Аңдатпа
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-structure-property 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 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 UTS, YS, and HV of a material dataset that comprises 5000 simulation analyses generated using “JMatPro” software, with input parameters including material compositions, grain size, cooling rates and temperature, factors relevant to the 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.