Experimental Investigation Using Ultrasonic Micro Machining on Engineering Ceramics

Abstract

Ultrasonic machining (USM) is a non-conventional process used for machining very hard and brittle materials such as glasses, ceramics, quartz, etc. In many industries, machined component precision is crucial, and for component sub-assemblies, hole geometric accuracy is especially significant. The two most important factors that impact the machining process are the environment and the time spent in the machining zone. Therefore, to address these issues, it is important to take experimental design into consideration, which is very helpful in the machining zone. This research uses zirconia (ZrO2) and silicon nitride (Si3N4) as workpiece materials during the ultrasonic micromachining (USMM) process to develop multiple micro- holes. This process uses silicon carbide (SiC) as an abrasive particle. Using past literature reviews and some pilot experiments, five levels of each for slurry concentration (SC), feed rate (FR), and power rating (PR) have been considered as process parameters. Material removal rate (MRR), overcut (OC), and taper angle (TA) have been considered as responses during USMM on ZrO2 and silicon nitride Si3N4 for developing multiple micro-holes. Fabricating the desired tool to develop multiple micro-holes in USMM is a challenging task. The tool development has also been investigated in this research work and structural analysis has been performed using ANSYS to analyze the indentation nature of the tool and workpiece. For better magnification factor performance, the horn has been newly designed with the help of ANSYS. This research work includes experimental investigation, modelling, and optimization in the ultrasonic micromachining of zirconia and silicon nitride ceramics. Experimental results have been analyzed by using analysis of variance (ANOVA) and statistical technique (here, desirability function analysis of RSM), followed by a computational approach (here, particle swarm optimization) for experimental investigation, predictive modelling, and single response optimization, respectively. Micro-ho