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首页> 外文期刊>Arabian Journal for Science and Engineering >Response Surface Methodology Integrated with Desirability Function and Genetic Algorithm Approach for the Optimization of CNC Machining Parameters
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Response Surface Methodology Integrated with Desirability Function and Genetic Algorithm Approach for the Optimization of CNC Machining Parameters

机译：结合期望函数和遗传算法的响应面法优化数控加工参数

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摘要

In this study, response surface method (RSM), desirability function (DF) and genetic algorithm (GA) techniques were integrated to estimate optimal machining parameters that lead to minimum surface roughness value of beech (Fagus orientalis Lipsky) species. Design of experiment was used to determine the effect of computer numerical control machining parameters such as spindle speed, feed rate, tool radius and depth of cut on arithmetic average roughness (). Average surface roughness values of the samples were measured by employing a stylus type equipment. The second-order mathematical model was developed by using response surface methodology with experimental design results. Optimum machining condition for minimizing the surface roughness was carried out in three stages. Firstly, the DF was used to optimize the mathematical model. Secondly, the results obtained from the desirability function were selected as the initial point for the GA. Finally, the optimum parameter values were obtained by using genetic algorithm. Experimental results showed that the proposed approach presented an efficient methodology for minimizing the surface roughness.
机译：在这项研究中，综合了响应面法（RSM），合意函数（DF）和遗传算法（GA）技术来估计最佳加工参数，从而使山毛榉（Fagus Orientalis Lipsky）物种的表面粗糙度值最小。实验设计用于确定计算机数控加工参数（如主轴速度，进给速度，刀具半径和切削深度）对算术平均粗糙度的影响。通过使用触针式设备测量样品的平均表面粗糙度值。利用响应面法和实验设计结果建立了二阶数学模型。为了使表面粗糙度最小化的最佳加工条件分三个阶段进行。首先，DF用于优化数学模型。其次，选择从期望函数获得的结果作为遗传算法的起点。最后，利用遗传算法获得了最优参数值。实验结果表明，所提出的方法提出了一种有效的方法来最小化表面粗糙度。

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