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Neural networks in quality function deployment
Pergamon
Computers ind. Engng, VoL31,No. 3/4,pp. 669- 673, 1996
Copyright© 1995ChinaMachinePress Publishedby ElsevierScienceLtd.P~tcd in GreatBritain S0360-8352(96)00252,5 0360-8352/96 $15.00+ 0.00
Neural Networks in Quality Function Deployment Xiping Zhang, Jiirgen Bode, Shouju Ren Dept. of Automation, Tsinghua University, Beijing 100084, PR China
Abstract: Quality Function Deployment (QFD) is a method of product planning in the early phases of the development of new products (pre-CAD phase). A major drawback of its application is the need to input a large amount of data and the necessity to estimate values on a rather subjective basis in order to complete the House of Quality. This data is plentiful and often designers lack the knowledge with satisfying accuracy. This paper suggests a machine learning approach in which a neural network automatically determines the data by learning from examples. Unlike conventional neural networks which are treated as "black boxes", the topology and the weight values are not random but represent real circumstances and can directly be interpreted in the terms of the application. A final section discusses problems arising from the small number of training sets which is usually available in the field of product design. Keyword$: Noaral networks, Quality Function Deployment, Concurrent Engineering, Product Development
1 Introduction 1.1 Concurrent Engineering and Quality Function Deployment The intensification of market competition and the progress of technology make modern manufacturing organizations pay more attention to their product design and engineering so that they can reduce the time between conceptualization of a new product and its final commercialization (time-to-market) and realize higher first mover market shares and margins. Concurrent engineering is a systematic approach to the integrated and concurrent design of products and their related processes including manufacture and service. It not only reduces time-to-market but offers the opportunity to improve quality and identify potentials for cost reduction. Research has shown that design determines up to 70% of total product cost although design itself only accounts for about 5% [O'Grady et al., 1991]. Among the methods to support concurrent engineering, Quality Function Deployment (QFD) allows the deployment of customer requirements throughout the firm to all units relevant to the product's quality attributes. The House of Quality as its major planning document illustrates, among others, customer requirements, technical requirements, their respective weights as well as their interrelationships [Hauser/Clansing, 1988; Sullivan, 1986; Wasserman, 1993]. A major drawback of Quality Function Deployment is the need to input a large amount of data and the necessity to estimate values on a rather subjective basis in order to complete the House of Quality. This data is plentiful and often designers lack the knowledge with satisfying accuracy. An example: A simple design task might involve, say, ten customer requirements and ten technical requirements. This results in ten ~igllts of customer requirements, one hundred possible relationships between the two sets and another forty-five among the possibly correlated technical requirements.
1.2 Neural Networks in New Product Development Among the properties of neural networks their ability to generalize functional relationships among example data is of utmost importance for design. This feature is valuable wherever these relationships are assumed, but not known. This is the case, for example, for some dependencies between design decisions (e.g. to detect incompatible solutions), or some impact of design decisions on downstream activities (e.g. to identify manufacturing or maintenance problems of a given design). Moreover, neural networks are adaptable. This gives rise to the hope that changes in the connection between design-relevant data (e.g. the reduction of development effort as experience with a technology grows) are recognized 669
670
18th International Conference on Computers and Industrial Engineering
without ¢xplicitiy reprogramming the system. For design applications of neural networks and discussions of machine learning approaches s¢¢ [Reich et al., 1993; Ivezic/Garrett, 1994; Bratko, 1993, p.162; Ehrlenspiel/Schaal, 1992, p.409f.; Bahrami/Dagli, 1992; Beckcr/Prischmann, 1993]. Within the I-MADIS laboratory of the national 863 Computer Integrated Manufacturing Systems Engineering Research Center (CIMS-ERC) the PENDES project investigates the possibilities to support decisions in the early phases of new product development ('PENDES is the reverse acronym of Support of early phases of the development of new products). In this paper we suggest a machine learning approach in which a neural network automatically determines the data by learning from examples. Unlike conventional neural networks which are treated as "black boxes", the topology and the weight values represent real circumstances and can directly be interpreted in the terms of the application. A final section discusses problems arising from the small number of training sets which is usually available in the field of product design.
2 Neural network representation of the design problem 2.1
Interrelationship between design concepts
In our approach we consider three major views which illustrate a product's quality attributes. Customer requirements (or customer needs) arc the product properties as seen by the customers (e.g. "sportive car"). Some or even all can only be expressed in a qualitative manner. However, it is possible to estimate the degree of satisfaction of a given product with respect to each customer requirement (usually applied in an evaluation of competing products and inserted in the right wing of tic House of Quality). Furthermore, not all requirements have an equal importance to the customer which makes it n~_s~ry to introduce a weight to each requirement. It might be necessary to aggregate the satisfaction degrees of all customer requirements to a single overall customer satisfaction value.
Technical requirements (also called engineering characleristics or engineering functions) are a product description from the engineering point ofvi~v (e.g. "maximum speed at least 160 kin/h"). They are all quantitative in the sense that their fulfillment by a given product can be measured by commonly accepted methods. Technical solutions specify concrete components, parts, or methods to achieve the technical requirements (e.g. "6 cylinders, 2.4 liters gasoline engine").
Customer satisfaction
Customer requirements
Technical requirements
Technical solutions
Figure 1: Design concepts and their interrelationships Customer requirements, technical requirements, and technical solutions are interconnected. A set of technical solutions is needed to achieve the technical requirements. A technical solution generally contributes to the fulfillment of at least one technical requirement. However, it might as well have a negative impact on other requirements which makes it necessary to resolve conflicts during the Quality Function Deployment planning process (e.g. a powerful engine has a desired effect on speed but a negative impact on gas consumption). Only the achievement of one or several technical requirements ensures satisfaction in the terms of a customer requirement (generally depicted in the relationship matrix of the House of Quality). Here again, positive and negative contributions of technical requirements to customer requirements let conflicts arise.
18th International Conference on Computers and Industrial Engineering
671
Beside,s this, technical requirements might be interrelated among each other (illustrated in the roof of the House of Quality). Figure I shows the above mentioned concepts and interrelationships as a graph.
2.2 Neural n e t w o r k t o p o l o g y a n d r e l a t i o n s h i p t o t h e H o u s e o f Q u a l i t y The graph in Figure 1 associatesa neural network topology, with each neuron representinga node and each linkage between neurons representing a relationship bet~een the nodes as described above. The output activation of each neuron in a trained network can be interpreted as the degree of achievement of the concept represented by this neuron. If, for instance, a set of technical solutions is fed into the network's input layer (on the right of Figure 1) the output activation of the neurons standing for the technical requirements tells about the amount by which these requirements are reached by the technical solutions. In addition to that, the weights of the connections between the neurons in a trained network represent the weights der,cribing the impact between the above mentioned design concepts. Thus, the connections between, for example, the neurons representing technical requirements and those standing for customer requirements display with their weights the values of the relationship matrix in the House of Quality. Positive weights mean positive contributions, negative weights mean negative impacts between design concepts. The weights of the connections between customer requirements neurons and the customer satisfaction output neuron symbolize the weights of the customer requirements in the House of Quality. The neural network topology therefore is a mapping of the application. Each neuron and each connection between neurons can be interpreted in the terms of the application. This is different from conventional neural networks which are regarded as "black boxes" and where the topology is determined more or less randomly according to the expected training behavior. It is desirable to obtain a standard multi layer perceptron structure because of its well knmvn behavior and powerful learning algorithms. The graph in Figure 1 has therefore to be transformed in order to eliminate the arcs between the nodes of the technical requirements layer. For this aim we introduce the following procedure. By [TR] and [TS] we denote the input activations of the neurons representing the technical requirements, and the output activations of the neurons representing the technical solutions, respectively. [W~] and [W~] are the weight matrices of the arcs linking the technical solution neurons with the technical requirements neurons, and the arcs interconnecting the technical requirements neurons, respectively. We make [TR] fully dependent of [TS] by the following simple algorithm.
[T/ I -- [w,,]( rsl + [w,,]i
IP1 [rn] = [c][ Ts] After this transformation the neural network associated ~qth Figure 1 turns into a standard multi layer perceptmn which can be trained using common and well knmvn procedures, i.e. the backpropagation algorithm and sigmoid activation functions.
2.3 Advantages The advantage of a neural network approach to Quality Function Deployment is the ability to extract knowledge from past examples instead of subjective guesses. Alter training the prospective technical solutions of a new design are fed into the network. The activation values of the output layer will return the forecast degree of satisfaction. Furthermore, the activation values of the neurons in the second and third layer represent the degrees by which the given technical requirements and customer requirements are reached respectively. At last, the weights of the neuron connections display the weights of the relationship matrices and the customer requirements as to be inserted in the House of Quality. The proposed architecture offers much flexibility. It can be used as a four layer perceptron with technical solutions as inputs and the single customer satisfaction degree as output. For more detailed information, the fourth layer can be omitted, thus using the neurons representing customer requirements as output layer. By omitting the first layer technical
672
18th International Conference on Computers and Industrial Engineering
Table 1: Results of neural network training and testing ([Bode et al., 1995]) No. of No. of test training sets sets
Testing Correct classierror (It7 ~) fication of test sets
27 18 12
7.25 8.27 10.22
64 64 64
44 (68.7%) 41 (64.1%) 40 (62.5%)
requirements are regarded as inputs. The neural network might even be trained in one mode (say, as a four layer perceptron) and applied after training in another mode (say, omitting the fourth layer). Neural network construction is simple and straightforward if all possible technical solutions, technical requirements, and customer requirements are known. These known concepts are to be mapped into the neurons of a neural network as depicted in Figure 1. Next, all neurons where a relationship in the design application is assumed have to be connected. After transformation of the network to eliminate connections within one layer (see the above equation) the perceptron is to be trained with examples from the design application. If training succeeds the activation values of the neurons in the output and hidden layers, and the weights of their connections can be interpreted as described above. If a relationships was assumed before training but the training samples show no evidence that it really exists the weight of the respective connection should be close to zero. If technical requirements or customer requirements are not known the construction of the hidden layers has to follow conventional procedures of neural network design. In this case only the activation values of the output neurons can be interpreted, just as with conventional neural networks.
3 Experimental results and discussion The main problem of the neural network approach is the large number of training sets that is usually required before a multi layer perceptron provides reliable results. We have conducted some experiments in order to assess the magnitude of this drm¢oack. In these experiments (partly,described in [Bode et al., 1995]) a three layer perceptron had to predict the cost of a given design after learning from no more than 27 training sets. Artificially generated data has been used to ensure that cost follow a sufficiently complicated, non-linear function. The correct predictions, depicted in Table 1, appear low. However, it should be considered that common applications of neural networks work with many hundreds, thousands, sometimes far more than ten thousand training sets in order to achieve satisfying results (IvezidGarretL 1994), (Pal/Mitra, 1992). From this point of view the performance is better than we expected. Nonetheless, the main focus of further research should be directed to measures that help to improve performance despite the small number of training sets as will be discussed below. We regard the small number of training data as the major point requiring attention. Several possibilities can be as_~,~___e~
to improve network performance [Bode et al., 1995]. 1. Based on design cases from the past experts are asked to alter case data systematically and estimate the outcome. Consequently, training data can be multiplied. Problems with the reliability of the simulated data and the fact that simulated data is likely to be situated close to the base cases in the'input space must be considered. 2. Often experts have substantial, yet approximate, background knowledge about the relationships between certain data. If this information is inserted into the neural network before training (prewiring) the learning process could be accelerated. The possibility to input background knowledge is an important advantage of the architecture presented above since it allows explicit representation of knowledge. As construction of the neural network topology follows the experts' assumptions about customer requirements, technical requirements, technical solutions, and their interrelationships, the neural network alrcady contains background knowledge before training. 3. Neural networks have problems learning relationships between very unevenly distributed data. It has been reported that transformation of data to an even distribution prior to learning can improve performance (Becker/Prischmann, 1993).
18th lnlernalional Conference on Computers and Industrial Engineering
673
4. Depending on the application neural networks might b¢ segmented, each segment being trainod separately with the same training sets. Segmentation results in a smaller number of input and/or output nodes and might speed up learning. This approach necessitates independence between the segments of input or output data, i.e. a segment of input nodes dctvrmines the activation of a segment of output nodes regardless of the values of input attributes from other segments. 5. The input attribut~ are concentrated, or cluste/od, into transformod attribute classes (e.g. by factor analysis) before construOing the neural network, or classification networks (e.g. Kohonen feature maps) cluster the input data automatically in real time. This method might be able to reduce the number of input dimensions and therefore requires less training.
References Bahrami, Ali / Dagli, Cihan H. (1992) Design retrieval by fuzzy neurocomputing. Journal of Engineering Design, 3, 4, pp.339--356. Becket, J6rg / Prischmann" M. (1993) Supporting the design process with neural networks - a complex application of cooperating neural networks and its implementation. Journal oflnformation Science and Technology, 3, 1, pp.7995. Bode, Jfirgen et al. (1995): Neural Networks in New Product Development. In" Sun, Q.N. (od.), Computer Applications in Production and Engineering, London, 1995 (in print). Bratko, Ivan (1993) Machine learning in artificial intelligence. Artificial Intelligence in Engineering, 8, pp. 159-164. Ehrlenspiel, K. / Schaal, S. (1992) In CAD integrierte Kostenkalkulation (Cost estimation integratod into CAD). Konstruktion, 44, pp.407--414 (in German). Hauser, John R. / Clausing, Don (1988): The House of Quality. Harvard Business Review, May-June, pp. 63-73. Ivczic, Nenad / Garrett, James H., Jr. (1994) A neural network-based machine learning approach for supporting synthesis. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 8, pp. 143-161. O'Orady, Peter I Young. Robert E. / Greef, Arthur / Smith, Larry (1991): An advice system for concurrent engineering. Int. J. Computer lntegrated Manufacturing, 4, 2, pp.63-70. Pal, Sankat K. / Mitra, Sushmita (1992) Multilayer pcrceptron" fuzzy sets, and classification. IEEE Transactions on Neural Networks, 3, 5, pp.683-697. Reich, Y. / Konda, S. / Levy, S.N./Monarch, I.A. / Subrahmanian, E. (1993) New roles for machine learning in design. Artificial Intelligence in Engineering, g, pp. 165-181. Rcn" Shouju: A system view of concurrent engineering. In: Proc. of the International Conferanc¢ on Computer Intcgratod Manufacturing (ICCIM '93), Beijing, May 12-14, 1993, pp.214-220. Sullivan, L. P.: Quality Function Deployment. Quality Progress, June 1986, pp. 39-50. Wasserman" Gary S.: On How to Prioritize Design Requirements during the QFD Planning Process. lie Transactions, 25 (1993) 3, pp.59-65.
Computers ind. Engng, VoL31,No. 3/4,pp. 669- 673, 1996
Copyright© 1995ChinaMachinePress Publishedby ElsevierScienceLtd.P~tcd in GreatBritain S0360-8352(96)00252,5 0360-8352/96 $15.00+ 0.00
Neural Networks in Quality Function Deployment Xiping Zhang, Jiirgen Bode, Shouju Ren Dept. of Automation, Tsinghua University, Beijing 100084, PR China
Abstract: Quality Function Deployment (QFD) is a method of product planning in the early phases of the development of new products (pre-CAD phase). A major drawback of its application is the need to input a large amount of data and the necessity to estimate values on a rather subjective basis in order to complete the House of Quality. This data is plentiful and often designers lack the knowledge with satisfying accuracy. This paper suggests a machine learning approach in which a neural network automatically determines the data by learning from examples. Unlike conventional neural networks which are treated as "black boxes", the topology and the weight values are not random but represent real circumstances and can directly be interpreted in the terms of the application. A final section discusses problems arising from the small number of training sets which is usually available in the field of product design. Keyword$: Noaral networks, Quality Function Deployment, Concurrent Engineering, Product Development
1 Introduction 1.1 Concurrent Engineering and Quality Function Deployment The intensification of market competition and the progress of technology make modern manufacturing organizations pay more attention to their product design and engineering so that they can reduce the time between conceptualization of a new product and its final commercialization (time-to-market) and realize higher first mover market shares and margins. Concurrent engineering is a systematic approach to the integrated and concurrent design of products and their related processes including manufacture and service. It not only reduces time-to-market but offers the opportunity to improve quality and identify potentials for cost reduction. Research has shown that design determines up to 70% of total product cost although design itself only accounts for about 5% [O'Grady et al., 1991]. Among the methods to support concurrent engineering, Quality Function Deployment (QFD) allows the deployment of customer requirements throughout the firm to all units relevant to the product's quality attributes. The House of Quality as its major planning document illustrates, among others, customer requirements, technical requirements, their respective weights as well as their interrelationships [Hauser/Clansing, 1988; Sullivan, 1986; Wasserman, 1993]. A major drawback of Quality Function Deployment is the need to input a large amount of data and the necessity to estimate values on a rather subjective basis in order to complete the House of Quality. This data is plentiful and often designers lack the knowledge with satisfying accuracy. An example: A simple design task might involve, say, ten customer requirements and ten technical requirements. This results in ten ~igllts of customer requirements, one hundred possible relationships between the two sets and another forty-five among the possibly correlated technical requirements.
1.2 Neural Networks in New Product Development Among the properties of neural networks their ability to generalize functional relationships among example data is of utmost importance for design. This feature is valuable wherever these relationships are assumed, but not known. This is the case, for example, for some dependencies between design decisions (e.g. to detect incompatible solutions), or some impact of design decisions on downstream activities (e.g. to identify manufacturing or maintenance problems of a given design). Moreover, neural networks are adaptable. This gives rise to the hope that changes in the connection between design-relevant data (e.g. the reduction of development effort as experience with a technology grows) are recognized 669
670
18th International Conference on Computers and Industrial Engineering
without ¢xplicitiy reprogramming the system. For design applications of neural networks and discussions of machine learning approaches s¢¢ [Reich et al., 1993; Ivezic/Garrett, 1994; Bratko, 1993, p.162; Ehrlenspiel/Schaal, 1992, p.409f.; Bahrami/Dagli, 1992; Beckcr/Prischmann, 1993]. Within the I-MADIS laboratory of the national 863 Computer Integrated Manufacturing Systems Engineering Research Center (CIMS-ERC) the PENDES project investigates the possibilities to support decisions in the early phases of new product development ('PENDES is the reverse acronym of Support of early phases of the development of new products). In this paper we suggest a machine learning approach in which a neural network automatically determines the data by learning from examples. Unlike conventional neural networks which are treated as "black boxes", the topology and the weight values represent real circumstances and can directly be interpreted in the terms of the application. A final section discusses problems arising from the small number of training sets which is usually available in the field of product design.
2 Neural network representation of the design problem 2.1
Interrelationship between design concepts
In our approach we consider three major views which illustrate a product's quality attributes. Customer requirements (or customer needs) arc the product properties as seen by the customers (e.g. "sportive car"). Some or even all can only be expressed in a qualitative manner. However, it is possible to estimate the degree of satisfaction of a given product with respect to each customer requirement (usually applied in an evaluation of competing products and inserted in the right wing of tic House of Quality). Furthermore, not all requirements have an equal importance to the customer which makes it n~_s~ry to introduce a weight to each requirement. It might be necessary to aggregate the satisfaction degrees of all customer requirements to a single overall customer satisfaction value.
Technical requirements (also called engineering characleristics or engineering functions) are a product description from the engineering point ofvi~v (e.g. "maximum speed at least 160 kin/h"). They are all quantitative in the sense that their fulfillment by a given product can be measured by commonly accepted methods. Technical solutions specify concrete components, parts, or methods to achieve the technical requirements (e.g. "6 cylinders, 2.4 liters gasoline engine").
Customer satisfaction
Customer requirements
Technical requirements
Technical solutions
Figure 1: Design concepts and their interrelationships Customer requirements, technical requirements, and technical solutions are interconnected. A set of technical solutions is needed to achieve the technical requirements. A technical solution generally contributes to the fulfillment of at least one technical requirement. However, it might as well have a negative impact on other requirements which makes it necessary to resolve conflicts during the Quality Function Deployment planning process (e.g. a powerful engine has a desired effect on speed but a negative impact on gas consumption). Only the achievement of one or several technical requirements ensures satisfaction in the terms of a customer requirement (generally depicted in the relationship matrix of the House of Quality). Here again, positive and negative contributions of technical requirements to customer requirements let conflicts arise.
18th International Conference on Computers and Industrial Engineering
671
Beside,s this, technical requirements might be interrelated among each other (illustrated in the roof of the House of Quality). Figure I shows the above mentioned concepts and interrelationships as a graph.
2.2 Neural n e t w o r k t o p o l o g y a n d r e l a t i o n s h i p t o t h e H o u s e o f Q u a l i t y The graph in Figure 1 associatesa neural network topology, with each neuron representinga node and each linkage between neurons representing a relationship bet~een the nodes as described above. The output activation of each neuron in a trained network can be interpreted as the degree of achievement of the concept represented by this neuron. If, for instance, a set of technical solutions is fed into the network's input layer (on the right of Figure 1) the output activation of the neurons standing for the technical requirements tells about the amount by which these requirements are reached by the technical solutions. In addition to that, the weights of the connections between the neurons in a trained network represent the weights der,cribing the impact between the above mentioned design concepts. Thus, the connections between, for example, the neurons representing technical requirements and those standing for customer requirements display with their weights the values of the relationship matrix in the House of Quality. Positive weights mean positive contributions, negative weights mean negative impacts between design concepts. The weights of the connections between customer requirements neurons and the customer satisfaction output neuron symbolize the weights of the customer requirements in the House of Quality. The neural network topology therefore is a mapping of the application. Each neuron and each connection between neurons can be interpreted in the terms of the application. This is different from conventional neural networks which are regarded as "black boxes" and where the topology is determined more or less randomly according to the expected training behavior. It is desirable to obtain a standard multi layer perceptron structure because of its well knmvn behavior and powerful learning algorithms. The graph in Figure 1 has therefore to be transformed in order to eliminate the arcs between the nodes of the technical requirements layer. For this aim we introduce the following procedure. By [TR] and [TS] we denote the input activations of the neurons representing the technical requirements, and the output activations of the neurons representing the technical solutions, respectively. [W~] and [W~] are the weight matrices of the arcs linking the technical solution neurons with the technical requirements neurons, and the arcs interconnecting the technical requirements neurons, respectively. We make [TR] fully dependent of [TS] by the following simple algorithm.
[T/ I -- [w,,]( rsl + [w,,]i
IP1 [rn] = [c][ Ts] After this transformation the neural network associated ~qth Figure 1 turns into a standard multi layer perceptmn which can be trained using common and well knmvn procedures, i.e. the backpropagation algorithm and sigmoid activation functions.
2.3 Advantages The advantage of a neural network approach to Quality Function Deployment is the ability to extract knowledge from past examples instead of subjective guesses. Alter training the prospective technical solutions of a new design are fed into the network. The activation values of the output layer will return the forecast degree of satisfaction. Furthermore, the activation values of the neurons in the second and third layer represent the degrees by which the given technical requirements and customer requirements are reached respectively. At last, the weights of the neuron connections display the weights of the relationship matrices and the customer requirements as to be inserted in the House of Quality. The proposed architecture offers much flexibility. It can be used as a four layer perceptron with technical solutions as inputs and the single customer satisfaction degree as output. For more detailed information, the fourth layer can be omitted, thus using the neurons representing customer requirements as output layer. By omitting the first layer technical
672
18th International Conference on Computers and Industrial Engineering
Table 1: Results of neural network training and testing ([Bode et al., 1995]) No. of No. of test training sets sets
Testing Correct classierror (It7 ~) fication of test sets
27 18 12
7.25 8.27 10.22
64 64 64
44 (68.7%) 41 (64.1%) 40 (62.5%)
requirements are regarded as inputs. The neural network might even be trained in one mode (say, as a four layer perceptron) and applied after training in another mode (say, omitting the fourth layer). Neural network construction is simple and straightforward if all possible technical solutions, technical requirements, and customer requirements are known. These known concepts are to be mapped into the neurons of a neural network as depicted in Figure 1. Next, all neurons where a relationship in the design application is assumed have to be connected. After transformation of the network to eliminate connections within one layer (see the above equation) the perceptron is to be trained with examples from the design application. If training succeeds the activation values of the neurons in the output and hidden layers, and the weights of their connections can be interpreted as described above. If a relationships was assumed before training but the training samples show no evidence that it really exists the weight of the respective connection should be close to zero. If technical requirements or customer requirements are not known the construction of the hidden layers has to follow conventional procedures of neural network design. In this case only the activation values of the output neurons can be interpreted, just as with conventional neural networks.
3 Experimental results and discussion The main problem of the neural network approach is the large number of training sets that is usually required before a multi layer perceptron provides reliable results. We have conducted some experiments in order to assess the magnitude of this drm¢oack. In these experiments (partly,described in [Bode et al., 1995]) a three layer perceptron had to predict the cost of a given design after learning from no more than 27 training sets. Artificially generated data has been used to ensure that cost follow a sufficiently complicated, non-linear function. The correct predictions, depicted in Table 1, appear low. However, it should be considered that common applications of neural networks work with many hundreds, thousands, sometimes far more than ten thousand training sets in order to achieve satisfying results (IvezidGarretL 1994), (Pal/Mitra, 1992). From this point of view the performance is better than we expected. Nonetheless, the main focus of further research should be directed to measures that help to improve performance despite the small number of training sets as will be discussed below. We regard the small number of training data as the major point requiring attention. Several possibilities can be as_~,~___e~
to improve network performance [Bode et al., 1995]. 1. Based on design cases from the past experts are asked to alter case data systematically and estimate the outcome. Consequently, training data can be multiplied. Problems with the reliability of the simulated data and the fact that simulated data is likely to be situated close to the base cases in the'input space must be considered. 2. Often experts have substantial, yet approximate, background knowledge about the relationships between certain data. If this information is inserted into the neural network before training (prewiring) the learning process could be accelerated. The possibility to input background knowledge is an important advantage of the architecture presented above since it allows explicit representation of knowledge. As construction of the neural network topology follows the experts' assumptions about customer requirements, technical requirements, technical solutions, and their interrelationships, the neural network alrcady contains background knowledge before training. 3. Neural networks have problems learning relationships between very unevenly distributed data. It has been reported that transformation of data to an even distribution prior to learning can improve performance (Becker/Prischmann, 1993).
18th lnlernalional Conference on Computers and Industrial Engineering
673
4. Depending on the application neural networks might b¢ segmented, each segment being trainod separately with the same training sets. Segmentation results in a smaller number of input and/or output nodes and might speed up learning. This approach necessitates independence between the segments of input or output data, i.e. a segment of input nodes dctvrmines the activation of a segment of output nodes regardless of the values of input attributes from other segments. 5. The input attribut~ are concentrated, or cluste/od, into transformod attribute classes (e.g. by factor analysis) before construOing the neural network, or classification networks (e.g. Kohonen feature maps) cluster the input data automatically in real time. This method might be able to reduce the number of input dimensions and therefore requires less training.
References Bahrami, Ali / Dagli, Cihan H. (1992) Design retrieval by fuzzy neurocomputing. Journal of Engineering Design, 3, 4, pp.339--356. Becket, J6rg / Prischmann" M. (1993) Supporting the design process with neural networks - a complex application of cooperating neural networks and its implementation. Journal oflnformation Science and Technology, 3, 1, pp.7995. Bode, Jfirgen et al. (1995): Neural Networks in New Product Development. In" Sun, Q.N. (od.), Computer Applications in Production and Engineering, London, 1995 (in print). Bratko, Ivan (1993) Machine learning in artificial intelligence. Artificial Intelligence in Engineering, 8, pp. 159-164. Ehrlenspiel, K. / Schaal, S. (1992) In CAD integrierte Kostenkalkulation (Cost estimation integratod into CAD). Konstruktion, 44, pp.407--414 (in German). Hauser, John R. / Clausing, Don (1988): The House of Quality. Harvard Business Review, May-June, pp. 63-73. Ivczic, Nenad / Garrett, James H., Jr. (1994) A neural network-based machine learning approach for supporting synthesis. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 8, pp. 143-161. O'Orady, Peter I Young. Robert E. / Greef, Arthur / Smith, Larry (1991): An advice system for concurrent engineering. Int. J. Computer lntegrated Manufacturing, 4, 2, pp.63-70. Pal, Sankat K. / Mitra, Sushmita (1992) Multilayer pcrceptron" fuzzy sets, and classification. IEEE Transactions on Neural Networks, 3, 5, pp.683-697. Reich, Y. / Konda, S. / Levy, S.N./Monarch, I.A. / Subrahmanian, E. (1993) New roles for machine learning in design. Artificial Intelligence in Engineering, g, pp. 165-181. Rcn" Shouju: A system view of concurrent engineering. In: Proc. of the International Conferanc¢ on Computer Intcgratod Manufacturing (ICCIM '93), Beijing, May 12-14, 1993, pp.214-220. Sullivan, L. P.: Quality Function Deployment. Quality Progress, June 1986, pp. 39-50. Wasserman" Gary S.: On How to Prioritize Design Requirements during the QFD Planning Process. lie Transactions, 25 (1993) 3, pp.59-65.