CBR Drawing Rev2

7/31/2019 CBR Drawing Rev2

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Indexing and Retrieval in Case-Based Process Planning

for Multi-Stage Non-Axisymmetric Deep Drawing

W. Y. Zhang a, b, S. B. Tor a, b and G. A. Britton b

a Singapore-MIT Alliance, SMA-NTU Office, Nanyang Technological University,

50 Nanyang Avenue, Singapore, 639798

b School of Mechanical and Production Engineering, Nanyang Technological University,

50 Nanyang Avenue, Singapore 639798

Abstract

This paper presents a case-based reasoning (CBR) methodology for computer-aided process

planning (CAPP) for multi-stage, non-axisymmetric sheet metal deep drawing. The

methodology addresses the indexing and retrieval of process planning cases. Planning cases

are indexed via a feature-based representation of deep drawn parts. Efficient case retrieval is

achieved by a feature-based similarity analysis between a new deep drawn part and existing

parts in the case library. An illustrative example is included to demonstrate the operation of

the proposed approach and show its effectiveness in speeding up CAPP for multi-stage non-

axisymmetric deep drawing.

Keywords: Case-based reasoning; Deep drawing; Feature-based representation; Process

planning


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1. Introduction

Sheet metal forming process has been widely applied in various industries such as aerospace,

electronics, machine tools, automobiles, refrigeration, etc., resulting in highly productive

automated processes that have high quality and low costs. Multi-stage deep drawn parts with

various cross-sectional shapes (cylindrical, square, rectangular and other non-axisymmetric

shapes), are widely used for electrical parts such as battery containers, semiconductor and

motor cases. However, the multi-stage deep drawing process planning remains more of an art

than a science because of the complex deformation during the multiple drawing stages. In

particular, in the multi-stage non-axisymmetric deep drawing process the non-uniform

drawing coefficients from elliptical or rectangular cross-sections produce complex non-

uniform metal flow compared to the conventional axisymmetric deep drawing process.

Consequently the process sequence design experience for multi-stage non-axisymmetric deep

drawing is often acquired through trial-and-error experimentation and is very hard to formally

articulate.

Recent advances in the field of Artificial Intelligence (AI) provide the opportunity to

construct AI-based systems, especially knowledge-based expert systems that incorporate

heuristic knowledge (e.g., production rules), that are applicable to solving the computer-aided

process planning (CAPP) problem for multi-stage deep drawing. However, most of these

systems have limited practicality or scalability because the heuristic knowledge in the

application domain of multi-stage deep drawing is tacit and ill-structured in nature. Such

information is difficult to acquire and represent well in a knowledge-based expert system.

Finite element method (FEM) has been explored to solve these problems to a great extent,

however the long computation time required for FEM is not suitable during the practical

planning stages. A discussion of some CAPP work for deep drawing related to our study can


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be found in the next section, which shows most attempts have been made to deal with

rotationally symmetric deep drawing problems, but few for multi-stage non-axisymmetric

deep drawing.

It is widely accepted that common design practices rely heavily on searching and reusing

of past design experiences to solve new problems, instead of designing everything from

scratch. From the viewpoint of AI, this is a human form of case-based reasoning (CBR)

paradigm. The foundation of CBR lies in the psychological theory of human cognition [1].

This paper presents a CBR methodology for CAPP for multi-stage non-axisymmetric sheet

metal deep drawing. The aim of this approach is to improve the productivity of CAPP. There

are two main advantages of using a CBR approach over a traditional knowledge-based (e.g.,

rule-based) reasoning approach or FEA. Firstly, the CBR approach reasons out process

planning solutions quickly by searching and reusing past planning cases, thus avoiding the

need to design everything from scratch. Secondly, process planning for multi-stage deep

drawing, is so complex that it is either difficult or not cost effective to write all knowledge as

succinct rules or build a comprehensive FEA model. On the other hand, process planning

cases can always be given even if the planning solutions are not completely understood.

It has been recognized that the retrieval mechanism plays a major role in a CBR system.

Its efficiency mainly depends on three factors representation, indexing, and similarity

analysis of cases in the case library, which are also the main focus of this paper. Indexing of

planning cases is guided by a feature-based representation of a deep drawn part, which models

the part at a high level of geometric abstraction. Efficient retrieval is achieved by a novel,

feature-based similarity analysis between a new deep drawn part and existing (old) parts in the

case library. This kind of approach has not been addressed in the literature of CAPP for

multi-stage axisymmetric or non-axisymmetric deep drawing.


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A prototype system of the retrieval mechanism has been implemented in a C Language

Integrated Production System (CLIPS) [2], and interfaced with the Solid Edge CAD system.

A process planning example is presented to illustrate the proposed approach.

2. Related Work

Research to improve the productivity of CAPP for sheet metal deep drawing has been widely

reported in the last two decades. Knowledge-based system is one popular AI technique

applied to intelligent process planning for deep drawing. Eshel et al. [3] developed a rule-

based automatic generation of forming process outlines (AGFPO) system to design, test,

rectify and compute the axisymmetric deep drawing process layout. Sitaraman et al. [4]

presented a knowledge-based computer-aided engineering (CAE) system for automatic

process planning for the manufacture of axisymmetric deep drawn parts. Sing & Rao [5]

proposed a decision table method in a knowledge-based CAPP system for axisymmetric deep

drawing. The logic rules contained within the decision table can be production rules, fuzzy

sets or frames. Choi et al. [6] developed an integrated design and CAPP system for

axisymmetric deep drawing, by standardizing design rules for formulating a process sequence.

Kang et al. [7] constructed a knowledge-based process planning system for multi-stage non-

axisymmetric deep drawing of parts with elliptical cross-sectional shape, with the surface area

of deep drawn parts being calculated through a 3D modeling technique.

These knowledge-based system prototypes are restricted to specific application domains

or require considerable interactive input from experienced designers. The limitations are

inherent in knowledge-based techniques, which have difficulty in acquiring tacit knowledge,

i.e., knowledge that is difficult to articulate.


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Alternatively FEM has been widely studied by many researchers to improve the process

sequence design for deep drawn parts. Parsa et al. [8] carried out a rigid-plastic FEM

simulation of the two-stage direct and reverse redrawing process. The simulation results show

that the success or failure of the redrawing process depends not only on the redrawing ratio

but also on the material and process parameters. Min et al. [9] used rigid-plastic FEM to

analyze the multi-stage deep drawing process and compared the predicted distributions of

thickness strains after each stage with the experimental results. In order to reduce possible

forming steps in the multi-stage axisymmetric deep drawing process, Cao et al. [10] combines

an optimization scheme, design rules and numerical tests using inverse and forward FEM

analysis incorporated with a damage model. Kim et al. [11] applied a multi-stage finite

element inverse analysis to multi-stage elliptical and rectangular deep drawing processes to

calculate the initial and the intermediate shapes and the thickness strain distribution in each

intermediate shape. Colgan & Monaghan [12] combined experimental and finite element

analysis to determine the most important factors influencing a drawing process. Though FEM

has proven to be suitable in verifying and improving the design parameters of CAPP for deep

drawing, it does not help reduce the number of design and process planning iterations [13].

In this paper, another fast emerging AI technique, CBR, is adopted to develop a case-

based process planning system for multi-stage non-axisymmetric deep drawing. Schank [14]

pioneered the CBR technique by representing human memory in computers, as an alternative

to the more fashionable knowledge-based reasoning techniques. In the last two decades, CBR

has been successfully applied in a wide number of areas, such as CYRUS [15] for story

understanding using semantic inference, WOK [16] for cooking advice, and BOLERO [17]

for clinical problem solving. CBR has also been used in the application domain of design and

manufacturing, e.g., Archie [18] for architectural design, Cadet [19] for mechanical design,

and a process planner [20] for machining process planning. In the past few years, CBR


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approach has proven to be suitable for tooling design such as fixture design [21], injection

molding design [22], and stamping die design [23]. However our previous work [23] can only

handle monotonic stamping features such as tab, curl, emboss, hole, slot and bend in a general

stamping part, but not combined deep drawing features of multi-stage deep drawn parts. No

research has been carried out thus far on the application of CBR to CAPP for multi-stage

axisymmetric or non-axisymmetric deep drawing.

3. Framework of the Proposed CBR System for CAPP for Multi-Stage

Non-Axisymmetric Deep Drawing

Figure 1 shows the framework of the proposed CBR system for CAPP for multi-stage non-

axisymmetric sheet metal deep drawing. The major modules are: case indexer, case retriever,

case adapter and case library.

[Insert figure 1 about here]

Successful CAPP cases for multi-stage deep drawing are stored in the case library in a

structured manner. Initially, the case library consists only of a few cases acquired using

traditional knowledge-based systems, FEM or industrial practices. To facilitate case retrieval,

each new deep drawn part with its object geometry is first described using a feature-based

representation (elaborated later). This is input to the case indexer, which can identify multiple

deep drawing features. The indexed case is then passed to the case retriever, which extracts a

case (from the case library) that most closely resembles the input case. The retrieval

mechanism employs a feature-based similarity analysis (elaborated later) between the new

deep drawn part and existing parts in the case library to maximize the retrieval efficiency. If

the retrieved closest case doesnt exactly match the query parts design, it is passed to a case

adapter that tailors the retrieved case to meet the requirements of the new part. Once the


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current problem is solved through retrieval or adaptation of a historical case, the final process

planning solution is output to the user and stored in a new historical case in the case library.

This has the effect of continuously improving the CBR system by expanding the case library

whenever a new CAPP problem is solved.

4. Case Representation

4.1. Feature-based representation of deep drawn parts

Since traditional geometric modeling techniques do not capture design intent they are, in

general, unable to support sophisticated and intelligent reasoning capabilities, such as case-

based process planning. Recently, the concept of machining features has been introduced to

create a direct link between design and manufacturing [24]. In a similar manner, a collection

of deep drawing features are used in this paper to model a sheet metal deep drawn part. Each

of these deep drawing features should encapsulate a set of design and manufacturing

information including geometric information such as shape, tolerance and surface finish, and

non-geometric information such as material parameters.

In this paper, a commercial CAD system, Solid Edge is used to support the representation

and extraction of the feature model for all the deep drawn parts in the case library. New parts

are also created in Solid Edge so the system can exploit the CAD systems design -by-feature

interface and its built-in functions (sub-routines) that facilitate feature recognition.

The proposed deep drawing features extraction strategy recognizes the deep drawing

features in the reverse direction of manufacture, starting from the final sheet metal object

geometry (figure 2). The first deep drawing feature is created from the deepest portion of

the geometry in the drawing direction, i.e., the bottom of the deep drawn part. Once this

feature has been identified, the prior geometry needed to form this feature is created. The


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prior geometry is based on the assumption of forming that the features are formed at one

station. In the example, the first deep drawing feature is a cup. It is assumed the cup has a

constant sheet thickness and the same area before and after deformation. The cup dimension

can be calculated easily through 3D modeling technique [7]. As figure 2 shows in step 1, the

deformation zone is decomposed into a drawing feature and its base object geometry, which in

turn is decomposed in step 2.


This backward decomposition procedure is carried out recursively until the base object

geometry is a flat blank. As a result, a set of deep drawing features are extracted, each of

which can be manufactured with one or a combination of deep drawing operations by taking

into account the forming limits such as minimum drawing coefficient and maximum aspect

ratio, which are determined by the material formability.

4.2. Case library design

The case library is composed of a number of historical process planning cases defined in a

frame structure that describes the design requirements and process planning solution. The

design requirements are defined using the feature representation described previously. The

process planning solution is defined as a complete process plan for forming a flat blank to the

final deep drawn part, including process sequence with intermediate object geometries, and

process parameters such as initial drawing coefficient, multiple redrawing coefficients,

punching force, blank holding force, punch profile radii, die profile radii and die clearance.

Due to the diversity of different applications, historical process planning cases may exist

in different forms, such as CAD files or data files, databases or libraries, graphs or data tables,

pictures produced by scanning blue-prints, hardcopy blue-prints, and so on; and may exist in


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local or remote digital sites (accessed by website or ftp) or physical archives. To create a

comprehensive case library and to save the development cost, it is recommended that the die

design information be retained in its original form (not compiled to a unified format). The

digital information is linked by data pointers stored in the case library, while the hardcopy

information is flagged with the reference location stored in the case library.

5. Case Indexing

One of the most important issues in CBR is the efficient retrieval of the most similar case

from a large case library. To find out whether two cases are similar, they have to be indexed

in a proper manner so that the system can identify the closest case easily.

The feature representation of a multi-stage deep drawn part discussed in the last section

can model the part explicitly and comprehensively, and so can support the indexing of cases

quickly and accurately. In this research, feature geometric parameters and material

parameters are used as indices. Feature geometric parameters include shape parameters,

tolerances and surface finishes. Material parameters include Youngs modulus, yield stress,

Poissons ratio, density, friction coefficient and plastic hardening coefficient.

The feature geometric parameters are the main factors for judging the similarity between a

new deep drawn part and existing parts in the case library. Material parameters also influence

the similarity analysis between cases, but are less critical. The indices may produce

contradictory results. For example, two parts may have similar values of their feature

geometric parameters, but low similarity in their material parameters values. Therefore, its

necessary to judge the similarity between cases based on the weight (importance) of each

index.


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6. Case Retrieval

6.1. Feature-based similarity analysis

Case retrieval requires a combination of searching and matching. For deep drawing

applications, the closest case is found by judging the similarity between a new deep drawn

part and existing parts using the following similarity metric:

materialgeometrypar t

materialmaterialgeometrypar tgeometrypar t

ww

SwSwSim

(1)

Where, Sim denotes the similarity metric between two deep drawn parts; Spart-geometry and

Smaterial respectively denote the part geometric similarity and material similarity between two

parts; wpart-geometry and wmaterial respectively denote the weights of the part geometry and

material. Usually, the former carries more weight than the latter since it is more important in

determining the deep drawing process plan.

The material similarity Smaterial between two parts in equation 1 is defined as the inverse of

the material resemblance distance between two parts:

n

j

jpa rametermaterial

n

jold

jpa rametermaterial

new

jpa rametermaterial

old

jpa rametermaterial

new

jpa rametermaterial

jpa rametermaterial

materialmaterial

w

PPMax

PPw

DS

1

,

1

2

,,

,,

,),(

11

(2)

Where,Dmaterial denotes the material resemblance distance between a new deep drawn part and

any existing (old) part in the case library, which is expressed as the normalized Euclidean

distance between the corresponding materials. new jpar ametermaterialP , andold

jpar ametermaterialP , (j = 1, 2,

, n) respectively denote the j-th material parameter of the new and existing parts. Their


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relative difference, i.e., the quotient of their absolute difference

newjpar ametermaterial

newjpar ametermaterial PP ,, divided by the larger value (i.e., maximum) of them

new

jpar ametermaterial

new

jpar ametermaterial PPMax ,,

, is used to calculate the j-th material parameter

resemblance distance, which ranges from 0.0 to 1.0. jpar ametermaterialw , denotes the weight of

thej-th material parameter. The material parameters include Youngs modulus, yield stress,

Poissons ratio, density, friction coefficient and plastic hardening coefficient. It can be

proven that Dmaterial ranges from 0.0 to 1.0, i.e., Dmaterial [0.0, 1.0], hence Smaterial [0.0,

1.0]. For the similarity value, 0.0 indicates most dissimilar and 1.0 indicates most

similar. For the resemblance distance value, 0.0 indicates closest, and 1.0 indicates most

distant. This annotation applies to the rest of this paper.

The part geometric similarity Spart-geometry in equation 1 is defined as the aggregation of all

the feature geometric similarities between corresponding deep drawing feature pairs (in short,

matched feature pairs) in the new and existing parts, and shown below:

nm

S

S

m

i

i

geometryfeatur e

geometrypa rt

2

21 (3)

Where Sfeature geometryi

(i= 1, 2, , m) denotes the feature geometric similarity between the i-th

matched feature pair in the new and existing parts. Since m matched feature pairs apply to

both the new and existing parts, the numerator is multiplied by 2. n denotes the number of

unmatched deep drawing features either in the new or existing parts. Further, Sfeature geometryi

is

defined as the inverse of the geometry resemblance distance between the i-th matched feature

pair, and shown below:


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finishtoleranceshape

iold

finish

inew

finish

ioldfinish

inewfinish

finishiold

tolerance

inew

tolerance

iold

tolerance

inew

tolerance

tolerance

i

shapeshape


i

finishfinish

i

tolerancetolerance

i

shapeshape

i

geometryfeatur e

i

geometryfeatur e

www

FFMax

FFw

FFMax

FFwDw

www

DwDwDw

DS

2

,,

,,2

,,

,,2

222

),(),(1

1

1

(4)

Where, Dfeature geometryi

denotes the geometry resemblance distance between the i-th matched

feature pair, which is expressed as the normalized Euclidean distance between the

corresponding features; Dshapei , Dtolerance

i and ifinishD respectively denote the shape, tolerance,

and surface finish resemblance distances between the i-th matched feature pair; wshape ,

wtolerance and finishw respectively denote the weights of the shape, tolerance and surface finish;

Ftolerancenew i, and Ftolerance

old i, respectively denote the tolerances of the i-th matched features in the new

and existing parts, whose relative difference is used to calculate Dtolerancei ; inewfinishF

, and ioldfinishF,

respectively denote the surface finishes of the i-th matched features in the new and existing

parts, whose relative difference is used to calculate ifinis hD . Note that, Dtolerance

i [0.0, 1.0],

and ifinis hD [0.0, 1.0].

The shape resemblance distance Dshapei between the i-th matched feature pair in equation 4

is then defined as the following normalized Euclidean distance between the corresponding

feature shapes:


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t

k

kpa rametershape

t

kiold

kpa rametershape

inew

kpa rametershape

iold

kpa rametershape

inew

kpa rametershape

kpa rametershape

i

shape

w

FFMax

FFw

D

1

,

1

2

,

,

,

,

,

,

,

,

,),(

(5)

Where, Fshape parameter knew i

,, and Fshape parameter k

old i

,, (k= 1, 2, , t) respectively denote the k-th shape

parameter of the i-th matched feature in the new and existing parts, whose relative difference

is used to calculate the k-th shape parameter resemblance distance, which ranges from 0.0 to

1.0; wshape parameter k , denotes the weight of the k-th shape parameter. Note that Dshapei [0.0,

1.0]. For an extracted deep drawing feature with non-axisymmetric cross-section shown in

figure 3, the shape parameters are expressed as:F

ma

F

mi

D

D,

F

ma

ma

D

D,

ma

mi

D

D,

miD

H,

ma

ma

R

D,

mi

mi

R

D,T

R1 ,

T

R2 ,

T

R3 and .

Where, FmaD andF

miD respectively denote the diameters of flange along major and minor axes;

maD and miD respectively denote the diameters of drawn cup along major and minor axes;

maR and miR respectively denote the curve radii of drawn cup along major and minor axes;H

and respectively denote the height and taper angle of drawn cup; Tdenotes the thickness of

sheet metal;1

R ,2

R and3

R respectively denote several important fillet radii.

The justification for using these shape parameters is based on their relevance to the deep

drawing process parameters. For example, a round drawing punch is required for an extracted

deep drawing feature with axisymmetric cross-sectionma

mi

D

D=1,

ma

ma

R

D=2, and

mi

mi

R

D=2. A

rectangular drawing punch is required for an extracted deep drawing feature with rectangular

cross-sectionma

ma

R

D=0, and

mi

mi

R

D=0.


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In summary, to calculate the similarity metric, the shape resemblance distance Dshapei

[0.0, 0.1] between the i-th matched feature pair is calculated first with equation 5, which is

then substituted into equation 4 to calculate the feature geometric similarity Sfeature geometryi

[0.0, 0.1] between the i-th matched feature pair. The part geometric similarity Spart-geometry

[0.0, 0.1] between two parts can be calculated by substituting Sfeature geometryi

into equation 3.

The material similarity Smaterial [0.0, 0.1] between two parts is also calculated with equation

2. Finally the overall similarity metric is calculated by substituting Spart-geometryand Smaterial

into equation 1, resulting in Sim [0.0, 0.1]. Note that all the weights in these equations are

determined by experience.

6.2. Case retrieval algorithm

The flowchart in figure 4 describes the search algorithm that the case retriever uses in the

similarity analysis to retrieve the most similar case. It is described in the following steps.


Step 1. Select an existing case in the library.

Step 2. Create a list called OLD-PART and put all deep drawing features of the existing

part into it.

Step 3. Create a list called NEW-PART and put all deep drawing features of the new part

into it.

Step 4. Create an empty list called MATCHED, which is used to add the corresponding

deep drawing feature pairs (in short, matched feature pairs) in the new and existing parts.


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Step 5. Select the first deep drawing feature from the list NEW-PART.

Step 6. Check whether the OLD-PART list is empty. Not empty means the OLD-

PART list involves one or several deep drawing features that can be used to match the one

selected from the list NEW-PART to produce potential matched feature pairs.

Step 7. If the list OLD-PART is not empty, i.e., potential matched feature pairs exist,

Sfeature geometryi

value between every pair is calculated with equation 4 in order to select the most

similar matched feature pair. Only the matching feature with the highest Sfeature geometryi

value

is removed from the list OLD-PART. The matched feature is also removed from the list

NEW-PART, and added to the list MATCHED. Go to step 9.

Step 8. If the list OLD-PART is empty, only remove the selected deep drawing feature

from the list NEW-PART.

Step 9. Check whether the NEW-PART list is empty.

Step 10. If the list NEW-PART is empty, calculate geometrypar tS with equation 3 as the

aggregation of all the matched feature pairs added to the list MATCHED, and then calculate

the similarity metric Sim of the present existing case. Go to step 12.

Step 11. If the list NEW-PART is not empty, go back to step 5 for further feature

matching process.

Step 12. Check whether all cases are over.

Step 13. If all existing cases are not over, go back to step 1 for another case for similarity

analysis.


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Step 14. If all existing cases are over, rank all cases according to their Sim values, and

retrieve the most similar case.

7. Case Study

A prototype system of the retrieval mechanism has been implemented using CLIPS embedded

in a C++ environment, thus providing both declarative and object-oriented windows-based

programming environments.

The system has been tested for a variety of multi-stage deep drawn parts and stored cases.

A simple example is presented here to explain how the retrieval mechanism works. For

conciseness, the feature-based similarity analysis process is described briefly and the final

result is elaborated. Figure 5 shows a new multi-stage deep drawn part and some parts of 100

successful cases in the case library. The parts were created using Solid Edge CAD system.

Feature geometric parameters including shape parameters, tolerances and surface finishes, and

material parameters including Youngs modulus, yield stress, Poissons ratio, density, friction

coefficient and plastic hardening coefficient, are used as indices for case indexing. All the

weights of these indices have been pre-determined by experience.


The retrieval algorithm shown in figure 4 is executed to perform the similarity analysis. It

is illustrated below for case #025. Table 1 shows the shape resemblance distance calculation

chart of the 1st matched feature pair (i.e., deepest portion of the deep drawn part) between

case #025 and the new case, which results in 4733.01 shapeD . Similarly, the shape

resemblance distance of the 2nd matched feature pair between case #025 and the new case is

calculated with the result 3433.02

shapeD . The tolerance resemblance distances of the 1st


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matched feature pair and 2nd matched feature pair between case #025 and the new case are

calculated respectively with the results 1121.01 toleranceD and 15.02 toleranceD . The finish

resemblance distances of the 1st matched feature pair and 2nd matched feature pair between

case #025 and the new case are calculated respectively with the results 1272.01 finishD and

1323.02 finishD . The material resemblance distances between case #025 and the new case is

calculated with the results 0877.0materialD .

Table 2 shows the similarity metric calculation chart between case #025 and the new case.

Here, the feature geometric similarities of the 1st matched feature pair and 2nd matched

feature pair between case #025 and the new case are calculated respectively with the results

5733.01 geometryfeatureS , and 6865.0

2 geometryfeatureS . The part geometric similarity between

case #025 and the new case is then calculated with the result 6299.0geometrypar tS . The

material similarity between case #025 and the new case is calculated with the result

9123.0materialS . Finally the overall similarity metric is calculated with the result Sim =

0.6723.

Figure 6 shows the the running results for the top five closest cases (#025, #031, #035,

#046, and #076). Though the parts all look similar to the new part, their detailed shape

parameters, tolerances and surface finishes, and material parameters are different (these have

been omitted for clarity). Ultimately, case #031 was retrieved as the most similar case. It has

a similarity metric of 0.8133.


The process planning solution including process sequence with intermediate object

geometries, and process parameters such as initial drawing coefficient, multiple redrawing


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coefficients, punching force, blank holding force, punch profile radii, die profile radii and die

clearance can be retrieved from this case. Figure 7 shows the retrieved process sequence with

intermediate object geometries from the most similar case, i.e., case #031. This sequence is

based on industrial best practice [7].


The prototype system is implemented on a personal computer with 2.4 GHz Pentium CPU

and 512 MB of memory. It is able to search over a 100 testing cases and retrieve the most

similar case in less than 2 seconds.

8. Conclusion and Future Work

This paper presents a CBR approach with a new indexing and retrieval strategy for CAPP for

multi-stage non-axisymmetric deep drawing. The retrieval mechanism uses a feature-based

representation, which is used to index cases quickly and accurately. A feature-based

similarity analysis is proposed to narrow down the search space and facilitate retrieval within

a reasonable period of time. Similarity metrics have been developed on the basis of feature

geometric parameters and material parameters.

A prototype of the proposed retrieval mechanism has been implemented using CLIPS

embedded in a C++ environment, and interfaced with the Solid Edge CAD system. The

technical feasibility of the proposed CBR approach was illustrated using a process planning

example.

The current system is limited to indexing and retrieval of design cases only. Further work

is in progress to develop an adaptation algorithm to automatically modify the retrieved case to

meet the requirements of the new multi-stage deep drawn part. Furthermore, retrieval of a


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most similar case is based largely on the completeness of the indexes from the practical

viewpoint. Besides feature geometric parameters and material parameters taken into account

in this paper, the future work will also look into additional indexes that need to be considered

in the real deep drawing process planning, including part weight, surface treatments, annual

production, etc.

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List of Figures and Tables

Figure 1. Framework of the proposed CBR system for CAPP for multi-stage non-

axisymmetric deep drawing.

Figure 2. Extraction of deep drawing features.

Figure 3. Feature shape parameters of an extracted deep drawing feature.

Figure 4. Flowchart of case retrieval algorithm for the similarity analysis.

Figure 5. A new multi-stage non-axisymmetric deep drawn part and some parts stored as

process planning cases in the case library.

Figure 6. Result of similarity analysis.

Figure 7. Retrieved process sequence

Table 1. Shape resemblance distance calculation chart of the 1st matched feature pair

between case #025 and the new case

Table 2. Similarity metric calculation chart between case #025 and the new case.


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Figure 1. Framework of the proposed CBR system for CAPP for multi-stage

non-axisymmetric deep drawing.

Required part

description

Case indexer

Case retriever

Case adapter

similarity analysis

Is the closest

case acceptable?

Case library

Process planning

solution

No

Yes

Is it satisfactory?

Yes

End

Reject the present

closest case

Store new case

Start

No


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Figure 2. Extraction of deep drawing features.

Deformation

zone for 1st

step of

Deformation zone

for 2nd step of

decomposition

Step 1: Decomposed into

Deep drawing

feature 1

Deep drawing

feature 2

Deep drawing

feature 3



And

And

And


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Figure 3. Feature shape parameters of an extracted deep drawing feature.

F

maD

maD

FmiD

miD

maR 3

R

miR

2R

1R

T

H


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Figure 4. Flowchart of case retrieval algorithm for the similarity analysis.

Select an existing case in the library

Start

Create a list called OLD-PART and put all deepdrawing features of the existing part into it

Create a list called NEW-PART and put all deep

drawing features of the new part into it

Create an empty list called MATCHED

Select the first feature from the list NEW-PART

Remove it from the list NEW-PART,

and add it to the list MATCHED

Remove it from the

list NEW-PART

Calculate Sfeature geometryi

value between every potential

matched feature pair, and remove the matching one with

the highest Sfeature geometryi

value from the list OLD-PART

No Yes

No

Yes

Is the list NEW-PART empty?

Calculate Sim

Are all cases over?No

Yes

Rank all cases according to their Sim values, andretrieve the most similar case

End

Is the list OLD-PART empty?


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#025 #031

#035 #046

#076 new

Figure 5. A new multi-stage non-axisymmetric deep drawn part and some partsstored as process planning cases in the case library.


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Figure 6. Result of similarity analysis.


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1/11st blanking & drawing 2/11st drawing 3/11st drawing 4/11st perform drawing

Major axis Minor axis



Major axis Minor

Major axis Minor axisMajor axis Minor axisMajor axis Minor axisMajor axis Minor axis

5/11st top drawing 6/11st top drawing 7/11st top drawing 8/11st top drawing

Figure 7. Retrieved process sequence


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Table 1. Shape resemblance distance calculation chart of the 1st matched

feature pair between case #025 and the new case

Feature 1 in

case #025

Feature 1

in new case

Shape parameters 1,,

oldkparametershapeF (k=1, 2, , 10)

Fma

F

mi

D

D F

ma

ma

D

D

ma

mi

D

D

miD

H

ma

ma

R

D

mi

mi

RD T

R1

T

R2

T

R3

0.4521 0.7123 0.3122 0.55 0 0 1 1.5 1.5 0

Shape

parameters1,

,

newkparametershapeF

(k=1, 2, , 10)

Fma

Fmi

D

D 0.48

Shape resemblance distance

4733.0

),(

10

1,

10

1

2

1,,

1,,

1,,

1,,

,

1

kkparametershape

kold

kparametershapenew

kparametershape

oldkparametershape

newkparametershape

kparametershape

shape

w

FFMaxFFw

D

Fma

ma

D

D 0.3333

ma

mi

D

D 1

miD

H 0.5

ma

ma

R

D 2

mi

mi

R

D 2

T

R1

1

T

R2 1.5

T

R3 10

0.0873


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Table 2. Similarity metric calculation chart between case #025 and the new case.

case #025

New case

Feature 1 Feature 2Material

Shape Tolerance Finish Shape Tolerance Finish

Feature

1

Shape4733.0

1

shapeD

Tolerance1121.0

1

toleranceD

Finish1272.0

1

finishD

Feature geometric similarity

5733.0

1

212121

1


finishfinishtolerancetoleranceshapeshape

geometryfeature

www

DwDwDw

S

Feature

2

Shape3433.0

2

shapeD

Tolerance15.0

2

toleranceD

Finish1323.0

2

finishD

Feature geometric similarity

6865.02 geometryfeatureS

Part geometric similarity

6299.0022

22

1

i

igeometryfeature

geometrypart

S

S

Material

0877.0

materialD

Material

similarity

9123.0

1

material

material

D

S

Overall similarity metric

6723.0

materialgeometrypart

materialmaterialgeometrypartgeometrypart

ww

SwSwSi m

Documents

CBR Drawing Rev2