Prediction of tool failure in metal hot extrusion process using artificial neural networks

Prediction of tool failure in metal hot extrusion process using artificial neural networks

Mosa Almutahhar, Ali Alhajeri, Rashid Ali Laghari, Syed Sohail Akhtar, Usman Ali

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Abstract. The variation of tool performance and nonuniform process parameters in metal forming are some of the factors that complicate the tool life modeling and analysis of such processes. In this work, a brief discussion about machine learning in analyzing metal extrusion process as well as tool life modeling, and an implemented work of using machine learning to predict failure modes for H13 Steel die used in 6063 Aluminum hot extrusion process is presented. The analysis is conducted on a set of steel dies used in 6063 aluminum hot extrusion process. The data for the failed dies used in this work is collected from a local hot extrusion manufacturer. Using artificial neural network, the prediction of the die failure modes was modeled. Moreover, the model’s accuracy and improvement recommendations are presented.

Hot Extrusion, Die Failure, H13 Steel, 6063 Aluminum, Artificial Neural Network

Published online 9/25/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Mosa Almutahhar, Ali Alhajeri, Rashid Ali Laghari, Syed Sohail Akhtar, Usman Ali, Prediction of tool failure in metal hot extrusion process using artificial neural networks, Materials Research Proceedings, Vol. 36, pp 8-15, 2023


The article was published as article 2 of the book AToMech1-2023 Supplement

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