20.500.12556/DKUM-96824 Unsupervised machine learning application in the selection of measurement strategy on Coordinate Measuring Machine It is indisputable that some type of coordinate measurement system (CMS) is generally used to assess the quality of dimensional and geometric characteristics. Considering the required accuracy, flexibility, and speed of measurement, a CMM with a scanning sensor may offer the best performance. These measurement systems are very complex, and many factors affect the reliability of the measurement results. A Metrologist’s choice represents the greatest variability in the measurement strategy. Previous research has shown that the measurement results can be changed up to 100 % by choosing a different measurement strategy when evaluating the form error. This paper conducts a detailed study of the impact of the measurement strategy on the cylindricity error when measuring eleven workpieces with the same nominal characteristics, but different real characteristics described by roughness and the reference value of cylindricity. To examine the influence and importance of certain factors and their levels, design of experiment (DoE) and unsupervised machine learning techniques of PCA (Principal Component Analysis) and Multiple Correspondence Analysis (MCA), were used. The results suggest that depending on the real geometry of the workpiece, different factors with different percentages influence the output characteristic. The objective was to choose a uniform measurement strategy when measuring cylindricity on the CMM, while the prior information about the actual geometry of the workpiece is lacking. Coordinate Measuring Machine CMM measurement strategy accuracy principal component analysis multiple correspondence analysis unsupervised learning strategije meritev natančnost nenadzorovano učenje true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Članek v reviji 2026-01-29 11:24:01 2026-01-29 11:24:02 2026-01-30 03:12:04 0000-00-00 00:00:00 2024 0 0 str.209-222 no. 2 Vol. 19 June 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-04-17 2024-06-30 2024-08-29 658.5 266618883 10.14743/apem2024.2.502 1854-6250 APEM19-2_209-222.pdf APEM19-2_209-222.pdf 1 3C0F47A3B4653904DA575FBBF9FA4BFA 15fd79f99fe3c03e197eaf08259f0fc519b04241c95599b33f8a7495d9861542 fb004266-fcfc-11f0-a012-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202772 https://apem-journal.org/Archives/2024/Abstract-APEM19-2_209-222.html 0 ffcb2d4f-fcfc-11f0-a012-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202773 Fakulteta za strojništvo Advances in production engineering & management 0 0 0