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Surface roughness model and parametric optimization in finish turning using coated carbide insert: Response surface methodology and Taguchi approach

机译:使用涂层硬质合金刀片精加工的表面粗糙度模型和参数优化:响应表面方法和田口方法

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This paper presents the experimental study, development of mathematical model and parametric optimization for surface roughness in turning D2 steel using TiN coated carbide insert using Taguchi parameter design and response surface methodology. The experimental plan and analysis was based on the Taguchi L27 orthogonal array taking cutting speed (v), feed (f) and depth of cut (d) as important cutting parameters. The influence of the machining parameters on the surface finish has also been investigated and the optimum cutting condition for minimizing the surface roughness is evaluated. The optimal parametric combination for TiN coated cutting insert is found to be v3-f1-d3. The ANOVA result shows that feed the most significant process parameter on surface roughness followed by depth of cut. The cutting speed is found to be insignificant from the study. The RSM model shows good accuracy between predicted values and experimental values with 95% confidence intervals and adequate. It is concluded that the developed RSM model can be effectively utilized to predict the surface roughness in turning D2 steel.
机译:本文介绍了使用Taguchi参数设计和响应表面方法对使用TiN涂层硬质合金刀片的D2车削D2钢进行表面粗糙度的实验研究,数学模型的开发和参数优化。实验计划和分析基于Taguchi L27正交阵列,将切削速度(v),进给量(f)和切削深度(d)作为重要切削参数。还研究了加工参数对表面光洁度的影响,并评估了使表面粗糙度最小的最佳切削条件。发现TiN涂层切削刀片的最佳参数组合为v3-f1-d3。方差分析结果表明,进给最重要的工艺参数是表面粗糙度,然后是切削深度。这项研究发现切削速度微不足道。 RSM模型显示出预测值和实验值之间的良好准确性,并具有95%的置信区间。结论是,所开发的RSM模型可以有效地用于预测车削D2钢的表面粗糙度。

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