An interpretable boosting-based predictive model for transformation temperatures of shape memory alloys
An interpretable boosting-based predictive model for transformation temperatures of shape memory alloys
复制标题
形状记忆合金转变温度的可解释的基于boosting的预测模型
DOI：
10.1016/j.commatsci.2023.112225
复制
发表时间：
2023
影响因子：
3.3
通讯作者：
Arroyave, Raymundo
中科院分区：
文献类型：
--
作者：
Zadeh, Sina Hossein;Behbahanian, Amir;Broucek, John;Fan, Mingzhou;Vazquez, Guillermo;Noroozi, Mohammad;Trehern, William;Qian, Xiaoning;Karaman, Ibrahim;Arroyave, Raymundo
在本研究中，我们展示了如何将适当的特征工程与选择最适合可用数据集的机器学习（ML）算法相结合，从而开发出可应用于各种形状记忆合金的转化温度预测模型。我们开发了一个梯度增强ML代理模型，能够预测马氏体开始、马氏体结束、奥氏体开始和奥氏体结束转变温度，通过明确地考虑不同合金体系建模时的潜在分布变化，平均精度超过95%。我们在模型输入特征中加入了热处理、轧制、挤压工艺参数和合金系统分类特征，以获得更准确、更真实的结果。此外，利用Shapley值（基于特征对所有可能组合的平均边际贡献计算），本研究能够深入了解控制特征及其对预测转变温度的影响，为研究马氏体转变温度的关键参数和特征提供了独特的机会。
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.