首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Multi‑Response Optimization During Dry Turning of Bio‑implant Steel (AISI 316L) Using Coated Carbide Inserts
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Multi‑Response Optimization During Dry Turning of Bio‑implant Steel (AISI 316L) Using Coated Carbide Inserts

机译:使用涂层硬质合金插入物干式转动干式转动过程中的多响应优化

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摘要

To comply with the remarkable demand of bio-implants at a reasonable cost, selection of appropriate material, efficient manufacturing processes and associated parameters play a vital role. Particularly, benign of green manufacturing is gaining huge attention in bio-implants manufacturing to comply with environmental concerns. The present work is an effort to exhibit the viability of green manufacturing during dry turning of bio-implant steel (AISI 316L) using coated carbide cutting tool. Cutting speed, feed rate, depth of cut and tool nose radius are considered as input variables, whereas main cutting force, tool flank wear and centreline mean surface roughness are taken as output responses. Range of the input variables has been decided on the bases of pilot studies. Using the design of experiment strategy, experimentation has been carried out on a computerized numerically controlled lathe machine in a dry environment. Significant input variables affecting the responses have been identified through the analysis of variance and response surface methodology. Further, mathematical regression models have also been derived. Desirability factor-based multi-response optimization analysis has been implemented and found the optimum main cutting force: 113.6 N, flank wear: 0.14 mm and mean surface roughness: 1.27 μm, at cutting speed: 125 m/min, feed: 0.05 mm/rev., depth of cut: 0.75 mm and tool nose radius: 0.4 mm.
机译:为了以合理的成本符合生物植入物的显着需求,选择适当的材料,有效的制造过程和相关参数起到重要作用。特别是,绿色制造业的良性在生物植入物制造业中取得了巨大的关注,以遵守环境问题。本工作是使用涂层碳化物切削工具在生物植入钢(AISI 316L)的干燥转动期间表现出绿色制造的可行性。切割速度,进料速率,切割深度和刀尖半径被认为是输入变量,而主要切削力,刀具磨损和中心线平均表面粗糙度被视为输出响应。输入变量的范围已经决定了试点研究的基础。使用实验策略的设计,在干燥环境中的计算机数控车床机上进行了实验。通过对方差分析和响应面方法来确定影响响应的重要输入变量。此外,还得到了数学回归模型。已经实施了可迭到基于因子的多响应优化分析,并找到了最佳的主切割力:113.6 n,侧面磨损:0.14mm和平均表面粗糙度:1.27μm,切割速度:125米/分钟,饲料:0.05 mm / Rev。,切割深度:0.75毫米,刀尖半径:0.4毫米。

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