Article (Scientific journals)
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 verified by ORBi
 

Files


Full Text
APEM19-2_209-222.pdf
Author postprint (1.95 MB) Creative Commons License - Attribution
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Keywords :
Accuracy; Coordinate Measuring Machine (CMM); Measurement strategy; Multiple correspondence analysis; Principal component analysis; Unsupervised learning
Abstract :
[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 :
Mechanical engineering
Author, co-author :
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.
External co-authors :
yes
Language :
English
Title :
Unsupervised machine learning application in the selection of measurement strategy on Coordinate Measuring Machine
Publication date :
2024
Journal title :
Advances in Production Engineering and Management
ISSN :
1854-6250
eISSN :
1855-6531
Publisher :
Production Engineering Institute
Volume :
19
Issue :
2
Pages :
209 - 222
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Physics and Materials Science
Funders :
Ministry of Science, Technological Development and Innovation
Funding number :
451-03-65/2024-03/200156
Funding text :
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).
Available on ORBilu :
since 20 January 2025

Statistics


Number of views
103 (4 by Unilu)
Number of downloads
40 (1 by Unilu)

Scopus citations®
 
3
Scopus citations®
without self-citations
3
OpenCitations
 
0
OpenAlex citations
 
1

Bibliography


Similar publications



Contact ORBilu