An investigation of burr formation and cutting parameter optimization in micro-drilling of brass C-360 using image processing

Abstract

A lot of research has been done in area of conventional micro-drilling but the measurement techniques for measuring burr size and holes circularity were either very expensive or inaccurate. This paper attempts to investigate role of input parameters like the spindle speed and the feed rate on burr height and burr thickness at hole exit for Brass C-360, which is a widely used material in micro-fabrication. The measurements were taken from scanning electron microscope (SEM) images of micro-drilled holes using image processing which makes the measurement simple, fast, and accurate. All the experiments were conducted using response surface methodology to develop second-order polynomial models for burr thickness and height of burr. Optimization by using multi-objective genetic algorithm and cohort intelligence algorithm using MATLAB is done to generate optimum output results. All the micro-hole SEM images were analyzed to detect the types of burrs formed during different experiments.