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Naslov:Unsupervised machine learning application in the selection of measurement strategy on Coordinate Measuring Machine
Avtorji:ID Strbac, Branko (Avtor)
ID Ranisavljev, M. (Avtor)
ID Orošnjak, M. (Avtor)
ID Havrlišan, Sara (Avtor)
ID Dudić, B. (Avtor)
ID Savković, Borislav (Avtor)
Datoteke:.pdf APEM19-2_209-222.pdf (1,88 MB)
MD5: 3C0F47A3B4653904DA575FBBF9FA4BFA
 
URL https://apem-journal.org/Archives/2024/Abstract-APEM19-2_209-222.html
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis: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.
Ključne besede:Coordinate Measuring Machine, CMM, measurement strategy, accuracy, principal component analysis, multiple correspondence analysis, unsupervised learning
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:17.04.2024
Datum sprejetja članka:30.06.2024
Datum objave:29.08.2024
Založnik:Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering
Leto izida:2024
Št. strani:str.209-222
Številčenje:Vol. 19, no. 2
PID:20.500.12556/DKUM-96824 Novo okno
UDK:658.5
COBISS.SI-ID:266618883 Novo okno
DOI:10.14743/apem2024.2.502 Novo okno
ISSN pri članku:1854-6250
Datum objave v DKUM:29.01.2026
Število ogledov:160
Število prenosov:1
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
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Gradivo je del revije

Naslov:Advances in production engineering & management
Skrajšan naslov:Adv produc engineer manag
Založnik:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1854-6250
COBISS.SI-ID:229859072 Novo okno

Gradivo je financirano iz projekta

Financer:the Ministry of Science, Technological Development and Innovation
Številka projekta:Contract No. 451-03-65/2024-03/200156

Financer:the Faculty of Technical Sciences, University of Novi Sad
Številka projekta:No. 01-3394/1
Naslov:Scientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:strategije meritev, natančnost, nenadzorovano učenje


Zbirka

To gradivo je del naslednjih zbirk del:
  1. Advances in production engineering & management

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