Modeling and Optimization of Grinding of Ceramics using Hybrid Artificial Neural Network and Genetic Algorithm Approach
In modern age, the technology is continually becoming more advance rapidly. The new materials are required in many technological fields. In manufacturing many engineering components, there is requirement of materials which has high strength at high temperature, good resistance to chemical degradation, good resistance to wear and having low density. Advance structural ceramics such as silicon carbide, silicon nitride etc. fulfill these requirements. These materials are used in many engineering applications to make various components. There are certain other parameters which are essentially required such as good surface finish and low surface damage. Grinding with superabrasive is one of the method to acquire good surface finish considering the factors such as grinding forces and material removal rate.
In the present work ceramic is chosen to perform various experiments and variables were grit size, depth of cut and feed using CBN grinding wheel to study the effect of control factors on grinding forces. Artificial neural network is used to develop the model for grinding force. Further, optimization has been done to achieve minimum grinding force using genetic algorithm.
Keywords - Artificial Neural Network, Ceramic grinding, Genetic Algorithm, Surface roughness, Grinding force.