| Title: | A comparative study of machine learning regression models for production systems condition monitoring |
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| Authors: | ID Jankovič, Denis (Author)
ID Šimic, Marko (Author)
ID Herakovič, Niko (Author) |
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| Files: |  APEM19-1_078-092.pdf (2,02 MB)
MD5: E848735C56CF09D1BC2DE40C1CE70CBC
 https://apem-journal.org/Archives/2024/Abstract-APEM19-1_078-092.html
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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: | This research investigates the benefits of different Machine Learning (ML) approaches in production systems, with respect to the given use case of considering the forming process and different friction conditions on hydraulic press response in between the phases of the sheet metal bending cycle, i.e. bending, levelling and movement. A framework for enhancing production systems with ML facilitates the transition to smarter processes and enables fast, accurate predictions integrated into decision-making and adaptive control. Comparative ML analysis provides insights into predictive regression models for hydraulic press condition recognition, enhancing process improvement. Our results are supported by performance evaluation metrics of predictive accuracy RMSE, MAE, MSE and R2 for Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Neural Network (NN) models. Given the remarkable predictive accuracy of the regression models with R2 values between 0.9483 and 0.9995, it is noteworthy that less complex models exhibit significantly shorter training times, up to 437 times shorter than more complex models. In addition, simpler models have up to 36 times better prediction rates, compared to more complex models. The fundamentals illustrate the trade-offs between model complexity, accuracy and computational training and prediction rate. |
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| Keywords: | hydraulic presses, metal forming, machine learning, linear regression, decision trees, support vector machines, gaussian process regression, artificial neural networks |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 01.02.2024 |
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| Article acceptance date: | 23.04.2024 |
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| Publication date: | 29.04.2024 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 78–92 |
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| Numbering: | Vol. 19, no. 1 |
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| PID: | 20.500.12556/DKUM-96755  |
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| UDC: | 621.7:004.85 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 201874947 |
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| DOI: | 10.14743/apem2024.1.494 |
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| Publication date in DKUM: | 27.01.2026 |
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| Views: | 135 |
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| Downloads: | 3 |
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| Metadata: |    |
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| Categories: | Misc.
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