A comparative study of machine learning regression models for production systems condition monitoring

Jankovič, Denis (avtor), Šimic, Marko (avtor), Herakovič, Niko (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: hydraulic presses, metal forming, machine learning, linear regression, decision trees, support vector machines, gaussian process regression, artificial neural networks
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: Str. 78–92
• Številčenje: Vol. 19, nr. 1
• PID: 20.500.12556/RUL-159626
• UDK: 621.7:004.85
• ISSN pri članku: 1854-6250
• DOI: 10.14743/apem2024.1.494
• COBISS.SI-ID: 201874947
• Datum objave v RUL: 16.07.2024

JANKOVIČ, Denis, ŠIMIC, Marko in HERAKOVIČ, Niko, 2024, A comparative study of machine learning regression models for production systems condition monitoring. Advances in production engineering & management [na spletu]. 2024. Vol. 19, no. 1. [Dostopano 5 maj 2025]. DOI 10.14743/apem2024.1.494. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=159626

Gradivo je del revije

• Naslov: Advances in production engineering & management
• Skrajšan naslov: Adv. produc. engineer. manag.
• Založnik: Univerza v Mariboru, Fakulteta za strojništvo, Katedra za proizvodno strojništvo
• ISSN: 1854-6250
• COBISS.SI-ID: 229859072

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: hidravlične stiskalnice, preoblikovanje kovin, strojno učenje, linearna regresija, odločitvena drevesa, podporni vektorski stroji, regresija Gaussovega procesa, umetne nevronske mreže