Numerical Simulation and Data-Driven Modeling of Metallic Materials Formed by Laser Additive Manufacturing

Dear Colleagues,

Additive manufacturing (AM) is well known for its rapid integrated forming ability for complex geometry, and as the AM industry flourishes, it is receiving increasing attention. Unlike traditional casting and other forming techniques, AM involves a rapid nonequilibrium melting and solidification process, which occasionally generates defects, such as pores, deformation, cracks, etc.

Even though significant research and experiments on various alloy systems and AM processes have been carried out to study melting and solidification behavior, some mechanisms remain unclear. In this context, numerical simulation and data-driven/physics-informed machine learning modeling are important approaches to computing the dynamic evolution of multiphysics fields or establishing relationships between process, microstructures, and mechanical properties.

These approaches can help us understand the fundamental principles and rules of AM processes and provide guidance for optimizing these processes and improving product quality.

For this Special Issue, we welcome original research and review articles that focus on the following topics:

• Numerical simulation of the multiphysics multiscale AM process, including molten pool dynamics, solidification microstructures in grain and dendrite scales, etc.;
• Numerical simulation of the gas atomization process for powder preparation;
• Data-driven/physics-informed machine learning modeling for predicting and optimizing AM processes;
• Development of computation methods, such as discrete element method, the volume of fluids/level set method, cellular automata, phase field, surrogate modeling, structure optimizations, etc.

Dr. Chaoyue Chen
Dr. Songzhe Xu
Guest Editors

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• additive manufacturing
• numerical simulation
• machine learning
• molten pool dynamics
• solidification microstructures
• powder preparation
• multiphysics/multiscale modeling