Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

Materials

Tomasz Trzepieciński¹  Marcin Szpunar²  Ľuboš Kaščák³ 

[1] Department of Materials Forming and Processing, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, Poland;Doctoral School of Engineering and Technical Sciences at the Rzeszow University of Technology, Rzeszow University of Technology, al. Powst. Warszawy 12, 35-959 Rzeszów, Poland;Faculty of Mechanical Engineering, Institute of Technology and Material Engineering, Technical University of Košice, Mäsiarska 74, 040 01 Košice, Slovakia;

关键词: coefficient of friction;    friction;    sheet metal forming;    strip drawing test;   

DOI  :  10.3390/ma14102570

来源: DOAJ

【 摘 要 】

This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: the quasi-Newton, Levenberg-Marquardt and back propagation. The values of root mean square (RMS) error and determination coefficient were adopted as evaluation criteria for ANNs. The minimum value of the RMS error for the training set (RMS = 0.0982) and the validation set (RMS = 0.1493) with the highest value of correlation coefficient (R² = 0.91) was observed for a multi-layer network with eight neurons in the hidden layer trained using the quasi-Newton algorithm. As a result of the non-linear relationship between clamping and friction force, the value of the COF decreased with increasing load. The regression model F-value of 22.13 implies that the model with R² = 0.6975 is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.

【 授权许可】

Unknown