{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T03:02:07Z","timestamp":1776222127081,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Centre for Research and Development, Poland","award":["POIR.01.01.01-00-1032\/19"],"award-info":[{"award-number":["POIR.01.01.01-00-1032\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The application of modern machine learning methods in industrial settings is a relatively new challenge and remains in the early stages of development. Current computational power enables the processing of vast numbers of production parameters in real time. This article presents a practical analysis of the welding process in a robotic cell using the unsupervised HDBSCAN machine learning algorithm, highlighting its advantages over the classical k-means algorithm. This paper also addresses the problem of predicting and monitoring undesirable situations and proposes the use of the real-time graphical representation of noisy data as a particularly effective solution for managing such issues.<\/jats:p>","DOI":"10.3390\/info16020079","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T07:55:32Z","timestamp":1737532532000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Real-Time Analysis of Industrial Data Using the Unsupervised Hierarchical Density-Based Spatial Clustering of Applications with Noise Method in Monitoring the Welding Process in a Robotic Cell"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6569-124X","authenticated-orcid":false,"given":"Tomasz","family":"Blachowicz","sequence":"first","affiliation":[{"name":"PROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, Poland"},{"name":"Institute of Physics\u2014CSE, Silesian University of Technology, S. Konarskiego 22B Str., 44-100 Gliwice, Poland"}]},{"given":"Jacek","family":"Wylezek","sequence":"additional","affiliation":[{"name":"PROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, Poland"}]},{"given":"Zbigniew","family":"Sokol","sequence":"additional","affiliation":[{"name":"PROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, Poland"}]},{"given":"Marcin","family":"Bondel","sequence":"additional","affiliation":[{"name":"PROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_2","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). 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