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结合神经网络与遗传算法的磨抛工艺参数优化

槐创锋 黄涛 贾雪艳

槐创锋, 黄涛, 贾雪艳. 结合神经网络与遗传算法的磨抛工艺参数优化[J]. 机械科学与技术, 2021, 40(7): 1025-1030. doi: 10.13433/j.cnki.1003-8728.20200190
引用本文: 槐创锋, 黄涛, 贾雪艳. 结合神经网络与遗传算法的磨抛工艺参数优化[J]. 机械科学与技术, 2021, 40(7): 1025-1030. doi: 10.13433/j.cnki.1003-8728.20200190
HUAI Chuangfeng, HUANG Tao, JIA Xueyan. Optimization of Processing Parameters in Grinding and Polishing Coupling Neural Networks with Genetic Algorithms[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(7): 1025-1030. doi: 10.13433/j.cnki.1003-8728.20200190
Citation: HUAI Chuangfeng, HUANG Tao, JIA Xueyan. Optimization of Processing Parameters in Grinding and Polishing Coupling Neural Networks with Genetic Algorithms[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(7): 1025-1030. doi: 10.13433/j.cnki.1003-8728.20200190

结合神经网络与遗传算法的磨抛工艺参数优化

doi: 10.13433/j.cnki.1003-8728.20200190
基金项目: 

高铁车体无尘干磨系统设计项目 2003618305

详细信息
    作者简介:

    槐创锋(1981-), 教授, 硕士生导师, 研究方向为机器人研究与应用、机械设计, hcf811225@163.com

  • 中图分类号: TH162

Optimization of Processing Parameters in Grinding and Polishing Coupling Neural Networks with Genetic Algorithms

  • 摘要: 针对机器人磨抛系统工艺参数的自主选择与优化问题, 提出一种基于神经网络与遗传算法的磨抛工艺参数优化方法, 采用基于人工神经网络的工件表面粗糙度预测模型解决各工艺参数间复杂的非线性问题, 结合粗糙度预测模型与磨抛效率公式, 通过遗传算法对各工艺参数进行全局寻优解决加工质量和效率的双目标优化问题并得到最优工艺参数组合。在满足加工质量要求的前提下, 加工效率提高了近三分之一, 证明此工艺参数优化方法是可行有效的。
  • 图  1  端面磨具磨抛过程示意图

    图  2  磨具转速对材料去除率的影响

    图  3  进给速度对材料去除率的影响

    图  4  神经网络模型结构图

    图  5  工件表面粗糙度预测值与实际值对比图

    图  6  磨抛工艺参数优化流程图

    图  7  工件表面粗糙度与进化代数的关系

    图  8  磨抛效率与进化代数的关系

    表  1  磨抛工艺参数表

    方法 步骤 G/# P/kPa ns/(r·min-1) va/(mm·min-1) N/次
    未优化 1 150 20 1 000 120 6
    2 180 20 1 000 120 4
    ANN-GA 1 150 15 1 000 180 7
    下载: 导出CSV

    表  2  磨抛工艺参数表

    方法 磨抛后Ra 预测Ra ΔRa/μm 总时间/s
    未优化 0.768 0.665 0.103 171
    0.192 0.176 0.016
    ANN-GA 0.216 0.195 0.021 116
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-28
  • 刊出日期:  2021-07-01

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