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Techniques for tool wear condition monitoring in drilling operations.

机译：钻井作业中刀具磨损状态监控技术。

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摘要

Monitoring of tool wear condition for the drilling process, which is one of the major cutting processes, is a very important economical consideration in order to prevent tool failures, increase machine utilization and decrease production costs in an automated manufacturing environment. New techniques are proposed in this study for on-line identification of tool wear status based on cutting force and power signal measurements during drilling.; Hidden Markov Models (HMM), which are very popular in speech recognition, are adopted for tool condition monitoring using two approaches in this study. The first is the bargraph monitoring of the HMM probabilities that shows the progress of tool wear during operation and the second is the classification of the sensor signals that correspond the various types of wear status, e.g. sharp, workable and dull, using the multiple modeling method.; Several other innovative techniques, namely the phase plane method, the transient time for the torque signals, parameter estimation of a mechanical model of torque and statistical analysis of all the sensor signals are also discussed.; Finally, a new algorithm, Decision Fusion Center Algorithm (DFCA), is proposed which combines the output of the methods developed in this work to produce a global decision variable that shows the tool wear status during the drilling operation. Experiment results demonstrate the effectiveness of the proposed methods and the DFCA algorithm.; Although this work focuses on on-line tool wear condition monitoring for drilling operations, the concepts introduced are general. Process and machine monitoring based on HMM and the decision algorithm (DFCA) developed in this thesis can be applied to many other cutting and machining operations.
机译：为了防止工具故障，提高机器利用率并降低自动化生产环境中的生产成本，监视钻削过程中的刀具磨损状况是一项非常重要的经济考虑，钻削过程是主要的切削过程之一，在经济上应如此。在这项研究中提出了新技术，用于根据钻削过程中的切削力和功率信号测量值在线识别刀具磨损状态。在语音识别中非常流行的隐马尔可夫模型（ HMM ）被用于工具状态监测，该方法采用两种方法。第一个是HMM概率的条形图监视，它显示了操作期间工具磨损的进度，第二个是对应于各种磨损状态（例如，磨损状态）的传感器信号的分类。使用多种建模方法，锋利，可行且无聊。还讨论了其他几种创新技术，即相平面法，扭矩信号的瞬态时间，扭矩机械模型的参数估计以及所有传感器信号的统计分析。最后，提出了一种新算法，即决策融合中心算法（ DFCA ），该算法结合了本工作中开发的方法的输出，以生成一个全局决策变量，该变量显示了钻孔过程中的工具磨损状态。实验结果证明了所提方法和DFCA算法的有效性。尽管这项工作的重点是用于钻井作业的在线工具磨损状态监控，但是引入的概念是通用的。本文开发的基于HMM和决策算法（DFCA）的过程和机器监控可应用于许多其他切割和加工操作。

著录项

  • 作者

    Ertunc, Huseyin Metin.;

  • 作者单位

    Case Western Reserve University.;

  • 授予单位 Case Western Reserve University.;
  • 学科 Engineering System Science.
  • 学位 Ph.D.
  • 年度 1999
  • 页码 174 p.
  • 总页数 174
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 系统科学;
  • 关键词

  • 入库时间 2022-08-17 11:48:05

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