Fuzzy logic and regression modelling of cutting parameters in drilling using vegetable based cutting fluids

Abstract

In machining industry, cutting fluids have an important role due to their lubrication, cooling and chip removal functions. Using of cutting fluids can improve machining process efficiency, tool life, surface quality and it can reduce cutting forces and vibrations. However, health and environmental problems are encountered with the use of cutting fluids. Therefore, there has been a high demand for using environmentally friendly cutting fluids such as vegetable based cutting fluids in order to diminish these harmful effects. For this purpose, two different vegetable based cutting fluids are used to improve machining performance. This study focuses on prediction of thrust force and surface roughness with vegetable based cutting fluids (VBCFs) using fuzzy logic and regression. The drilling experiments are conducted according to Taguchi's L9 orthogonal array. Thrust force and surface roughness during drilling of AISI 304 with HSS-E tool are measured. Spindle speed, feed rate and drilling depth are considered as machining parameters. A model depended on fuzzy logic approach is established and the results obtained from fuzzy logic are compared with the results based on regression and experiment. Effectiveness of fuzzy logic and regression models has been determined by analyzing correlation coefficient and by comparing experimental results. Fuzzy logic models give closer values to experimental measured values than regression models. It has been concluded that fuzzy rule based modelling can be used to predict the thrust force and surface roughness successfully.