Physics-Based Machine Learning Framework for Predicting Structure-Property Relationships in DED-Fabricated Low-Alloy Steels †

Rahman A. Ali M.H. Malik A.W. Mahmood M.A. Liou F.
September 2025 Multidisciplinary Digital Publishing Institute (MDPI)

Metals
2025 #15 Issue 9

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process parameters, material constituents, grain size, cooling rates, and thermal history. This study introduces an ML model capable of accurately predicting the UTS, YS, and HV of a material dataset comprising 4900 simulation analyses generated using the “JMatPro” software, with input parameters including material compositions, grain size, cooling rates, and temperature, all of which are relevant to DED-processed low-alloy steels. Subsequently, an ML model is developed using the generated dataset. The proposed framework incorporates a physics-based DED-specific feature that leverages “JMatPro” simulations to extract key input parameters such as material composition, grain size, cooling rate, and thermal properties relevant to mechanical behavior. This approach integrates a suite of flexible ML algorithms along with customized evaluation metrics to form a robust foundation to predict mechanical properties. In parallel, explicit data-driven models are constructed using Multivariable Linear Regression (MVLR), Polynomial Regression (PR), Multi-Layer Perceptron Regressor (MLPR), XGBoost, and classification models to provide transparent and analytical insight into the mechanical property predictions of DED-processed low-alloy steels.

DED , explainable AI , low-alloy steels , machine learning , mechanical property prediction , process–structure–property relationships

Text of the article Перейти на текст статьи

Department of Mechanical & Aerospace Engineering, Missouri University of Science and Technology, Rolla, 65409, MO, United States
Department of Mechanical and Aerospace Engineering, SEDS, Nazarbayev University, Qabanbay Batyr Ave 53, Astana, 010000, Kazakhstan
National Strategic Planning & Analysis Research Center, Mississippi State University, Starkville, 39762, MS, United States
Intelligent Systems Center, Missouri University of Science and Technology, Rolla, 65409, MO, United States

Department of Mechanical & Aerospace Engineering
Department of Mechanical and Aerospace Engineering
National Strategic Planning & Analysis Research Center
Intelligent Systems Center

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026