Papers by Keyword: Micro Drilling

Abstract: Rapid growths in the electronic industries provide opportunities for the industries to expand the using of silicon wafer in modern and advanced technologies especially Micro Electromechanical Systems (MEMS). Laser cutting is one of the most significant and important application for fabrication MEMS components. The objective of this study is to investigate the cutting capability of industrial laser, CO₂ laser, in silicon wafer processing. Experiment was conducted in two conditions which is with assisted of pyrex glass and without assist of pyrex glass. In this work, silicon wafer with thickness 525 μm were cut into several rectangular sample shapes. The surface roughness produce at the cutting edge on each sample were investigated and analyzed. Analysis of Variance (ANOVA) was used to analyze the result and generated an appropriate model for the laser cutting processing. The laser parameters involved were laser power, cutting speed, and pulse frequency. Experimental results evident that, the laser cutting process with assist of glass give a better surface roughness on silicon cutting edge compared to without assist of glass.

Abstract: The acceptability of polymer matrix composites for various industrial applications has been increased over the years due to their better mechanical properties. However, nonconductive nature and fibrous residuals produced during micro drilling of these materials has become major challenge for the research fraternity. The conventional machining of these materials causes high tool wear due to presence of abrasive particles. Recently, the electrochemical discharge machining (ECDM) process has emerged as potential contender for the machining of nonconductive hard and brittle materials. Keeping in mind all these challenges, the present research work focuses on micro drilling of PMCs using ECDM process. The experimentation was planned as per Taguchi’s methodology using L₉ orthogonal array. Voltage, electrolyte concentration and duty factor were considered as process parameters whereas material removal rate and taper were observed as output quality characteristics. The regression equation and coefficients were obtained using regression analysis. Using this regression equations, further solutions were obtained by genetic algorithm.