Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks

• Published in: Lubricants Vol. 12; no. 2; p. 50
• Main Authors: Trzepieciński, Tomasz, Szwajka, Krzysztof, Szewczyk, Marek
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2024
• Subjects: Analysis; Artificial intelligence; Artificial neural networks; Blankholders; Coefficient of friction; Contact pressure; Deep drawing; Deformation; Friction; Genetic algorithms; Kinematics; Low carbon steels; Lubricants; Lubricants & lubrication; Lubrication; Lubrication and lubricants; Machine learning; Mechanical properties; Metal forming; Metal sheets; Multilayers; Neural networks; Performance prediction; Pressure effects; Process parameters; Sheet-metal; Steel products; Surface roughness; surface topography; Titanium alloys; Viscosity; Poland; Germany
• ISSN: 2075-4442; 2075-4442
• DOI: 10.3390/lubricants12020050

This article presents the results of an analysis of the influence of friction process parameters on the coefficient of friction of steel sheets 1.0347 (DC03), 1.0338 (DC04) and 1.0312 (DC05). A special tribometer was designed and manufactured in order to simulate the friction phenomenon occurring in the blankholder area in deep drawing operations. Lubricant was supplied to the contact zone under pressure. The value of the coefficient of friction was determined under various contact pressures and lubrication conditions. Multi-layer artificial neural networks (ANNs) were used to predict the value of the coefficient of friction. The input parameters considered were the kinematic viscosity of lubricants, contact pressure, lubricant pressure, selected mechanical properties and basic surface roughness parameters of sheet metals. The value of the coefficient of friction of 1.0312 steel sheets was predicted based on the results of friction tests on 1.0347 and 1.0338 steel sheets. Many ANN models were built to find a neural network that will provide the best prediction performance. It was found that to ensure a high performance of ANN prediction, it is necessary to simultaneously take into account all the considered roughness parameters (Sa, Ssk and Sku). The predictive performance of the ‘best’ network was greater than R2 = 0.98. The lubricant pressure had the greatest impact on the coefficient of friction. Increasing the value of this parameter reduces the value of the coefficient of friction. However, the greater the contact pressure, the smaller the beneficial effect of pressure-assisted lubrication. The third parameter of the friction process, the kinematic viscosity of the oil, exhibited the smallest impact on the coefficient of friction.