Design of an Adaptive Fuzzy Logic 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. This should be achieved through shorter production cycle times with reduced operator intervention and increased exibility. In order to accomplish this, the trend is towards increased use of machine intelligence in machining processes. Grinding process is usually em- ployed to machine harder materials, or, as a nishing process. A fast, accurate and e cient grinding process contributes greatly to the productivity in a production setup. In the current work, a theoretical model was developed and used to predict the vi- brations resulting from the grinding process. A controller based on adaptive neural fuzzy inference system (ANFIS), was developed for the cylindrical grinding machine- tool. The main aim of this study was to optimize the grinding process by adaptively controlling the speed of the grinding wheel based on the infeed and the speed of the workpiece. This would help in prevention of excessive vibrations that would a ect the machining process, resulting in poorly nished surfaces and degraded grinding wheel. Also, experimental work was carried out to validate the model. From this study, it was demonstrated that, ANFIS based controller controlled vibra- tions during grinding through in-process adjustment of speed of the grinding wheel so that there would be minimal vibrations. The proposed controller was tested ex- perimentally and was seen to be e ective in reducing the machining vibrations by as much as 90 percent.