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Optimization of machining parameters in a turning operation of austenitic stainless steel to minimize surface roughness and tool wear

机译:优化奥氏体不锈钢车削加工中的加工参数,以最小化表面粗糙度和刀具磨损

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摘要

The present work concerned an experimental study of turning on Austenitic Stainless steel of grade AISI 202 by a TiAlN coated carbide insert tool. The primary objective of the ensuing study was to use the Response Surface Methodology in order to determine the effect of machining parameters viz. cutting speed, feed, and depth of cut, on the surface roughness of the machined material and the wear of the tool. The objective was to find the optimum machining parameters so as to minimize the surface roughness and tool wear for the selected tool and work materials in the chosen domain of the experiment. The experiment was conducted in an experiment matrix of 20 runs designed using a full-factorial Central Composite Design (CCD). Surface Roughness was measured using a Talysurf and tool wear with the help of a Toolmaker’s microscope. The data was compiled into MINITAB ® 17 for analysis. The relationship between the machining parameters and the response variables (surface roughness and tool wear) were modelled and analysed using the Response Surface Methodology (RSM). Analysis of Variance (ANOVA) was used to investigate the significance of these parameters on the response variables, and to determine a regression equation for the response variables with the machining parameters as the independent variables, with the help of a quadratic model. Main effects and interaction plots from the ANOVA were obtained and studied along with contour and 3-D surface plots. The quadratic models were found to be significant with a p-value of 0.033 and 0.049. Results showed that feed is the most significant factor affecting the surface roughness, closely followed by cutting speed and depth of cut, while the only significant factor affecting the tool wear was found to be the depth of cut. The top three optimum settings for carrying out the machining were obtained from Response Surface Optimizer and are shown in the results section.
机译:目前的工作涉及通过TiAlN涂层硬质合金刀片对AISI 202级奥氏体不锈钢进行车削的实验研究。接下来的研究的主要目的是使用响应表面方法来确定加工参数的影响。切削速度,进给量和切削深度,取决于加工材料的表面粗糙度和刀具的磨损。目的是找到最佳的加工参数,以使所选工具和工作材料在实验的所选范围内的表面粗糙度和工具磨损最小化。该实验在使用全要素中央复合设计(CCD)设计的20个实验的实验矩阵中进行。使用Talysurf和工具制造商的显微镜在工具磨损下测量表面粗糙度。数据被编译成MINITAB®17进行分析。使用响应曲面方法(RSM)对加工参数与响应变量(表面粗糙度和工具磨损)之间的关系进行建模和分析。方差分析(ANOVA)用于研究这些参数对响应变量的重要性,并借助二次模型确定以加工参数为自变量的响应变量的回归方程。从方差分析中获得了主要效果和相互作用图,并与等高线图和3-D表面图一起进行了研究。发现二次模型是有意义的,p值为0.033和0.049。结果表明进给是影响表面粗糙度的最重要因素,紧随其后的是切削速度和切削深度,而影响刀具磨损的唯一重要因素是切削深度。可从Response Surface Optimizer获得进行加工的前三个最佳设置,并显示在结果部分。

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    Agrawalla Y;

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