Cooling Rate (CR) definitively influences the microstructure of metallic parts manufactured through various processes. Factors including cooling medium, surface area, thermal conductivity, and temperature control can influence both predicted and unforeseen impacts that then influence the results of mechanical properties. This comprehensive study explores the impact of CRs in diffusion, microstructural development, and the characterization of aluminum alloys and the influence of various manufacturing processes and post-process treatments, and it studies analytical models that can predict their effects. It examines a broad range of CRs encountered in diverse manufacturing methods, such as laser powder bed fusion (LPBF), directed energy deposition (DED), casting, forging, welding, and hot isostatic pressing (HIP). For example, varying CRs might result in different types of solidification and microstructural evolution in aluminum alloys, which thereby influence their mechanical properties during end use. The study further examines the effects of post-process heat treatments, including quenching, annealing, and precipitation hardening, on the microstructure and mechanical properties of aluminum alloys. It discusses numerical and analytical models, which are used to predict and optimize CRs for achieving targeted material characteristics of specific aluminum alloys. Although understanding CR and its effects is crucial, there is a lack of literature on how CR affects alloy properties. This comprehensive review aims to bridge the knowledge gap through a thorough literature review of the impact of CR on microstructure and mechanical properties.

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.

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.