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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 415

Special Issue Editors


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Guest Editor
School of Materials Science & Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: additive manufacturing; blue laser; molten pool; in situ monitoring

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Guest Editor
Department of Mechanical Engineering, Shantou University, Shantou 515063, China
Interests: sustainable manufacturing; life cycle assessment; process monitoring
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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

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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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Published Papers (1 paper)

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Research

23 pages, 5417 KiB  
Article
Enhancing Powder Bed Fusion—Laser Beam Process Monitoring: Transfer and Classic Learning Techniques for Convolutional Neural Networks
by Piotr Sawicki and Bogdan Dybała
Materials 2025, 18(13), 3026; https://doi.org/10.3390/ma18133026 - 26 Jun 2025
Viewed by 228
Abstract
In this work, we address the task of monitoring Powder Bed Fusion–Laser Beam processes for metal powders (PBF-LB/M). Two main contributions with practical merit are presented. First, we consider the comparison between a large deep neural network (VGG-19) and a small model consisting [...] Read more.
In this work, we address the task of monitoring Powder Bed Fusion–Laser Beam processes for metal powders (PBF-LB/M). Two main contributions with practical merit are presented. First, we consider the comparison between a large deep neural network (VGG-19) and a small model consisting of, among others, four convolutional layers. Our study shows that the small model can compete favorably with the big model, which takes advantage of transfer learning techniques. Secondly, we present a filtering method using a semantic segmentation approach to preselect a region for the classification algorithm. The region is selected based on post-exposure images, and preselection can be easily adopted for any machine independently of the software used for the translation of process input files. To consider the task, a master dataset with over 260,000 samples was prepared, and a detailed process of preparing the training datasets was described. The study demonstrates that the classification time can be reduced by a factor of 4.51 while still maintaining the model’s necessary performance to detect errors in a PBF-LB process. Full article
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