Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method

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• 作者：Mozammel Mia ; Nikhil Ranjan Dhar
• 关键词：Hard turning ; HPC ; Taguchi optimization ; ANOVA ; Surface roughness ; Cutting temperature ; DoE ; CDF
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：88
• 期：1-4
• 页码：739-753
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：88

文摘

In this article, the effects of material hardness and high-pressure coolant jet over dry machining are evaluated in respect of surface roughness and cutting temperature using Taguchi L₃₆ orthogonal array. The experimental data was analyzed using empirical cumulative distribution function and box plot with respect to material hardness and machining environment. Afterward, optimization of the quality responses is performed using signal-to-noise ratio. As part of Taguchi optimization, the “smaller is better” was adopted as optimization principle; the design of experiment was used for parameters orientation, and the analysis of variance was used for determining the effects of control factors. For the present experimental studies, three types of hardened steels (40 HRC, 48 HRC, and 56 HRC) were turned by coated carbide insert at industrial speed–feed combinations under both dry and high-pressure coolant jet. Depth of cut, being a less significant parameter, was kept fixed. The high-pressure coolant jet was found successful in reducing cutting temperature, surface roughness, and tool wear. The statistical analysis showed that work material hardness is the most significant factor for both cutting temperature and surface roughness. However, for surface roughness, other variables exerted somewhat similar contribution, while in determining the cutting temperature, the environment demonstrated crucial role. The confirmation tests showed 15.85 and 0.28 % error in predicting surface roughness and cutting temperature, respectively.