Classification and Selection of Sheet Forming Processes with Machine Learning

Elia Hamouche, Evripides Loukaides

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
20 Downloads (Pure)

Abstract

Sheet metal forming is a critical component of modern manufacturing. The procedure for selecting a suitable manufacturing process to achieve the final geometry of a metal part is unstructured and heavily reliant on human expertise. Similarly, classification and design of new metal forming processes has yet to be automated. In this study, a Machine Learning approach was used for the first time to identify the manufacturing process that formed a part solely from the final geometry. Several Neural Network configurations were tested with different geometry representation methods. The best performing classifier employed a deep Convolutional Neural Network and achieved an accuracy of 89%, namely when the geometry was given through a mapping of the Mean and Gaussian curvatures. The high accuracy rate establishes that automated methods can perform this step between design and manufacture, thus eliminating the need for human experts in matching each product to a suitable forming method.
Original languageEnglish
Pages (from-to)921-932
Number of pages12
JournalInternational Journal of Computer Integrated Manufacturing
Volume31
Issue number9
Early online date28 Jan 2018
DOIs
Publication statusPublished - 2018

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