Improving an inverse model of sheet metal forming by neural network based regression

Frayman, Yakov, Rolfe, Bernard and Webb, G. 2002, Improving an inverse model of sheet metal forming by neural network based regression, in Proceedings of the 2002 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, New York, N.Y., pp. 1-12.

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Title Improving an inverse model of sheet metal forming by neural network based regression
Author(s) Frayman, Yakov
Rolfe, Bernard
Webb, G.
Conference name ASME Design Engineering. Technical Conferences (2002 : Montreal, Quebec)
Conference location Montreal, Canada
Conference dates September 29 - October 2 2002
Title of proceedings Proceedings of the 2002 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Editor(s) Kazerounian, Kazem
Publication date 2002
Conference series ASME Design Engineering Technical Conferences
Start page 1
End page 12
Publisher American Society of Mechanical Engineers
Place of publication New York, N.Y.
Keyword(s) sheet metal forming
inverse models
regression
classification
neural networks
Summary An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.
ISBN 0791836215
9780791836217
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005 Monash University
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004908

Document type: Conference Paper
Collection: School of Information Technology
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