A neural network-based approach for dynamic quality prediction in a plastic injection molding process

Authors:
Wen-Chin Chen;Pei-Hao Tai;Min-Wen Wang;Wei-Jaw Deng;Chen-Tai Chen
Affiliations:
Graduate Institute of Industrial Engineering and System Management, Chung Hua University, 707 Wufu Road, Section 2, Hsinchu 300, Taiwan;Graduate Institute of Industrial Engineering and System Management, Chung Hua University, 707 Wufu Road, Section 2, Hsinchu 300, Taiwan;Department of Mechanical Engineering, National Kaohsiung University of Applied Sciences, 415 Chien Kung Road, Kaohsiung 807, Taiwan;Graduate School of Business Administration, Chung Hua University, 707 Wufu Road, Section 2, Hsinchu 300, Taiwan;Department of Computer Science and Information Engineering, Ta Hwa Institute of Technology, 1 Tahwa Road, Chiunglin, Hsinchu 307, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi's parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.