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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Optimal process parameters for minimizing the surface roughness in CNC lathe machining of Co28Cr6Mo medical alloy using differential evolution
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Optimal process parameters for minimizing the surface roughness in CNC lathe machining of Co28Cr6Mo medical alloy using differential evolution

机译:最佳工艺参数,以最小化CO28CR6MO医用合金CNC车床加工的表面粗糙度使用差分演进

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摘要

This study is conducted to observe the optimal effect of rotational speed, feed rate, depth of cut, and tool tip radius on the surface roughness of a material. In the machining processes, surface roughness value should be made as low as possible and is determined by the value of the optimal process parameters. Currently, the application of differential evolution (DE) optimization technique in optimizing the process parameters for achieving minimum surface roughness, especially in CNC lathe machining of Co28Cr6Mo medical alloy, is still not given any consideration by the researcher. Therefore, in this study, a new approach of CNC lathe parameters optimization using DE algorithm is introduced. At first, a regression model is developed from the actual machining data provided by Asilturk, NeAYeli, and A degrees nce [1]. The regression model of the surface roughness is formulated as a fitness function for DE algorithm. The results of this study have proven that the DE optimization technique is able to estimate the optimal process parameters that yield minimum surface roughness. The application of DE as a solution approach in process parameter optimization has significantly improve the surface roughness (Ra) where the Ra value is reduced by 81, 72, and 30% when compared to the experiments, regression modeling, and response surface methodology (RSM) respectively.
机译:该研究进行了观察旋转速度,进料速度,切割深度和刀尖半径的最佳效果在材料的表面粗糙度上。在加工过程中,表面粗糙度值应尽可能低,并且由最佳过程参数的值确定。目前,差分演化(DE)优化技术在优化实现最小表面粗糙度的过程参数中,特别是在CO28CR6MO医用合金的CNC车床加工中,仍未考虑研究人员的任何考虑。因此,在本研究中,介绍了使用DE算法的CNC车床参数优化的新方法。首先,从Asilturk,Neayeli提供的实际加工数据和NCE [1]提供回归模型。表面粗糙度的回归模型作为DE算法的适应性函数配制。该研究的结果证明,DE优化技术能够估计产生最小表面粗糙度的最佳过程参数。作为方法参数优化中的解决方案方法的应用显着改善了与实验,回归建模和响应表面方法(RSM ) 分别。

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