AI-Driven Modeling and Monitoring Towards Advanced Additive Manufacturing
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 123
Special Issue Editors
Interests: additive manufacturing; blue laser; molten pool; in situ monitoring
Interests: process monitoring; additive manufacturing; AI for science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Additive manufacturing (AM) represents a transformative approach to industrial production that allows for the creation of lighter, stronger parts and systems. AM technologies show rapid and wide applications in aerospace, automotive, healthcare, and other critical industries. However, the complexity of the processes involved in AM presents challenges reagarding process reliability, quality assurance, and materials properties. Artificial Intelligence (AI) emerges as a powerful ally in addressing these challenges. AI-driven modeling and monitoring can significantly enhance the capabilities of AM through more efficient process monitoring, predictive modeling, and real-time adjustments. AI models can predict the microstructural characteristics of materials, optimize mechanical properties, and adaptively control the process parameters to minimize defects and improve the quality of the final product. Integrating AI into AM for simulation or monitoring is expected to enhance understanding of the intricate dynamics of AM processes and guarantee the mechanical/material properties of AM fabricated parts.
This special issue here seeks original research articles, review articles, and case studies that address the various aspects of AI-driven modeling and monitoring in additive manufacturing. Contributions may address, but are not limited to, the following topics:
- Process monitoring approaches for additive manufacturing
- Numerical or data-driven simulations of thermal/stress/strain etc.
- Advanced AI modeling for additive manufacturing applications
- Innovations in quality control and defect detection
- Mechanical/material property prediction or optimization for additive manufacturing
Dr. Zijue Tang
Dr. Shitong Peng
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- process monitoring
- additive manufacturing
- AI-based modeling
- microstructure simulation
- in-situ sensing
- quality control and defect detection
- real-time and adaptive control
- numerical modeling
- data-driven modeling
- material property prediction/optimization
- mechanical property prediction/optimization
- thermal/stress/strain analysis
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