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

Y. Frayman, B. F. Rolfe, G. I. Webb

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An inverse model for a sheet metal 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 winnertakes- 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.

Original languageEnglish
Title of host publicationProceedings of the ASME Design Engineering Technical Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
Number of pages8
ISBN (Electronic)0791836215
Publication statusPublished - 2002
Externally publishedYes
EventASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE) 2002 - Montreal, Canada
Duration: 29 Sept 20022 Oct 2002


ConferenceASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE) 2002


  • classification
  • inverse models
  • neural networks
  • regression
  • sheet metal forming

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