Optimization of Machining Parameters In A Turning Operation of Austenitic Stainless Steel To Minimize Surface Roughness And Tool Wear

Abstract

The current study used a TiAlN coated carbide insert tool to turn on austenitic stainless steel of grade AISI 202. The primary purpose of the following research was to use Response Surface Methodology to evaluate the impact of machining parameters such cutting speed, feed rate, and depth of cut on the surface roughness of the machined material and tool wear. The purpose was to find the best machining parameters for the specified tool and work materials in the experiment's chosen domain in order to reduce surface roughness and tool wear. The experiment was carried out in a 20-run experiment matrix with a full-factorial Central Composite Design (CCD) (CCD). A Talysurf was used to quantify surface roughness, and a Toolmaker's microscope was used to measure tool wear. MINITAB ® 17 was used to compile the data for analysis. To develop and analyse the link between the machining parameters and the response variables (surface roughness and tool wear), the Response Surface Methodology was employed (RSM). Analysis of Variance (ANOVA) was used to analyse the importance of these parameters on the response variables and to develop a regression equation for the response variables with the machining parameters as independent variables using a quadratic model. We obtained and evaluated ANOVA main effects and interaction graphs, as well as contour and 3-D surface plots. The quadratic models were found to be significant with p-values of 0.033 and 0.049. The findings revealed that feed had the greatest impact on surface roughness, followed by cutting speed and depth of cut, whereas depth of cut was shown to be the only significant factor affecting tool wear. The top three optimal settings for carrying out the machining were offered by Response Surface Optimizer, which are listed in the results section.