Optimization and Prediction of Cutting Parameters in the End Milling Process for Cast Aluminium B4C Based Composite

Abstract

End milling process is a very common and important machining process not only due to its ease of machining but also due to the availability of various cutter profiles and curved surfaces. This research work investigates the effect of various process parameters, such as rotational speed of the cutting tool, feed rate, depth of cut on the machined surface of the composite, experimentally. The composite material is synthesized by using the stir casting process with reinforcement of B₄C particulate into Al5083 aluminium alloy. The Taguchi design of experiments is used to calculate the optimum process parameters for machining with minimum variability. In this study, RSM (Response surface methodology) based equation is applied to Teaching-Learning-Based Optimization (TLBO) algorithm to optimize the process parameters. The mathematical model is developed with a confidence level of 95% with a prediction error of less than ±5%. The efficiency and effectiveness of the TLBO algorithm has been observed with the help of convergence graph of the value outcome from experiment. The optimized results obtained from TLBO are almost nearer to the average results of 10 runs.