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首页> 外文期刊>Periodica polytechnica >Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and?Desirability Approach
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Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and?Desirability Approach

机译:使用RSM和WRSM和αsi304奥氏体不锈钢AISI304在加工过程中的建模与优化

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In the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The?cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The?effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc?=?350?m/min, f?=?0.088?mm/rev, and ap?=?0.9?mm).
机译:在本发明的纸张中,使用响应表面方法(RSM)和期望方法研究和优化了AISI 304奥氏体不锈钢转动过程中的切割参数。在该工作中使用的切削工具插入物是CVD涂层碳化物。 θ切割速度(Vc),进料速率(F)和切割深度(AP)是本研究中考虑的主要加工参数。通过ANOVA分析分析这些参数对表面粗糙度(Ra),切割力(Fc),特定切割力(Kc),切割功率(PC)和材料去除率(MRR)的影响。结果表明F是影响RA的最重要参数,贡献为89.69%,而AP被鉴定为最显着的参数(46.46%)影响FC后跟F(39.04%)。 KC受F(38.47%)的影响,然后是AP(16.43%)和VC(7.89%)。然而,PC受VC(39.32%)的影响,然后是AP(27.50%)和F(23.18%)。由RSM获得的二次数学模型,呈现RA,FC,KC和PC的演变(提出了VC,F和AP)。实验和预测值之间的比较与所发现的模型呈现出良好的协议。通过期望函数进行加工参数的加工参数,以实现最大MRR和更好的RA。结果表明,最大MRR和最佳RA的最佳参数被发现为(VC?= 350?350?M / min,F?= 0.088?mm / Rev,AP?=?0.9?mm)。

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