Machine Learning for Material and Process Optimization in Additive Manufacturing
A special issue of Crystals (ISSN 2073-4352).
Deadline for manuscript submissions: 26 February 2026 | Viewed by 114
Special Issue Editors
Interests: 3D printing; machine learning; process modeling and optimization
Special Issues, Collections and Topics in MDPI journals
Interests: 3D printing; additive manufacturing; advanced manufacturing; powder materials
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the application of machine learning in additive manufacturing (AM) has established a new paradigm for material design and performance optimization. AM, also known as 3D printing, has revolutionized the production of complex, high-performance components across industries including the aerospace, biomedical engineering, and energy industries. However, the inherent process complexity of AM and the challenges in optimizing material behavior under variable manufacturing conditions have limited the full realization of its potential. Machine learning (ML) offers powerful solutions to these challenges through data-driven modeling, real-time process control, intelligent material design, and predictive maintenance capabilities.
This Special Issue will focus on predicting material structure–property relationships and examine how machine learning models can enable intelligent control of microstructural evolution, defect formation, and mechanical properties in additive manufacturing processes. The research highlights the potential of data-driven approaches to overcome limitations of traditional empirical models while accelerating the development of novel materials, thereby providing theoretical foundations for cross-scale design in AM.
We cordially invite manuscript submissions addressing these topics for consideration in this Special Issue.
Dr. Haining Zhang
Dr. Joon Phil Choi
Dr. Xingchen Liu
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- additive manufacturing
- process optimization
- material design
- artificial intelligence
- material development
- digital twins
- process control
- data-driven modeling
- advanced manufacturing
- sustainable manufacturing
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