In Situ Monitoring of Manufacturing Processes

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2203

Special Issue Editor

School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA, USA
Interests: 3D imaging; in situ inspection; 3D optical metrology; fringe analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As manufacturing continues to advance toward greater precision, automation, and sustainability, in situ monitoring has become an essential approach for ensuring real-time quality control, process optimization, and predictive maintenance. By integrating advanced sensing technologies, data analytics, and machine learning, in situ monitoring enables early defect detection, enhances process efficiency, and reduces material waste. Recent developments in optical metrology, acoustic sensing, and multi-sensor fusion have further expanded the capabilities of real-time monitoring across various manufacturing domains, including additive manufacturing, machining, and surface finishing. This Special Issue aims to highlight recent innovations and address key challenges in implementing reliable and scalable in situ monitoring solutions.

Potential topics include, but are not limited to, the following:

  • In situ monitoring;
  • Sensing technologies;
  • Image or signal processing methods;
  • Data analytics;
  • Machine learning for predictive analysis;
  • Defect detection;
  • Optical metrology;
  • Surface characterization.

Dr. Beiwen Li
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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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

  • in situ monitoring
  • manufacturing
  • automation
  • machine learning
  • defect detection

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Published Papers (1 paper)

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Review

33 pages, 4897 KB  
Review
Recent Advances in Sensor Fusion Monitoring and Control Strategies in Laser Powder Bed Fusion: A Review
by Alexandra Papatheodorou, Nikolaos Papadimitriou, Emmanuel Stathatos, Panorios Benardos and George-Christopher Vosniakos
Machines 2025, 13(9), 820; https://doi.org/10.3390/machines13090820 - 6 Sep 2025
Viewed by 1832
Abstract
Laser Powder Bed Fusion (LPBF) has emerged as a leading additive manufacturing (AM) process for producing complex metal components. Despite its advantages, the inherent LPBF process complexity leads to challenges in achieving consistent quality and repeatability. To address these concerns, recent research efforts [...] Read more.
Laser Powder Bed Fusion (LPBF) has emerged as a leading additive manufacturing (AM) process for producing complex metal components. Despite its advantages, the inherent LPBF process complexity leads to challenges in achieving consistent quality and repeatability. To address these concerns, recent research efforts have focused on sensor fusion techniques for process monitoring, and on developing more elaborate control strategies. Sensor fusion combines information from multiple in situ sensors to provide more comprehensive insights into process characteristics such as melt pool behavior, spatter formation, and layer integrity. By leveraging multimodal data sources, sensor fusion enhances the detection and diagnosis of process anomalies in real-time. Closed-loop control systems may utilize this fused information to adjust key process parameters–such as laser power, focal depth, and scanning speed–to mitigate defect formation during the build process. This review focuses on the current state-of-the-art in sensor fusion monitoring and control strategies for LPBF. In terms of sensor fusion, recent advances extend beyond CNN-based approaches to include graph-based, attention, and transformer architectures. Among these, feature-level integration has shown the best balance between accuracy and computational cost. However, the limited volume of available experimental data, class-imbalance issues and lack of standardization still hinder further progress. In terms of control, a trend away from purely physics-based towards Machine Learning (ML)-assisted and hybrid strategies can be observed. These strategies show promise for more adaptive and effective quality enhancement. The biggest challenge is the broader validation on more complex part geometries and under realistic conditions using commercial LPBF systems. Full article
(This article belongs to the Special Issue In Situ Monitoring of Manufacturing Processes)
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