NDLI: Application of a Taguchi-based neural network prediction design of the film coating process for polymer blends

Content Provider	Springer Nature Link
Author	Kuo, Chung Feng Jeffrey Wu, Yi Shiuan
Copyright Year	2005
Abstract	In this paper, an approach for developing the prediction model for polymer blends using a back-propagation neural network (BPNN) combined with the Taguchi quality method is presented in an attempt to improve the deficiencies in current neural networks associated with the design of network architecture, including the selection of one optimal set of learning parameters to accomplish faster convergence during training and the desired accuracy during the recall step. The objective of the prediction model is to explore the relationships between the control factor levels and surface roughness in the film coating process. In addition, the feasibility of adopting this approach is demonstrated in the study optimizing the learning parameters of the BPNN structure to forecast the target characteristics of the product or process with various control conditions in the manufacturing system.
Starting Page	455
Ending Page	461
Page Count	7
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	27
Issue Number	5-6
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-01-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Back-propagation neural network (BPNN) Polymer blends Surface roughness Taguchi quality method Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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