Mechanical Property

Mechanical property research focuses on understanding and predicting the material response to external forces, encompassing strength, stiffness, elasticity, and other related characteristics. Current research heavily utilizes machine learning, employing diverse models like Gaussian processes, random forests, XGBoost, and neural operators, to predict mechanical properties from various inputs such as processing parameters, microstructures, and even chemical compositions. This work is crucial for optimizing material design in diverse fields, including additive manufacturing, biocomposites, and robotics, enabling the creation of materials with tailored properties for specific applications. The integration of physics-based models with machine learning is a significant trend, improving prediction accuracy and interpretability.