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Title:Unsupervised machine learning application in the selection of measurement strategy on Coordinate Measuring Machine
Authors:ID Strbac, Branko (Author)
ID Ranisavljev, M. (Author)
ID Orošnjak, M. (Author)
ID Havrlišan, Sara (Author)
ID Dudić, B. (Author)
ID Savković, Borislav (Author)
Files:.pdf APEM19-2_209-222.pdf (1,88 MB)
MD5: 3C0F47A3B4653904DA575FBBF9FA4BFA
 
URL https://apem-journal.org/Archives/2024/Abstract-APEM19-2_209-222.html
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract: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.
Keywords:Coordinate Measuring Machine, CMM, measurement strategy, accuracy, principal component analysis, multiple correspondence analysis, unsupervised learning
Publication status:Published
Publication version:Version of Record
Submitted for review:17.04.2024
Article acceptance date:30.06.2024
Publication date:29.08.2024
Publisher:Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering
Year of publishing:2024
Number of pages:str.209-222
Numbering:Vol. 19, no. 2
PID:20.500.12556/DKUM-96824 New window
UDC:658.5
ISSN on article:1854-6250
COBISS.SI-ID:266618883 New window
DOI:10.14743/apem2024.2.502 New window
Publication date in DKUM:29.01.2026
Views:156
Downloads:1
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Advances in production engineering & management
Shortened title:Adv produc engineer manag
Publisher:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1854-6250
COBISS.SI-ID:229859072 New window

Document is financed by a project

Funder:the Ministry of Science, Technological Development and Innovation
Project number:Contract No. 451-03-65/2024-03/200156

Funder:the Faculty of Technical Sciences, University of Novi Sad
Project number:No. 01-3394/1
Name:Scientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:strategije meritev, natančnost, nenadzorovano učenje


Collection

This document is a part of these collections:
  1. Advances in production engineering & management

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