Multaneous enhancement in the strength and toughness of cast Al–Si–Mg alloys via interpretable machine learning

Resolving the strength-ductility trade-off in Al-Mg-Si alloys via physics-informed machine learning

Composition-based machine learning for Al-Mg-Si alloys lacks generalization. A physics-informed strategy incorporating physicochemical descriptors was proposed, improving the coefficient of determination (R2) by 30% over composition-only models. Shapley additive explanations (SHAP) identified electronegativity differen...

Machine Learning-ANN Driven Hardness Modeling in SPS-Processed AlCuCrFeMnWx High-Entropy Alloys

Machine Learning Modeling of Hardness–Depth Distribution of Nitrided Steels

A comprehensive database was constructed, comprising over twenty thousand hardness values from steels with various compositions and nitriding parameters, and two predictive models were developed incorporating physically based parameters such as nitrogen diffusion distance and nitriding layer thickness.

MACHINE LEARNING PREDICTION OF TENSILE PROPERTIES OF ENGINEERED CEMENTITIOUS COMPOSITES

Engineered Cementitious Composites (ECC) exhibit exceptional strain-hardening behavior, yet predicting their tensile properties remains challenging due to complex nonlinear interactions among mix constituents and fiber characteristics. This study presents a comprehensive machine learning framework for predicting both t...

Prediction of tensile properties in fiber-reinforced composites by machine learning models

The results validate the feasibility of applying ML, particularly instance-based and ensemble learning models, to predict the tensile behavior of fiber-reinforced composites using a limited experimental dataset, thereby reducing reliance on destructive testing and accelerating the design of high-performance hybrid comp...

Explainable Machine Learning for Compressive Stress Prediction in Cement-Based Construction Materials: A Systematic Benchmarking and SHAP Interpretability Study

This study addresses three recurring weaknesses of the machine learning literature in this area: model comparisons are seldom made under one shared data split and are rarely validated statistically; raw mix-design variables are usually passed to the learner without encoding established concrete science; and analysis co...