Applying neural network approach to achieve robust design for dynamic quality characteristics

This study presents an effective means of applying neural networks to achieve robust design with dynamic characteristic considerations. Two neural networks are constructed to train the data set in the Taguchi's orthogonal array (OA): one to search for the optimal condition, and the other to for...

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Bibliographic Details
Published in:The International journal of quality & reliability management 1998-08, Vol.15 (5), p.509-519
Main Authors: Su, Chao-Ton, Hsieh, Kun-Lin
Format: Article
Language:English
Subjects:
Algorithms
Back propagation
Design
Industrial engineering
Neural networks
Noise
Quality control
Semiconductors
Statistical data
Studies
Taguchi methods
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Summary:This study presents an effective means of applying neural networks to achieve robust design with dynamic characteristic considerations. Two neural networks are constructed to train the data set in the Taguchi's orthogonal array (OA): one to search for the optimal condition, and the other to forecast the system's response value. A measuring system employed in semiconductor manufacturing demonstrates the proposed approach's effectiveness. According to those results, the proposed approach outperforms the conventional Taguchi method. By using the proposed approach, the adjustment factors are not a prerequisite for the dynamic characteristic problem. Moreover, the proposed approach enhances the generalization capability.
ISSN:0265-671X
1758-6682
DOI:10.1108/02656719810196243