30

CHAPTER 2

ISSUES IN CAPP BASED ON AUTOMATIC FEATURE RECOGNITION

TECHNIQUE FROM CAD MODELS

2.1 INTRODUCTION

"A picture is worth a thousand words" [4], is a perfect phrase to

begin the discussion on process planning. The first step in process

planning is to understand the engineering design. Manufacturing

aspects in product development expect a precise and detailed model of

the component to be produced.

Ever since the advent of computer graphics, CAD models have

been used extensively for engineering applications. Geometric solid

modeling facilitates the unambiguous representations of objects as 3D

CAD models which have wide applications in areas such as product

modeling, visualization, engineering analysis, interference checking,

CNC code generation etc. While there are many techniques of solid

modeling, the literature documents that Constructive solid geometry

(CSG) and Boundary representation (B-rep) have gained tremendous

acceptance.

In CSG based systems, an object is modeled by combining pre-

defined solid primitives like blocks, cylinders, wedges, tori etc. with

the help of geometric transformations and Boolean operations, while,

in B-rep, the solid model is constructed from entities like faces, edges,

and vertices. Although these techniques are mathematically sound

and robust, they contrast with the engineer's view of the component,

31

which are in terms of functionally important regions known as form

features.

Features often form the basis of knowledge about various design

and manufacturing tasks like process planning, fixture design,

inspection, assembly planning etc. Feature based modeling essentially

deals with this concept. CAD modelers most extensively use Wire-

frame, Surface and Solid models. A feature based design system is a

front end to a solid modeler [Kleeman, 1988; Bhat et.al, 1989; Chang,

1990][9,10,6].

Solid models provide the highest level of details and accuracy

regarding the objects. Solid models resemble surface and wire-frame

models and are almost created in a similar way. But the major

distinguishing feature of the solid models is that these have the

properties of mass, volume, moment of inertia, etc. which the other

two fail to possess. These are the ideal ones for the design of

engineering materials.

Also, these models claim to diminish the amount of

calculations, i.e. it is much simpler to calculate the relevant properties

of a solid model rather than a surface or a wire-frame model by the

computer (by simple numerical integration), especially for complex

shaped objects. Solid models too are not free from negative points

such as consumption of greater amount of memory as well as time

during development. They require extensive and exhaustive processing

etc.

32

The concept of product model has been of great interest for the

researchers. Following section discusses the same.

2.1.1 Product models

Any CAD model, when associated with the manufacturing

attributes, can be termed as a product model in a general sense.

Nevertheless, a product model need not be a solid model. It can also

be represented as a 2D model. In fact, until recently, the same was

done before the advent of solid models. However, the current trends

and requirement in CAD research demand that, the product model

should be preferably based on a solid model.

Any basic manufacturing process expects the representation of

the finished product in terms of a technical drawing, i.e. a model. As

mentioned earlier, solid models have replaced the 2D models owing to

the trend of the industry towards automation. In either case, the

conversion of a 2D solid model into a product model precedes all other

activities involved in the manufacture of a product. Product models

have retained their importance despite continuous altering and

updating of the forms of product models (2D to 3D, physical to digital,

etc.). Also, the emergence of STEP interface has added to the

significance of product models.

2.2 ROLE OF CAD IN CAPP

CAPP systems usually serve as a link between CAD and CAM.

However, this is a partial link, because most of the existing CAD

drafting systems does not provide part feature information, which is

33

the essential data for CAPP. The clear and complete information of

geometrical and technological aspects of the Feature's in the CAD

models is important for CAPP decisions.

2.2.1 Features

Features are the characteristics of the models having some

manufacturing significance; for example, slots, holes, pockets, etc.

Each feature can be associated with certain amount of manufacturing

knowledge. Every manufacturing operation is associated with certain

technological attributes, which are represented by a feature.

Features guide in judging the overall shape and size of a given

component. For convenience, features are classified as follows:

1. Geometric form features.

2. Manufacturing features.

• Geometric form features

A geometric form feature can be defined as "a portion of the part

boundary which comprises a set of connected faces having certain

recognizable manufacturing characteristics". This is one of the many

definitions of geometric form features. Form features have been

classified as protrusions, depressions, passages, rotational, prismatic,

etc. on the basis of the type and arrangement of faces that constitute

these features.

• Manufacturing features

A manufacturing feature is one that gives technological

information, associated with the manufacturing operations and tools.

These include the dimensions of the model in different axes of

34

representation. These also incorporate the linear and geometric

tolerances. For example, a dimension of 100 mm may be associated

with a tolerance of about 0.01 mm, which may be represented

conventionally as: 100 + 0.01. Manufacturing features also include

geometric tolerances of surface finish, cylindricity etc.

2.2.2 Automated Feature Extraction

The process of recognizing the features of a model, analyzing

them and correctly interpreting the model is known as Feature

Extraction or Feature Recognition. Usually Feature extraction involves

the use of an interface between the modeling software and the

database.

Humans recognize features in a model, visually. The process of

recognition of features involves the setting of separate rules for each

feature which consumes large amount of computations and time. This

leads to the necessity of Automated Feature Recognition. In this

process, each feature, its shape, their numbers of appearance and

dimensions etc, are sequentially submitted to the user, who then can

interpret the model precisely. Lots of work has already been done on

these lines.

• Importance of Automated Feature Extraction

Feature extraction plays an important role in manufacturing

and design systems. It acts as a bridge between CAD and CAM

(Computer Aided Manufacturing). This is justified as the models

during the design stage are recognized by a feature extraction system

35

before the part is actually forwarded to the various manufacturing

operations.

For the designing processes, these systems provide a feature

based user interface and for the manufacturing processes, these

determine the precedence and sequence of operations. In case of the

designing processes, validation, and re-validation of features is

essential after each modeling operation. Here, it is necessary to check

whether a new feature has been correctly installed in the model or if it

has destroyed the validity of certain previously created feature.

In such a system of designing processes, some standard rules

are used to match the extracted features to establish appropriate type

(class) of features.

Determining the features in case of manufacturing systems too

involves the recognition of a set of elements matching the standard

rules.

From the above, it is evident that Automated Feature Extraction

plays a vital role in CAD/ CAM on long term basis. Most of the present

day manufacturing industries prefer the drawings presented through

computer based drawing packages, which promote CAPP. In other

words, CAPP (Computer Aided Process Planning) forms an essential

part of such an environment. Therefore, the automatic feature

recognition of these drawings becomes a necessary tool for the

automation of CAPP and other such automated manufacturing

planning.

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In early 90's, the concept of using Automatic approach in CAPP

has been proposed [Chang, 1990][6]. This approach essentially tries to

eliminate the human factor in CAPP to a greater extent. Here, the CAD

model (preferably product model) is the input and Process sequence,

Process plan with the CNC Part program is the outcome.

Any Computer Aided Process Planning system, based on

Automatic approach involves the procedures as shown in the

flowchart below. Viewing the depiction shown in Fig., one can assess

the importance of feature extraction in CAPP.

Fig 2.1 Flow diagram of CAPP system based on Automatic approach

37

Thus it can be inferred from the above diagram that feature

extraction forms the foundation of the entire structure of a CAPP

system.

Some of the CAD interfaces available for the interpretation of

CAD models are DXF, IGES, SAT, STEP etc. Most of the CAD systems

support one or more of these interfaces. [Stokes 1995; Pal et.al. 1998]

[12, 13]

An overall glance through the research in feature extraction

indicates that many researchers have focused their attention on

automatic feature extraction from CSG as well as B-rep based solid

models. [Kulkarni et.al., 1995][14].

A number of approaches to part feature recognition for

rotational as well as prismatic components have been proposed. They

include Syntactic pattern recognition [Jakubowski, 1982][86],

Geometry decomposition, Expert system rule logic [Joshi etal., 1988;

Kleeman, 1989; Bhat, 1989; Chang, 1990] [15, 9, 10, 6], Graph based

approach and Set theoretic. [Joshi et.al., 1988; Mortensen, 1989;

Gavankar et.al., 1990] [16, 17, 18]

Feature extraction techniques employed in rotational part

feature recognition systems are mainly based on the syntactic and/or

expert logic approach.

The logic for feature recognition in prismatic parts is complex

and needs a proper representation of generic model. Most of the

systems take the CAD interface file as the input and analyze it for

38

feature interpretation in the program. In general, artificial intelligence

based systems are being extensively used for this task.

Much of the research is currently in progress on the

augmentation of such models with technological and other lifecycle

data with reference to product model.

As mentioned earlier, CAD interfaces play a dominant and

decisive role in automatic extraction of features from CAD models.

They are the autonomous and neutral media, aiding the data transfer

amongst various CAD systems. An effective CAD interface must be

capable of performing the following activities:

� Determine the raw material to be removed,

� Identify the machined parts of the model,

� Recognize features formed by the machined faces,

� Obtain precedence between features based on geometry.

In the present work, STEP interface has been used in an

intelligent way, for the feature extraction task. STEP is preferred over

other interfaces. Following section discusses the importance of STEP.

2.3 IMPORTANCE OF STEP (STANDARD FOR THE EXCHANGE

OF PRODUCT MODEL DATA)

In the communication between and CAD/CAM, CAPP and other

CA systems it is necessary to provide transmission of two types of

information [19]:

� Geometric data – describing design of the part.

39

� Technological data – describing the way of

manufacturing/ machining the part.

The combination of the above two types of information

constitutes Product Data. The purpose of STEP is to build a common

standard that ensures the product data can be communicated

electronically across different platforms, e.g. CAD, CAM and CAPP.

The STEP standard differs from IGES by incorporating a formal object-

oriented model for data exchange [20].

STEP enables all individuals contributing to the design,

manufacturing, marketing and supply of a product and its

components to contribute to, to access, and to share information.

STEP aims at eliminating the concept of “islands of automation”. STEP

also attempts to unite manufacturing efforts among corporate

partners, distant subsidiaries and suppliers across diverse computer

environments. STEP addresses the issues of diversified engineering

applications and covers security aspects, which become relevant now

that several companies would be sharing the same product

information [21].

Fig 2.2 Transfer of data between CAX systems

40

The STEP neutral file is a text file that contains geometrical data

of a component including boundary representation data such as

shells, faces, vertices; surface geometric data such as planes,

cylinders, cones, curve geometric such as lines, circles, ellipses, b-

spline curves [22].

In STEP standard the entire model is represented by a variety of

geometrical entities and topological elements arranged in, the data

section. A brief description of some STEP data elements is provided in

the Figure while more detailed definitions along with their attributes

are available in the ISO/TC/ 184/SC4N141 committee draft standard

[23, 24]. The data elements are shown in Fig.2. & the description

follows.

Fig 2.3 Data Structure of Prismatic Feature in STEP

CLOSED-SHELL: A collection of one or more faces, which bounds a

region in three-dimensional space and divides the space into two

regions, one finite and the other infinite.

FACE-SURFACE: A type of face in which the geometry is defined by

the associated surface, boundary and vertices.

FACE-BOUND: A loop used for bounding a face.

EDGE-LOOP: A path in which the start and end vertices are the same.

41

ORIENTED-EDGE: An edge constructed from another (original) edge

and containing the direction (orientation) information. The

ORIENTED-EDGE will be equivalent to the original edge if the

orientation information is not included.

EDGE-CURVE: A type of edge that has its geometry fully defined.

VERTEX-POINT: A point defining the geometry of a vertex.

CARTESIAN-POINT: Address of a point in Cartesian space.

The current commercial STEP versions found in commonly

available CAD modelers have AP’s (AP203) that do not support

storage/ transfer of technological information. Hence, taking all these

aspects into consideration the current research work has been carried

out.

A sample STEP file for a Plate with a Hole component has been

shown in Appendix A.

2.4 SELECTION OF THE PROCESS

Immediately after feature identification, next important task is

to select the manufacturing processes capable of producing them.

This has not been a simple task. A detailed study of process

capabilities need to be made and same should be utilized in the CAPP

system intelligently.

Other than the shape producing capability, each process/tool

also has its own dimension, tolerance, and surface properties

producing capabilities. It is obvious that the drilling process cannot

42

drill an infinitely large and deep hole. Neither can it drill a hole

infinitely small in size. As a matter of fact, it can only produce holes

with discrete size increments. Beside the size capability, every process

has tolerance and surface finish producing capabilities. For process

planning purpose, collection of this information and its representation

in a way that promotes its effective use is sought after. The

capabilities of some processes have been discussed below.

2.4.1 Dimension capability

The dimension capability is determined by both the tool size

and/or the machine tool work envelope. For a process, which uses a

form generating method, the dimension capability usually is

determined by the tool dimension. For generating the machining, the

dimension is not only limited by the tool but also by the machine tool

where the process is conducted. On three axes machining, usually the

Z - axis is the spindle axis. If a cavity is being machined, the

maximum depth is limited by tool length. When trying to go any

deeper, the spindle will begin to interfere with the workpiece. However,

if the cavity opening is big enough to allow the spindle to go in, then

the limitation is the maximum travel of the spindle. For the X and Y

axes, the dimension limits are the machine travel limits. A database of

available tools and machines must be kept current in order to supply

such information to the process planning system.

For hole producing processes, the depth of the hole that is

machinable is also related to the diameter of the tool used. An

ordinary drilling process can drill a depth from three to eight times the

43

hole diameter. However, in practice, the limit of the depth / diameter

ratio is four. Any hole that has a depth-to-diameter ratio greater than

this has to be drilled by a deep hole drilling process. This capability is

limited by the deflection of the tool, the friction between the cutter, the

hole wall and the chip flow.

2.4.2 Tolerance capability

The cause of tolerance capability is more complex. Many factors

affect the accuracy of a process, i.e. tool wear, tool deflection, chatter,

thermal deformation of machine tool elements, tools and workpiece,

control inaccuracy, round out of tool assembly, fixture error, etc. The

tolerance capability is caused by a combination of these factors. It is

not possible to predict the tolerance precisely. Therefore the only

feasible way is to rely on the experience base. From various

handbooks and textbooks, tolerance data can be collected. For a

specific shop, the capability has to be modified by collecting data from

within the shop. The following tables (Table 2.1 and 2.2) show some

tolerance and surface finish information summarized from several

sources [6].

Table 2.1 Process Capabilities - Milling

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Table 2.2 Process Capabilities - Turning

Table 2.3 Process Capabilities - Drilling and Reaming

Table 2.4 Process Capabilities - Grinding

45

In the present work, once the features have been identified,

further processing is delegated to the Intelligent System for

Manufacturing Information (ISMI) module developed in the Windows

environment. Here, the user is provided with a facility to give

dimensional as well as geometric tolerances. These input values are

then compared with the standard database values correlated with the

process capabilities as shown in the tables 2.1 and 2.2 to finally arrive

at the sequence of operations to be recommended.

2.5 CAPP - A LINK BETWEEN CAD AND CAM

The communication between CAD and CAM is a key link in CIM,

which to a great extent determines the success of a CIM. CAPP serves

as the bridge between CAD and CAM. CAPP determines how a design

will be made in a manufacturing system. Without a successful CAPP,

it is impossible to transform the design information into

manufacturing.

CAPP is the critical link between CAD and CAM, both of which

need this indispensable interface. It is for this reason that CAPP is

often referred so as a critical step in achieving Computer Integrated

Manufacturing [Ham and Lu, 1988][25].

CAD and CAM have undergone a relatively long period of

development. Some of their techniques, such as computer graphics,

programming for numerically controlled (NC) machine tools, etc., have

been well developed. However, it was only in the late 1960s that CAPP

46

began to evolve. The significance of CAPP was not realized in

manufacturing industry in general until the last decade.

Although many technical problems arising in CAD and CAM are

complicated and are difficult to solve, most of them are deterministic

and involve a limited number of factors. CAPP, however, involves

substantial technological decision-making, and the relationships

among these CAPP decisions are intricate. To make it worse, many

technical and organizational problems are nondeterministic and some

of the decision making can only be done by experimental methods.

This briefly indicates the level of difficulty associated with CAPP.

In recent years, a large number of CAPP systems have been

developed around the world. However, only a few actually can be used

by industry. High level CAPP systems, which can really achieve the

interface between CAD and CAM, have not yet appeared. In this

situation, the attempt to achieve the interface of CAD and CAM is in

fact only a good intention; So-called "Integrated CAD/CAM systems"

have become available on the commercial market during the past

decade. However, these systems are nothing more than CADMC or

CAD/APT systems, which save the geometry definition during post

processing [Chang and Wysk, 1985][4].

However, a great upsurge in CAPP has arrived. Many

researchers and practitioners around the world have been focusing

their efforts on the development of new CAPP systems, as well as on

the research of the special subjects of CAPP techniques. Those

47

subjects involve some key problems hindering the development of

CAPP. It is believed that, through world-wide mutual effort and co-

operation, a better understanding will be achieved. Therefore, the

development of CAPP will meet the needs of PLM implementation and

the ever-increasing challenge in manufacturing industry.

CAPP is not just computer work. Of course, the implementation

of CAPP depends on the development and ingenious application of

various decision logic, artificial intelligence and expert systems,

computer graphics, database structure and management, computer

language and programming, etc. However, it is the principles and

methodologies of process planning that provide the basis for

developing CAPP systems.

The development of a high-level CAPP system must be based on

the thorough understanding of process planning principles and

methodologies. It should be pointed out that not all the technological

knowledge, used for the decision making in CAPP systems, is

experiential knowledge. On the contrary, a large portion of the

technological knowledge has already been theorized and has become

specialized CAPP knowledge. Along with the development of the theory

and methodology of process planning, more technological knowledge

will enter the class of specialized knowledge. The theories, principles

and methods of process planning provide the important technological

basis for CAPP.

48

2.6 LITERATURE SURVEY

Computer Aided Process Planning (CAPP) is an essential

component of a Computer Integrated Manufacturing (CIM)

environment. The purpose of CAPP is to automate process planning

tasks so that the process plans can be generated consistently.

The basic Process planning activity involves determining the

necessary manufacturing processes and their sequence in order to

produce a given part economically and competitively. In terms of

machining processes, the major process planning activities are

interpretation of product design data, selection of machining

processes, determination of datum surfaces and fixtures, sequencing

the operations, selection of inspection devices, determination of

production tolerances, determination of proper cutting conditions,

calculation of overall production times, generation of process sheets

and NC data.

It has been more than thirty years since the first noticeable

efforts to automate process planning using computers began. In this

period mainly two approaches emerged.

� Variant approach: relies on standard plans developed from

previously manufactured parts.

� Generative approach: involves generation of process plans

automatically without referring to existing plans of previously

manufactured components.

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At the same time, some CAPP developers have attempted to

combine some features of both approaches forming another category

called Semi-generative CAPP.

The idea of developing process plans using computers was first

presented by Seibel [27]. In the following year, Schenk [28] discussed

the feasibility of automated process planning. Subsequently, Berra

and Barash [29] presented an investigation of automated planning

and optimization of metal working processes.

In 1976, the first CAPP system was developed under the

sponsorship of Computer Aided Manufacturing International (CAM-I).

In the same year, MIPLAN was developed by the OIR (Organization of

Industrial Research) and presented by Houtzcel [30]. Both utilized the

variant approach, where parts firstly are grouped into families

considering their geometric or manufacturing similarities and a

unique code is assigned for each family based on Group Technology

(GT) coding systems like OPITZ. MICLASS, KK-3 and DCLASS [31,32].

Subsequently, a standard process plan is generated for each family,

stored in a computer and whenever a plan is needed for a new part, a

standard plan for a similar part is retrieved and if necessary, modified.

Wysk [33] presented a generative system called APPAS which

focused on detailed process selection. Ideally, a generative system is

set up to emulate the thinking of a human process planner and

develops the process plans without any help. The process plans have

been generated for each individual part by means of a decision logic

50

using part and process information. In general, the decision logic may

be based on decision trees, decision tables, artificial intelligence

methods, rule-based decision trees, constraint-based methods, hard

coded algorithms, and problem oriented languages.

With these encouraging developments more and more

researchers and companies became interested in developing CAPP

systems, mainly due to the declining number of process planners in

industry. Numerous variant and generative process planning systems

have been developed for various applications. MIAPP, MITURN,

MIPLAN/MIPREP, IPROS, TIDY, TOJICAPP, DOPS, ICAPP and

Microplan constitute representative examples of variant CAPP systems

and are reviewed in [34, 35, 31] and [36]. AUTAP [37], EXCAP [38],

XPLAN [39], Turbo-CAPP [40], SIPP [41], KAPPS [42] are some

examples of generative systems developed during the 1980’s.

In spite of enormous efforts a truly generative general purpose

CAPP system has not been accomplished yet. All researchers have

restricted their problem domains to handle only some aspects of such

a system. Some considered only rotational parts while others

concentrated on prismatic ones only incorporating a very limited

number of manufacturing features.

In the presence of serious difficulties with a purely generative

system, some researchers have proposed a semi-generative approach

to CAPP, which is basically a combination of the variant and

generative methods. The aim of such systems is to reduce user

51

interaction by incorporating standard operation sequences, decision

tables and mathematical formulas to the system. In most cases, a part

family is identified for a given part and a standard plan is retrieved.

The system may just modify this standard plan or may build a new

one for a specific part. Sometimes the system may build a new and

complete process plan using standard process descriptions stored in

the computer. The semi-generative approach is considered a good

direction for current industrial applications.

A review of several literature surveys on CAPP systems

demonstrates that the issues challenging the research community are

well understood. It has been clear from these surveys that the

developments in the CAPP area have not kept pace with those in CAD

and CAM. As a result the interface between these three domains is

still a topic of many research activities. Furthermore, the lack of a

theoretic basis for process planning, coupled with its dynamic nature

has obstructed the development of general process planning systems

to date. Several researchers have attempted to highlight the research

areas that need further attention. Steudel [36] reviewed the state-of-

the-art in CAPP and outlined the anticipated developments in the

establishment of a scientific base for manufacturing processes and

technology, the development of common language of geometry to

relate the parts to processes, the development of CAD and CAM

systems that include CAPP considerations and the development of

software and databases compatible and transportable among users.

52

Erickson [26] reviewed over 127 CAPP systems and related

projects. It has been outlined that incompatibilities in computer

software, hardware and different representations of product, resource

and process plan data prohibited the development of a general-

purpose integrated system. The author suggested that future CAPP

systems should be modular, available with standard interfaces, easy

to integrate, user friendly and easy to maintain. In the same year,

Ham and Lu [43] in an assessment of the current status of CAPP

suggested that future research should include the integration of

design and manufacturing and also apply Artificial Intelligence (AI)

techniques.

Alting and Zhang [44] reviewed more than 200 references and

provided an extensive list of CAPP systems developed until 1988. Their

survey indicated that it was still very difficult to interface various CAD

and CAPP systems due to the lack of both powerful descriptive

languages to represent geometric entities and of semantic information

attached to CAD entities. The need to develop CAD data interpreters

capable of identifying regions of a part with manufacturing

significance was strongly stressed. It was suggested that the CAPP

systems should be interfaced with NC tool path generation systems,

MRP systems and production simulation systems as well. It was also

predicted that AI techniques would be utilized more extensively in

developing CAPP systems. However, more user friendly software for AI

applications with strong mathematical calculation capabilities is

clearly necessary. The importance of systems with learning

53

capabilities was also pointed out. It was suggested that the next

generation CAPP systems should monitor the actual production and

feed the changes in the status of the shop floor back to the process

planner. The rise of distributed planning systems was also

anticipated. It was indicated that future common knowledge bases

would be divided into individual segments for knowledge at different

level, such as factory, cell, or workstation. It has been suggested that

the integration of design, process planning and manufacturing should

be the ultimate goal. Moreover, it was believed that the information

involved in producing a part should be integrated into a single

database. It has been strongly emphasized that CAPP systems should

be built in such a way that they are portable to different platforms like

PC’s, workstations and mainframes. The development of tolerancing

and dimensioning packages has also been advocated. Recently, H.C.

Zhang and L. Alting [45] compiled their survey and research work in a

book.

Gupta and Ghosh [46] focused on the use of expert systems

technology in process planning and manufacturing and included brief

descriptions of such well-known CAPP systems as GAR1 [47], TOM

[48], EXCAP [38], HI-MAPP [49], SIPP [4I] etc. Gouda and Taraman

[31] surveyed CAPP systems and included an overview of

fundamentals of expert-system-based CAPP.

Shah et al. [50] reported on various process planning and NC

programming techniques. Their survey indicated features to be the

54

primary method for part description. It was observed that most CAPP

systems were based on artificial intelligence techniques, using

production rules and inference engines. However, it was suggested

that a general purpose CAPP system was far from reality due to

insufficient work in the area of fixtures, scheduling and lack of shop

floor feedback mechanisms. It was also noted that except for 2.5D and

axisymmetric parts, the automatic generation of tool paths was still at

its infancy.

Elmaraghy el al. [51] discussed the evolution and state-of-the-

art in CAPP. They also identified the integration of CAPP and product

design, the integration of process planning and production planning

and control, distributed process planning, process planning for

quality, non-traditional process planning applications as the major

trends in CAPP systems research.

Ali and Motavalli [52] presented a number of requirements for

the new generation systems. It was stressed that future CAPP systems

should use relational databases and have electronic sign-off capability

for security reasons. It was also emphasized that text and graphics

should be merged and systems should have graphical user interfaces,

together with standard printing utilities. The importance of dynamic

data links, in order to attach notes to process plans or other related

documents was also noted. It was suggested that the systems should

allow the user to define new data fields for GT codes, time standards,

etc.

55

A broad but brief state-of-the-art review of CAPP was presented

by Eversheim and Schneewind [53]. It was suggested that future CAPP

systems would also involve product assembly operations. It was also

predicted that CAPP and NC programming would be integrated and

that artificial intelligence methods would be employed for decision

making and shared databases would be used for CAD data

integration.

Kamrani et al [54] presented an overview of process planning

techniques and discussed the characteristics and critical issues

associated with the evaluation and selection of a CAPP system. These

are identified as the range of product support, classification and

coding capabilities, graphic capabilities, work instruction creation and

maintenance, process planning approach, time analysis capabilities,

machining parameters, material and tooling database, systems

requirement, cost, commercial availability in addition to user

friendliness, vendor qualification and support.

Kiritsis [55] reviewed knowledge-based expert systems for

process planning. The basic problem areas in CAPP were identified as:

product and part representation methods, process planning logic and

knowledge, databases and feature recognition. In addition the basic

features of expert systems were described and practical aspects

together with development problems were highlighted. Finally, a brief

discussion of over 50 knowledge-based expert systems was included

and the systems were presented by area of application and method of

56

development in a tabular format. It was suggested that knowledge

based systems can provide the means of arriving at integrated and

intelligent process planning systems capable of working together with

CAD and production planning systems within the CIM concept,

however more attention should be paid to knowledge elicitation and

choice of a suitable representation technique.

Park [56] considered that a knowledge base should be not

merely a set of rules, but a framework of process planning that can be

controlled and customized using rules and proposed a knowledge

capturing methodology, in which four knowledge elements, facts,

constraints, the way of thinking and rules for process planning, were

derived from the model of process planning that was represented by a

traditional three-phase modeling framework consisting of object

model, functional model and dynamic model (decision logic and

decision variables).

Ramana [57] summarized the previous work and key issues

related to data and knowledge modeling for product design and

process planning.

Once knowledge bases have been constructed, the

corresponding artificial intelligence-based decision-making

techniques, such as expert systems, rule-based reasoning, case-based

reasoning, fuzzy logical, neural network, genetic algorithms, etc.,

could be used to explore the large solution space for a valid and

optimal process plan under various constraints. Li et al. [58]

57

developed a hybrid genetic algorithm and simulated an annealing

approach to consider concurrently the processes of selecting

machining resources, determining set-up plans, and sequencing

operations for a prismatic part. Wong et al. [59] describes a fuzzy

expert system and genetic algorithms for solving the process selection

and sequencing problem under uncertainty. Other researchers like

Zao [60] and Shakeri [61] also addressed the implementation of the

automated operation planning and optimum operation sequencing,

and tool selection algorithms.

Achieving automated process planning to reduce dependence on

human judgment is the ultimate objective of CAPP. Though numerous

studies have been reported, they are limited to academic discussion

and prototype demonstration in principle and it is difficult to find a

commercial CAPP system applicable to complicated objects as

projected by Shin [62].

The process planning development requires product design data

which includes geometric and technological information as input.

Most of the research carried out so far in Product data technology

focuses on DXF, IGES & other design interfaces. However, the current

focus in CAD/CAM technology is Product model & the associated

design interfaces. A Product model is one which has the geometric as

well as the technological information embedded into it.

So far the world of CAD/CAM has viewed IGES as its translation

standard for years, using the system to move 2-Dimensional models

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from one program to another. While IGES does, in fact, do a good job

of transmitting basic geometry, another translator – STEP (Standard

for the exchange of Product model data) has gained popularity. STEP

goes considerably further than just transmitting geometry; it provides

users with the ability to express & exchange digitally useful product

information throughout a products lifecycle, including design,

analysis, manufacturing & support. The objectives of STEP include

the creation of a single international standard to cover all aspects of

CAD/CAM data exchange & the implementation of this standard

within industry, superseding all others as highlighted by Liu [63] and

Owen[64].

Further Manufacturing or CAM modules expect the complete

geometrical as well as technological information from CAD models in a

form acceptable to them, as discussed by Appukuttan [65]. STEP

helps serve this purpose. STEP is a proactive effort, the focus being

placed on developing a standard that caters for various user groups.

These user groups are usually associated with an industry or

according to a common application such as CAD data, which can be

used throughout multiple industries, as observed by Ravat [66]. STEP

is an international effort that goes beyond geometry & aims to

represent product data throughout a products life cycle.

The primary goal of the standard is to provide a neutral

platform for product data exchange over the entire life cycle of a

product. It is possible to avoid redundancy of data needed across the

59

applications such as finite element analysis, CAPP etc., as projected

by Rao [67]. This makes it the keystone for integrating a company’s

engineering processes. STEP if used with World Wide Web technology

can enable a company to build an open system and make engineering

information available throughout its operations, as envisaged by

Baumgartner [68].

Zhang et al. [69] introduced a STEP-based model data exchange

framework for virtual enterprises and many papers demonstrated the

related data translation, such as one to one translator between an

IGES file and a STEP AP203 file for data exchange of heterogeneous

CAD systems, a STEP AP 203 file and an AP 209 file for CAD/CAE

data exchange, and STEP AP 203 and AP 224 [70, 71] and AP 238

(STEP-NC) files [72] for CAD, CAPP, CAM and CNC data

communication, whilst Pratt et al. [73] described the progress of work

aimed at extending the STEP standard to provide some important new

capabilities such as allowing the transfer of procedural (construction

history) feature-based CAD models with parameterization and

constraints between different CAD/CAM systems.

A majority of papers addressed such a neutral file-based

solution for the integration of CAD and CAM, where the CAD model of

a part is exported via a STEP file from a certain commercial CAD

system to an external feature recognition system and the recognized

features have been used in conjunction with knowledge and/or AI-

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based methods to prepare a process plan for the part. And again the

process plan is exported via STEP to CAM systems.

However, this has been a kind of one-way integration from CAD

to CAPP and further to NC code generation. Furthermore, its

practicability depends on the development of a mature feature

recognizing system.

The notion of features and feature technology emerged in order

to give a higher level meaning to the form aspects or other attributes

of a part or an assembly.

In essence, Feature Recognition (FR) has been defined as a

search process in which a pattern of the entities in the geometric

model is compared with the generic definitions of previously defined

features. The values of the feature parameters and feature

interactions are also determined during feature recognition.

The term "feature" does not imply the same meaning in different

engineering disciplines. This has resulted in several ambiguous

definitions for feature. A feature, in Computer Aided Design (CAD)

software can be called a region of a part with some interesting

geometric or topological patterns as defined by Pratt and Wilson [74].

This meaning can refer to all sorts of information, such as for

example, shape, functional or manufacturing information as

suggested by Regli [75]. Although many types of features have been

investigated by Shah and Mäntylä [76], the most common type of

feature is the form feature, which contains both shape information

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and parametric information. Examples of form features which are

common in many shape models are round holes, slots, bosses, and

pockets.

Features can also be used to represent manufacturing

information of the part. Different manufacturing domains require

different feature representations. Some of the properties that need to

be encoded by features are assembly method, manufacturing process

and tolerances. As quoted by Regli [75], a manufacturing feature can

be defined as a form feature, but not necessarily vice versa. Among

manufacturing features, the ones received extensive attention is the

machining features. According to Chang [77], a machining feature can

be regarded as the volume swept by a cutting tool. In this sense, it is

always a negative (subtracted) volume in contrast with form features

which are sometimes positive (added) volumes.

Feature data in a CAD model can be represented either as a

collection of surfaces or volumetrically. Surface features are naturally

used to describe manufacturing tolerances or locating surfaces in

fixture design. Volumetric features on the other hand, are used in

process planning since manufacturing information (particularly in

machining) is better portrayed volumetrically.

The first published work on features was for the original

boundary representation modeling system, BUILD, and was performed

by Kyprianou [78] in 1980. Soon other work followed based on

different solid representations. Overviews on the work on features can

62

be found in Shah et al [79], Subrahmanyam and Wozny [80] and

Salomons et al [81].

Work on features (generally called feature technology) can be

divided into two rough categories:

� Design-by-features and

� Feature recognition.

In Design-by-features, also known as feature-based design

(FBD), feature structures are introduced directly into a model using

particular operations or by sewing in shapes. By using features to

build up shape models, the design process is made more efficient

because the shape of features can be pre-defined. According to Shah

and Rogers [82] features in FBD can be directly associated to

manufacturing information, so that this information can be retrieved

in downstream applications. In this way CAD/CAM system can be

fully automated; however, the idea of using manufacturing features to

design a part has its own shortcomings as pointed out by Regli [75].

The features used to design the part do not necessarily represent the

best way to manufacture it. It would be therefore the responsibility of

the designer to evaluate all the methods that can produce the part.

Furthermore, manufacturing features are not the most natural way of

designing a part.

The goal of Feature recognition has been to algorithmically

extract higher level entities (e.g. manufacturing features) from lower

level elements (e.g. surfaces, edges, etc) of a CAD model. The classical

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Kyprianou's method [78] was aimed to encode parts for group

technology (GT). The purpose of GT is to systematically classify objects

based on their manufacturing method. Kyprianou's work involved

classifying faces into primary and secondary groups and then

identifying features according to patterns of these primary or

secondary faces. A primary face is one in which there are multiple

boundaries (also called "hole-loops") or mixed concave and convex

boundaries. A concave boundary is a set of concave edges, where the

solid angle over the edge is more than 1800. Secondary faces are all

other faces. Kyprianou's work was continued and extended by other

researchers to cover a number of important special cases where

features interacted.

Automatic Feature Recognition (AFR) is regarded as an ideal

solution to automate design and manufacturing processes. Successful

automation of CAD and CAM systems is a vital connection in building

Computer Integrated Manufacturing (CIM) systems as indicated by

Scholenius [83]. This is the part of the FR research that has attracted

much of the attention. Another important application of AFR as shown

by Gupta and Nau [84] is for manufacturability evaluation. The AFR

system should be able to interpret the design differently based on

alternative features and feed back the manufacturability and cost of

those interpretations to the designer.

Current developments in automatic feature recognition systems

have been directly related to the specific geometric representation

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scheme utilized by the solid modeler. In this respect, advances in

automatic feature recognition systems have been presented

considering two widely used geometric representation schemes,

namely, Boundary Representation (B-rep) and Constructive Solid

Geometry (CSG).

In B-rep, a physical object is considered to be bounded by a set

of faces which are closed and orientable. In this representation

scheme, the information attached to any topological element of a solid

object is locally complete.

A number of approaches have appeared in automatic feature

recognition using B-rep. Some of these are syntactic pattern

recognition, logical inference, graph-theoretic, hint-based approach,

volume decomposition, declarative feature description language,

neural network etc.

Nevertheless, only the first three have been widely used. In all

approaches, predefined patterns of topological elements (like faces)

and geometric values are matched with those in B-rep of the design

part or difference (removal) volume (blank minus design part) to

identify manufacturing (or machining) features.

According to Geelink [85], feature recognition methods that

directly use the product model are referred to as 'direct' methods and

those that use a derivative of the product model such as the volume to

be removed or the convex hull are termed 'indirect' methods.

65

In syntactic pattern recognition, the basic idea is to define

features by a graph grammar, and then search for the occurrences of

these features in the B-rep of an object by parsing it. The graph

grammar is given in a pictorial or graphical language whose alphabet

consists of symbols that represent the topological elements, such as

faces and edges or certain geometric values like edge concavity of an

object. In the graph grammar, features are modeled as rewrite rules. A

feature rule describes all of the topological components and

geometrical constraints of that particular feature using the symbols in

the grammar's alphabet. Then, the B-rep of an object is parsed

according to the rules in the feature grammar. By parsing a language

construct, the sequence of rewrite rules that generated this construct

is obtained. Finally, these rewrite rules correspond to the

manufacturing features. The method is limited to 2.5D features.

Kyprianou [78], Jakubowski [86], Staley et al [87], Liu and

Srinivasan [88], Choi et al [89], Choi and Barash [90], Falcidieno and

Giannini [91], Pinilla et al [92] are some of the researchers who

pursued this approach.

In Logical inference (expert system approach), features are

described using inference rules that define topological components

and geometrical constraints for each class of features in a symbolic

language. Then, the B-rep of an object is searched to identify the

regions that satisfy the feature descriptions given by inference rules.

Since the logical inference is used as the computational method. Such

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systems are developed in logical programming languages or

production systems such as Prolog, OPS-5 or other AI programming

languages.

Henderson [93, 94] and Kung [95] used expert system

techniques in their feature finders. Hummel [96] employed a rule-

based approach to automated feature recognition. Vandenbrande [97,

98] developed his prototype feature finder in KnowledgecraftTM (an AI

environment) and addressed the issue of feature recognition when

complex feature interactions occur. In a recent work, Donaldson and

Corney [99] also used a rule-based approach to determine features for

2.5D machined components.

In Graph-theoretic approach, features are modeled as

stereotypical subgraphs, representing faces, edges, adjacency

information and attributes such as edge convexity. The solid object is

also described by a similar graph. Feature finding becomes a search

for subgraphs that match the feature subgraph, using subgraph

isomorphism as the computational mechanism.

Several researchers adopted graph-theoretic approaches. Joshi

[100] represented the B-rep of the part as an Attributed Adjacency

Graph (AAG) and split this AAG into subgraphs. Then, he matched

these subgraphs with those representing certain feature families or

feature types. Later, Marefat and Kashyap [101] extended Joshi's work

to deal with general feature interactions. De Floriani [102] also used

graph algorithms like connected-component algorithms to recognize

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certain features attached to a face or pair of faces. Sakurai and

Gossard [103, 104] employed subgraph isomorphism in which the

whole B-rep of a part is searched for each feature subgraph.

Graph based approaches have been criticized for several

shortcomings. They fail to account for manufacturability of the

recognized features due to their strong reliance on topological patterns

rather than geometry. The intersection of features causes an explosion

in the number of possible feature patterns that spoils any attempt to

formulate feature patterns.

To address these difficulties, Vandenbrande and Requicha [105]

proposed to search for "minimal indispensable portion of a feature's

boundary", called hints, rather than complete feature patterns, thus

giving rise to another AFR technique called Hint based approach. For

example, presence of two opposing planar faces is a hint for potential

existence of a slot feature. Hints are not necessarily restricted to the

part geometry. They can be extracted from tolerances and design

attributes as well. For example, "a thread attribute may be taken as a

hole hint" (Han et al [106]). This approach has been more successful

in recognizing intersecting features. However, the efficiency of the

approach has been argued, as there could be a huge number of traces

that won't lead to valid features. Some authors have been in favor of

using a hybrid of graph based and hint based FR (Gao and Shah

[107], Rahmani and Arezoo [108]).

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In CSG a physical object is formed by combining a set of

primitives using Boolean operations. Feature recognition using CSG

has been problematic due to the non-uniqueness of CSG

representation. That is, an infinite number of CSG trees can represent

the same object. Another problem results from the fact that

information related to the construction of a solid object is scattered

throughout the CSG tree. Therefore in most cases, CSG

representations are preprocessed to overcome these problems. In

contrast, a B-rep model provides explicit model information about

vertices, edges and faces of an object, so that it is then possible to

develop heuristic rules and algorithms to carry out geometric

reasoning to extract features from the hierarchical face-edge-vertex

data structure.

With respect to the neutral formats particularly STEP (AP203)

which contains the geometrical data of the object, several researchers

concentrated on identifying and extracting the cylindrical, prismatic,

interaction features from the STEP file of the CAD model, applying

complex algorithms and logic to the feature extraction system. Some

of the works have been highlighted in the continuing discussion.

Specific to Cylindrical Feature Recognition, Gao [109] discussed

conversion algorithms on coaxial hole-series machining feature, based

on the design feature model for gearbox components. The planar-type

machining features and non-geometrical attribute features are also

studied. The converted machining features model can be transferred

to process planning system using STEP file.

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Another work by Cicirello [110] presented the approach of using

machining features as an index retrieval mechanism for solid models.

One of the technical approaches for this research is to perform

machining features extraction to map the solid model to a set of STEP

machining features. The approach is using automatic feature

recognition, based on the FBMach system from Allied Signal to

generate feature data to be used in indexing algorithms.

The next work by Han [111] proposed to integrate feature

recognition and process planning in the machining domain. The

purpose of the work is to achieve the goal of CAD/CAM integration.

The system that was proposed uses STEP as input and output

formats. STEP is the interface for portability between CAD and

planning systems, feature recognition for manufacturability and setup

minimization, feature dependency construction, and generation of an

optimal feature-based machining sequence.

A feature extraction system was developed by Bhandarkar [71]

to take STEP file as input and to define the geometry and topology of a

part. In addition, the system generates STEP file, as output with form

feature information is AP224 format for form feature process planning.

The STEP file can be exchanged between various companies and can

serve as input to further downstream activities such as process

planning, scheduling and material requirement planning (MRP).

Finally the focus shifts to Feature interactions since they have

tremendous consequences on a feature model and its applications,

subsequently leading to violation of feature validity. Handling feature

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interactions in feature-based design system is still an unsolved

research issue. Research on feature-interactions in the area of

feature-based design approach has had limited discussion in the

literature, one being Su [112] but has been described widely in feature

recognition research by Narang [113]. The research involves analyzing

the interaction relationship, decomposing the interacted features into

single features and defining their relationship as described by Suh

[114]. Thus, this area of research has become prominent in feature-

based modeling.

All the techniques and systems that have been discussed have

limitations in terms of their feature extraction & identification

technique, interface & data exchange problems, incompatibilities in

computer software, hardware and different representations of product,

resource and process plan etc.

The current work addresses these problems/ issues and is an

effort to develop an intelligent manufacturing system which has a

powerful feature extraction module, has all the necessary

technological data needed to take intelligent decisions w.r.t. all

industrial functions concentrating on machining process, part

program generation and process planning, takes STEP file of CAD

model as input and can be used for training and industrial purpose.

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2.7 RESEARCH ISSUES

Manufacturing technology and CAPP in particular exists in a

highly dynamic environment, with numerous technologies embedded

in to it. Furthermore, the constant developments in Computer Aided

technologies make the task of predicting the future very difficult.

However, it has become vital to review the past methods and

anticipate future developments.

A number of CAPP systems exist in today's dynamic

manufacturing environment. Hence some of the issues emanating

from these systems need to be addressed and managed with proper

consideration. It’s a fact that CAPP systems have been designed and

developed over the years but none of them have been able to address

the real issues in an integrated manner. In this perspective, the

following discussion highlights few such issues.

The conventional CAPP techniques viz. Variant and Generative

have both positive as well as negative aspects. An integrated, feature

based approach, deriving benefits from both the methods would be the

most suitable approach. Depending on the task, the standard process

sequence can be retrieved for specific task, whereas details of each

operation in it have to be generated based on theoretical

considerations, standard databases or practical constraints.

However, it remains an undisputable fact that a truly

Generative type CAPP system would be the most desirable for an

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automated environment. This fact compels the researchers to use the

AI component more effectively in the development of CAPP systems.

It has been recommended for a perfect integration that, features

must be used as a technological and communicational interface

between design and process planning since they depict geometric as

well as technological data, in unison called the product data. Hence,

there is a need for a system which uses product data for all functions

of product realization.

Further, the previous works have concentrated on prismatic or

cylindrical or complex or interacting features only, but not all of them

together on a single, integrated platform. Therefore the system should

be designed to process all these features and to automatically extract

all the product data for catering to all the functions of the industry.

Thus the essence lies in developing a system which has a scope

more than that of any CAPP system and which can cater not only to

the Manufacturing and Process planning functions but also to

functions like Costing, Marketing, Sales, Quality control, in an

organization. Such a system utilizes the product data in an intelligent

way and thus can be termed as an Intelligent System for

Manufacturing Information (ISMI).

CAD models have been used extensively in the engineering

industry and currently majority of the product data is available with

them. Three Dimensional solid models form the core of the CAD

models as far as product development and analysis is concerned.

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Considerable research work has been done in the area of

Automatic Feature extraction from CAD models. These efforts are

based on complex interfaces such as DXF, IGES etc and algorithms

[13, 115]. These interfaces could transfer only geometric data and had

data translation problems which resulted in inconsistent data being

transmitted from one Computer Aided system to another. Therefore, a

need was felt for an interface which could transfer product data

(geometric and technological) consistently. STEP standard gained

popularity due to these reasons. But the commercially available

version i.e. STEP (AP203) transfers only the geometric data. Hence

there is a need to develop a standard /format or an interface to

capture & transmit both geometric and technological data.

It has been always desirable to have a system based on simple

interface and logic. The concept of product model has the potential to

become a powerful technology in the field of CAD/CAM. The product

model [116] takes care of all the manufacturing and design related

attributes apart from the geometrical and topological information of

the product. Hence, it is necessary that, the research in CAPP should

orient towards this fact.

Process planning system must suggest the actions, considering

the constraints, capacities and capabilities of the manufacturing

processes, equipment in the manufacturing shops. Selection of the

manufacturing process has to be based on matching the design and

74

functional requirements with process capabilities which can be a

knowledge data base consisting of following factors [11].

� Shape and size a process can generate

� The dimensional and geometric tolerances that can be obtained

by various processes

� The surface finish obtainable

� The relative cost

� Other cutting characteristics/constraints

One of the primary objectives of any process planning system

has been to generate an optimum, well designed process plan and

CNC part program to suit the present trend of flexible automation. In

order to do that the process capabilities need to be integrated into the

CAPP system.

Another requirement has been to keep the user interaction to a

minimum so as to avoid mistakes. For ex: in cleanup/ modifications

during CAD models import, process and toolpath selection, selection

of optimum speed, feed, DOC etc.

Capacity utilization has always been top priority of

manufacturing industries. A Scheduling module developed by taking

input from realistic shop floor data is a must for achieving this goal.

Most of the existing CAPP systems deal with only specific

operations such as turning, milling, grinding etc [11, 117, 118, 119].

But a component has to undergo different processing procedures

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before reaching the final stage. This concept has to be given

significance in the process planning systems by considering most of

the operational procedures performed on manufacturing shops. Also a

realistic database for machine and tool selection based on their

availability on the shop floor has to be provided. Furthermore, the

user may need process planning backup for individual manufacturing

tasks while working in a general machine shop.

Additionally, in the whole crowd of CAPP systems, the

requirements of small machine shops have not been considered much.

A simplified CAPP system based on all the fundamentals, integrating

all the components intelligently, for general machine shops is the need

of around 70% of the manufacturing sector. Such a simple system can

also serve the purpose of training for the future requirements of CIM.

Finally, the system being developed should be capable of

working in a networked environment and be able to connect to the

World Wide Web for information and help.

Appendix B discusses and highlights the key differences/

comparisons in terms of various factors in the working of a

commercially available CAPP system with the Research Application

developed in the present research work.

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2.8 PROBLEM DEFINITION

It has been proposed to design and develop an Intelligent

System for Manufacturing Information based on Automatic Extraction

of Product Data from CAD Models for prismatic, cylindrical,

interacting and complex features, with a focus on general and

commonly observed product manufacturing environment and

procedures.

This application of Product Data Technology involves generation

of time-cost calculations, process sequence, process plan, part

program, machining status monitoring and reports for various

departments, provided the Product Data is available. The Geometric

data has to be extracted using an Automatic Feature Extraction and

Recognition System from CAD Models with the help of a neutral

interface, thereby eliminating the human interpretation errors in

understanding the product related features. The Technological data

has to be generated using the Product Expert, standard references,

machining parameters database and user input.

2.9 SCOPE

� The system is being developed for a small scale manufacturing

enterprise; the reason being that 70% of enterprises belong to

this category.

� The system will have a focus on machining operations.

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� An Effort has been made to develop a format which contains

entire “Geometric & Technological” data, i.e. “STEP+” file which

will be used for product realization.

� A simple scheduling system needs to be developed for effective

utilization of machines and tools on the shop floor.

� Hardware & software support required for the system should be

minimum.

� The system developed should be able to work in a networked

environment in order to support CIM & DNC setups, which are

currently in use in industries worldwide.

� The work aims to develop a conceptual system, which can be

extended to larger manufacturing enterprises.

� The system should provide understanding of CAPP systems to

students and industry personnel.