Modelling of Friction Phenomena Existed in Drawbead in Sheet Metal Forming

The article presents the results of friction tests of a 0.8 mm-thick DC04 deep-drawing quality steel sheet. A special friction simulator was used in the tests, reflecting friction conditions occurring while pulling a sheet strip through a drawbead in sheet metal forming. The variable parameters in the experimental tests were as follows: surface roughness of countersamples, lubrication conditions, sample orientation in relation to the sheet rolling direction as well as the sample width and height of the drawbead. Due to many factors that affect the value of the coefficient of friction coefficient, artificial neural networks (ANNs) were used to build and analyse the friction model. Four training algorithms were used to train the ANNs: back propagation, conjugate gradients, quasi-Newton and Levenberg-Marquardt. It was found that for all analysed friction conditions and sheet strip widths, increasing the drawbead height increases the COF value. The chlorine-based Heavy Draw 1150 compound provides a more effective friction reduction compared to a LAN-46 machine oil.

Automated summary

This study analyzed the impact of different parameters on the coefficient of friction of steel sheets through friction tests, constructed a friction model using artificial neural networks, and found that increasing the drawbead height will increase the coefficient of friction. In addition, the chlorine-based Heavy Draw 1150 compound provides more effective friction reduction compared to LAN-46 machine oil.