基于神经网络与遗传算法的刀具磨损检测与控制

秦国华; 谢文斌; 王华敏

doi:10.3788/OPE.20152305.1314

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基于神经网络与遗传算法的刀具磨损检测与控制

微纳技术与精密机械 | 更新时间：2020-08-13

- 基于神经网络与遗传算法的刀具磨损检测与控制
- Detection and control for tool wear based on neural network and genetic algorithm
- 光学精密工程 2015年23卷第5期页码：1314-1321
- 作者机构：
  
  南昌航空大学无损检测教育部重点实验室,江西南昌,330063
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51165039,No.51465045)();江西省自然科学基金资助项目(No.20142BAB206018)();无损检测技术教育部
- DOI：10.3788/OPE.20152305.1314
  中图分类号： TG711
- 收稿日期：2014-11-12，
  
  修回日期：2014-12-20，
  
  纸质出版日期：2015-05-25
- 稿件说明：
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秦国华, 谢文斌, 王华敏. 基于神经网络与遗传算法的刀具磨损检测与控制[J]. 光学精密工程, 2015,23(5): 1314-1321

QIN Guo-hua, XIE Wen-bin, WANG Hua-min. Detection and control for tool wear based on neural network and genetic algorithm[J]. Editorial Office of Optics and Precision Engineering, 2015,23(5): 1314-1321
秦国华, 谢文斌, 王华敏. 基于神经网络与遗传算法的刀具磨损检测与控制[J]. 光学精密工程, 2015,23(5): 1314-1321 DOI： 10.3788/OPE.20152305.1314.

QIN Guo-hua, XIE Wen-bin, WANG Hua-min. Detection and control for tool wear based on neural network and genetic algorithm[J]. Editorial Office of Optics and Precision Engineering, 2015,23(5): 1314-1321 DOI： 10.3788/OPE.20152305.1314.

摘要

针对切削参数对刀具磨损状况和使用寿命的影响

研究了基于神经网络和遗传算法的刀具磨损检测与控制方法。采用多因素正交试验设计方法进行了马氏体不锈钢平面的铣削实验

通过万能工具显微镜测量后刀面的磨损量得到训练样本。借助BP神经网络的非线性映射能力

通过有限的训练样本建立了关于切削速度、每齿进给量、背吃刀量和切削时间的刀具磨损预测模型。实验显示该神经网络预测模型的预测误差不超过5.4%。最后构建了使刀具磨损量为最小的切削参数优化模型

根据每一代的刀具磨损量定义个体的适应度评价函数

提出了切削参数优化模型的遗传算法求解技术。与Taguchi法相比

基于遗传算法的优化方法所获得的最优切削参数减小了6.734%的刀具磨损量。实验显示:提出的刀具磨损检测与控制技术提高了刀具磨损量的计算效率与精度

并为切削参数的合理选择提供了基础理论。

Abstract

For the influence of machining parameters on tool wear and tool life

a detection and control technology for the tool wear based on neural network and genetic algorithm was explored. The orthogonal experimental design method was used to carry out the plane-milling experiment of the martensitic stainless steel and a universal tool microscope was adopted to measure the tool flank wear to obtain training samples. And then

with the nonlinear mapping of BP neural network

the finite training samples were employed to formulate the prediction model of the tool wear for cutting speeds

feed per tooth

the depth of cut

and cutting time. Experimental results show that the prediction error of the proposed neural network model is no more than 5.4%. Finally

the optimal model of machining parameters was established with the objective of minimizing the tool wear. According to the wear of each generation tool parameter

the evaluation function was defined for the fitness of the individual and the genetic algorithm was skillfully developed to solve the optimal model of tool wear. In comparison with the Taguchi method

the optimal machining parameters obtained by the genetic algorithm based optimal model decrease the tool wear by 6.734%. The proposed method not only improves the calculation efficiency and precision

but also provides a basic theory for the selection of machining parameters.

关键词

Keywords

references

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