Smart Metal Additive Manufacturing
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 5
Special Issue Editor
Interests: advanced manufacturing; additive manufacturing; autonomous systems; AI manufacturing; digital twins; digital manufacturing; laser processing; machine learning and AI; manufacturing automation; mechatronics; micro-manufacturing; multi-criteria decision making; physics-based process simulations; physics-informed learning; precision machining; process sensing; process monitoring; process optimization; smart manufacturing; surface integrity
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Special Issue Information
Dear Colleagues,
Laser-based metal additive manufacturing (AM) technologies—including Laser Powder Bed Fusion (LPBF), Directed Energy Deposition (DED), and multi-laser systems—are transforming aerospace, biomedical, automotive, energy, and defense industries by enabling the fabrication of complex, lightweight, and high-performance components. Despite remarkable progress, significant scientific and technological challenges remain in understanding and controlling melt-pool dynamics, thermal–fluid phenomena, microstructure evolution, defect formation, residual stresses, and process variability under highly dynamic laser processing conditions.
This Special Issue aims to provide a unified platform for the latest advances in physics-informed and data-driven digital twinning for laser-based smart metal additive manufacturing. The Special Issue seeks contributions that integrate process physics, computational modeling, sensing and monitoring, artificial intelligence, machine learning, and real-time optimization frameworks for predictive and autonomous manufacturing systems.
We particularly encourages interdisciplinary studies that bridge multiphysics simulation methods—such as finite element modeling (FEM), finite-difference time-domain (FDTD), thermo-mechanical simulations, computational fluid dynamics (CFD), and phase transformation modeling—with emerging AI approaches including deep learning, physics-informed neural networks (PINNs), surrogate modeling, uncertainty quantification, and cyber-physical digital twin architectures.
This Special Issue will highlight state-of-the-art methodologies for sensor-driven modeling, in situ monitoring, defect prediction, closed-loop control, and scalable industrial deployment of intelligent AM systems. Contributions featuring reproducible computational workflows, experimental validation, and industrial case studies are especially welcome.
You may choose our Joint Special Issue in JMMP.
Prof. Dr. Tuğrul Özel
Guest Editor
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 250 words) can be sent to the Editorial Office for assessment.
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. Machines is an international peer-reviewed open access monthly 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 2400 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
- laser-based metal additive manufacturing
- process physics
- digital twins
- melt-pool modeling
- data-driven modeling
- defect detection
- machine learning for AM
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