Technology for the Monitoring and Control of Laser Powder Bed Fusion
A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).
Deadline for manuscript submissions: 31 March 2026 | Viewed by 14
Special Issue Editor
Interests: additive manufacturing (AM); thermal–mechanical modeling; residual stress and distortion; machine learning (CNN, GPR, QR); microstructure evolution; creep modeling; finite element method (FEM); computational fluid dynamics (CFD); material characterization; process optimization; advanced manufacturing; metal forming; digital twins; heat transfer analysis; numerical simulation; physics-informed AI
Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to this Special Issue of the Journal of Manufacturing and Materials Processing titled “Technology for the Monitoring and Control of Laser Powder Bed Fusion”. As LPBF continues to mature as a leading additive manufacturing (AM) technology for fabricating complex, high-performance metal components, the demand for robust in situ monitoring and intelligent control systems becomes more critical than ever. Key challenges such as melt pool instability, porosity, residual stress, and geometric inaccuracies remain persistent, often limiting part quality and repeatability.
This Special Issue aims to bring together recent advances in sensor-based monitoring, real-time data analytics, closed-loop control, machine learning integration, and multi-physics modeling for LPBF. We welcome both experimental and theoretical studies, as well as numerical simulations and hybrid approaches, that advance our understanding of process behavior, defect mitigation, and process optimization.
We encourage contributions in the following areas:
- In situ monitoring technologies (optical, acoustic, thermal, etc.);
- Defect detection and quality assurance in LPBF;
- Closed-loop and adaptive control strategies;
- Sensor fusion and data interpretation;
- Machine learning and AI for LPBF process prediction;
- Real-time data processing and digital twins;
- Melt pool dynamics and modeling;
- Process stability, repeatability, and reliability;
- Multiscale simulations and feedback models.
We look forward to your high-quality submissions that contribute to advancing the state of the art in LPBF manufacturing.
Dr. Fan Chen
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 100 words) can be sent to the Editorial Office for announcement on this website.
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Keywords
- laser powder bed fusion (LPBF)
- additive manufacturing
- process monitoring
- closed-loop control
- defect detection
- sensor fusion
- in situ monitoring
- machine learning
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