• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of productivity and quality management >Multi-objective optimisation of machining parameters in wire electrical discharge machine using non-dominating sorting genetic algorithm
【24h】

Multi-objective optimisation of machining parameters in wire electrical discharge machine using non-dominating sorting genetic algorithm

机译：基于非支配排序遗传算法的电火花线切割机加工参数多目标优化

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The present work is aimed at optimising the cutting rate, surface roughness and dimensional deviation of EN-31 steel considering the simultaneous effect of various input parameters such as pulse on time, pulse off time, wire tension, spark gap set voltage and servo feed. Response surface methodology (RSM) is adopted to study the effect of independent variables on responses and develop predictive models. It is desired to obtain optimal parameter setting that can decrease surface roughness and dimensional deviation while increasing cutting rate. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying all the objectives in one solution. Therefore, it is essential to explore the optimisation landscape to generate the set of dominant solutions. Non-sorted genetic algorithm (NSGA) has been adopted to optimise the responses such that a set of mutually dominant solutions can be found out.
机译：考虑到各种输入参数（例如脉冲接通时间，脉冲断开时间，线张力，火花隙设定电压和伺服进给）的同时影响，当前的工作旨在优化EN-31钢的切削速度，表面粗糙度和尺寸偏差。采用响应面方法（RSM）来研究自变量对响应的影响并建立预测模型。期望获得最佳的参数设置，该参数设置可以减小表面粗糙度和尺寸偏差，同时提高切削速率。由于响应本质上是冲突的，因此很难在一个解决方案中获得满足所有目标的切削参数的单个组合。因此，探索优化环境以生成主导解决方案集至关重要。已采用非分类遗传算法（NSGA）来优化响应，以便可以找到一组相互主导的解决方案。

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