Innovative Computational and Data-Driven Strategies for Advancing Materials and Processes in Additive Manufacturing
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 57
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,
Additive Manufacturing (AM), often referred to as 3D printing, is revolutionizing modern manufacturing by enabling the production of complex, customized, and high-performance components across various sectors, including aerospace, biomedical, energy, and automotive industries. As AM technologies mature, there is a growing need to move beyond traditional trial-and-error approaches and adopt more intelligent, predictive, and data-centric methods to accelerate innovation and ensure process reliability, reproducibility, and scalability.
Recent advancements in computational modeling, machine learning, artificial intelligence, and data analytics are unlocking new opportunities to enhance AM materials, optimize process parameters, and design next-generation structures with tailored properties. These innovative strategies offer the potential to significantly improve printing precision, reduce defects, lower costs, and shorten development cycles. Moreover, the fusion of physics-based modeling with data-driven approaches is driving the development of digital twins and closed-loop control systems, enabling real-time monitoring and adaptive process optimization.
Given the growing relevance and transformative potential of these approaches, this Special Issue of Materials is devoted to “Innovative Computational and Data-Driven Strategies for Advancing Materials and Processes in Additive Manufacturing.” It seeks to showcase cutting-edge research that bridges materials science, computational engineering, and data science to advance the fundamental understanding and practical capabilities of AM technologies.
This Special Issue welcomes original research articles, reviews, and case studies on, but not limited to, the following topics:
- Multiscale and multiphysics computational modeling of AM processes and materials behavior.
- Machine learning, deep learning, and data-driven optimization for process design and control.
- Integration of digital twins and smart sensing for in situ monitoring and real-time decision-making.
- Data fusion techniques for quality assurance, defect prediction, and failure analysis in AM parts.
- Inverse design and topology optimization enabled by artificial intelligence.
- Development and discovery of novel materials for AM using computational and informatics tools.
- Simulation-informed AM process planning and parameter tuning.
- High-throughput experimentation and data generation pipelines for AM research.
This Special Issue aims to serve as a comprehensive reference for researchers and practitioners working at the intersection of additive manufacturing, computational science, and data analytics. Contributions that explore interdisciplinary methodologies, demonstrate practical applications, or propose new frameworks for intelligent AM systems are particularly encouraged.
Dr. Haining Zhang
Dr. Joon Phil Choi
Guest Editors
Manuscript Submission Information
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Keywords
- computational modeling
- machine learning
- additive manufacturing
- process optimization
- material design
- artificial intelligence
- material development
- digital twins
- data-driven modeling
- advanced manufacturing
- sustainable manufacturing
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