Pergamon 0360-8352(95)00026-7

Computers ind. Engng Vol. 30, No. I, pp. 83-93, 1996 Copyright © 1996 Elsevier Science Lid

Printed in Great Britain. All rights reserved 0360-8352/96 $15.00 + 0.00

M A N U F A C T U R I N G P R O C E S S P L A N N I N G I N A

C O N C U R R E N T D E S I G N A N D M A N U F A C T U R I N G

E N V I R O N M E N T

JIAN (JOHN) DONG 1, HAMID R. PARSAEI 2 and H E R M A N R. LEEP 2 'Department of Mechanical Engineering, University of Connecticut, Storrs, CT06269-3139, U.S.A.

2Department of Industrial Engineering, University of Louisville, Louisville, KY 40292, U.S.A.

(Received May 1995)

Abstract--Concurrent design and manufacturing attempts to consider all aspects of the product during the early stage of design to avoid the costly and time consuming downstream traditional design and manufacturing processes. Process planning links design and manufacturing. This paper discusses the roles of manufacturing process planning in a concurrent engineering environment and the research on the development of prototype feature-based automated process planning (FBAPP) system for concurrent design and manufacturing.

INTRODUCTION

In today's intensively competitive global market place, manufacturing companies are facing a wide range of critical issues such as how to develop a product in less time, at lower cost and with higher quality. Companies are finding that traditional engineering practices and tools can no longer keep pace with the fast changing global market. Increasingly, companies are turning to strategic initiatives such as concurrent engineering (concurrent design and manufacturing), total quality control, just-in-time manufacturing and so forth.

Concurrent design and manufacturing have been recently considered as essential ways to further improve product quality and shrink the time to bring a product to market. The philosophy of concurrent design and manufacturing is to consider all aspects of product during the early stage of design, in order to avoid the costly and time consuming activities downstream associated with traditional design and manufacturing processes [1].

Concurrent design and manufacturing requires quick information exchange so that design engineers can be aware of the manufacturing approaches and be able to select the design with the lowest manufacturing cost. In a traditional design and manufacturing environment, the relationship between designers and manufacturing personnel has always been one of initiator and implementer [2]. This relationship makes design engineers feel that their only responsibility is to create a design which meets functionality requirements. How to realize a design is the responsibility of manufac- turing personnel. If the design violates manufacturing rules, either the manufacturing environment is redesigned to accommodate the new design or the design is sent back to the designers for modification.

There is an invisible wall between design personnel and manufacturing personnel in this traditional design and manufacturing environment. This wall blocks the information flow between design and manufacturing, and greatly increases product development time and cost. Lack of communication between design and manufacturing personnel has become one of the major drawbacks for U.S. manufacturing firms to compete in the global market. According to Ref. [3] 70-80% of the manufacturing productivity can be determined during the design stage. Many of designers make million-dollar decisions every minute without ever knowing it.

Concurrent design and manufacturing are aiming to remove the wall and enhance the communication between design and manufacturing engineers. There are two approaches to concurrent design and manufacturing. One approach is to have design and manufacturing engineers work more closely during product development. The second approach is to create computer-based tools to assist both design and manufacturing personnel to communicate easily and be aware of

83

84 Jian (John) Dong et al.

the mutual requirements during the early stage of design. This paper discusses the development of a computer-based tool for concurrent design and manufacturing and the role of manufacturing process planning in bridging the gap between design and manufacturing.

DESIGN, PROCESS PLANNING AND FEATURE-BASED TECHNOLOGY

Manufacturing process planning plays a key role in the information exchange between design and manufacturing. It is a linkage activity between design and manufacturing as shown in Fig. 1.

Process planning is defined as the function within a manufacturing facility that establishes which processes and parameters are to be used (as well as those machines capable of performing these processes) to convert a piece of material from its initial form to its final predefined form [4].

In a manufacturing company, after a designer finishes a design, the design is sent to a process planner. The process planner will decide whether or not the designed part can be manufactured in the company. If answer is no, the design may be sent back to design engineers for modifications, or sent to an outside company for manufacturing.

Traditional manual process planning involves several steps. The first step is the interpretation of the design data which are usually displayed by blueprints, or by a computer-aided design (CAD) system. In this stage, batch size, geometric configuration, raw material properties, dimensions, tolerances, surface roughness, heat treatment and hardness, as well as some special requirements, are studied and interpreted. The second step is the selection of manufacturing operations and suitable machines. Interpreted design information, shop capabilities and production knowledge are needed to perform the tasks in the second step. The third step is the determination of operation sequences. Economic considerations and company's strategies are needed to perform this step. The fourth step is the selection of clamping devices and the orientation of the cutting tools. The appropriate cutting tools and cutting conditions such as cutting speed, feed rate and depth of cut, a r e also needed. Finally, the overall machining time and nonmachining time are then calculated and process sheets, operation sheets and route sheets are prepared [5].

One of the major concerns in concurrent design and manufacturing is the automatic interpret- ation of design information for manufacturing process planning. Although CAD and computer- aided process planning (CAPP) have been implemented for more than two decades, most CAD

Design [ I-

I Design data interpretation I

I Shop capacities I Production rules]

Human experience I

Process planning

1 7:q

i Sohe ulin I I NCProgrammin I

IFixtorin l I Purchasin l Manufacturing

Fig. 1. Process planning links design and manufacturing.

Manufacturing process planning 85

and CAPP activities are still performed separately. The gap between CAD and CAPP is one of the major obstacles in design and manufacturing automation.

Group technology (GT) has been quite successfully used in aiding engineers to convert the design information into process plans. GT is a manufacturing philosophy in which similar parts are identified and grouped to take advantage of their similarities in manufacturing and design [6]. However, since a human operator is usually needed for the interpretation of design information and the classification of parts, it is difficult to use GT for concurrent design and manufacturing.

Feature-based technology has been recently considered as a promising way to automate process planning activities from design to manufacturing. Feature-based technology is the efficient way to understand design part geometry, tool geometry and tool paths. A feature is defined as any geometric form or entity which is used in reasoning through one or more design and manufacturing activities [7].

The applications of feature-based technology falls into two areas, feature-based design and feature extraction and recognition. Feature-based design provides designers with a feature library in which a number of features are predefined. When designers design a part, they can select different features to form the part.

Feature-based design attempts to match the design process with the physical reality of a manufacturing process, however, this may cause some barriers. Firstly, in feature-based design, designers concentrate too much on the manufacturing process requested to make a part. This may leave too little time to study the functionality which is the most important task during the design stage. Secondly, since features are application-oriented, features with the same shape may be given different names in different application areas. Some features may have the same name but carry different meanings. Thirdly, the limitation of the shape complexity and the number of features available greatly narrow the application areas [8].

Besides feature-based design, there is another feature-based concept for integrating design and manufacturing that seems to match more closely with the nature of design and manufacturing processes. This technique, called feature recognition and extraction, provides much more flexibility to designers.

The main task of feature recognition and extraction is to animate the human geometric reasoning process to automatically extract design information for manufacturing. It is probably the most important and the most difficult task in concurrent design and manufacturing.

The existing feature recognition and extraction approaches can be summarized into three categories: graph/pattern matching [9-12], geometry decomposition [13, 14] and knowledge-based system [15].

A considerable amount of research has been conducted in feature-based geometric reasoning. Several researches [15, 16] have attempted to integrate feature-based geometric reasoning with manufacturing process planning [1, 8, 17]. The research conducted in geometric reasoning research is still far from applications. Only simple and limited features and their combinations can be handled automatically.

In feature-based automated process planning (FBAPP) prototype system, a hybrid algorithm which integrates graph matching, decomposition and knowledge approaches is developed. The objective of this algorithm is to fully and automatically interpret design information for process planning.

PROCESS PLANNING, MANUFACTURING AND PRODUCTION KNOWLEDGE

After the design information has been properly interpreted, a process plan is created for manufacturing the designed part. The information in the process plan is shared by many manufacturing individuals in a company such as machinists, quality engineers, tool designers as diagramed in Fig. 2.

Process planning generation need not only design information but also production knowledge. According to the experimental work for analyzing the process planner's reasoning process which was conducted by Wright and Bourne [18], process plans are generated by first comparing the design part information with the process planner's production knowledge of the company, and then

86 Jian (John) Dong et al.

Cost accounting

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I

Process plans Facilities layout

Tool design

i I \

Machining Inventory&purchase

Fig. 2. Process plan information shared by different manufacturing activities.

using human reasoning capacities to reach conclusions. An automated process planning system, therefore, needs the design part information, production knowledge and reasoning capacities.

Production knowledge may include information such as process capabilities, tool selection knowledge, fixture selection knowledge and sequence heuristics. Process capabilities can be described by the part shape, dimensions, tolerances, surface finish, geometric and technological constraints, and economics of a process.

Due to the difficulty of modeling the interrelationships between workpiece motions and cutting tool motions, representing the shape-producing capabilities is a difficult task. In manual process planning, the operator often has a good understanding about the machine tool motion and its producing capabilities. However, in a computer system, an explicit expression is necessary. For example, when face milling with inserts, a fiat bottom volume can be removed. The quality of the surface finish depends on the machine tool and cutting tool used. Experimental data can be found in handbooks such as The Machining Data Handbook [19].

In order to investigate the feasibility of concurrent design and manufacturing by using computer-based tools, a prototype FBAPP system was developed. A feature-based CAD interface was used to automatically provide the necessary design information. In this study, a feature refers to a certain shape of volume which is to be removed from a blank to produce a designed part. Features carry all the design information for manufacturing process planning.

Production knowledge is captured and coded into expert system rules and files. Human reasoning capacities is realized by using an expert system inference engine. In the FBAPP system, a designed part and blank are built in a CAD system, and the overall removable volume (ORV) is generated by graphically comparing the designed part with a blank. General manufacturing features (GMFs) are extracted based on a heuristic approach. The shape of each GMF is recognized based on the

Manufacturing process planning 87

built-in expert system rules. The design data are converted into manufacturing-specific semantic information and carried by each GMF. Process plans can then be generated directly from the CAD system. A designer, therefore, can know how to manufacture a part before the part is sent to the manufacturing department.

STRUCTURE OF THE FBAPP PROTOTYPE SYSTEM

The FBAPP prototype system consists of four components. These components are the CAD interface, production knowledge, process planning and post processing as shown in Fig. 3.

1. CAD interface

To automatically generate process plans from a CAD, a CAD interface is needed to animate human geometric reasoning capabilities in order to transfer design data into manufacturing-specific semantic information. Five steps are involved in the CAD interface. These steps include the ORV generation, GMF extraction, GMF recognition, compound manufacturing feature (CMF) recog- nition and detailed design information transformation (GMF information scheme).

The ORV can be obtained by graphically comparing a designed part with a blank. GMFs are extracted by decomposing the ORV as shown in Fig. 4. A concave edge-based heuristic approach is used to decompose the ORV into the GMFs [1]. A GMF is a volume without any concave edges.

The shape of each GMF is recognized by using expert system rules. For example, the rules for a standard cylinder can be written as follows:

IF an object has three surfaces, one is a cylindrical surface and other two are planar surfaces AND only two edges, they are the closed circles THEN the object is a standard cylinder

Because many features with different combinations of topological and geometrical information can have the same shape, the developed expert system can only recognize the features with the simplest combination of topological and geometrical information. In order to recognize various features, a smoothing process is performed before using the expert system rules. During the

Solid modelcr CAD system

Feature H extraction

Feature

/ Feature

recognition

/

relationship management

CAD interface

Mac,line II tools Fixtures Machinist

1 Human I Company strategies experience

Production knowledge

Production rules

Graphical [ [ Workshop process p l a n communication

Post-processing

H Inference H Process I/ engine plan

Process planning

Fig. 3. The structure of prototype FBAPP system.

CA|E 3 0 / 1 ~

88 Jian (John) Dong e t al.

Designed part

SSI ORV2 I ;

/ SS2

O R V 2 2 ~ / \ SS3 5 P \

ORV222/ '

SS4

/ J

BS1

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ORV = overall removable volume CE = concave edge

J GMFI (ORV 1)

i |

: : I GMF21 (ORV21) I :

GMF221 (ORV221)

, GMF2221 (ORV2221) SS = splitting surface BS = blank surface

g GMF2222 (ORV2222) DPS = designed part surface GMF = general manufacturing feature

Fig. 4. Extraction of general manufacturing features.

smoothing process, redundant edges, lines and points on a feature are smoothed out. The feature, therefore, has the simplest combination of topological and geometrical information, and the expert system rules can then be used to recognize the shape of the feature [1].

Some features, such as a counterbore and countersink, are CMFs formed by more than one GMF. a CMF can usually be manufactured in one machine set-up. In FBAPP, CMFs are recognized by considering the adjacent GMFs.

Feature information is considered to be vital in the selection of the manufacturing operations, machine tools and cutting tools. However, for a complete manufacturing application, other information such as dimensions, sizes, tolerances, materials and feature relations are also needed. In the FBAPP system, a general manufacturing feature information scheme (GMFIS) was designed for retaining all the information.

The GMFIS includes three levels: feature extraction flow, object information and detail information. Each level carries different information. Feature extraction flow retains information about the relations among the GMFs, ORV, designed part and blank. Object information contains high-level CAD/CAM information such as the shape of a feature and the relations between an object and surfaces. Detail information includes items such as dimensions, tolerances, edges and points [1].

2. Production knowledge

Process capabilities are the only production knowledge currently considered in the FBAPP system. Process capabilities are described in terms of the part shape, dimensions, tolerances, surface finish as well as geometric and technological constraints.

Due to the difficulty in mathematically modeling the geometry of a part and the path of a cutting tool, the representation of the shape-producing capabilities of a machine in a computer environ- ment is a difficult task. However, based on the common knowledge, some shape-producing-capa-

Manufacturing process planning 89

bilities are,. represented in the FBAPP system. For example, a milling machine can usually be used to machine flat slots and holes. If the machine tool has more than one shape-producing capability, preferences for each shape should be listed. For example, a Bridgeport R2E3 CNC milling machine has shape-producing-capabilities, such as face milling (flat bottom shape), end milling (slot or pocket) and drilling (hole). Preferences should be listed as face milling, end milling and then drilling.

Dimensional capabilities of a machine tool are based on the work envelope of the machine tool. The accuracy and repeatability of a machine tool, which affects part tolerances, depend on many factors such as the structure of the machine tool, workpiece materials, work environment and human experience. However, available data in the Machine Data Handbook [19] can be used as a reference for the tolerance of each operation.

3. Process planning

The process plans required to produce each GMF or CMF can be obtained from the production rules coded within an expert system (Fig. 5). The following is an example of the CLIPS expert system for producing a hole:

(feature-shape cylinder-with-two-plane-ends); a through hole

(assert(drilling and/or reaming operation)))

(drilling and/or reaming operation) (blank-shape rect-prism)

(assert (br-227 tapping machine)) (assert (bridgeport milling machine)))

(hole-dimension height, radius) (hole-radius-tolerance up-t, low-t) (hole-high-tolerance up-t, low-t) (hole-material) (hole-heat-treatment) (hardness) :::o.

(assert (drilling and remaining operation are needed)) (assert (drilling-feed-rate, cutting-speed, tool-material)) (assert (reaming-feed-rate, cutting-speed, tool material)))

Feature shape information and the relations among features

Size, dimension and tolerance from GMFIS

Design information

Producing capacities from a workshop

Production knowledge

Expert system

I Process plans with alternatives I

Fig. 5. Process plans generated with an expert system.

90 Jian (John) Dong et al.

4. Post processing

Based on each feature, both graphical and textual representations of a process plan can be generated for GMFs or CMFs. These representations greatly help machine operators in under- standing the required manufacturing process. Figure 6 illustrates a process plan and its graphical representation for drilling a hole.

I II II IlUi

he e -d s tut ter 40 hole- length 50

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The hole wi th diameter 40 (nm) and depth S0 (nm) can not he made in the Bridgeport R2E3 d~illing Machine

4~¢s i t t tt~test a n i s e s s t 4n tae t t t t e t®t It¢Jmte¢ • s • sa~¢

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FEI~D Finishing ope ra t i nn - -Re tming ! ! Po t the hole with diameter 40 01111) and uptolerance 0.1(rim) and log to le tance -0 .1 (rim)

n t '~e~l eq14q41~4~4~4~ ¢S¢ ~ qUl a u¢4gqnt • 8 g

| INPUT DATA (FEATURE INFORMATION)

|

I ! !

OUTPUT (MANUFACTURING METHODS AND PARAMETERS)

! f

Hole with diameter 40rim and depth 50nm Rev blank is ar ter ial 1117 with hardness 140Bhn wrought and annealed heat- t rcatment . T h e hole has to lerance S . l and -0 .1

t O ~ FOR OPERATION I !

1st OPERATION: dr i l l lng-rec t -b lank MACHINES: 1st t ap ing-TC-227 T O O L - M A T E R I A L - G R A D E s2-or-s3. SPEED: 21m/rain to 32m/rain FEED: 0 .75rnhev

| t

OUTPUT FOR OPERATION 11 !

| 2nd OPERATION : reaming | MACHINES :' 1st taping-TC-227

T O O L - M A T E R I A L - G R A D E : s3 .s4 .s2 .k20

| : ***Rough Reaming*** SPEED 37 to 43m/rain FEED 0.75 to 0.9m/rev

***Finish reaming*** SPEED : fls to 21m/rain FEED : 0.9 to 0,9ndr©v

SDRC I-DEPS VI: Solid M o d e l l i m l

Update Level Full

25-JAN-93 19:29:21 Units: ram

Display: No stored Option Bin I -NAIN

Fig. 6. Post processing for a hole feature.

I Solid modeling ] I

I [ Drafting I

I FBAPP I_ I-

I-DEAS families

Manufacturing process planning

~ P e a r l d a t a b a s e ~

FBAPP C main program

CLIPS C subroutines inference engine

~ P o s t - p r o c e s s ~ planning

Process planning CF recognition

Fig. 7. I-DEAS, CLIPS and FBAPP.

GMF recognition

ASCII file ]

I Pearl data [ interface

I-DEAS interface

91

I Release design I

I Manufacturing ]

Mechanical design with CAD system I-DEAS

. . . . . . .

FBAPP prototype system I

Manufacturing process plans I

Modify design

Fig. 8. FBAPP prototype system and concurrent design and manufacturing.

92 Jian 0ohn) Dong et al.

ROLE OF THE FBAPP PROTOTYPE SYSTEM IN CONCURRENT DESIGN AND MANUFACTURING

The FBAPP prototype system was written in the C programming language embedded with the CLIPS expert system rules. CLIPS is an expert system shell developed by NASA. Because feature extraction and recognition require many geometric computations, and the CLIPS expert system lacks computat ion capability, C programs and the CLIPS expert system were, therefore, mixed as an integrated system. Most geometric reasoning and process planning reasoning were done with the CLIPS system, and computat ion and user-interfaces were done with the C program. I-DEAS Vi. 1 software was used as a solid modeling package for the prototype FBAPP system. I-DEAS is a mechanical computer aided design and analysis software developed by the Structural Dynamics Research Corporat ion (SDRC). Three-dimensional models of a designed part and blank were created with I-DEAS. The FBAPP system integrated with I -DEAS was used for feature extraction, feature recognition, process planning and post processing. A schematic of the relationship between I-DEAS, CLIPS and FBAPP is shown in Fig. 7.

With the FBAPP system, designers can check manufacturing process plans for each feature and designed part during the design stage. The best design with lowest manufacturing cost can, therefore, be achieved through concurrent design and manufacturing. Figure 8 illustrates the basic concepts for concurrent design and manufacturing with the FBAPP system.

CONCLUSIONS

Feature-based automated process planning is a link between design and manufacturing. The FBAPP prototype system presented in this paper shows a way to integrate design and manufac- turing activities so that decisions for product design and process development can be made during the early state of design. The FBAPP system can be used by a designer to check manufacturing process plans (for simple parts in the current FBAPP system) during the design stage. The conflicts between design and manufacturing may be avoided. The costly downstream traditional product design and manufacturing process development, therefore, may be performed concurrently. The techniques used, such as ORV generation, G M F extraction and recognition, G F M I S and knowledge-based process planning, make the FBAPP a promising system for further concurrent design and manufacturing research.

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