cold forging

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ELSEVIER Journal of Materials Processing Technology 69 (1997) 264-272 Joumalef Materials Processing Technology Cold forging process design based on the induction of analytical knowledge Quang-Cherng Hsu a,*, Rong-Shean Lee b National Kaohsmng Institute of 7cchnology, Department of Mechanical Engineering, 415 Chien Kung Road, Kaohsiung 807. Taiwan, ROC b Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC Received 27 October 1995 Abstract In this paper, a cold forging process design method based on the induction of analytical knowledge is proposed. The analysis engine, which is a finite-element-based program, is used to analyze various multi-stage cold forging processes based on pre-defined process condition parameters and tooling geometry. According to the simulation results, a knowledge-acquisition procedure is instituted, i.e. a neural network model is constructed, in which the multi-layer network and the back-propagation algorithm are utilized to learn the training examples from the simulation results. In the last part of this paper, an industrial case study for the multi-stage cold forging process design of a low-carbon steel speaker tip is discussed. The optimal process condition parameters, such as the preform punch geometry and the preform punch stroke are decided, based on the requirement of homogeneous plastic deformation of the cold-forged product. The proposed method is useful for the shop floor to decide the cold forging process parameters for producing a sound product within the required minimum quantity of the die set. © I997 Elsevier Science S.A. Keywords: Cold forging; Design method; Finite-element-based program 1. Introduction The use of a knowledge-based system in cold forging has become a topic of wide interest since the original concept was proposed by Sevenler [1]. Following Seven- let's work, several prototype systems for multi-stage process sequence design have been developed [2-8]. Further, these design process sequences can be evalu- ated by using a plasticity numerical model, which pro- cedure can be named as an integrated approach [9-11]. In the previous developments, the design rules were based on the heuristic approach and past experiences. However, due to the lack of universal and reliable human knowledge, it is rather difficult to acquire pre- cise design rules from human experts. There/ore, the induction of analytical knowledge obtained from nu- merical simulation results is important and essential to assist the process sequence design. Osakada and co- workers [12,13] have proposed a framework for an expert system for a cold forging process based on *Corresponding author. Fax: +886 7 3831373; e-mail: [email protected] 0924-0136/97/$17.00 © 1997 Elsevier Science S.A. All rights reserved. PIIS0924-0136(97)00028..9 finite-element simulation, in their work several forging cases having been discussed, which include: the determi- nation of single-stroke forming methods, the prediction of the number of forming steps, and shape defects in backward extrusion. They concluded that the ratio of correct answers in the neural network model are better than those for the statistical method. Fig. 1 shows the decision-making framework for the current intelligent cold forging process. Realization of this concept de- pends on the development and breakthrough of an induction engine, the adaptive remesh of the finite-ele- ment software, and the high computational power and speed of the modern computer. In the current study, the neural-network model for the knowledge acquisition of a multi-stage process se- quence design of cold forging has been developed, being a continuation of the research work from the authors' previous developed Geometry-Oriented Pro- cess Design Expert 'GEOPDE' system for cold forging [4,6,11]. The system is able to design multi-stage cold forging processes and consists of the following compo- nents: (i) a knowledge-based system for process design; (ii) a finite-element plasticity method to generate ana-

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Page 1: cold forging

ELSEVIER Journal of Materials Processing Technology 69 (1997) 264-272

Joumalef

Materials Processing Technology

Cold forging process design based on the induction of analytical knowledge

Quang-Cherng Hsu a,*, Rong-Shean Lee b National Kaohsmng Institute of 7cchnology, Department of Mechanical Engineering, 415 Chien Kung Road, Kaohsiung 807. Taiwan, ROC

b Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC

Received 27 October 1995

Abstract

In this paper, a cold forging process design method based on the induction of analytical knowledge is proposed. The analysis engine, which is a finite-element-based program, is used to analyze various multi-stage cold forging processes based on pre-defined process condition parameters and tooling geometry. According to the simulation results, a knowledge-acquisition procedure is instituted, i.e. a neural network model is constructed, in which the multi-layer network and the back-propagation algorithm are utilized to learn the training examples from the simulation results. In the last part of this paper, an industrial case study for the multi-stage cold forging process design of a low-carbon steel speaker tip is discussed. The optimal process condition parameters, such as the preform punch geometry and the preform punch stroke are decided, based on the requirement of homogeneous plastic deformation of the cold-forged product. The proposed method is useful for the shop floor to decide the cold forging process parameters for producing a sound product within the required minimum quantity of the die set. © I997 Elsevier Science S.A.

Keywords: Cold forging; Design method; Finite-element-based program

1. Introduction

The use of a knowledge-based system in cold forging has become a topic of wide interest since the original concept was proposed by Sevenler [1]. Following Seven- let's work, several prototype systems for multi-stage process sequence design have been developed [2-8]. Further, these design process sequences can be evalu- ated by using a plasticity numerical model, which pro- cedure can be named as an integrated approach [9-11]. In the previous developments, the design rules were based on the heuristic approach and past experiences. However, due to the lack of universal and reliable human knowledge, it is rather difficult to acquire pre- cise design rules from human experts. There/ore, the induction of analytical knowledge obtained from nu- merical simulation results is important and essential to assist the process sequence design. Osakada and co- workers [12,13] have proposed a framework for an expert system for a cold forging process based on

*Corresponding author. Fax: +886 7 3831373; e-mail: [email protected]

0924-0136/97/$17.00 © 1997 Elsevier Science S.A. All rights reserved. PIIS0924-0136(97)00028..9

finite-element simulation, in their work several forging cases having been discussed, which include: the determi- nation of single-stroke forming methods, the prediction of the number of forming steps, and shape defects in backward extrusion. They concluded that the ratio of correct answers in the neural network model are better than those for the statistical method. Fig. 1 shows the decision-making framework for the current intelligent cold forging process. Realization of this concept de- pends on the development and breakthrough of an induction engine, the adaptive remesh of the finite-ele- ment software, and the high computational power and speed of the modern computer.

In the current study, the neural-network model for the knowledge acquisition of a multi-stage process se- quence design of cold forging has been developed, being a continuation of the research work from the authors' previous developed Geometry-Oriented Pro- cess Design Expert 'GEOPDE' system for cold forging [4,6,11]. The system is able to design multi-stage cold forging processes and consists of the following compo- nents: (i) a knowledge-based system for process design; (ii) a finite-element plasticity method to generate ana-

Page 2: cold forging

Q.-C. Hsu. R.-S. Lce /Journal of Material.~ Processing Techmdogy 69 (1997)264-272 265

lytical re,,ults; and (iii) a neural network model for analytical knowledge acquisition. The following sec- tions present a description of the system together with an industrlal case study and discussion/conclusions.

2. Knowledge-based system for process design

The relationship between the product model and the process model is the major consideration on GEOPDE [4,6]. The product model of cold forging consists of ~, form feature and the material feature. The geometric,~ shape of products for axisymmetric cold forging are arranged in groups based on their outer/inner dimen- sions and other associated attibutes. According to the classifications of product shapes, the system describes product geometry by using constructive solid geometry (CSG) or building blocks, such as solid and hollow cylinders, truncated cones and truncated spheroids• This CSG representation or primitives can be manipu- lated easily for cold forging products.

In GEOPDE the basis processes of cold forging arc classified into three groups, which are unit processes, connected processes and combined processes. The unit processes are also classified into three sub-groups: upset forging, Ibrward extrusion and backward extrusion. If the limit of lbrmability or the limit to buckling are exceeded, the forging operation fails to produce the desired shape in one blow and consequently multi-stage processes must be applied. The substituted multi-stage processes, which are called connected processes, must operate successively. A combined process is defined as a combination of two unit processes that operate simulta- neously, in one pass. Combined processes are usually used for reducing the pass. The process model of unit processes comprises the process variables and the forge- ability of the material.

The relationship between the product model and the process model for each basic process is represented by a rule. The architecture of this knowledge-based system includes three main components: an inference engine, a knowledge based and a database. Based on the built-in deduction method and control strategy, the inference engine can find the rules for which the conditions are satisfied by facts and data. An artificial intelligence language, 'Prolog', was selected to develop the system. The built-in control strategies, including backward chaining and back-tracking, and the formation ap- proach 'generate and test', are used to represent the strategic knowledge that controls the design procedures of the process sequence. The knowledge base contains the facts, the rules and the strategic knowledge for the process sequence design of cold forging. The material properties related to the basic processes, such as the limits of forgeability, the limit to buckling, the flow stress, the friction coefficient and the billet dimensions,

are stored in a separate database and can be invoked into the system as facts.

By matching the form feature and the process vari- ables and checking for forging limits, all possible pro- cess sequence are generated. Each of these possibilities is associated with a weighting value, which latter is calculated from practical industrial considerations, such as the total sequence number, the die configuration, the forging load and process rationalization. Taking a typi- cal single-axis disc part as an example, its cold forging processes can be generated as shown in Fig. 2. Methods 1 and 2 are backward rod extrusion and forward rod extrusion in a vertical press, respectively• If the area reduction is less than the formability limit, these two methods can be performed successfully. In method 2, if the area reduction is greater than the formability limit, but the length-to-diameter ratio is below the buckling limit, then a connected process 'extrude-heading' in the vertical press can be utilized successfully, such as shown in method 3. If the upset strain is less than the forge- ability limit and the upset ratio is less than the buckling limit, then single-stage upsetting can be used, as shown in method 4. When the upset ratio exceeds the buckling limit, then a preform heading process is necessary, as depicted in method 5. This technique is used frequently by industry for its high productivity in a horizontal upsetter. Therelbre, the design of preform heading parameters is important in order to meet the product requirements both in terms of geometry and properties

User level

t __

Decision level

Knowledge level

Information level

Task level

,

Conclusions and actions]

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resulisl ]

• . ] Cold forging I /[\ . [ experitment ]

[ Plasticity model[

& |

Kn°w]edge I engineer ]

l J Cold Forging[ - I expert I

Fig. I. Decision-making framework for intelligent cold forging.

Page 3: cold forging

266 Q.-C. Hsu, R.-S. Lee/Journal of Materials Processing Technology 69 (1997) 264-272

Method 1.

Method 2. [

Method 3.

Method 4.

Method 5.

I

I

rigid viscoplastic, linear thermo-elastic and sintered porous preform. The finite-element analysis is done in a series of steps. Processing steps often are done simulta- neously, and often are transparent, in the computer. The solution is based on variational mechanics, in which the equilibrium equations of the material or the virtual work equations are derived. The deformation nodal velocity tensors in the discrete element domain, the material constitutive equations, and the boundary conditions are then substituted into the virtual work equation in order to derive the governing equations. The solution procedures are finalized through a Taylor series expansion of the governing equations, and the Newton-Raphson iteration process. The user must in- tervene in the pre-processing stage by providing data in order to construct the geometry of the dies and the billet, and to define the material properties and the press specifications. The detail analysis results, such as the material flow, and the distributions of stress, strain, pressure and temperature can be obtained. Conse- quently, DEFORM, as an analysis e~agine, can provide information on die filling and free surface geometry, and the occurrence of process defects such as folding, laps and cracks in the designed product.

4. Neural-network model for analytical knowledge acquisition

Fig. 2. Cold forging processes for a single-axis disc part.

after finish heading. The finite-element plasticity method can be used to generate the required design knowledge.

3. Finite-element plasticity method to generate analytical results

In this section, the mathematical description of the back-propagation network (BPN) operation, which is the learning algorithm for the network, is present briefly [15]. Fig. 3 shows the three-layer network. An input vector, .Vp = (.Vpi, .xp2 ...... Vpn)', is applied to the input layer of the network. When the pth training set is input, the gain of the j th node in the hidden layer is:

N net[~j = ~ Xpi Wh + 0]' (l}

i=1

The application of a powerful numerical simulation technology, such as the Finite-Element Method (FEM), has become a very popular approach these days for the following reasons: (1) to reduce costly trials in a newly designed die before actual forming; (ii) to improve tool and die design to reduce production and material costs; and (iii) to shorten lead time in bringing a new product to market by reducing die design time. In this paper, a FEM-based software, called DEFORM 2D [14] has been used to generate analytical results for a cold- forged speaker tip. The automatic remeshing capability of DEFORM is a powerful feature, which can be used to simulate various multi-stage forging processes, and it is transparent to the user during the simulation stage.

The material constitutive models in DEFORM for plasticity deformation analysis include: rigid plastic,

where v ,h is the weight on the connection from the ith -t ij input unit to the j th node in the hidden layer, and 011 is the bias term. The "h' superscript refers to quantities on the hidden layer. Assuming that the activation of this

Ork ~ / ~ f o u t p u t layer

Xpi tnput layer

Fig. 3. The three-layer BPN architecture [15].

Page 4: cold forging

Q.-C. Hsu, R.-S. Lee/Journal of Materials Proces'smg Teclmology 69 (1997) 264-272 267

node is equal to the net input, the output of this node is: • h h tpj = f j (netvj) (2)

Therefore, the equations for the k th output node are: 1.

= ~ ' " - - j k (ne tpk) (3) net[;k ~ /pj jk -~- Ok Opk j = l

where the 'o" superscript refers to the quantities of the output layer.

The initial set of weight values represents a first guess as to the proper weights for the problem. The technique of the neural network model does not depend on making a good first guess. The procedures are to accu- mulate the changes as each pattern was processed, sum them up, and make one update to the weights, the process being repeated until the error reaches the ac- ceptable level. The error function is defined as:

E = ~ Z (Ypk - opk)-" (4) - - p = l k - i

where the subscript "p' refers to the p th training vector, and 'k ' refers to the kth output unit, Ypk is the desired output value, and Opk is the actual output from the kth unit. The factor of 1/2 is there for convenience in calculating derivatives later. T is the number of patterns in the training set and M is the total node number of the output layer. The algorithm of adjusting weights is to minimize the error function by the gradient-descent method. Therefore, the increment of weight from the ith unit in the input layer to the j th node in the hidden layer is:

?E Aw~j = - q ~ (5)

() |t'i. I

and the increment of the weight from the j th node in the hidden layer to the kth unit in the output layer is:

8E (6) A Wjk = - q 0 Wjk

where q is a multiplier factor.

cPrc-defme a set of process ondition parameters tl

IUs ing the Analytical I~owledge from the I J trained Neural ~ /

Network Model J

'~ . . . . Re free

Fig. 4. Flow chart for process design in the forward manner.

I .~d

¢1

U p s e t t i n g Cone Guide ratio angle length

2tr, s = 9,0/d 0 degrees a

2.5 15 0.6 d o 3.3 | 5 1.0 d O 3.9 | 5 1.4 d o 4.3 20 1.7 d o 4.5 25 1.9 d o

__=

Fig. 5. Design guide of

Length of conical portion

of preheader

1.37 d o 1.56 d o 1.66 d o

.56 d o 1.45 d o

the solid prefon-n header [16].

condition parameters the product will be based. The second is the synthesis phase, which uses the knowledge in a backward manner, i.e. to optimize the process-con- dition parameters based on the required product infor- mation.

5.1. Forward manner to design process

5. Results and discussion

The following paragraphs demonstrate how to induce the analytical knowledge from FEM simulation to as- sist cold forging process sequence design using the back-propagation neural network model. The aim is to produce a T-type speaker tip made of low-carbon steel by means of open-die upsetting, so as to obtain nearly homogeneous deformation within the required mini- mum quantity of the die set. The methodology can be divided into two phases. Phase one is the analysis phase, which uses the FEM analytical knowledge in a forward manner, i.e. to seek what pre-defined process

In the forward manner, the procedures to use the analytical knowledge are: (i) to choose a pre-defined process-condition parameters such as the die cone an- gle, the die stroke and guide length, etc.; (ii) to input these parameters into the trained neural network to obtain the product geometry; (iii) to check whether the product geometry is acceptable or not; (iv) if yes then stop, otherwise go to step (i). Fig. 4 shows the flow chart of the design process in the forward manner.

According to the built-in knowledge collected from Ref. [16] shown in Fig. 5, the combination of the preform heading and the finish heading of the cold- forging process is shown in Fig. 6. In the preform

Page 5: cold forging

268 Q,-C, Hsu, R.-S. Lee/Journal of Materials Processing Technology 69 (1997)264-272

heading, the cone angle is 15*, the length of the cone is 27,4 ram, and the guide length is 12.0 mm. Since open-die upsetting was adapted in the last operation for its low forging force and lesser number of die sets, and the material flow in the boundary is not homogeneous due to the friction effect, the flatness of the product bounda~ is not good. In order to understand the relationship between the product geometry and the parameters of preform heading, 33 cases of different parameters have been analyzed by DEFORM. In this analysis, the constant shear factors are 0.25 in preform heading with dry friction and 0.15 in finish heading with a phosphate coating, respectively. The flow stress model of the low-carbon steel is defined as a = 577 ¢o.233 MPa. There are five values for the cone angle, namely 15, 20, 25, 30 and 35% and there are three guide

I I i !

I I

, 64

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Cu~-oF¢

f- .4 I 1170.11

L ~ - - ~ . - -~ r ' I , / i I__i _+_

I::)refor• heo01ln O Finish t-~c~dmg

Fig. 6. The combination of preform heading and finish heading in cold forging processes for a single-axis disc.

Fig. 8. Neural network model and root mean square error for four input and 11 output units in analysis phase.

I Obtaining the 1 Optimum Process Condition Parameters

Knowledge from the trained Neural Network Model

io-.l Required Product Geometry

?b "?1 Cone angle, 15 degrees

'°1") '") '31 ,~,~.: . . .~ .... : . . ~ ...... .'~....~ Cane angle, 1 5 degrees

9;8"''"" ~ " ~ ...... "~b"~ Cane angle, 15 degrees

Cane angle: 20 degrees

"l'"') "") "") I q ; g : ~ " ' ' " ~ ...... "%" '6

Cane angle, ;=0 degrees

Cane angle, 20 degrees

Cone Qngte, 2~ d e g r e e s

18 "'

9 ; . ' " : ' " ~ .... " ' ~ ...... %""1~ Cane angle: 25 degrees

;8

~4~. e . . . . . . . ~ . . . . . . . ~ . . . . . . . 4b. • .~,la Cone ¢ngte: 25 degree..;

Cane Q.gle: 30 degrees

Cone angle, 35 degrees

10 Mr~

Fig. 7. Free surface contours of a single.axis disc.

Fig. 9. Flow chart for process design in the backward manner.

lengths, namely 0, 12, 28 mm. The stroke of the punch is also chosen with three different values.

Note that when the die angle is 30 or 35 ° , no guide length is allowable, as with the guide length present with a cone angle of greater than 30 °, the folding defect will occur. The free surface boundary of these 33 cases after DEFORM simulation is shown in Fig. 7. Using cubic spline curve fitting and interpolation, the original nodal points in the free surface can be transformed into data points with equal y-axis distance. Because the length of the free surface in the y-direction is 4 mm, every 0.4 mm is chosen for interpolation. Therefore each case now has 11 interpolated data points and their x-coordinate values can be used as the output layer for neural network learning.

The 33 training cases for neural network model are listed in Table 1. There are four input units to represent process condition parameters and 11 output units to represent product geometry. The neural network model that is shown in Fig. 8 is to use one hidden layer, the cumulative-delta rule and the sigmoid function. If there

Page 6: cold forging

Q.-C. Hsu, R.-S. Lee/Journal of Materials Processing Technology 69 (1997)264-272 269

I"

~ ~ ~ - ~ _ ~ , ~ _ ~ ~ ..... ~ ~ , ~ ~ _

~ ~ o o ~ ~ o ~ : = ~ ~ ~ o ~ _ -~ . . . .

o ~ ~ - ~ ~ ~ ~ ~ = ~ o , ^ ~ _ ~ . ~ . ~ - ~ . - ~ , _ _ _

~ - , ~ - , e~, ~ - , e~, ~ - . ~ - , ~ , ~ - , e~, r~ , r l i,,--, ~ . ~ - , ew, r~ . ew, ~ - , r e . ~ I e~, ~ - . ~ ' . ~ , ~ - , e~, ~ , ~ - , ~ ' , ~ - , ~ ' , e~,

Page 7: cold forging

270 Q.-C. Hsu, R.-$. L e e / J o u r n a l of Materials Processhtg Technology 69 (1997) 264-272

D E F O R H P o s t - P l o t ( - 1 )

8 0 . 0

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D E F O R M P o s t - P l o t ( 4 4 5 )

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I1 .ll I I . I 4 . I 53 .Q .o IO i i l .1 11 . ! 42.4 ~1.o

Raa tus (C) Raa t u s

Fig. 10. Speak tip product and its cold forging process: (a) billet; (b) preform heading; (c) finish heading.

are six hidden nodes, the root-mean-square error is less than 0.01, which means that the learning accuracy attends +0.02 mm. The learning results (i.e. the weights from the input to the hidden layer and from the hidden layer to the output layer) can be abstracted and integrated into a knowledge-based expert system for assisting cold forging process design.

5.2. Backward manner to design process

The procedures to use the analytical knowledge in the backward manner are: (i) to define the required product geometry; and (ii) to input the geometry data into the trained neural network model to obtain the suitable and optimum process condition parameters. Fig. 9 shows the flow chart to design the process in backward manner, ~shilst Fig. 10 shows the speaker-tip

product and its cold-forging process. Using DEFORM to simulate the 15 different cases with changing in the preform heading geometry, the simulation results are listed in Table 2. Fig. 11 shows the free surface con- tours of the speaker-tip product. In Table 2 there are 11 input units to represent the product geometry and three output units to represent the process condition parame- ters. Fig. 12 shows the neural network model combined with three-layer BPN architecture. The network con- sists of one hidden layer, the norm-cumulative-delta rule and the hyperbolic tangent function. If the hidden layer has 14 nodes, the root-mean-square error is less than 0.0035, which means that the netwerk completely fits in the simulation data generated by DEFORM. To use the analytical knowledge from the trained neural network is just to input the required product geometry, whereupon the optimum process condition parameters will be obtained.

Page 8: cold forging

Q.-C. Hsu, R.-S. t_,~'~:' Journal of Materials Process#~g Technology 69 (1997) 264-272 271

0

0

.o

.~_ r~

o

.3.= ~

0

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . ~ ~ ~ ~ ~ !

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~ ~ ~ ~ ~ . . ~ ~ .

Page 9: cold forging

272 Q.-C. Hsu, R.-S. Lee/Journal of Materials Processing Technology 69 (1997) 264-272

Cane angle, 30 deorees

36 0 I . . . . . . . . . . . ! . . . . . . . . . . . ! . . . . . . . . . . . ! .. Cane Qngte, 32 degrees

it)! :i 0 Cone cngle, 34 degrees

10 ~M

Fig. 11. Free surface contour of the speaker tip.

6. Conclusions

In this paper, a concept for designing a cold-forging process based on analytical knowledge has been pro- posed. By integrating a knowledge-based system, the finite-element plasticity method and the back-propaga- tion neural network model, the decision-making frame- work has been implemented. Two industrial cases have been studied to demonstrate how to use the analytical knowledge for process design, accomplished with two different approaches: one is the forward manner seeks what the product will be, based on the pre-defined process conditions; and the other is the backward man- ner, which optimizes the process condition parameters based on the required product information. According to previous work, the following conclusions can be drawn. The analytical knowledge can be used easily on the shop floor to optimize the process condition parameters, as the time-consuming analysis task (FEM simulation) can be

results can be well trained in neural network model by

Fig. 12. Neural network model and root mean square error for i ! input and three output units in systhesis phase.

the backward manner. In the case study of a single-axis disc, the convergence is bad (for its large root-mean- square error) if the backward manner is adopted, because of the high divergence of the original data patterns.

Acknowledgements

The authors would like to acknowledge Dr I.M. Bidhendi, Professor at the University of South Australia, for his kindly discussion of the manuscript. Mr Tasi at Metal Industry Research and Development Centre is also acknowledged for assisting in the neural-network compu- tation.

References

[1] K. Sevenler, P.S. Raghupathi, T. Altan, Forming-sequence de- sign for multistage cold forging, J. Mech. Work. Tech. 14 (1987) 121-135.

[2] K. Osakada, T. Kado, G.B. Yang, Application of Al-technology to process planning of cold forging, Ann. CIRP 37 (1988) 239-242.

[3] K. Lange, G. Du, A Formal Approach to Designing Forming Sequences for Cold Forging Operation, Proc. 17th North Amer- ican Manufacturing Research Conference, 1989, pp. 17-22.

[4] Q.C. Hsu, R.S. Lee, Application of a Knowledge-Based System for Axisymmetrical Cold-Forging Process Design, Proc. ASME Winter Annual Meeting on Concurrent Product and Process Design, San Francisco, CA, 1989, pp. 185-194.

[5] T. Mahmmod, B. Lengyel, "i.M. Husband, Expert System for Process Planning in the Cold Forging of Steel, Proc. Int. Conf. Expert Systems, Brighton, 1990.

[6] Q.C. Hsu, R.S. Lee, Geometry-oriented knowledge-based system for preliminary process design of cold forging parts, Int. J. Adv. Manuf. Tech. 6 (1991) 45-61.

[7] H.S. Kim, S.M. Yoon, Y.T. im, Development of Expert System for Multistage Cold Forging Process Design, Proc. 26th Int. Cold Forging Group Conf., Osaka, 1993, pp. 5.1-12.

[8] K. Nakanishi, A. Danno, O. Takata, T. Yamazaki, Expert System for Process Design of Multistage Cold Forging, ibid., pp. 21.l-10.

[9] P. Bariani, G. Berti, L. D'Angelo, M. Marengo, A. Rossi, An Integrated CAD/CAE System for Cold Forging Process Design, Proc. 3rd Int. Conf. Technol. Plasticity, Kyoto, 1990, pp. 7-12.

[10] N. Alberti, L. Cannizzaro, F. Micari, Knowledge-based system and F.E. simulations in metal-forming processes design--an integrated approach, Ann. CIRP 40 (1991) 295-298.

[l l] R.S. Lee, Q.C. Hsu, Development of an integrated process planning-based CAE system for cold forging, J. Eng. Manuf. Proc. Inst. Mech. Eng. 206 0992) 215-225.

[12] K. Osakada, G.B. Yang, T. Nakamura, K. Mori, Expert System for cold forging process based on FEM simulation, Ann. CIRP 39 (1990) 249-252.

[13] K. Osakada, G.B. Yang, Application of Neural Networks to an Expert System for Cold Forging, Int. J. Machine Tools and Manuf. 31 (1991) 577-587.

[14] J.P. Tang, W.T. Wu, DEFORM 2D User Manual, Ver. 4.0, SFTC, USA, 1994.

[15] J.A. Freeman, D.M. Skapura, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley, Reading, MA, 1991.

[16] K. Lange (Ed.), Handbook of Metal Forming, McGraw-Hill, New York, 1985.