Abstract
The key successful factor of the new product design (NPD) of sensor manufacturing industry is the selections of the best parameter level. For above reasons, the selection of best parameter level sometimes causes more cost increasing and job reworking. Previous studies focus on try and error test and structured approach for the replacement and management of selection of the parameter level in product design, but rarely on a dynamic environment. Therefore, this work presents a novel algorithm, the Taguchi System-two steps optimal algorithm, which combines the Taguchi System (TS) with neural network (NN) method, which is shown how product adjusted under a dynamic environment in product design. From the results, the proposed method might possibly be useful for our problem by selecting of parameter level size and adjusting the parameters by NN in the DSPDS is observed in this study.
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© 2013 Springer Science+Business Media Dordrecht
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Huang, CL., Wan, TL.J., Wang, LC., Hung, CJ. (2013). The Taguchi System-Neural Network for Dynamic Sensor Product Design. In: Park, J., Barolli, L., Xhafa, F., Jeong, HY. (eds) Information Technology Convergence. Lecture Notes in Electrical Engineering, vol 253. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6996-0_71
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DOI: https://doi.org/10.1007/978-94-007-6996-0_71
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