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Title:Enhancing manufacturing precision: Leveraging motor currents data of computer numerical control machines for geometrical accuracy prediction through machine learning
Authors:ID Berus, Lucijano (Author)
ID Hernavs, Jernej (Author)
ID Potočnik, David (Author)
ID Šket, Kristijan (Author)
ID Ficko, Mirko (Author)
Files:.pdf sensors-25-00169_(1).pdf (4,44 MB)
MD5: D88C43CFCA902089A3CB8B7A0CB45F50
 
URL https://www.mdpi.com/1424-8220/25/1/169
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract:Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards
Keywords:smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy
Publication status:Published
Publication version:Version of Record
Submitted for review:11.12.2024
Article acceptance date:16.12.2024
Publication date:31.12.2024
Publisher:MDPI
Year of publishing:2024
Number of pages:19 str.
Numbering:Vol. 25, iss. 1, [article no.] 169
PID:20.500.12556/DKUM-91985 New window
UDC:658.5:004.6
ISSN on article:1424-8220
COBISS.SI-ID:225665795 New window
DOI:10.3390/s25010169 New window
Publication date in DKUM:10.03.2025
Views:0
Downloads:6
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
BERUS, Lucijano, HERNAVS, Jernej, POTOČNIK, David, ŠKET, Kristijan and FICKO, Mirko, 2024, Enhancing manufacturing precision: Leveraging motor currents data of computer numerical control machines for geometrical accuracy prediction through machine learning. Sensors [online]. 2024. Vol. 25, no. 1,  169. [Accessed 23 April 2025]. DOI 10.3390/s25010169. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=91985
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Record is a part of a journal

Title:Sensors
Shortened title:Sensors
Publisher:MDPI
ISSN:1424-8220
COBISS.SI-ID:10176278 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0157-2020
Name:Tehnološki sistemi za pametno proizvodnjo
Funder:ARIS - Slovenian Research and Innovation Agency
Project number:L2-3167-2021
Name:Kognitivna geometrijska kontrola mehansko obdelanih odkovkov na osnovi množičnih podatkov iz obdelovalnega procesa

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:pametni proizvodni stroji, podatkovno vodena proizvodnja, algoritmi strojnega učenja, podatki CNC krmilnika, geometrijska natančnost


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