Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.5
Journals
Machines
Special Issues
In Situ Monitoring of Manufacturing Processes
share announcement

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 May 2026 | Viewed by 8423

Special Issue Editor

Dr. Beiwen Li

E-Mail Website
Guest 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 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

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

36 pages, 12016 KB  
Article
Federated Learning-Enabled Secure Multi-Modal Anomaly Detection for Wire Arc Additive Manufacturing
by Mohammad Mahruf Mahdi, Md Abdul Goni Raju, Kyung-Chang Lee and Duck Bong Kim
Machines 2025, 13(11), 1063; https://doi.org/10.3390/machines13111063 - 18 Nov 2025
Cited by 1 | Viewed by 1202
Abstract
This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor [...] Read more.
This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor streams, including current, voltage, travel speed, and visual bead profiles, necessitates a decentralized learning paradigm capable of handling non-identical client distributions without raw data pooling. To this end, the proposed framework integrates reversible data hiding in the encrypted domain (RDHE) for the secure embedding of sensor-derived features into weld images, enabling confidential parameter transmission and tamper-evident federation. Each client node employs a domain-specific long short-term memory (LSTM)-based classifier trained on locally curated time-series or vision-derived features, with model updates embedded and transmitted securely to a central aggregator. Three FL strategies, FedAvg, FedProx, and FedPer, are systematically evaluated against four robust aggregation techniques, including KRUM, Multi KRUM, and Trimmed Mean, across 100 communication rounds using eight non-independent and identically distributed (non-IID) WAAM clients. Experimental results reveal that FedPer coupled with Trimmed Mean delivers the optimal configuration, achieving maximum F1-score (0.912), area under the curve (AUC) (0.939), and client-wise generalization stability under both geometric and temporal noise. The proposed approach demonstrates near-lossless RDHE encoding (PSNR > 90 dB) and robust convergence across adversarial conditions. By embedding encrypted intelligence within weld imagery and tailoring FL to WAAM-specific signal variability, this study introduces a scalable, secure, and generalizable framework for process monitoring. These findings establish a baseline for federated anomaly detection in metal additive manufacturing, with implications for deploying privacy-preserving intelligence across smart manufacturing (SM) networks. The federated pipeline is backbone-agnostic. We instantiate LSTM clients because the sequences are short (five steps) and edge compute is limited in WAAM. The same pipeline can host Transformer/TCN encoders for longer horizons without changing the FL or security flow. Full article
(This article belongs to the Special Issue In Situ Monitoring of Manufacturing Processes)
►▼ Show Figures

Figure 1

Review

Jump to: Research

49 pages, 13896 KB  
Review
A Review on In-Situ Monitoring in Wire Arc Additive Manufacturing: Technologies, Applications, Challenges, and Needs
by Mohammad Arjomandi, Jackson Motley, Quang Ngo, Yoosuf Anees, Muhammad Ayaan Afzal and Tuhin Mukherjee
Machines 2026, 14(1), 19; https://doi.org/10.3390/machines14010019 - 22 Dec 2025
Cited by 2 | Viewed by 1367
Abstract
Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate [...] Read more.
Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate reliable in-situ monitoring for process understanding, quality assurance, and control. While several reviews exist on in-situ monitoring in other additive manufacturing processes, systematic coverage of sensing methods specifically tailored for WAAM remains limited. This review fills that gap by providing a comprehensive analysis of existing in-situ monitoring approaches in WAAM, including thermal, optical, acoustic, electrical, force, and geometric sensing. It compares the relative maturity and applicability of each technique, highlights the challenges posed by arc light, spatter, and large melt pool dynamics, and discusses recent advances in real-time defect detection and control, process monitoring, microstructure and property prediction, and minimization of residual stress and distortion. Apart from providing a synthesis of the existing literature, the review also provides research needs, including the standardization of monitoring methodologies, the development of scalable sensing systems, integration of advanced AI-driven data analytics, coupling of real-time monitoring with multi-physics modeling, exploration of quantum sensing, and the transition of current research from laboratory demonstrations to industrial-scale WAAM implementation. Full article
(This article belongs to the Special Issue In Situ Monitoring of Manufacturing Processes)
►▼ Show Figures

Figure 1

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
Cited by 3 | Viewed by 5299
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop