Optimization of Precision Grinding Parameters of Silicon for Surface Roughness Based on Taguchi Method

Abstract

Silicon being a typical hard-brittle material is difficult to machine to a good surface finish. Although ductile-mode machining (DMM) is often employed to machine this advanced material but this technique requires the use of expensive ultra-precision machine tools therefore limiting its applicability. However, by proper selection of grinding parameters, precision grinding which can be performed on conventional machine tools can be used to generate massive ductile surfaces thereby reducing the polishing time and improving the surface quality. Precision grinding should be planned with reliability in advance and the process has to be performed with high rates of reproducibility. Therefore, this study investigated the effect and optimization of grinding parameters using Taguchi optimization technique during precision grinding of silicon. Experimental studies were conducted under varying depths of cut, feed rates and spindle speeds. An orthogonal array (OA), signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) were employed to find the minimum surface roughness value and to analyze the effect of the grinding parameters on the surface roughness. Confirmation tests were carried out in order to illustrate the effectiveness of the Taguchi method. The results show that feed rate mostly affected the surface roughness. The predicted roughness (Ra) of 34 nm was in agreement with the confirmation tests. Massive ductilestreaked surface was also found corresponding to the minimal surface finish determined from the optimal levels.

Automation of the Edge Deburring Process and Analysis of the Impact of Selected Parameters on Forces and Moments Induced during the Process

“…Both indicators are considered, and experimental results have shown that the comprehensive target can be effectively used as a metric to control grinding performance and process improvement. In [29][30][31][32], the Taguchi design method was widely used to optimize the milling parameters for surface roughness. In [33], the authors presented and discussed the concept of the project of the sensor-less force control of automatic grinding and deburring through an adjustable mechanism.…”

Optimization of Grinding Parameters for the Workpiece Surface and Material Removal Rate in the Belt Grinding Process for Polishing and Deburring of 45 Steel

“…Periyasamy et al [9] found the optimum parameters for minimum surface roughness by using RSM with design expert software in the grinding process of AISI 1080 steel plates. In references [10][11][12][13], the Taguchi design method was widely used to optimize milling parameters for surface roughness. In fact, focusing on only a single goal will not meet the grinding requirements of an actual production process, since at least two objectives must be considered simultaneously.…”

Multi Objective Optimisation for High Speed End Milling Using Simulated Annealing Algorithm

“…These methods can be classified into two main parts: conventional and non-conventional methods. The conventional methods based on the mathematical methods such as linear programming, nonlinear programming, geometric programming and dynamic programming or the statistical methods such as the desirability function method [2,3] and Taguchi method [4]. The nonconventional methods usually give a near optimal solution and based on the artificial intelligence methods such as genetic algorithm [5][6], neural network [7], Particle swarm algorithm [9], ant colony algorithm [9] and simulated annealing algorithm [10,11].…”