The use of artificial neural networks to the analysis of lubricating performance of vegetable oils for plastic working applications

Sheet metal forming is the basic method of processing of deep-drawing quality steel sheets used in the automotive industry. A properly planned technological process of forming should include guidelines for friction conditions, or rather the coefficient of friction. Determination of the coefficient of friction is carried out using various methods. In this article, the strip drawing test was used to analyse the friction of low-carbon DC04 steel sheets. The tests were carried out at different contact pressures and with the use of different vegetable-oil based biolubricants. The most common edible and non-edible oils were selected for the tests: sunflower, rape-seed, moringa and karanja. The analysis of the experimental results was carried out using multilayer artificial neural networks (ANNs). Different learning algorithms and different transfer functions were considered in ANNs. Based on the analysis of experimental data, it was noticed that the coefficient of friction decreased with increasing contact pressure. The lowest values of the coefficient of friction, in the entire range of analysed pressures, were observed during lubrication with karanja oil. It was also found that Levenberg-Marquardt training algorithm with log-sigmoid transfer function provided the lowest values of performance errors and at the same time the highest value of the coefficient of determination R2 = 0.94719.