacturing information, ensuring its clarity and

providing a framework for future modification, it is not only necessary but also inevitable to develop a

good knowledge structure from wide spectrum of knowledge. Flowchart, decision trees, decision

tables, algorithms, concepts of unit machined surfaces, pattern recognition techniques, and artificial

intelligent based tools are used to serve the purpose. A brief discussion on decision table is given

below.

The basic elements of decision tables are condition, action and rules. They are represented in the form

of allocation matrix. Figure 9.4 is one such representation where condition states the goal that we

want to achieve and action states the operation that we have to perform. On the basis of experience the

expert rules are formed by entry values to establish the relationship between condition and action.

Table 9.1 is one such representation where entry are of Boolean-types (true, false, don’t care).

Similarly, in Table 9.2, continuous value type entries are shown.



The decision making process works as follow.

For a particular set of condition entries, look for its corresponding rule from that rule determine the

action.

Advantages of Generative Process Plan Generative process plans have a number of advantages. Among the major ones are the following :

(i) They rely less on group technology code numbers since the process, usually uses decision tree to

categorize parts into families.

(ii) Maintenance and updating of stored process plans are largely unnecessary. Since, any plan may be

quickly regenerated by processing through the tree. Indeed, many argue that with generable systems,

process plans should not be stored since if the process is changed, and out-of-dated process plan might

find its way back into the system.

(iii) The process logic rules however must be maintained up to dated and ready for use. This provides

the process planner with an assurance that the processes generated will reflect state-of-the-art

technology.



Variant or Generative, Which to Use?

What CAPP approach (Variant or Generative) is better? This question has been constantly asked but,

there is no definite answer to it.

Generally speaking, a variant system is better for manufacturing setting where similar parts are

manufactured repetitively. Because parts are similar, Group Technology can easily be implemented

and shows quick and significant return on investment (ROI). Because similar parts are produced

repetitively, process plan can be retrieved, slightly modified and used, without going through too

much trouble. On the other hand, generative process planning is better suited for a manufacturing

environment in which part does not exhibit too much similarity and new part are introduced on a

regular basis. In this case, benefits cannot be gained from Group Technology due to dissimilarity of

parts. Because, new parts are regularly introduced, historical data does not have too much value to the

process planner. However, aforementioned approach is a rough guideline for selecting the appropriate

CAPP approach.



GROUP TECHNOLOGY

Group technology is a manufacturing philosophy in which similar parts are identified and

grouped together to take advantage of their similarities in manufacturing and design. Similar parts are

arranged into part families. Each family would possess similar design and manufacturing

characteristics. Hence processing of each member of a given family would be similar and this results

in manufacturing efficiencies. These efficiencies are achieved in the form of reduced set-up times,

lower in-process inventories, better scheduling, improved tool control and the use of standardized

process plans. The design retrieval system is a manifestation of group technology principle applied to

the design function. To implement such a system some form of parts classification and coding is

required.

Part classification and coding is concerned with identifying the similarities among parts and

relating these similarities to a coding system. Part similarities are of three types:

i. Design attributes (such as geometric shape and size)

ii. Manufacturing attributes (sequence of processing steps required to make the part)

iii. Design and manufacturing attributes (combination of the design and manufacturing attributes)

When implementing a parts classification and coding system most companies elect to purchase a

commercially available package rather than develop their own. The following factors are considered

in selecting a parts coding and classification system:

Objective

Scope and application

Costs and time

Adaptability to other systems

Management problems

Overview of Group Technology (GT)

Parts in the medium production quantity range are usually made in batches

Disadvantages of batch production:

Downtime for changeovers

High inventory carrying costs

GT minimizes these disadvantages by recognizing that although the parts are

different, there

are groups of parts that possess similarities



GT exploits the part similarities by utilizing similar processes and tooling to produce

them

GT can be implemented by manual or automated techniques

When automated, the term flexible manufacturing system is often applied

Group Technology Defined

An approach to manufacturing in which similar parts are identified and grouped

together in order to take advantage of their similarities in design and production

Similarities among parts permit them to be classified into part families

In each part family, processing steps are similar

The improvement is typically achieved by organizing the production facilities into

manufacturing cells that specialize in production of certain part families

Part Family

A group of parts that possess similarities in geometric shape and size, or in the

processing steps

used in their manufacture

Part families are a central feature of group technology

There are always differences among parts in a family

But the similarities are close enough that the parts can be grouped into the same

family

MANUFACTURING RESOURCE, GROUPS AND THEIR CHARACTERISTICS

Following are the six characteristics for effective grouping

1. The Team. Groups contain a specified team of workers who solely or generally form the group.

2. Product. Groups produce a specified family or set of products. In an assembly department, the

products are assembled. In a machine shop these products will be machine parts e.g. in foundry products

will be casting.

3. Facilities. Groups are equipped with a specified set of machines and/or other production

equipment, which are used solely or generally in group.

4. Group layout. The facilities are laid out together in one area reserved for the group.

5. Independence. The group should, as far as possible, be independent of each other.

6. Size. The group should be limited to restrict the number of workers per group of 6 to 15 workers has

been widely recommended. Larger group up to 35 workers may be necessary for technology reasons in some

cases.

GROUPING PARTS INTO FAMILIES The biggest single obstacle in changing over to group technology from a traditional shop is the problem of

grouping parts into families. There are three general methods for solving this problem. All the three

methods are time consuming and involve the analysis of much data by properly trained personnel. The

methods are

(i) Visual inspection

(ii) Production flow analysis (PFA)

(iii) Component classification and coding system (a) By design features (b) By production

features

(i) Visual Inspection. The visual inspection method is the least sophisticated and least expensive

method. It involves the classification of parts into families by looking at either the physical parts or

photographs and arranging them into similar groupings. This method is generally considered to be the

least accurate of the three.



(ii) Production Flow Analysis (11FA). Production flow analysis (PFA) method, was developed by J.L.

Burbidge. PFA is a method of indentifying part families and associated machine tool groupings by analyzing the

route sheets for parts produced in a given shop. It groups together the parts that have similar operation sequences

and machine routings. The disadvantage of PFA is that it accepts the validity of existing route sheets, with no

consideration given to whether these process plans are logical or consistent.



The classical GT cell allows parts to move from any machine to any other machine. Flow is not

Unidirectional. However, since machines are located in close proximity short and fast transfer

is possible.

The GT center may be appropriate when large machines have already been located and cannot be

moved, or product mix and part families are dynamic and would require frequent relayout. Then,

machines may be located as in a process layout by using functional departments (job shops), but

each machine is dedicated to producing only certain Part families. This way, only the tooling and

control advantages of GT can be achieved. Compared to a GT cell layout, increased material

handling is necessary.

(iii) Components classification and coding system. This method is the most time con-

suming and complicated of the three methods. However, it is the most frequently

applied method and is generally recognised to be the most powerful of the three. This

method of grouping parts into families involves an examinations of the individual design

and/or manufacturing attributes of each part. The attributes of the parts are uniquely

identified by means of a code number. This classification and coding may be carried out

on the entire list of active parts of the firm, or a sampling process may be used to establish

the part families.

Many parts classification and coding system have been developed throughout the world,

and there are several commercially available packages being sold to industrial

concerns. It should be noted that none of them has been universally adopted. One of the

reason for this is that a classification and coding system should be custom - engineered for a

given company or industry. One system may be best for one company while another

system is more suited to another company.

(a) The Opitz Classification System. This classification and coding system for parts was

developed by H. Opitz of the University of Aachen in West Germany. It represents one of the

pioneering efforts in the GT area and is perhaps the best known of the classification and coding

schemes. The Opitz coding system has the following digit sequence :



The basic code consists of 9 digit, which can be extended by adding four more digits. The first nine

digits are intended to convey both design and manufacturing data. The first five digits, 12345, are

called the "Form Code" and describe the primary design attribute of the part. The next four digits,

6789, constitute the "Supplementary Code". It indicates some of the at¬tribute that would be of use to

manufacturing (Dimensions. Work Material, Starting Raw Work Piece Shape and Accuracy).The

extra four digits. ABCD are referred to as the "Secondary Code" and are intended to identify the

production operation type and sequence. The secondary code can be designed by the firm to serve its

own particular needs.





(b) The MICLASS System: MICLASS stand for Metal Institute Classification System arid was

developed by TNO, the Netherland's Organisation for Applied Scientific Research. It was started in

Europe in 1969. The MICLASS system was developed to help automation and standardize a number

of designs, production, and management functions. These include

Standardization of engineering drawings

Retries al of drawings according to classification number

Standardization of process routing

Automated process planning

Selection of parts for processing on particular groups of machine tools

Machine tool investment analysis

The MICLASS classification number can range from 12 to 30 digits. The first 12 digits are a universal

code that can be applied to any part and up to 18 additional digits can be used to code data that are

specific to the particular company or industry.

For example, lot size, company drawing no., piece time, machine tool te be used, cost data and

operation sequence might be included in the 18 supplementary digits. The work part attributes coded

in the first 18 digits of the MICLASS number are as follows.

One of the unique features of the MICLASS system is that parts can be coded using a computer

interactively. To classify a given part design, the user responds to a series of questions asked by the

computer. The number of questions depends on the complexity of the part. For a simple part, as few

as seven questions are needed to classify the part. For an average part, number of questions ranges

between 10 and 20. On the basis of the responses to its questions, the computer assigns a code number

to the part.





(c) The CODE System. The CODE system is a parts classification and coding system developed and

marketed by Manufacturing Data System. Inc. (MDSI) of Ann Arbor, Michigan. Its most universal

application is in design engineering for retrieval of part design data, but it also has applications in

manufacturing process planning, purchasing, tool design, and inventory control.

The CODE number has eight digits. For each digit there are 16 possible values (Zero through 9 and A

through F) which are used to describe the part's design and manufacturing characteristics.

1st Digit indicates the basic geometry of the part and is called the major division of the CODE system.

This digit would be used to specify whether the shape was a cylinder, flat piece, block, or other. The

interpretation of the remaining seven digits depends on the value of first digit, but these remaining

digits from a chain-type structure. Hence the CODE system possesses a hybrid structure.

2nd

and 3rd

Digit provide additional information concerning the basic geometry and manufacturing

process for the part.

4th5th

and 6th

digits specify secondary manufacturing processes such as threads grooves, slots

and so forth.

7th

and 8th Digits are used to indicate overall size of the part (e .g .Diameter and Length of the

turned part) by classifying it into one of 16 size ranges for each of two dimension.

Fig. 19.3.Coding a component by the CODE system.

For example : Coding the component given in Fig. 19.3 is to be found out by code system and is given

as under :

So that the code for a given component is 13188D75

19.4.1. (i) Coding System Structure. Parts coding scheme consists of a sequence of symbols that

identify the part's design and/or manufacturing attributes. The symbols in the code can be all numeric,



all alphabetic, or a combination of both types. However, most of the classification and coding system

use number digits only. There are three basics code structures used in GT application:

(a) Hierarchical Code Structure

(b) Poly Code Structure

(c) Hybrid Code Structure, a combination of hierarchical and polycode structures known as

decision - Tree coding.

(a) Hierarchical Code Structure. With the hierarchical structure, the interpretation of each

succeeding symbol depends on the value of the preceding symbols. Other names commonly used for

this structure are monocode and tree structure. The hierarchical code provides a relatively compact

structure which conveys much information about the part in a limited number of digits.

(b) Poly Code Structure. In the poly code structure, the interpretation of each symbol in the sequence

is fixed and does not depend on the value of preceding digits. Another name commonly given to this

structure is chain type structure. The problem associated with poly code is that they tend to be

relatively long. On the other hand, the use of a polycode allows for convenient identification of

specific part attributes. This can be helpful in recognizing parts with similar processing requirements.

To illustrate the difference between the hierarchical structure and chain-type structure, consider a two-

digit code, such as 15 or 25. Suppose that the first digit stands for the general part shape. The symbol,

1 means round work part and 2 means flat rectangular geometry. In a hierarchical code structure, the

interpretation of the second digit would depend on the value of first digit. If, preceded by 1, the 5

might indicate some length/diameter ratio, and by 2, the 5 might be interpreted to specify some

overall length. In the chain-type code structure, the symbol 5 would be interpreted the same way

regardless of the value of the first digit. For example, it might indicate overall part length, or whether

the part is rotational or rectangular.

(c) Decision - Tree Codes. Most of the commercial parts coding system used in industry are a

combination of two pure structures. The hybrid structure is an attempt to achieve the best features of

monocodes and polycodes. Within each of these shorter chains, the digits are independent, but one or

more symbols in the complete code number are used to classify the part population into groups, as in

the hierarchical structure. This hybrid coding seems to best serve the needs of both design and

production.

Part Design Attributes

Major dimensions

Basic external shape

Basic internal shape

Length/diameter ratio

Material type

Part function

Tolerances

Surface finish



Part Manufacturing Attributes

Major process

Operation sequence

Batch size

Annual production

Machine tools

Cutting tools

Material type

Benefits of Group Technology

Standardization of tooling, fixtures, and setups is encouraged

Material handling is reduced

Parts are moved within a machine cell rather than entire factory

Process planning and production scheduling are simplified

Work-in-process and manufacturing lead time are reduced

Improved worker satisfaction in a GT cell

Higher quality work

Problems in Group Technology

Identifying the part families (the biggest problem)

If the plant makes 10,000 different parts, reviewing all of the part drawings and

grouping

the parts into families is a substantial task

Rearranging production machines in the plant into the appropriate machine cells

It takes time to plan and accomplish this rearrangement, and the machines are not

producing during the changeover

Artificial Intelligence in Process Planning Artificial Intelligence

Artificial Intelligence (AI) is the area of computer science focusing on creating machines that

can engage on behaviors that humans consider intelligent. The ability to create intelligent

machines has intrigued humans since ancient times, and today with the advent of the

computer and 50 years of research into AI programming techniques, the dream of smart

machines is becoming a reality. Researchers are creating systems which can mimic human

thought, understand speech, beat the best human chessplayer, and countless other feats never

before possible. Find out how the military is applying AI logic to its hi-tech systems, and how

in the near future Artificial Intelligence may impact our lives.

AI textbooks define the field as "the study and design of intelligent agents" where an

intelligent agent is a system that perceives its environment and takes actions that maximize its

chances of success. John McCarthy, who coined the term in 1956, defines it as "the science

and engineering of making intelligent machines." Artificial intelligence takes the intelligence

of humans, such as perception, natural language processing, problem solving and planning,



learning and adaptation, and acting on the environment and applies them with machines,

systems, and virtual objects.

Use of AI in PP

The use of artificial intelligence has proved to be beneficial in various areas since the mid-

1960s. Recently, more and more studies on AI applications to manufacturing systems have

been reported. Process planning is one of the areas to which artificial intelligence can be

successfully applied. AI techniques which may have a substantial influence on process

planning include:

Natural language processing

Voice/speech recognition

Pattern recognition and

Expert systems

Natural Language Processing

Formal communication between users and computers is designed to be more considerate of

computers than humans. Users must enter data in a pre-defined, rigid format without any

flexibility. Therefore even a minor error in data entry may lead to a confusing result. Natural

language processors have been developed to solve this problem, by allowing humans to

communicate with computers in formal English. Natural language understanding could be

applied to both variant and generative process planning systems. In a variant system, a natural

language interface would be convenient to the user in entering, retrieving and editing the data

and process plans.

For instance, a user may request:

• Give me all the parts in part family 1.

• What are those parts in part family 1?

• List all the parts which belong to part family 1.

The computer should be able to interpret these lexically different sentences as having the

same meaning. As to a generative system, although in the long run the shape, tolerance, and

surface finish for a part can be directed from a CAD's database, it would be advantageous if

the user could enter the required data in English.

Pattern Recognition

Pattern recognition is another AI technique that can contribute to process planning. One can

use this technique to design a pattern-directed expert system[29], or apply it to design an

interface scheme to communicate with a CAD's database such that the computer can access

the position, the dimension and machining requirements of each shape entity in order to

recognise the work piece that it is going to plan. Several different schemes are being used to

resolve the pattern recognition problem. Algorithms have been developed which can

construct a 3D object from 2D drawings under certain assumptions. These algorithms,

however, work well only within narrow ranges; there is no universal algorithm to resolve this

type of problem.



Speech/Voice Recognition

Speech recognition, also known as voice data entry, involves translating spoken languages

into machine-readable forms[30]. Because speech is the natural method of communication

amongst human beings, the use of speech I/O technology at the man-machine interface

accommodates the machine to the user rather than forcing the user to conform to the

computer. Therefore, voice recognition will very probably surpass mouse interface and CRT

usage in the near future. Process planning would then benefit more from this technique than

from natural language

processing.

Expert Systems

Expert systems are probably the most promising AI technique for process planners. An expert

system gains its name from the fact that the system contains massive domain-specific

knowledge designed to successfully solve problems in a field which would normally be

thought of as requiring a human expert. An expert system approach is ideally suited to[31]:

• ill-structured problems;

• problems which can be formulated analytically but for which the number

of alternative solutions is large;

• situations where the domain knowledge is vast and relevant knowledge must

be used selectively;

• problems where the solution is related to human expertise.

An ideal process planning expert system should be equipped with domain knowledge such

that it can behave like an experienced process planner in preparing plans.

Expert Systems Approach to Process Planning

Early research in APP has provided a basic structure for process planning analysis. However,

an expert system technique may replace traditional approaches for the following reasons:

(1) Various factors influence process selection and sequencing; currently, it is not obvious

what all of these factors are, nor what their effects might be.

(2) During the development of an APP system, it would be necessary to make extensive

modification of the facts and decision logic. This can be done more easily in an expert system

structure than in a conventional program.

(3) Decision trees and decision tables, as usually used in a traditional generative APP system,

work effectively only for simple processes. If one wants a fully automated process planning

function, intelligent reasoning must be performed.

(4) Expert process planning systems can be designed to accumulate knowledge as time goes

by, which is more appropriate for a process planning function that requires considerable

experiential knowledge.



Ideally, an expert system, able to emulate human thinking, reasoning, and behavior, should

consist of most of the following components :

• a language processor that manages user-system inferences;

• domain-related facts;

• domain-related rules for drawing inferences;

• an interpreter that controls and executes the rules;

• a justifier that explains the system's action or reasoning;

• a consistency enforcer that maintains a consistent representation of the

emerging solution;

• a scheduler that arranges the order of rules to be executed;

• a blackboard or working space where intermediate hypotheses and decision making that the

system manipulates can be stored.

The actual system architecture depends on the special needs of each application arena. For

example, in order to design a perfect APP system, a CAD database, a machining database, a

CAD interpreter, and a process plan generator should be included in the system. A conceptual

framework for designing such an expert system for process planning purposes is given in

Figure F, where:

• a CAD interpreter extracts geometric data from a CAD data base;

• a machining database is used to store the machine tool and tooling information;

• domain-specific facts and rules are stored in a knowledge base;

• an inference/control module serves as the mechanism to interpret, schedule



and resolve conflicts among candidate rules; and

• a queries/answers processor manages the interaction between users and

the system.

For a computer to emulate human experience and access domain-specific facts, the experts'

knowledge and facts must be extracted, represented and organized in a specific format that

the computer can recognize. One of the popular representing methods is production rule or

production system. The basic idea of production systems is that the database (or rule base)

consists of rules, called productions, in the form of IF-THEN or CONDITION-ACTION

pairs. Three components are normally required for a product system:

• rule bases, which are a set of productions;

• context; a short-term memory buffer-like data structure, which is to be

matched by the left-hand side of productions; and

• inference engine (or interpreter), which controls the system's activities.

Figure F

A typical system built according to the above framework, using a rule-based approach,

should be able to function as follows:

(1) Knowledge is extracted from experienced process planners and stored in a rule base in

CONDITION-ACTION pairs.

(2) When one starts the system, the control program will first examine the left-hand side of a

rule. If the condition matches the input, the right-hand side of this rule is triggered, i.e. put



into action. At the same time, this piece of action is accumulated with inputs and the

condition-match procedure continues.

(3) The inference terminates whenever a satisfactory result (succeeded) or

a conflict (failed) is generated.

(4) A process plan based on the successful result is prepared and displayed (or printed).

Process Planning Software

Programming Language

The choice of language for expert systems, although not as critical as other issues because of

the variety of programming languages available [37], should not be neglected. At the lowest

level, an expert system can be programmed in a high-level programming language, such as

FORTRAN, PASCAL, BASIC or C. A builder using this type of tool must write everything

from scratch which is very time consuming; however, some sort of flexibility is retained. At

the other extreme, an expert system can be built via an environment called an expert system

shell (EMYCIN and OPS5 are examples). This type of tool, which has blocks or subroutines

built in internally, can save time for the system builder, but does not offer much flexibility for

selection. Their functions are normally limited to certain specific systems that can be

constructed. At the middle of the range are those AI-oriented languages such as LISP or

PROLOG, which usually provide the logic for facilitating the tasks of acquiring knowledge

and building an inference engine. Many early expert systems were constructed using this type

of language. Among the existing expert APP systems, GARI uses LISP, and SIPP and

AGMPO utilize PROLOG. TOM and EXCAP are coded in PASCAL, and the system used by

Preiss and Kaplansky is written in FORTRAN 77.

Manufacturing Software: Scheduling & Production Planning

The PIMSS production software toolset provides advanced planning and optimization

functionality, including manufacturing capacity planning, master production scheduling and

factory scheduling:

Production Planning Systems manufacturing software - factory planning systems -

production scheduler

Advanced Manufacturing Planning real-time production planning system - JIT

manufacturing planning

Production Capacity Planning master schedule - capacity planning - batch scheduling -

capacity planner

Material Planning & MRP material planning - MRP systems - production order

scheduling

Strategic Production System strategic optimization - production schedule analysis -

plant planning

http://www.mjc2.com/Frameset_products_manufacturing.htm#production-planning-systems

http://www.mjc2.com/Frameset_products_manufacturing.htm#real-time-manufacturing-scheduling-software

http://www.mjc2.com/Frameset_products_manufacturing.htm#capacity-planning-software

http://www.mjc2.com/Frameset_products_manufacturing.htm#manufacturing-resource-planning

http://www.mjc2.com/Frameset_products_manufacturing.htm#strategic-production-system



Lean Manufacturing Software manufacturing ERP software - factory & production

optimization

PIMSS (Process Industry Manufacturing Scheduling System) offers an integrated solution

spanning the manufacturing supply chain, ranging from long term strategic optimization to

real-time manufacturing scheduling. PIMSS is the fastest production scheduler on the market

and is capable of automatically planning and optimizing very large manufacturing

operations in seconds. Its flexibility enables it to model highly complex production processes

accurately without the need for approximation or compromise on performance.

PIMSS maintains high customer service levels while minimizing inventory and maximizing

manufacturing efficiency. PIMSS is designed for manufacturing operations that process

1000s of products in an environment where minimizing changeovers and balancing resource

usage are vital.

MJC²'s advanced manufacturing software is particularly applicable to operations which

produce short shelf-life or fast-turnover goods against a backdrop of tight order fulfillment

windows and increased demand for flexibility from the customer.

The PIMSS production planning software takes into account:

Demands & orders: actual or forecast orders by customer and due date(s); branded

& own label volumes;

Ideal stock levels: taken as a constraint or calculated by the production system;

Materials planning: raw materials planning requirements, as a constraint or

calculated by PIMSS;

Labour planning: shift patterns, break cover, rosters, skills, teams, working time

constraints, cost calculation;

Process capacities: by product/type, machines & packing/bottling areas, plant

scheduling constraints;

Machine routing: links to secondary processes, fixed production order scheduling,

product scheduling sequences;

Work in progress: intermediate products, core blends, inter-factory, on-line/off-line

packing, assembly planning;

Master scheduling: batch planning, master production schedule constraints (color,

formers, moulds, etc.);

Factory scheduling constraints: product mix changes, plan size, pack size,

packaging material, product type, etc.

MJC²- Process Planning & Optimization

MJC²'s process planning software is designed for large production and manufacturing

operations which need to manage complex processing lines and facilities. Our manufacturing

process optimization tools include:

Advanced process control software: real-time planning of manufacturing operations

taking account of feedback from production control (e.g. SCADA), process

automation software and inventory management systems.

http://www.mjc2.com/Frameset_products_manufacturing.htm#lean-manufacturing-software

http://www.mjc2.com/Frameset_products_manufacturing.htm



Process planning tools: automated scheduling of manufacturing lines and factories,

keeping stock levels within defined limits while optimizing production capacity and

throughput.

Capacity planning: strategic forward planning of production and related process

control to assess long-term throughput and availability profiles along the supply

chain.

Process scheduling: modelling and production process optimization to improve

efficiency and customer service levels.

Our process control software is applicable to all large manufacturing operations, particularly

those which produce high-turnover or short shelf life goods. The planning tools can model

complex multi-stage (and multi-site) automated processes and inter-linking lines.

Aras Solutions | Manufacturing Process Planning

Aras Manufacturing Process Planning ensures information accuracy and quality for

manufacturing processes by providing a reliable, single version of the truth in a secure, online

location. Users throughout Engineering, Production Control, Manufacturing and Quality rely

on Aras as the source of official, released information about production processes and related

technical data.

Manufacturing Process Planning:

Automates and manages all the information and processes required to manufacture

parts, assemble final products and conduct product quality inspections

Provides revision and version control of manufacturing process plans, production

routing, product descriptions, operation sequencing, tooling assets, and set-up and

run-time instructions

Ensures the accuracy of Lean Six Sigma methods and integrates with ERP systems for

business analytics and more

SMARTer Manager's

SMARTer Manager offers a wide range of capabilities and a more complete manufacturing

management software solution than any other manufacturing software technology.

Unparalleled functionality at a fraction of the cost and the ability to implement, execute and

operate across any manufacturing enterprise with one single solution. SMARTer Manager's

innovative applications are completely scalable for job shops or larger product oriented

organizations.

Reference:

1. Principles of Automation and Advanced Manufacturing Systems- Dr. K.C. Jain

2. CAD/CAM-Concepts and Applications- Chennakesava R. Alavala

3. CAD/CAM- M Groover, E. Zinners

4. Computer Aided Manufacturing-P.N Rao, N.K. Tewari, T.K. Kundra

5. CAD/CAM-P.N Rao

http://www.smartermanager.com/smarter_manager/



Date post:	07-Aug-2015
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