• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >Predicting tool life in turning operations using neural networks and image processing
【24h】

Predicting tool life in turning operations using neural networks and image processing

机译：使用神经网络和图像处理预测车削操作中的刀具寿命

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

A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, V_B, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.
机译：提出了一种两步法来自动预测车削操作中的刀具寿命。首先，在相同的恒定加工条件下收集三个切削刃的实验数据。在这些实验中，使用常规方法测量刀具磨损参数V_B，并使用Neural Wear（一个结合了侧面磨损图像识别和人工神经网络（ANN）的定制软件包）估算相同的参数。其次，使用从前两个切削刃收集的数据训练刀具寿命的ANN模型，然后在第三个切削刃的两个不同子集上评估后续模型：第一个子集是直接测量刀具磨损和第二个是从神经磨损软件获得的，该软件使用边缘图像估算工具磨损。尽管采用了全自动解决方案，用于刀具磨损识别的神经磨损软件加上刀具寿命预测的ANN模型，其误差比直接测量的误差略高，但误差在相同范围内，可以满足所有工业要求。这些结果证实，图像识别软件和ANN建模的组合可以潜在地发展成为一种有用的工业工具，以低成本估算车削操作中的工具寿命。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2018年第may1期|503-513|共11页
  • 作者

    T. Mikolajczyk; K. Nowicki; A. Bustillo; D. Yu Pimenov;

  • 作者单位

    Department of Production Engineering, UTP University of Science and Technology, Al. prof. S. Kaliskiego 7, Bydgoszcz 85-796, Poland;

    Department of Computer Methods, UTP University of Science and Technology, Al. prof. S. Kaliskiego 7, Bydgoszcz 85-796, Poland;

    Department of Civil Engineering, University of Burgos, Avda Cantabria s, Burgos, 09006, Spain;

    Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080, Russia;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Tool life prediction; Image analysis; Tool wear; Neural networks;

    机译：刀具寿命预测;图像分析;工具磨损;神经网络;

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