首页> 外文会议>CIRP Conference on Modelling of Machining Operations >Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm
【24h】

Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm

机译:用遗传算法耦合遗传算法,使加工参数的预测建模与加工参数优化,以最小化表面粗糙度

获取原文

摘要

This paper develops a predictive and optimization model by coupling the two artificial intelligence approaches-artificial neural network and genetic algorithm-as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters.
机译:本文通过耦合两个人工智能方法 - 人工神经网络和遗传算法 - 作为预测最小表面粗糙度的加工参数的最佳值的替代方法,开发预测和优化模型。本研究中提到了真实加工实验,检查所提出的模型的预测和优化表面粗糙度的能力。所提出的模型预测的结果表明了预测值与实验值之间的良好一致性。本研究的分析证明了所提出的方法能够确定最佳加工参数。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号