基于人工智能的使用低成本加速度传感器改造的专用铣削机械的刀具磨损和缺陷预测
Artificial Intelligence based tool wear and defect prediction for special purpose milling machinery using low-cost acceleration sensor retrofits
摘要：
铣床是许多工业加工链的组成部分。因此，近年来提出了几种基于机器学习的刀具磨损检测方法，但这些方法大多针对标准铣床，而专为更专业任务设计的机械迄今为止只获得了有限的关注。本文演示了加速度传感器的应用，以方便这样一个特殊用途的机器，即圆缝铣床的状态监测。我们检查各种情况，包括刀片磨损和刀片断裂，以及不正确的机器安装或传动带张力不足。此外，我们提出了不同的方法来监督故障识别有限的训练数据量。因此，除了理论见解外，我们的分析具有很高的实际重要性，因为用加速度传感器和边缘分类设置改造旧机器的成本和工作量很低，但对机器和工具的状态以及一般的生产过程提供了有价值的见解。
英文摘要：
Milling machines form an integral part of many industrial processing chains. As a consequence, several machine learning based approaches for tool wear detection have been proposed in recent years, yet these methods mostly deal with standard milling machines, while machinery designed for more specialized tasks has gained only limited attention so far. This paper demonstrates the application of an acceleration sensor to allow for convenient condition monitoring of such a special purpose machine, i.e. round seam milling machine. We examine a variety of conditions including blade wear and blade breakage as well as improper machine mounting or insufficient transmission belt tension. In addition, we presents different approaches to supervised failure recognition with limited amounts of training data. Hence, aside theoretical insights, our analysis is of high, practical importance, since retrofitting older machines with acceleration sensors and an on-edge classification setup comes at low cost and effort, yet provides valuable insights into the state of the machine and tools in particular and the production process in general.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
