Version 1
: Received: 20 September 2024 / Approved: 23 September 2024 / Online: 24 September 2024 (09:10:47 CEST)
How to cite:
Cruz Neto, R. M. D. A.; Brandi, S. D.; Fávero, L. P.; Belfiore Fávero, P. Longitudinal Hardness Prediction of a Weld Bead Using Linear and Nonlinear Mixed‐Effects Models and Artificial Neural Networks. Preprints2024, 2024091842. https://doi.org/10.20944/preprints202409.1842.v1
Cruz Neto, R. M. D. A.; Brandi, S. D.; Fávero, L. P.; Belfiore Fávero, P. Longitudinal Hardness Prediction of a Weld Bead Using Linear and Nonlinear Mixed‐Effects Models and Artificial Neural Networks. Preprints 2024, 2024091842. https://doi.org/10.20944/preprints202409.1842.v1
Cruz Neto, R. M. D. A.; Brandi, S. D.; Fávero, L. P.; Belfiore Fávero, P. Longitudinal Hardness Prediction of a Weld Bead Using Linear and Nonlinear Mixed‐Effects Models and Artificial Neural Networks. Preprints2024, 2024091842. https://doi.org/10.20944/preprints202409.1842.v1
APA Style
Cruz Neto, R. M. D. A., Brandi, S. D., Fávero, L. P., & Belfiore Fávero, P. (2024). Longitudinal Hardness Prediction of a Weld Bead Using Linear and Nonlinear Mixed‐Effects Models and Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints202409.1842.v1
Chicago/Turabian Style
Cruz Neto, R. M. D. A., Luiz Paulo Fávero and Patrícia Belfiore Fávero. 2024 "Longitudinal Hardness Prediction of a Weld Bead Using Linear and Nonlinear Mixed‐Effects Models and Artificial Neural Networks" Preprints. https://doi.org/10.20944/preprints202409.1842.v1
Abstract
At the beginning of any electric arc welding process, a regime called transient regime will always occur, characterized by the continuous variation of the energy flow, also resulting in the variation of the cooling rate of the welded joint. Consequently, the weld bead hardness varies from a maximum value to an approximately constant value, and this phenomenon can be represented by an asymptotic exponential function. For several welded specimens, it would be possible to fit a non-linear regression for each specimen, however, it would be a difficult task to combine different regressions in a single analysis. The objective of this study is to evaluate the applicability of mixed-effects models and artificial neural networks to predict the longitudinal hardness of the weld bead in the initial transient regime. To this end, linearized and nonlinear mixed-effects models and artificial neural networks with different architectures were fitted. Finally, valid models were obtained with all approaches, with artificial neural network model slightly better fitted. However, the explainability capacity of the linearized mixed-effects model was superior, showing that the relative thickness, one of the predictor variables, was responsible for 48% of the variation in longitudinal hardness of weld bead.
Engineering, Metallurgy and Metallurgical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.