一种用于形状记忆合金相变温度的、基于可解释提升方法的预测模型
An Interpretable Boosting-based Predictive Model for Transformation Temperatures of Shape Memory Alloys
AI总结：
本文提出一种结合特征工程与梯度提升的形状记忆合金相变温度预测模型，平均准确率超过95%，并利用 Shapley 值解释关键工艺与合金特征的影响。
摘要：
在本研究中，我们展示了如何通过纳入适当的特征工程，并选择最适合可用数据集的机器学习（ML）算法，来开发一个可应用于广泛形状记忆合金的相变温度预测模型。我们开发了一种梯度提升机器学习代理模型，该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相变温度，平均准确率超过95%；其做法是在对不同合金体系进行建模时，显式处理潜在的分布变化。我们将热处理、轧制、挤压工艺参数以及合金体系类别特征纳入模型输入特征，以获得更准确、更符合实际的结果。此外，利用基于特征对所有可能联盟的平均边际贡献计算得到的 Shapley 值，本研究得以深入理解主导特征及其对预测相变温度的影响，从而为考察马氏体相变温度中的关键参数和特征提供了独特机会。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
