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

TL;DR

This paper presents a gradient boosting machine learning model that accurately predicts transformation temperatures of shape memory alloys, incorporating feature engineering and interpretability to understand key parameters affecting the transformations.

Contribution

The study introduces a highly accurate, interpretable ML model for predicting shape memory alloy transformation temperatures, integrating diverse processing parameters and alloy features.

Findings

Achieved over 95% prediction accuracy.

Identified key features influencing transformation temperatures.

Provided insights into critical parameters via Shapley values.

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.