Predictive modelling and analysis of surface roughness in CNC milling of green alumina using response surface method and genetic algorithm

Abstract

Modelling and optimization of machining parameters are essential in Computer Numerical Control (CNC) milling process. The objective of current study is to develop a functional relationship between various factors and responses of CNC machined alumina green ceramic compact. As, ceramic material is notch sensitive in nature, the measurement of average surface roughness (Ra) is vital as it influences the quality and performance of the finished product. In this context, optimization of surface roughness is of maximum importance in manufacturing sectors. To accomplish the required optimal levels of surface quality, the proper selection of machining parameters in CNC milling is highly needed. In this study, four significant machining parameters including spindle speed, XY speed, Z speed and depth of cut in CNC milling process have been selected and along with various combination experiments were conducted. A mathematical regression model was developed to predict the average surface roughness in CNC milling machined surface of alumina based green ceramic compact. The developed model was validated with the new experimental data. Further, the model was coupled with Genetic Algorithm (GA) technique, to predict the optimum possible surface roughness. The results demonstrate the potential to improve the efficacy of production and quality of the finished product as well.