Article (Périodiques scientifiques)
Unsupervised machine learning application in the selection of measurement strategy on Coordinate Measuring Machine
Štrbac, B.; Ranisavljev, M.; OROSNJAK, Marko et al.
2024In Advances in Production Engineering and Management, 19 (2), p. 209 - 222
Peer reviewed vérifié par ORBi
 

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Mots-clés :
Accuracy; Coordinate Measuring Machine (CMM); Measurement strategy; Multiple correspondence analysis; Principal component analysis; Unsupervised learning
Résumé :
[en] 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. © 2024 Production Engineering Institute. All rights reserved.
Disciplines :
Ingénierie mécanique
Auteur, co-auteur :
Štrbac, B.;  University of Novi Sad, Faculty of Technical Sciences, Department of Production Engineering, Novi Sad, Serbia
Ranisavljev, M.;  University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Management, Novi Sad, Serbia
OROSNJAK, Marko  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) ; University of Slavonski Brod, Mechanical Engineering Faculty, Slavonski Brod, Croatia
Havrlišan, S.;  Comenius University Bratislava, Faculty of Management, Bratislava, Slovakia
Dudić, B.;  Faculty of Economics and Engineering Management, University Business Academy, Novi Sad, Serbia
Savković, B.
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Unsupervised machine learning application in the selection of measurement strategy on Coordinate Measuring Machine
Date de publication/diffusion :
2024
Titre du périodique :
Advances in Production Engineering and Management
ISSN :
1854-6250
eISSN :
1855-6531
Maison d'édition :
Production Engineering Institute
Volume/Tome :
19
Fascicule/Saison :
2
Pagination :
209 - 222
Peer reviewed :
Peer reviewed vérifié par ORBi
Focus Area :
Physics and Materials Science
Organisme subsidiant :
Ministry of Science, Technological Development and Innovation
N° du Fonds :
451-03-65/2024-03/200156
Subventionnement (détails) :
This research has been supported by the Ministry of Science, Technological Development and Innovation (Contract No. 451-03-65/2024-03/200156) and the Faculty of Technical Sciences, University of Novi Sad through project \u201CScientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad\u201D (No. 01-3394/1).
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depuis le 20 janvier 2025

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