Magnetic abrasive finishing (MAF) process is one of non-traditional or advanced finishing methods which is suitable for different materials and produces high quality level of surface finish where it uses magnetic force as a machining pressure. A set of experimental tests was planned according to Taguchi orthogonal array (OA) L27 (36) with three levels and six input parameters. Experimental estimation and optimization of input parameters for MAF process for stainless steel type 316 plate work piece, six input parameters including amplitude of tooth pole, and number of cycle between teeth, current, cutting speed, working gap, and finishing time, were performed by design of experiment (DOE) and response surface methodology (RSM).These six input parameters in this research were optimized for all input parameters to improve the surface layer for work piece by using signal-to-noise ratio technique. The obtained results showed that all six input parameters have an influence on the change in surface roughness(∆Ra). In addition, the results showed that the surface roughness of the work piece decreased from 1.130 to 0.370µm that means high level of improvement in the change of surface roughness (0.760)µm. Keywords: MAF process, MINITAB software, parameters, Signal-to-Noise ratio, surface roughness, Taguchi orthogonal array.
Chemical Mechanical Polishing (CMP) is the polishing process where the top surface of a wafer is smoothed using a slurry containing abrasive grit as well as reactive chemical agents. The polishing process is partly mechanical and partly chemical. The mechanical element's main advantage is that it is achieved without great effort to manufacture and supplies good-quality general mechanical and electrical properties. In the current study, the invention reckons on the chemical and mechanical properties of the composition particles (abrasive slurry) utilized to polish silicon surfaces traveling through chemical-mechanical polishing (CMP). MINITAB 17 software was used to estimate the influence of the (CMP) input variables on the surface roughness (Ra) of the silicon workpiece. Other process input variables were disk speed (rpm), the dose of abrasive, the grain size of the abrasive, and the type of slurry. In order to get the best response surface roughness, the current findings show that the constant coefficient of determination (R2) is 95.80%. Furthermore, the effects of disk speed (X1), abrasive dose (X2), abrasive grain size (X3), and type of slurry (X4) on achieving a superior surface roughness finish were 21.05%, 4.34%, 50.00%, and 24.59%, respectively.
Metal matrix composites (MMCs) have been increasingly used in industries, nuclear plant, and automobiles due to their superior properties compared to other alloys, and that is owing to the tough and abrasive hard reinforced particles. It's complicated to machine hard materials by traditional processes methods, therefore the present study focused on the investigation of parameters in electrochemical machining (ECM) like electrolyte concentration (EC), voltage(V), and Inter-electrode gap (IEG) on the radial over cut (ROC) and material removal rate (MRR) in the ECM of Al-7.5%B4C. Stir casting method was used to fabricate metal matrix composites. Based on Taguchi design, the process parameters were optimized. A Multiple Regression Model (MRM) was employed as model for radial over cut and material removal rate. The mathematical model was examined using analysis of variance (ANOVA). The EC 10 g/L, V 10 v, and IEG 0.3 mm are the optimal parametric combination for ROC. Also, the EC 30 g/L, V 18 V, and IEG 0.2 mm are the optimal parametric combination for MRR.
In this paper, Response Surface Method (RSM) is utilized to carry out an investigation of the impact of input parameters: electrode type (E.T.) [Gr, Cu and CuW], pulse duration of current (Ip), pulse duration on time (Ton), and pulse duration off time (Toff) on the surface finish in EDM operation. To approximate and concentrate the suggested second- order regression model is generally accepted for Surface Roughness Ra, a Central Composite Design (CCD) is utilized for evaluating the model constant coefficients of the input parameters on Surface Roughness (Ra). Examinations were performed on AISI D2 tool steel. The important coefficients are gotten by achieving successfully an Analysis of Variance (ANOVA) at the 5 % confidence interval. The outcomes discover that Surface Roughness (Ra) is much more impacted by E.T., Ton, Toff, Ip and little of their interactions action or influence. To predict the average Surface Roughness (Ra), a mathematical regression model was developed. Furthermore, for saving in time, the created model could be utilized for the choice of the high levels in the EDM procedure. The model adequacy was extremely agreeable as the constant Coefficient of Determination (R2) is observed to be 99.72% and adjusted R2-measurement (R2adj) 99.60%.
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