Application of neural networks to an expert system for cold forging

Int. J. Mach. Tools Manufact. Vol. 31, No. 4. pp.577-587, 1991. 089(~-6955/9153.00 + .00 Printed in Great Britain Pergamon Press pie

A P P L I C A T I O N O F N E U R A L N E T W O R K S T O A N E X P E R T

S Y S T E M F O R C O L D F O R G I N G

KOZO OSAKADA* a n d GUOBIN YANG*

(Received 12 January 1990; in final form 15 January 1991)

Abstract--The technique of neural networks is applied to an expert system for cold forging in order to increase the consultation speed and to provide more reliable results. A three-layer neural network is used and the back-propagation algorithm is employed to train the network.

By utilizing the ability of pattern recognition of neural networks, a system is constructed to relate the shapes of rotationally symmetric products to their forming methods. The cross-sectional shapes of the products which can be formed by one blow are transformed into 16 × 16 black and white points and are given to the input layer. After learning about 23 products, the system is able to determine the forming methods for the products which are exactly the same or slightly different from the products used in the network training. To exploit the self-learning ability, the neural networks are applied to the prediction of the most probable number of forming steps, from information about the complexity of the product shape and the materials of the die and billet, and also to the generation of rules from the knowledge acquired from an FEM simulation. It is found that the prediction of the most probable number of forming steps can be made successfully and that the FEM results are represented better by the neural networks than by the statistical methods.

1. INTRODUCTION

To REALIZE automatic process planning of forging, research into computer aided process planning systems, including those based on the artificial intelligence (AI) techniques, is being carried out actively. This sort of work was begun by Noack [1] in the field of forging at the beginning of the 1970s and since then a significant progress has been made, especially in the last decade [2-39]. Almost all of the previous work has been concerned with the process planning of relatively simple shaped products and is found to be at an experimental prototype stage.

A prototype of a knowledge based process planning expert system was developed by the authors [28, 35, 38]. As shown in Fig. 1, this system includes the functions of process generation, determination of the priority of the generated processes, evaluation of the feasibility of the generated processes and FEM simulation of the obtained processes. Although the skeleton of the system has been constructed, there still remain many unsolved problems, such as the realization of rapid process generation, effective determination of the priority degree of the obtained processes, acquisition and self- learning of reliable knowledge, etc., for the system to be applicable in industry.

It has become clear that not all the problems can be solved by the approach of artificial intelligence, and some new methods are required to improve the ability of the present expert 'systems. A method to combine the expert system for process planning of cold forging with the finite element method (FEM) simulation and neural networks has been put forward [38] and the method has proved to be effective in some cases. The conventional AI-technique is effective in getting the correct answers by checking all the possible cases if a complete set of rules is given, but it usually takes a long time to test the answers. On the other hand, neural networks have a characteristic feature that very rapid response is possible, although the correct answer is obtained only when the problem is within the well trained range. The combination of both methods may allow expert systems to provide correct answers in a short time for most cases, and further, the self-learning function of expert systems may be realized by the application of the neural network techniques.

*Faculty of Engineering Science, Osaka University, Toyonaka, Osaka, Japan.

577

578 K. OSAKADA and G. YANG

[Re isratiOnof IIPr°cess I10nlofOr erof Product Generation Evaluation

$ $ $ Data Bases of System -geometric primitive definition data base -billet and die materials data base -sample processes data base -fundamental processes data base -rule base etc.

~ p~ut I Visualizati°nof II Evaluating [I C°m ~r

Process System Simulation

Fl~. 1. Structure of the expert system for process planning of cold forging.

Knowledge Acquisition from Experts

Knowledge Accumulation from Conclusions ofl Consultation

In this paper, the application of neural networks to the expert system is extended to some problems such as determination of forming method from product shapes, efficient determination of priority degree of the proposed processes and extraction of forging rules from the data bases of FEM simulation.

2. NEURAL NETWORKS

Artificial neural network models or simply "neural networks" go by many names such as connectionist models, parallel distributed processing models, and neuromorphic systems. Whatever the name, all these models attempt to achieve good performance via dense interconnection of simple computational elements. Instead of performing a program of instructions sequentially as in the von Neumann computers, the neural network models explore many competing hypotheses simultaneously by using massively parallel networks composed of many nonlinear and typically analog computational elements connected by links with variable weights [40]. The functions of the neural network models could be simulated by the von Neumann computers. It is considered that completely new approaches by the neural network models may bring about a break-through in the problems that the expert systems encounter.

There are mainly three types of neural network models, i.e. the single and multi- layer perceptron, the Hopfield network and the Boltzmann machines, depending on the network topology, node characteristics, and training or learning rules. In the present work, the back-propagation training algorithm [41] is employed to train some multi- layered perceptron networks.

Figure 2 shows a three-layer neural network model with N input units, No output units and M hidden units. In the formulas given in the figure, li are the inputs of nodes in the input layer, Hj and Ok are the outputs of nodes in the hidden and output layers, C~ and Dk are the internal offsets in those nodes, Wji is the connection strength from the input to the hidden units, and Vkj is the connection between the hidden and the output units, f is a nonlinearity function:

f(x) = [1 + tanh(x/Uo)]/2

where Uo is a constant. Given the desired state vector of the output units for each state vector of the input

units, the back-propagation training algorithm adjusts the weights of the connections repeatedly in the network; the weights are self-organized to minimize the square root average of the difference between the actual and the desired output vector. The internal hidden units represent important features of the task domain although no explicit meaning is given to them. As a result, the regularities in the task are captured by the interactions between these units.

Application of Neural Networks 579

Output layer

Hidden H0

layer

Input layer

Oo Ok ) (

)

Io I i

01%-i

HM-1

IN-1

M-I Ok=f( Z VkjHj+Dk)

j=o (O~_k.~No- I)

N-1 Hj=f (~ Wjili+Cj)

i=o

(O-<j-<M - 1)

(0S_i.SN- 1)

Fro. 2. A three-layer neural network.

This kind of self-organizing ability distinguishes neural networks from existing artificial intelligence systems or conventional von Neumann computer systems. After the network has been trained, necessary dynamics to solve the problem will be generated simply by giving the state vector of the input units to the network. The weights of the connections in the trained neural network can be utilized just as the rules in "an expert system can, except that the response of the neural network is much faster.

3. DETERMINATION OF SINGLE STROKE FORMING METHODS

Process generation is one of the most important functions of the expert system for cold forging. To increase the generation speed of forming processes, a three-layer neural network with 256 (16 x 16) units for image data input, 5 hidden units and 5 output units (]Fig. 3) is applied to relate some kinds of product shapes to forming methods using pattern recognition.

First, the shape of the axi-symmetric product for which process is to be designed is created by a computer-aided design (CAD) system of the expert system [28, 35] and is changed to a black and white image of the cross-section, or a black and white image is read directly by an image scanner. Then the data are standardized to 16 × 16 image data through a series of image processing operations such as noise reduction, location adjustment, scale change (Fig. 4), etc. The standardized image data are then sent to the 256 input units.

The first 4 output units of the network refer to the cold forging methods which may form the product with one blow: forward extrusion, backward extrusion, combined extrusion and upsetting. The fifth output unit indicates that the product cannot be formed by one of the above methods or with one blow. This unit will respond when the shapes of products are quite different from those of the products used in the network training. If the final output unit responds or if the predicted one-blow process is judged to be inappropriate by the process evaluation system, the process generation will then be carried out by the expert system for processs planning.

Training of the network for pattern recognition of images was begun with a test of recognizing 5 alphabetic characters: A, B, C, D and E, written by 10 persons. Although it was a relatively small network, ratios of correct answers of the 5 characters recognition achieved about 100%. Then the same network was retrained to recognize the 4 forming methods by using 23 simple shaped products which could be cold forged by only one

~ 3 1 : 4 - J


Classification Neural Network orw Ex o oo

Backward Exlr usion

Combined Extrusion Up,~tting Impossible

Classification Impossible

Shapeof ProductData ~ Conventional Expert System ]

~._~ Process

____) Evaluation

~ S~ System

FiG. 3. Determination of single stroke forming methods.

Noise Slant

tn Location and Scale Difference

! | m n u u m m m m B u n m n n | m m n m m l l u B u m m B i m | n n n u m u m n u n mmm m m l m I m | u m m m m u n m m u u m m m m m u m n n u n u n u m b n u n n n u m n m u mmm a n n n u m B n m n n u n u m u m m u m I l l N a i l n i l B D N N u t nun n u n D i n l i d l l m m m m i m m m i i i m i l u m m m u m m m m m m u m u m m

Standardized Image

Ftc. 4. Image data preprocessing.

blow. The mean square errors between the outputs and the teacher signals for both cases of the network training process are given in Fig. 5. It can be seen that both cases gave satisfactory rates of training; about 31 min were spent to train the network with 23 products using a 32-bit engineering work-station, U-station/S, with a computing speed of about 12 MIPS.

After being trained, the network could instantly determine a forming method correctly for almost all the products which are exactly the same or slightly different from the products used in the network training.


1.0

0.8 LU

0.6 t~

G~0.4 I ¢ -

0.2

0.0

Forming Method Suggeslion Character Recognition

i

1000 2000

Number of Iterations 3000

FIG. 5. Mean square errors in network training processes.

4. PREDICTION OF THE NUMBER OF FORMING STEPS

As a result of automatic process generation, several processes with a different number of forming steps are usually obtained. It is necessary to evaluate the feasibility of all the processes obtained because there may be physically impossible processes among them due to fracture of the dies or occurrence of defects in the product. To avoid long computation of time during testing, a statistical method was proposed to determine the priority degrees for evaluating the obtained processes [35]; the processes were tested in the order of priority degrees until a suitable process was found.

The number of forming steps usually increases with complexity of the shape, number of primitive shapes which belong to the product, hardness of the material to be formed and the die strength. To express this tendency, a factor describing the complexity of the product shape, i.e. shape complexity, is defined. An imaginary cylindrical preform (Fig. 6), which has the same volume and length as the product (the diameter of which can therefore be determined from the volume and length) is considered and the shape complexity (SC) is defined by

N

s c = ~ (vi. Iln (S,/Si,,)l}/V (4.1) i = l

where Vi and Si are the volume and the area of side surface of the ith primitive, respectively. S,o is the surface area of a part of the imaginary preform corresponding to the ith primitive of the product, V is the whole volume of the product and N is the number of primitives.

Si

Product Imaginary Preform

0

The imaginary preform is a cylindrical preform which has the same volume and length as the product.

Fl~. 6. Imaginary cylindrical preform.


Another factor which indicates the severity of deformation (forming severity, FS) is defined as

FS = S C . o - a / O - d i e (4.2)

where o a is the flow stress of the material and ~die is the die strength. The previous system [35] assumed that the number of forming steps had a linear

relation with the forming severity (FS) and the number of primitives (K) of the cold forged products as

N = [3(pl.FS + p2) + (1 - [3)(ql.K + q2) (4.3)

where the factors p l , p2, ql, q2 and 13 were determined statistically by using 60 sample products.

Since the above linear relation is too simple, the reliability of the result is considered not to be high enough. To predict the number of forming steps more effectively, a method that utilizes the self-learning ability of neural networks was tried.

The constructed network (Fig. 7) consisted of 117 input units, two hidden units and 4 output units that indicated the number of forming steps. Within the 117 input units, 100 were used for the shape complexity, 15 for the number of primitives of the product, one for the billet material and one for the die material, respectively. Although the values of the shape complexities were continuous, the data scope of the shape complexity was divided into 100 sections which corresponded to 100 input units. The section where the value of the shape complexity was located was valued 1, otherwise, valued 0. This approximate method was used to improve the performance of the training process of the network. The 4 units in the output layer indicate the number of forming steps 1, 2, 3 and 4, respectively.

About 60 sample products and their actual number of forming steps were used to train the network. Due to the lack of information about the influence of combination of billet and die material on the numbers of forming steps, the present network was trained for only one combination of billet and die material (billet material: mild steel $25C, die material: cold forging die SKD11).

The training for the 60 sample products took only one minute with the engineering work-station. The trained network gave answers completely for the number of forming steps for the sample processes while the statistical method gave only 68% of correct answers. It is confirmed that very good prediction is possible for the number of forming steps of the products, of which the shape complexities and primitive numbers are within the well trained range. By making use of this number of forming steps, the priority degrees of the obtained processes for further evaluation are determined according to the previously proposed method [35].

5. NEURAL NETWORK SYSTEMS BASED ON FEM SIMULATION

After the priority degrees have been determined, the proposed processes are evalu- ated to determine whether fracture of the die and defects in the product will occur or not in the order of the priority degree until some feasible processes are found. For this purpose, a great amount of empirical knowledge is needed. However, it is extremely difficult to collect universal and reliable knowledge for evaluation. Further, it is difficult to convert variety of empirical knowledge to evaluation rules. As a result, the reliability of the conventional knowledge base is uncertain.

To compensate the deficiency of the conventional knowledge base, a method to utilize the results of FEM simulation as a sort of experience for the expert system was proposed [38]. And the results of the further evaluation of the method are presented below, taking the backward extrusion model as an example.


Shape Complexity: SC

Num her of Primitives: K

Flow Stress: t~a

Die Strength: Odie

Input Layer Hidden Layer Output Layer

N=I

N=2

N=3

N=4

N: Number of Forming Steps

FIG. 7. Prediction of the number of forming steps.

5.1. Data base construction by FEM simulation

A computer simulation system RIPLS-FORGE [42] for axi-symmetric cold forging by rigid-plastic FEM was utilized with the model for backward extrusion shown in Fig. 8. To avoid excessive computational time, only the final stages of the forming processes were simulated by assuming regular shapes of products and uniform flow stress distributions. Calculations were made for 1680 combinations of different levels of the fundamental parameters: reduction in area (R), height/diameter ratio (H = hJd2), punch angle (et) and friction coefficient (Ix) in backward extrusion. The number of the levels of R, H, ot and Ix were 7, 12, 4 and 5, respectively.

Two parameters; MAXPRES (maximum pressure/flow stress) for predicting the fracture of die, and DEFECT, which has the binary value of 1 or 0 for expressing whether the surface defect (lift-up) of product will occur or not, were used for predicting the failure of the process (Fig. 9).

The constructed data base contained the parameters denoting the conditions for the FEM simulation and the parameters for expressing the success or failure of the process as described above. Figure 10 shows a part of the constructed data base.

5.2. Construction of neural networks based on FEM simulation

The results of the FEM simulation were treated with the neural network techniques, and the die fracture and surface defect were predicted by the neural network systems.

d2

Die Fl6. 8. Simulation model for backward extrusion.


Punch " ~ " ~ /

/

; /

t / Cavity u

L./ / / / / / / / / / / / , , I [ Die

FIG. 9. Shapc defect in backward extrusion.

d I d 2 h 0 R(=d12/d22) # a MAXPRES DEFECT

4 . 4 7 2 10 5 .477 10 6 . 3 2 5 10 7 .071 10 7 .746 10 8 .367 10 8 . 9 4 4 10 4.472 10 5.477 10 6.325 10

0 0 0 3 . 0 0 0 0 . 2 0 0 0 . 0 0 0 1 8 0 . 0 0 . 5 1 5 0 0 0 0 3 . 0 0 0 0 . 3 0 0 0 . 0 0 0 1 8 0 . 0 0 . 7 4 1 0 0 0 0 3 . 0 0 0 0 . 4 0 0 0 . 0 0 0 1 8 0 . 0 0 . 9 7 0 0 000 3 . 0 0 0 0 . 5 0 0 0 . 0 0 0 1 8 0 . 0 1 . 2 1 8 0 000 3 . 0 0 0 0 . 6 0 0 0 . 0 0 0 1 8 0 . 0 1 . 5 1 0 1 000 3 . 0 0 0 0 . 7 0 0 0 . 0 0 0 1 8 0 . 0 1 . 9 8 5 1 000 3 . 0 0 0 0 . 8 0 0 0 . 0 0 0 1 8 0 . 0 3 . 1 1 8 1 000 4 . 0 0 0 0 . 2 0 0 0 . 0 0 0 1 8 0 . 0 0 . 4 7 6 0 0 0 0 4 . 0 0 0 0 . 3 0 0 0 . 0 0 0 1 8 0 . 0 0 . 7 0 1 0 000 4 . 0 0 0 0 . 4 0 0 0 . 0 0 0 1 8 0 . 0 0 . 9 5 1 0

FiG. 10. A part of the constructed data base.

The common structure of the neural networks based on the FEM simulation is shown in Fig. 11, in which the die fracture prediction system is taken as an example. In the network training, the forming conditions and the corresponding results of the FEM simulation were used as the state vectors of the input units and the teacher signals of the output units, respectively.

The convergence in the training of neural networks is influenced greatly by the way in which the data are expressed. For this reason, two methods of representing the numerical data by neuron state variables with continuous valued inputs and binary inputs were proposed previously by the authors and both proved to give satisfactory results [38]. At the present research, the same methods are used for the numerical data representation.

5.2.1. Die fracture prediction. It is assumed that die fracture occurs when the contact pressure exceeds the die strength, although this assumption may be too simple for practical use. Fracture of die is assumed to be determined by the parameters R, H, or, Ix, the strength of die (rm~x and the flow stress of billet material (r,. At present, 16 billet materials and 18 die materials are usable in the expert system developed. To take these two parameters into consideration the following three methods were tried.

(1) Adding the information of billet and die material to the input layer: 34 input units are added to the input layer by allocating 16 units to the 16 billet materials and 18 units to the 18 die materials. Since the number of combinations of billet and die materials is very large, a very long training time is necessary for this method.

(2) Establishing a neural network model for each of the combinations of billet and die materials: it takes relatively short time to train each model, and new combinations of billet and die material can be easily added later to the system.


Input

Reduction in Area: R Ratio of Height to

Diameter: H Friction Factor: ].t Cone Angle of Punch: Flow Stl ess: Oa Die Strength: (Ydie

?T

f Three-Layer Neural Networ_..__~k Output

Fracture of Die

Will Occur

""- ' l

Fracture of Die Will Not Occur

| Conditions of Simulation Results of Simulation

FEM Simulation

FIG. 11. A neural network based on FEM simulation.

(3) Expressing the information about die material in the output layer of the network: for each die material one unit of output layer is assigned. Every output unit has a value of 1 or 0 denoting whether fracture of the die will occur or not. This method may be convenient for advising appropriate die materials.

5.2.2. Surface defect prediction. In backward extrusion, it is assumed that surface defect (lift-up) occurs when the calculated contact pressure turns negative at the die corner and the billet leaves the die surface. The occurrence of surface defect is assumed to be determined by the parameters R, H, a and Ix. The constructed neural network consists of the inputs of the above four neuron state variables, two hidden units and two outputs that indicate the occurrence of surface defects and no surface defect in the product, respectively.

6. COMPARISON OF RESULTS OF STATISTICAL METHODS AND NEURAL NETWORKS

The ratios of correct answers obtained by statistical methods are compared with those by the neural network techniques in Table 1. The ratios are obtained by comparing the calculated results with the data within the well trained range of the network training and of the statistical treatments. Thus there exists a possibility that quite different results may be obtained when the methods are compared for completely new sets of data beyond the well trained range.

Since the number of data used for network training is too small in the cases of determination of the forming method and prediction of the number of forming steps, it is necessary to test under the industrial conditions with sufficiently large number of actual data. But in the cases of die fracture prediction and surface defect prediction, the number of data is sufficiently large and the results show the superiority of the neural networks. Moreover the answers are obtained almost instantly by the neural networks while fairly long time is needed by the statistical methods because the local

TABLE 1 • COMPARISON OF RATIOS OF CORRECT ANSWERS OBTAINED BY STATISTICAL METHODS AND NEURAL NETWORKS

Suggestion of forming method Prediction of number of forming steps Die fracture prediction Surface defect prediction

Ratios of correct answers (%)

Statistical methods Neural networks

- - 1 0 0 . 0

68.8 100.0 9(I.0 99.0 95.0 99.6


da ta a re s ea rched for. In genera l , it may be poss ib le to say that the neura l ne tworks are g o o d not only f rom the v iewpoin t of re l iab i l i ty but also f rom the speed of r e sponse , which is an i m p o r t a n t fac tor for an expe r t sys tem.

7. CONCLUSIONS

The neura l n e t w o r k techniques have been app l i ed to some p r o b l e m s as soc ia t ed with the cold forging expe r t sys tem; d e t e r m i n a t i o n of the fo rming m e t h o d f rom the final p roduc t shape , p r ed i c t i on of the most p r o b a b l e n u m b e r of fo rming s teps by cons ider ing the shape complex i t y and ma te r i a l p r o p e r t y , and p red i c t i on of the die f rac ture and surface defec t in the f o r m e d p roduc t . Sa t i s fac tory resul ts are o b t a i n e d by combin ing the neura l ne tworks with the expe r t sys tem for most cases.

I t may be conc luded tha t the app l i ca t ion of neura l ne tw ork t echn iques can grea t ly i m p r o v e the p e r f o r m a n c e of the p re sen t expe r t sys tem by ob ta in ing a first guess in a shor t t ime.

Acknowledgements--The authors would like to thank Mr T. Nakamura and Mr T. Inoue for assisting the computer programming. The financial support by AMADA Foundation for Metal Work Technology is also acknowledged.

REFERENCES

[1] P. NOACK, Computer-aided determination of operation sequence and costs in cold forging of rotation- symmetric workpieces, SME Technical Paper, MF73-141 (1973).

[2] S. K. BISWAS and W. A. KNIGm', Computer-aided preform design for long hot forgings, Proc. 17th MTDR Conf'., Birmingham. p. 26 (1976).

[3] P. NOACK, Computer aided design of process planning cost calculation for cold extrusion of steel. Report .(No. 48) form University of Stuttgart, Germany (1979). (In German.)

[4] M. REBHOLZ, Interactive programming system for developing layout for cold bulk forming, Report No. 60, Metal Forming Institute, University of Stuttgart (1981). (In German.)

[5] M. I. GOKLER, W. A. KNIGHT and C. R. POLl, Classification for systematic component and process design for forging operations, Proc. 9th NAMRC, pp. 158-164 (1981).

[6] M. I. GOKLER, T. A. DEAN and W. A. KNIGHT, Computer-aided sequence design for hot upset forgings, Proc. 22nd MTDR Conf., Manchester, pp. 457-466. Macmillan, London (1982).

[7] W. A. KNIGHT and C. R. POLl, Design for economical use of forging: indication of general relative forging costs, Ann. CIRP 31, 159-163 (1982).

[8] M. I. GOKLER, T. A. DEAN and W. A. KNIGHT, Computer-aided die design for upset forging machines, Proc. ll th NAMRC, SME, pp. 217-223 (1983).

[9] A. A. BADAWY, D. J. KUHLMANN, P. S. RAGHUPATHI and T. ALTAN, A computer-aided design system for determining the forming sequence in forging solid round parts, Interim Report to NSF, Battelle Columbus Division, Columbus, Ohio, p.10 (1983).

[10] M. I. GOKLER, T. A. DEAN and W. A. KNmm, Computer-aided sequence design for piercing on horizontal forging machines, J. Mech. Work. Technol. 8, 13-26 (1983).

[11] C. R. POLl and W. A. KNIGHT, Computer-aided part design for economical fabrication by forging, Proc. l l th Conf. on Prod. Res. Technol., Pittsburgh, pp. l l l - l l 8 (1984).

[12] T. P. DAVlSON and W. A. KNIGHT, Computer-aided process design for cold forging operations, Ann. ICTP 1,551-556 (1984).

[13] A. A. BADAWY, D. J. KUHLMANN, P. S. RAGHUPATHI and T. ALTAN, Computer-aided design of multistage forging operations for round parts, J. Mech. Work. Technol. II, 259-274 (1985).

[14] P. BARIANI and W. A. KNIGHT, Computer-aided cold forging process design: determination of machine setting conditions, Ann. CIRP 34, 245-248 (1985).

[15] M. REBHOLZ, Computer-aided checking of sequences in forging, Proc. 17th Int. Conf. on Cold Forging, Birmingham, pp.117-125 (1985).

[16] J. TANG, S. I. OH and T. ALTAN, The application of expert system to automatic forging design, Proc. 13th NAMRC, pp. 456-463 (1985).

[17] K. R. VEMURI, P. S. RAGHUPATHI and T. ALTAN, The application of expert systems to automatic forging design, Proc. 14th NAMRC, pp. 372-378 (1986).

[18] R. H. PHILLIPS, V. ARUNTHAVANATHAN and X. D. ZHOU, Symbolic representation of CAD data for artificial intelligence based process planning, Tech. Presentation, ICFG (1986).

[19] P. S. RAGHUPATHI and K. SEVENLER, FORMEX: a computer system for establishing forming sequence in cold forging, Tech. Presentation, ICFG (1986).

[20] K. SEVENLER, P. S. RAGHUPATHI, T. AI,TAN and R. A. MILLER, A rule-based system for forming sequence design for multistage cold forging, Proc. IEEE Comp. Soc. Conf. on A.L , pp.1013-1017 (1986).

[21] A. J. LAMES, T. A. DEAN, P. HARTLEY and C. E. N. STURGESS, An integrated computer system for forging die design and flow simulation, Proc. 1st Int. ConJ~ on Comput. Aided Prod. Engng, pp. 231-236 (1986).

[22] K. SEVENLER, P. S. RAGHUPATHI and T. ALTAN, Forming-sequence design for multistage cold forging, J. Mech. Work. Technol. 14, 121-135 (1987).

[23] P. BARIANI, E. BENUZZI and W. A. KNIGHT. Computer-aided design of multistage cold forging process:


load peaks and strain distribution evaluation, Ann. CIRP 36, 145-148 (1987). [24] K. R. VEMURI, S. I. OH, T. ALTAN and R. A. MILLER, A knowledge-based approach to geometrical

design of blockers, AAAI-87 (1987). [25] A. J. LAMES, T. A. DEAN, P. HARTLEY, C. E. N. STURGESS and G. W. RowE, An intelligent knowledge-

based system For upset forging design, Proc. 2nd Int. Conf. on Comput. Aided Prod. Engng, pp. 37-41 (1987).

[26] G. W. ROWE, An intelligent knowledge based system to provide design and manufacturing data for forging, Comput. Aided Engng J. 2, 56-61 (1987).

[27] P. HARTLEY, C. E. N. STURGESS, T. A. DEAN and G. W. ROWE, Forging die design and flow simulation: their integration in intelligent knowledge based systems, J. Mech. Work. Technol. 15, 1-13 (1987).

[28] K. OSAKADA, T. KADO and G. B. YANG, Application of AI-technique to process planning of cold forging, Ann. C1RP 3'7, 239-242 (1988).

[29] P. HARTLEY, C. E. N. STURGESS, T. A. DEAN, G. W. ROWE and A. J. LAMES, Development of a forging expert system. In Expert Systems in Engineering (edited by D. T. PHAM), pp. 425--443. IFS Publications (1988).

[30] P. BARIANI and W. A. KNIGHT, Computer-aided cold forging process design: a knowledge-based system approach to farming sequence generation, Ann. CIRP 37, 243-246 (1988).

[31] B. LENGYEL and M. L. TAY, Expert system in the cold forging of steel, Proc. 27th Int. MTDR Conf., UMIST, England, pp. 345-352 (1988).

[32] K. LANGE and G. H. Du, A formal approach to designing forming sequences for cold forging, Proc. 17th NAMRC, pp.17-22 (1989).

[33] B. LENGYEL and T. MAHMOOD, Expert system for cold forging steel hollows, Proc. 17th NAMRC, pp. 30-33 (1989).

[34] A. DANNO, K. NAKANISHI, O. TAKATA and T. YAMAZAKI, A knowledge-based system for forming- sequence generation in multistage cold forging Tech. Presentation, ICFG, (1989).

q

[35] G. B. YANG and K. OSAKADA, An expert system for process planning of cold forging, Ann. ICTP I, 109-114 (1990).

[36] P. BARIAN1, G. BERTI, L. D'ANGELO, M. MARENGO and A. Rossl, An integrated CAD/CAE system for forging proce,;s design, Ann. ICTP l, 7-12 (1990).

[37] R. EHRISMAN~* and J. REISSNER, The integration of simulation techniques in expert systems, Ann. ICTP l, 197-202 (1'990).

[38] K. OSAKADA, G. B. YANG, T. NAKAMURA and K. MORI, Expert system for cold forging process based on FEM simulation, Ann. CIRP 39, 249-252 (1990).

[39] A. AZUSHIMA, M. KIM and H. KtJoo, A PC-based expert system for forming sequence design, Ann. CIRP 39, 245-248 (1990).

[40] R. P. LIPPMANN, An introduction to computing with neural nets, IEEE ASSP Mag. 4, 4-22 (1987). [41] D. E. RUMEI_HART, G. E. HINTON and R. J. WILLIAMS, Learning internal representations by error

propagation. In Parallel Distributed Processing, Vol. 1, pp. 318-362. The MIT Press, Cambridge, London, England (1986).

[42] K. OSAKADA and K. MORI, The use of micro- and super-computers for simulation of metal forming processes, Ann. CIRP 34, 241-244 (1984).

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Application of neural networks to an expert system for cold forging