Machining process influence on the chip form and surface roughness by neuro-fuzzy technique

Abstract

The main aim of the study was to analyze the influence of six machining parameters on the chip shape formation and surface roughness as well during turning of Steel 30CrNiMo8. Three components of cutting forces were used as inputs together with cutting speed, feed rate, and depth of cut. It is crucial for the engineers to use optimal machining parameters to get the best results or to high control of the machining process. Therefore, there is need to find the machining parameters for the optimal procedure of the machining process. Adaptive neuro-fuzzy inference system (ANFIS) was used to estimate the inputs influence on the chip shape formation and surface roughness. According to the results, the cutting force in direction of the depth of cut has the highest influence on the chip form. The testing error for the cutting force in direction of the depth of cut has testing error 0.2562. This cutting force determines the depth of cut. According to the results, the depth of cut has the highest influence on the surface roughness. Also the depth of cut has the highest influence on the surface roughness. The testing error for the cutting force in direction of the depth of cut has testing error 5.2753. Generally the depth of cut and the cutting force which provides the depth of cut are the most dominant factors for chip forms and surface roughness. Any small changes in depth of cut or in cutting force which provide the depth of cut could drastically affect the chip form or surface roughness of the working material.