{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:46:52Z","timestamp":1785250012748,"version":"3.55.0"},"reference-count":112,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability.<\/jats:p>","DOI":"10.3390\/s23156821","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T10:08:14Z","timestamp":1690798094000},"page":"6821","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions"],"prefix":"10.3390","volume":"23","author":[{"given":"Ryanne Gail","family":"Kim","sequence":"first","affiliation":[{"name":"Research and Development Center, Philippine Coding Camp, 2401 Taft Ave, Malate, Manila 1004, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mideth","family":"Abisado","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technologies, National University, Manila 1008, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jocelyn","family":"Villaverde","sequence":"additional","affiliation":[{"name":"School of Electrical, Electronics and Computer Engineering, Map\u00faa University, Manila 1002, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2354-4409","authenticated-orcid":false,"given":"Gabriel Avelino","family":"Sampedro","sequence":"additional","affiliation":[{"name":"Research and Development Center, Philippine Coding Camp, 2401 Taft Ave, Malate, Manila 1004, Philippines"},{"name":"Faculty of Information and Communication Studies, University of the Philippines Open University, Laguna 4031, Philippines"},{"name":"College of Computer Studies, De La Salle University, 2401 Taft Ave, Malate, Manila 1004, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ahlers, D., Wasserfall, F., Hendrich, N., and Zhang, J. 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