Version 1
: Received: 10 June 2023 / Approved: 12 June 2023 / Online: 12 June 2023 (05:23:12 CEST)
How to cite:
Silva, V.C.E.D.; Gouveia, G.; Silva, B.L.; Tomazella, V.L.D.; Spinelli, J.E. Solder Alloy Design: Investigation of Predictive Models for Tensile Properties Based on Different Alloy Compositions and Microstructures. Preprints2023, 2023060771. https://doi.org/10.20944/preprints202306.0771.v1
Silva, V.C.E.D.; Gouveia, G.; Silva, B.L.; Tomazella, V.L.D.; Spinelli, J.E. Solder Alloy Design: Investigation of Predictive Models for Tensile Properties Based on Different Alloy Compositions and Microstructures. Preprints 2023, 2023060771. https://doi.org/10.20944/preprints202306.0771.v1
Silva, V.C.E.D.; Gouveia, G.; Silva, B.L.; Tomazella, V.L.D.; Spinelli, J.E. Solder Alloy Design: Investigation of Predictive Models for Tensile Properties Based on Different Alloy Compositions and Microstructures. Preprints2023, 2023060771. https://doi.org/10.20944/preprints202306.0771.v1
APA Style
Silva, V.C.E.D., Gouveia, G., Silva, B.L., Tomazella, V.L.D., & Spinelli, J.E. (2023). Solder Alloy Design: Investigation of Predictive Models for Tensile Properties Based on Different Alloy Compositions and Microstructures. Preprints. https://doi.org/10.20944/preprints202306.0771.v1
Chicago/Turabian Style
Silva, V.C.E.D., Vera Lucia Damasceno Tomazella and José Eduardo Spinelli. 2023 "Solder Alloy Design: Investigation of Predictive Models for Tensile Properties Based on Different Alloy Compositions and Microstructures" Preprints. https://doi.org/10.20944/preprints202306.0771.v1
Abstract
In this study, an extensive data set was based on existing literature records in order to enable the suitability of several predictive models, from Multiple Linear Regression (MLR) to Neural Networks (NN). The main objective was to, through regression analyses, generate model computations to correlate tensile properties (UTS- Ultimate Tensile Strength, YTS – Yield Tensile Strength and EF – Elongation-to-Fracture) to a given alloy composition and microstructural spacing. This investigation led to positive results, as the highest accuracies of the trained modules (in 80% of the database) were found to be above ~82% (UTS and EF) and a maximum of ~98% (YTS), when analyzing the results to a test data set. Later, these models were used to define trends for possible next solder alloy commercial compositions. Overall, using the standard model’s setup, the Random Forest and Decision Tree models showed the highest accuracy results, with 0.958 for YTS as opposed to 0.907 for MLR. Moreover, Multilayer Perceptron (MLP)-optimized models yielded the best results for each variable, with the highest increases in accuracy associated with the YTS and EF. The present contribution might imply an important milestone towards alloy design research based on data science guidelines to unlock the full potential of former experiments and their extensive set of results.
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.
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