Integrated genetic programming and genetic algorithm approach to predict surface roughness
Integrated genetic programming and genetic algorithm approach to predict surface roughness
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DOI：
10.1081/amp-120022023
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发表时间：
2003-01-01
影响因子：
4.8
通讯作者：
Kovacic, M
中科院分区：
文献类型：
--
作者：
Brezocnik, M;Kovacic, M
在本文中，我们提出了一种新的综合遗传规划和遗传算法的方法来预测端面铣削的表面粗糙度。测量了主轴转速、进给速度、切削深度和振动四个独立变量。这些变量影响因变量（即表面粗糙度）。在训练数据集的基础上，采用遗传规划方法建立了不同的表面粗糙度模型。采用遗传算法对最佳模型的浮点常数进行了优化。在测试数据集上验证了模型的准确性。采用该方法对表面粗糙度进行预测，比仅采用遗传规划方法进行建模更准确。结果表明，表面粗糙度受进给速度的影响最大，而振动提高了预测精度。
In this article we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimized by a genetic algorithm. Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modeling by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.