Experimental Study and Optimization of lathe Machining Parameters

Abstract

In modern manufacturing, optimizing machining parameters is crucial for enhancing productivity, reducing costs, and improving surface quality. Turning, a widely used metal cutting process, often suffers from inefficiencies due to poor parameter selection, leading to increased tool wear and energy use. This study investigated how machining variables like cutting speed, feed rate, depth of cut, and spindle speed affect the Material Removal Rate (MRR), aiming to determine the optimal combination for maximum efficiency. Central Composite Design (CCD) of Response Surface Methodology (RSM) was used to model and optimize the machining process. Thirty experimental runs were conducted on a conventional lathe, varying the selected parameters within practical limits. MRR was calculated for each run, and regression modeling was applied to develop a predictive quadratic model. Analysis of Variance (ANOVA) confirmed the statistical significance of the model and highlighted the contribution of each parameter. Findings showed that feed rate and depth of cut had the most significant influence on MRR, followed by cutting speed, with spindle speed having a moderate effect. The optimal machining conditions were identified as: cutting speed of 170.89 m/min, feed rate of 0.49 mm/rev, depth of cut of 1.14 mm, and spindle speed of 751.13 rpm, achieving a maximum MRR of 2.46%. The developed model can serve as a predictive tool to set machining parameters, reducing guesswork and improving efficiency. Future research could extend this approach to include other factors such as surface roughness, tool wear, for comprehensive process optimization.