An ANN based approach for the friction stir welding process intrinsic uncertainty
QUARTO Mariangela, BOCCHI Sara, GIARDINI Claudio, D’URSO Gianluca
download PDFAbstract. Friction Stir Welding is a solid-state bonding process that during last years has caught the researcher’s attention for the mechanical characteristics of the welded joints that are quite similar to the properties of the base material. The Friction Stir Welding is affected by several process parameters leading to intrinsic variability in the process. The present paper would introduce a new approach for predicting the surface hardness in different areas of the welded parts. Specifically, this method is based on the hypothesis that multiple Artificial Neural Networks, characterized by the same architecture but different weights, can be used for forecasting both the punctual value of the local hardness and its confidence interval, resulting in taking into account the intrinsic variability of the process.
Keywords
Artificial Neural Network, Process Variability, Friction Stir Welding
Published online 4/19/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: QUARTO Mariangela, BOCCHI Sara, GIARDINI Claudio, D’URSO Gianluca, An ANN based approach for the friction stir welding process intrinsic uncertainty, Materials Research Proceedings, Vol. 28, pp 1067-1074, 2023
DOI: https://doi.org/10.21741/9781644902479-117
The article was published as article 117 of the book Material Forming
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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