| Title: | Enhancing manufacturing precision: Leveraging motor currents data of computer numerical control machines for geometrical accuracy prediction through machine learning |
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| Authors: | ID Berus, Lucijano (Author)
ID Hernavs, Jernej (Author)
ID Potočnik, David (Author)
ID Šket, Kristijan (Author)
ID Ficko, Mirko (Author) |
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| Files: |  sensors-25-00169_(1).pdf (4,44 MB)
MD5: D88C43CFCA902089A3CB8B7A0CB45F50
 https://www.mdpi.com/1424-8220/25/1/169
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| 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 |
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| Keywords: | smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 27.11.2024 |
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| Article acceptance date: | 16.12.2024 |
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| Publication date: | 31.12.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | 19 str. |
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| Numbering: | Vol. 25, iss. 1, [article no.] 169 |
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| PID: | 20.500.12556/DKUM-91985  |
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| UDC: | 658.5:004.6 |
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| ISSN on article: | 1424-8220 |
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| COBISS.SI-ID: | 225665795 |
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| DOI: | 10.3390/s25010169 |
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| Copyright: | © 2024 by the authors |
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| Publication date in DKUM: | 10.03.2025 |
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| Views: | 148 |
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| Downloads: | 20 |
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| Metadata: |    |
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| Categories: | Misc.
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