Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Interpretable Boosting-based Predictive Model for Transformation Temperatures of Shape Memory Alloys

Sina Hossein Zadeh
* ORCID logo ,
Amir Behbahanian
,
John Broucek
,
Mingzhou Fan
,
Guillermo Vazquez Tovar
,
Mohammad Noroozi
,
William Trehern
,
Xiaoning Qian
,
Ibrahim Karaman
,
Raymundo Arroyave
Version 1 : Received: 4 February 2023 / Approved: 6 February 2023 / Online: 6 February 2023 (04:46:33 CET)

A peer-reviewed article of this Preprint also exists.

Zadeh, S.H.; Behbahanian, A.; Broucek, J.; Fan, M.; Vazquez, G.; Noroozi, M.; Trehern, W.; Qian, X.; Karaman, I.; Arroyave, R. An Interpretable Boosting-Based Predictive Model for Transformation Temperatures of Shape Memory Alloys. Computational Materials Science 2023, 226, 112225, doi:10.1016/j.commatsci.2023.112225. Zadeh, S.H.; Behbahanian, A.; Broucek, J.; Fan, M.; Vazquez, G.; Noroozi, M.; Trehern, W.; Qian, X.; Karaman, I.; Arroyave, R. An Interpretable Boosting-Based Predictive Model for Transformation Temperatures of Shape Memory Alloys. Computational Materials Science 2023, 226, 112225, doi:10.1016/j.commatsci.2023.112225.

Abstract

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

Keywords

Shape Memory Alloys, Machine Learning, Martensitic Transformation, Phase Transformation, Feature Engineering

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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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