Analyzing the effect of parameter setting on surface roughness of turning process - Alias, Mohd Fairuz (2022)
Turning on a high carbon martensite-coated carbide insertion of 440C grade stainless steel was the subject of this experiment (UNS S44004). Following trials and investigations, the major goal was to verify that the Reaction Surface Methodology was used to assess the impacts of machining parameters, such as cutting speed, feed, and cutting depth, on machine material surface roughness. The goal is to discover the best machining parameters for the specified tools and workpieces in the chosen experimental area to reduce surface roughness. The tests were carried out using a Response Surface with Historical Data Design experimental matrix. The Surtronic S-100 from Taylor Hubson was used to evaluate surface roughness. The data to be obtained will be compiled and entered Design Expert 6 for analysis. The relationships between machining parameters and response variables (surface roughness) were modelled and analyzed using the Response Surface Methodology (RSM). Analysis of Variance (ANOVA) was used to investigate the importance of this parameter on the responding variable, and to determine the regression equation for the responding variable with the machining parameter as the independent variable, using the aid of a quadratic model. The main effects and interaction plots from the ANOVA were obtained and studied along with the contours and 3-D surface plots. The results would be expected to show that feed was the most significant factor influencing surface roughness, followed closely by cutting speed and cutting depth, while the only important factor influencing tool wear was found to be cutting depth. The information gathered will be collated and analyzed. The Response Surface Methodology was used to construct and analyses the interactions between machining parameters and response variables (surface roughness). With the help of a quadratic model, analysis of variance (ANOVA) was performed to determine the impact of this parameter on the responding variable, as well as the regression equation for the responding variable with the machining parameter as the independent variable. Along with the contours and 3-D surface plots, the main effects and interaction plots from the ANOVA were collected and evaluated. The findings should reveal that feed was the most important factor impacting surface roughness, followed by cutting speed and cutting depth. (Bertinetto et al., 2020).