Modelling of micro-turning process based on constant cutting force

Abstract

In this research, an evaluation of the external transverse micro-turning with conventional cutting inserts was performed with a constant cutting force in a dry environment. During machining, the number of revolutions, machining time and cutting forces was varied. Before and after machining, the diameter of the workpiece, circularity and the roughness of the machined surface was measured. The obtained results indicate that with increasing number of revolutions, time and cutting force, the cutting depth increases. The results show that this type of machining can achieve very small cutting depths and reduce circularity deviation and roughness of the machined surface. Based on the experimental results, the modelling of the artificial neural network (ANN) was performed which reliably predicted the change in diameter, cylindricity, and roughness after micro-turning operation, with a mean percentage error smaller than 3 %. It can be concluded that the application of ANN is adequate during the machining process with the constant cutting force, since the output parameters can be predicted with small error, while also reducing effort and costs.