Applications of Artificial Intelligence in Metal Additive Manufacturing

Dear Colleagues,

The rapid growth of metal additive manufacturing continues to create a clear demand for advanced modeling tools that can describe the mechanical behavior of metallic materials from the level of melt-pool solidification to their final macroscopic properties. Traditional multi-scale models that combine thermal histories, microstructure evolution, and constitutive descriptions are powerful, yet often are associated with a high computational cost and complex calibration. In parallel, the increasing availability of high-quality experimental data has opened up the possibility of applying artificial intelligence techniques to accelerate simulation, improve parameter identification, and support the prediction of stress–strain responses in additively manufactured metals.

For these reasons, the development of AI-driven mechanics remains a significant direction for both scientific understanding and industrial applications. The integration of machine learning with established numerical methods such as phase-field modeling, cellular automata, crystal plasticity, and continuum mechanics allows for reducing the computational cost while maintaining physical interpretation. These approaches offer the potential to link microstructure, defects, and anisotropy with the resulting mechanical response, producing models that are more efficient and easier to adapt to different alloys and process conditions.

In this Special Issue, we welcome articles focused on the multi-scale mechanical modeling of metal AM parts supported by AI-based or hybrid strategies, including parameter identification, surrogate modeling, and the prediction of microstructure-dependent mechanical behavior. Works combining simulations with experimental validation remain especially encouraged, with the aim of advancing reliable and practical tools for metal additive manufacturing.

Dr. Fan Chen
Dr. Guiwei Li
Dr. Shuhao Wang
Guest Editors

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