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Contribution of Factors such as Machining Parameters, MQL Nozzle Orientation (Angle & Distance) and MQL Nano-Fluid Type on Surface Finish of Turned Steel Work-Pieces Using DOE Approach

机译:使用DOE方法的转向钢工件的表面光洁度(角度和距离)和MQL纳米流体类型等因素的贡献

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摘要

Study of input factors play a vital role in controlling of process responses such as surface finish, cutting temperature, energy consumption etc. in machining process. Design of Experiment (DOE) is one such tool used by researchers to identify the key factors and levels and optimize the process. An attempt was made to identify and experiment turning of AISI 4340 steel using 6 factors viz. cutting speed, feed rate, depth of cut, MQL nozzle orientations (distance from the cutting tool-chip interface, nozzle angle) and different cutting fluid (Coolant). The response variable selected for study was surface roughness of the work-piece which needed to fit criteria smaller-the-better. L_(25) Orthogonal Array-OA design was selected for 6 factors and 5 levels. Comparison of results of average responses of different levels of factors, analysis of variance (ANOVA) of the process is detailed. Experimental results showed that the key contributors in the turning process are due to cutting speed, feed and depth of cut covering from 12% to 40%. The major contributor to the process was the cutting speed. Selection of MQL fluids and nozzle orientation contributed to 10% showing least significance. This experiment helps us to understand the importance of machine cutting conditions as key success factors which can be assisted with MQL fluids and other input factors.
机译:对输入因素的研究在控制加工过程中的过程响应时发挥了至关重要的作用。实验设计(DOE)是研究人员使用的一种工具,以确定关键因素和水平并优化该过程。尝试使用6因素Ziz来识别和实验AISI 4340钢的转向。切割速度,进料速率,切割深度,MQL喷嘴取向(距切割工具芯片界面,喷嘴角度)和不同切削液(冷却剂)。选择用于研究的响应变量是工件的表面粗糙度,需要拟合规范更好的标准。 L_(25)选择正交阵列-OA设计6个因素和5级。详细说明了不同因素的平均响应结果的比较,详细介绍了该过程的方差分析(ANOVA)。实验结果表明,转弯过程中的关键贡献者是由于切割速度,饲料和深度从12%到40%。该过程的主要贡献者是切割速度。选择MQL液体和喷嘴取向导致10%表示最小意义。该实验有助于我们了解机器切割条件作为关键成功因素的重要性,可以协助MQL流体和其他输入因素。

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