Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks.

中文翻译：

---

使用卷积神经网络和转移学习的定性工具状态监测框架

刀具状态监控是制造过程中的经典问题之一，尚待解决，该解决方案可以在世界各地的机械车间中实施。在工具状态监视中，我们主要尝试定义工具更改策略。该工具更改策略将识别出产生不合格零件的工具。当识别出不合格零件生产工具时，可以对其进行更改，并且可以采取主动的加工质量方法来节省在不合格零件上投资的资源。现有研究突出了在实现工具状态监视解决方案以在世界各地的机械车间中自主，独立地执行工具更换决策之前需要解决的三个障碍。第一，这些系统不够灵活，无法满足机械车间的不同质量要求。现有研究仅考虑一个质量方面（例如，表面光洁度），很难将其概括为不同的质量要求，例如同心度或机加工车间常见的边缘毛刺。其次，研究试图量化工具的状况，而重要的问题是工具产生的是合格零件还是不合格零件。第三，对工具是生产合格零件还是不合格零件的定性答案需要大量数据来训练预测模型。拟议的模型使用计算机视觉，卷积神经网络（CNN），