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Using the Machine Vision Method to Develop an On-machine Insert Condition Monitoring System for Computer Numerical Control Turning Machine Tools

机译：用机器视觉方法开发计算机数控车床机床的插入状态监测系统

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This study uses the machine vision method to develop an on-machine turning tool insert condition monitoring system for tool condition monitoring in the cutting processes of computer numerical control (CNC) machines. The system can identify four external turning tool insert conditions, namely fracture, built-up edge (BUE), chipping, and flank wear. This study also designs a visual inspection system for the tip of an insert using the surrounding light source and fill-light, which can be mounted on the turning machine tool, to overcome the environmental effect on the captured insert image for subsequent image processing. During image capture, the intensity of the light source changes to ensure that the test insert has appropriate surface and tip features. This study implements outer profile construction, insert status region capture, insert wear region judgment, and calculation to monitor and classify insert conditions. The insert image is then trimmed according to the vertical flank, horizontal blade, and vertical blade lines. The image of the insert-wear region is captured to monitor flank or chipping wear using grayscale value histogram. The amount of wear is calculated using the wear region image as the evaluation index to judge normal wear or over-wear conditions. On-machine insert condition monitoring is tested to confirm that the proposed system can judge insert fracture, BUE, chipping, and wear. The results demonstrate that the standard deviation of the chipping and amount of wear accounts for 0.67% and 0.62%, of the average value, respectively, thus confirming the stability of system operation.
机译：这项研究使用机器视觉方法开发了一种机上车刀插入状态监视系统，用于监视计算机数控（CNC）机器切削过程中的刀具状态。该系统可以识别四个外部车刀插入条件，即断裂，积屑瘤（BUE），崩刃和后刀面磨损。这项研究还使用周围的光源和补光设计了用于刀片尖端的视觉检查系统，可以将其安装在车床机床上，以克服环境对捕获的刀片图像的影响，以进行后续图像处理。在图像捕获期间，光源的强度会发生变化，以确保测试插入物具有适当的表面和尖端特征。这项研究实现了外部轮廓构造，插入状态区域捕获，插入磨损区域判断以及用于监视和分类插入条件的计算。然后根据垂直侧面，水平刀片和垂直刀片线修剪插入图像。捕获磨损区域的图像，以使用灰度值直方图监视后刀面或碎屑磨损。使用磨损区域图像作为评估指标来计算磨损量，以判断正常磨损或过度磨损状况。测试了机上刀片状态监测，以确认所建议的系统可以判断刀片断裂，BUE，崩裂和磨损。结果表明，崩刃的标准偏差和磨损量分别占平均值的0.67％和0.62％，从而确认了系统运行的稳定性。

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