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
download PDFAbstract. 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.
Keywords
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
DOI: https://doi.org/10.21741/9781644902790-2
The article was published as article 2 of the book AToMech1-2023 Supplement
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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