Sensors | |
Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force | |
Agne Paulauskaite-Taraseviciene1  Laura Kizauskiene2  Darius Eidukynas3  Vytautas Ostasevicius3  Ieva Paleviciute3  Vytautas Jurenas3  | |
[1] Department of Applied Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania;Department of Computer Sciences, Kaunas University of Technology, 51368 Kaunas, Lithuania;Institute of Mechatronics, Kaunas University of Technology, 51424 Kaunas, Lithuania; | |
关键词: incremental sheet forming; failure prevention; friction force; robotized manufacturing; prediction model; | |
DOI : 10.3390/s22010018 | |
来源: DOAJ |
【 摘 要 】
This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.
【 授权许可】
Unknown