Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis

Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis

This study facilitates the data-driven design of novel cast TiAl alloys by systematically investigating the critical elements and their interactions affecting room-temperature (RT) tensile properties by the machine learning method based on SHAP analysis. Comparative analysis of three algorithms with...

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Bibliographic Details
Main Authors: Shiqiu Liu, Li Liang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Crystals
Subjects:
cast titanium aluminide
machine learning
SHAP analysis
room-temperature tensile properties
random forest regression (RFR)
Online Access:https://www.mdpi.com/2073-4352/15/5/468
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