The journal Sensors (MEiN 100 pts) has published a new article co-authored by employees of the Complex Systems Department: Andrzej Paszkiewicz, Marek Bolanowski and Mateusz Salach.
Paszkiewicz, A.; Piecuch, G.; Żabiński, T.; Bolanowski, M.; Salach, M.; Rączka, D. Estimation of Tool Life in the Milling Process—Testing Regression Models. Sensors 2023, 23, 9346. https://doi.org/10.3390/s23239346
The article is entitled "Estimation of Tool Life in the Milling Process-Testing Regression Models" and was written in collaboration with the staff of the Department of Complex Systems and the Department of Computer and Control Engineering of the Rzeszów University of Technology as part of the project "Regional Initiative of Excellence".
Abstract:
The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R2 parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used.
Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools.
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