Optimization of cutting parameters during grinding of flat glass with statistical approach

In this research, three different grinding wheels are used and compared in terms of surface roughness in different cutting speeds and feed rates with a constant depth of cut value. The microstructure analysis of the grinding tools is performed with a scanning electron microscope (SEM) and an energy dispersive X-ray analysis device (EDX). All the tests are carried out on the production line in a flat glass plant. ANOVA (Analysis of Variance) is used for the analysis of relationship between the surface quality and the grinding parameters. According to the uncertainty of the grinding process, the statistical methods are being used for estimating the performance of cutting parameters and optimizing them. The machining parameters are optimized considering response surface methodology (RSM). Finally, the probabilistic uncertainty analysis is performed by using Monte Carlo simulation as a function of feed rate and wheel speed. By the optimization, the most suitable grinding parameters are determined for the surface roughness value desired by the customers.