Design and Analysis of Elliptical Nozzle in AJM Process using Computational Fluid Dynamics

Abrasive jet machining (AJM) is a micromachining process, where material is removed from the work piece by the erosion effect of a high speed stream of abrasive particles carried in a gas medium, which are emerging from a nozzle. Abrasive machining includes grinding super finishing honing, lapping polishing etc. The common nozzle shape presently used in AJM machining process is rectangle and circular shape nozzle which gives a low flow rate and further demands on decreasing the material removal rate (MRR), so this research mainly focuses on designing nozzle geometry to improve flow rate and MRR in AJM machining process. The elliptical shape nozzle has been designed and analyzed using computational fluid dynamics software (CFD). CFD is the most efficient software for flow rate analysis. The result shows the improvement in flow rate about 574.2m/sec and MRR of newly designed nozzle geometry i.e elliptical shape in abrasive jet machining

Experimentation and Prediction of Temperature Rise in Turning Process using Response Surface Methodology

Reducing the temperature rise during turning operation improves the quality of the product and reduces tool wear. Experiments are conducted as per the Design of Experiments (DoE) of Response Surface Methodology (RSM) to predict the temperature rise by varying the cutting parameters such as cutting speed, feed rate and depth of cut. In the present study, the experiment was conducted on Aluminium Al 6061 by coated carbide tool. A second order mathematical model in terms of machining parameters was developed for temperature rise prediction using RSM. This model gives the factor effects of the individual process parameters. Values of Prob> F less than 0.05 indicate model terms are significant. The cutting speed is the most important parameter that cause the temperature of the turning process compared to the other factors such as feed rate and depth of cut. Validation results show good agreement between the actual process output and the predicted temperature rise

Determining the Effect of Cutting Parameters on Surface Roughness Using Genetic Algorithm

The aim of present research focuses on the prediction of machining parameters that improve the quality of surface finish. The surface  roughness is one of the important properties of work piece quality in the CNC (Computer Numerical Control) turning process. An effective approach of optimization techniques genetic algorithm (GA) and response surfacemethodology (RSM) was implemented to investigate the effect of the  cutting parameters such as cutting speed, feed rate, and depth of cut on the surface roughness. In this study, the surface roughness is measured during turning operation at different cutting parameters such as speed, feed, and depth of cut on Alumunium 6063 using coated carbide tool. Thesecond order mathematical model is developed using RSM of central  composite method to predict the surface roughness standards. The  regression equation is solved using genetic algorithm approach for   optimizing the cutting parameters for minimizing surface roughness, this study attempts the application of GA technique using Matlab 8.0 is  recommends 1.512ìm as the best minimum predicted surface roughness value for the optimal solution of the cutting conditions was 80 m/min, 0.18 mm/rev,0.3mm