Design of an Adaptive Controller for Cylindrical Plunge Grinding Process

Abstract

In modern competitive manufacturing industry, machining processes are expected to deliver products with high accuracy and good surface integrity. Cylindrical plunge grinding process, which is a final operation in precision machining, suffers from occurrence of chatter vibrations which limits the ability of the grinding process to achieve the desired surface finish. Further, such vibrations lead to rapid tool wear, noise and frequent machine tool breakages, which increase the production costs. There is therefore a need to increase the control of the machining processes to achieve shorter production cycle times, reduced operator intervention and increased flexibility. In this paper, an Adaptive Neural Fuzzy Inference System (ANFIS) based controller for optimization of the cylindrical grinding process is developed. The proposed controller was tested through experiments and it was seen to be effective in reducing the machining vibration amplitudes from a 10^-1 µm to a 10^-2 µm range.