Faculty of Mechanical Engineering, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia.
Sensors (Basel). 2021 Mar 9;21(5):1917. doi: 10.3390/s21051917.
This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which-unlike common cameras-captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator's knowledge and competence.
本文提出了一种用于切削刀具状况监测的控制系统，该系统可在车削过程中自动识别刀具磨损。我们使用红外摄像机进行过程控制，与普通摄像机不同，它不仅可以捕捉到过程的视觉状态，还可以捕捉到热成像状态。尽管存在挑战性的环境条件（例如，热切屑），我们还是保护了摄像机并将其放置在刀具旁边，以便近距离观察加工情况。在实验中，对（低碳合金钢 1.7225 或 42CrMo4）的干式加工设置了恒定的切削条件。为了构建超过 9000 张图像的数据集，我们在车床上使用具有不同磨损程度的刀具刀片进行加工。我们使用卷积神经网络（CNN）为刀具磨损和刀具损坏预测开发了一个模型。它根据热成像过程数据自动确定刀具的状态（无磨损、低磨损、中磨损、高磨损）。分类的准确率为 99.55%，这证实了所提出方法的充分性。无论操作人员的知识和能力如何，此类系统都能在刀具磨损或损坏的情况下立即采取行动。
