论文标题
基于人工智能的工具磨损和缺陷预测，用于使用低成本加速传感器改造的特殊用途铣削机器
Artificial Intelligence based tool wear and defect prediction for special purpose milling machinery using low-cost acceleration sensor retrofits
论文作者
论文摘要
铣床构成了许多工业加工链的组成部分。结果，近年来已经提出了几种基于机器学习的方法检测方法，但是这些方法主要涉及标准的铣床，而设计用于更专业任务的机械迄今为止仅获得了有限的关注。本文展示了加速传感器的应用，以便对这种特殊用途机器（即圆形接缝铣床）进行方便的状态监视。我们检查了各种条件，包括刀片磨损和刀片断裂，以及不当的机器安装或变速箱皮带张力不足。此外，我们还提供了不同的培训数据来识别监督失败识别的方法。因此，除了理论见解之外，我们的分析具有很高的实际意义，因为使用加速传感器改造较旧的机器，并且在边缘分类设置中以低成本和精力却以低成本和精力提供了对机器和工具的状态以及一般生产过程的宝贵见解。
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.
