{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T22:21:32Z","timestamp":1777933292402,"version":"3.51.4"},"reference-count":17,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"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>The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.<\/jats:p>","DOI":"10.3390\/s24134239","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:14:46Z","timestamp":1719828886000},"page":"4239","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Advanced Monitoring of Manufacturing Process through Video Analytics"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7820-3356","authenticated-orcid":false,"given":"Nisar","family":"Hakam","sequence":"first","affiliation":[{"name":"Arts et M\u00e9tiers, Institute of Technology (AMIT), 75013 Paris, France"},{"name":"ERM Automatismes, 84200 Carpentras, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-021X","authenticated-orcid":false,"given":"Khaled","family":"Benfriha","sequence":"additional","affiliation":[{"name":"Arts et M\u00e9tiers, Institute of Technology (AMIT), 75013 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8713-4946","authenticated-orcid":false,"given":"Vincent","family":"Meyrueis","sequence":"additional","affiliation":[{"name":"Arts et M\u00e9tiers, Institute of Technology (AMIT), 75013 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cyril","family":"Liotard","sequence":"additional","affiliation":[{"name":"ERM Automatismes, 84200 Carpentras, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tambare, P., Meshram, C., Lee, C.-C., Ramteke, R.J., and Imoize, A.L. (2021). Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review. Sensors, 22.","DOI":"10.3390\/s22010224"},{"key":"ref_2","first-page":"100277","article-title":"Industrial parts change recognition model using machine vision, image processing in the framework of industrial information integration","volume":"26","author":"Mirbod","year":"2022","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.cirp.2019.03.021","article-title":"Machine learning-based image processing for on-line defect recognition in additive manufacturing","volume":"68","author":"Caggiano","year":"2019","journal-title":"CIRP Annals"},{"key":"ref_4","unstructured":"Korzun, N.B.E.D. (2019, January 5\u20138). Event-Driven Video Services for Monitoring in Edge-Centric Internet of Things Environments. 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(IJIDeM)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1016\/j.promfg.2018.07.111","article-title":"Automated Process Monitoring in 3D Printing Using Supervised Machine Learning","volume":"26","author":"Delli","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39934","DOI":"10.1109\/ACCESS.2020.2976860","article-title":"Noise-Robust, Reconfigurable Canny Edge Detection and its Hardware Realization","volume":"8","author":"Kalbasi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.procir.2020.02.292","article-title":"Image-based Measurement of Material Roughness using Machine Learning Techniques","volume":"95","author":"Giusti","year":"2020","journal-title":"Procedia CIRP"},{"key":"ref_13","first-page":"3259","article-title":"Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training","volume":"34","author":"Cai","year":"2021","journal-title":"Adv. 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