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Robust Parameter Design via Taguchi’s Approach and Neural Network

Robust Parameter Design via Taguchi’s Approach and Neural Network 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal on Quality Emerald Publishing

Robust Parameter Design via Taguchi’s Approach and Neural Network

Asian Journal on Quality , Volume 6 (1): 10 – Apr 17, 2005

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Publisher
Emerald Publishing
Copyright
Copyright © 2005 Emerald Group Publishing Limited. All rights reserved.
ISSN
1598-2688
DOI
10.1108/15982688200500009
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Asian Journal on QualityEmerald Publishing

Published: Apr 17, 2005

Keywords: Taguchi methods; Parameter design; Neural networks

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