欢迎访问机床与液压官方网站!

咨询热线:020-32385312 32385313 RSS EMAIL-ALERT
冷滚打花键表面粗糙度神经网络预测模型建立
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金资助项目(51475146)


Establish Neural Network Predictive Model for Surface Roughness of Cold Roll-beating
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为降低冷滚打花键表面粗糙度,获得冷滚打加工最优参数组合,以滚打轮公转转速和工件进给量两个影响表面粗糙度的主要因素作为变量,设计了冷滚打花键及测量实验方案,采用白光共聚干涉显微镜测量冷滚打花键分度圆处表面粗糙度,依据实验数据通过试凑法建立了冷滚打花键表面粗糙度BP神经网络预测模型,最终确定的神经网络结构为2-6-2-1,对预测值与训练样本值及测试样本值进行了对比分析,结果表明:预测值与训练样本最大误差6.5%,与测试样本最大误差7.9%,预测值与训练样本之间的相关系数为0.996,与测试样本之间的相关系数为0.973,进一步说明了神经网络预测模型的有效性和精确性。

    Abstract:

    In order to reduce the surface roughness of cold roll-beating spline and to get the optimal parameter combination, rotating speed of roller revolution and feeding rate of workpiece two main factors affecting surface roughness as variables, the cold roll-beating spline and experimental project are designed. Surface roughness of cold roll-beating spline's pitch circle was measured through white light copolymerization interference microscope. The Back Propagation (BP) neural network prediction model for surface roughness of cold roll-beating spline was established based on the experimental data through trial and error method. The optimal neural network structure 2-6-2-1 was determined. The predicted values and the training samples and testing samples were contrasted and analyzed. The results show that the maximum error between the predicted values and the training sample is 6.5% and the maximum error between the predicted values and the training sample is 7.9%. The correlation coefficient between the predicted values and the training samples is 0.996 and the correlation coefficient between the predicted values and the testing samples is 0.973. The validity and accuracy of neural network prediction model are further illustrated.

    参考文献
    相似文献
    引证文献
引用本文

王晓强,刘佳,卜敏,韩坤鹏.冷滚打花键表面粗糙度神经网络预测模型建立[J].机床与液压,2017,45(17):99-104.
. Establish Neural Network Predictive Model for Surface Roughness of Cold Roll-beating[J]. Machine Tool & Hydraulics,2017,45(17):99-104

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2018-03-13
  • 出版日期: 2017-09-15