International Journal of Quality Innovation

  • 제6권1호
  • /
  • Pages.109-118
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  • 2005
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  • 1598-2688(pISSN)
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  • 2363-7021(eISSN)
한국품질경영학회 (Korean Society for Quality Management)
권호 표지

Robust Parameter Design via Taguchi's Approach and Neural Network

  • Tsai, Jeh-Hsin (Department of Business Administration National Sun Yat-Sen University) ;
  • Lu, Iuan-Yuan (Department of Business Administration National Sun Yat-Sen University)
  • 발행 : 2005.04.01
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초록

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

키워드

참고문헌

  1. Castillo, E.D. Montgomery, D.C. and McCarville,D.R.(1996): 'Modified Desirability Functions for Multiple Response Optimization,' Journal of Quality Technology, 28, (3), 337-345
  2. Chattopadhyay, S. and Sellars, C.M.(1982): 'Kinetics of Pearlite Spheroidization during Static Annealing and during Hot Deformation,' Acta Metallurgical, 28, 157-170
  3. Chiang, T.L., Su, C.T., Li, T.S. and Huang, R.C.C.(2001): 'Improvement of Process Capability through Neural Networks and Robust Design: A Case Study,' Quality Engineering, 14(2), 313-318 https://doi.org/10.1081/QEN-100108689
  4. Kim, K.J.and Lin, K.J.(2000): 'Simultaneous Optimization of Mechanical Properties of Steel by Maximizing Exponential Desirability Functions,' Applied Statistics, 49, (3), 311-325
  5. Logothetis, N. and Haigh, A.,(1988): 'Characterizing and Optimizing Multi-response Process by the Taguchi methods,' Quality and Reliability Engineering International, 4, (2), 159-168 https://doi.org/10.1002/qre.4680040211
  6. Paqueton, H. and Pineau A.(1971): 'Acceleration of Pearlite Spheroidization by Thermomechanical Treatment,' Journal of The Iron and Steel Institute, Dec., 991-998
  7. Phadke, M.S. and Dehnad, K., (1988): 'Optimization of product and process design for quality and cost,' Quality and Reliability Engineering International, 4, 105-112 https://doi.org/10.1002/qre.4680040205
  8. Pignatiello, J.J., (May 1993): 'Stategies for Robust Multiresponse Quality Engineering,' IIE Transactions, (25), 5-15
  9. Phadke, M.S. (1989): Quality Engineering Using Robust design, AT & T Bell Laboratories
  10. Robbins, J.L., Shepard, O.C. and Sherby, O.D.,(1964): 'Accelerated Spheroidization of Eutectoid Steels by Concurrent Deformation,' Journal of The Iron and Steel Institute, Oct., 804-807
  11. Su, C.T, and Chiang, T.L. (2003): 'Optimizing the IC Wire Bonding Process Using a Neural Networks/Genetic Algorithms Approach,' Journal of Intelligent Manufacturing, 14(2), 229-238 https://doi.org/10.1023/A:1022959631926