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

conference contribution

posted on 2002-01-01, 00:00 authored by Yakov Frayman, Bernard RolfeBernard Rolfe, G WebbAn 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.

## History

## Title of proceedings

Proceedings of the 2002 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference## Event

ASME Design Engineering. Technical Conferences (2002 : Montreal, Quebec)## Pagination

1 - 12## Publisher

American Society of Mechanical Engineers## Location

Montreal, Canada## Place of publication

New York, N.Y.## Start date

2002-09-29## End date

2002-10-02## ISBN-13

9780791836217## ISBN-10

0791836215## Language

eng## Publication classification

E1 Full written paper - refereed## Copyright notice

2005 Monash University## Editor/Contributor(s)

K Kazerounian## Usage metrics

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