This paper presents the use of vision-based methods for cutting tool motion registration and modal analysis. Motion of three illustrative tools were recorded using low- and high-speed cameras with sufficiently high resolutions. The tool’s own features are used to register motion. Pixels within images from recordings of the vibrating tools are treated as non-contact motion sensors. Comparative analysis of three different methods of motion registration are presented to evaluate their suitability for the application of interest. These include variants of expanded edge detection and tracking schemes, expanded optical flow-based schemes, and established digital image correlation methods. Performance of different methods was observed to be governed by the tool’s own features, illumination conditions, noise, and the image acquisition parameters. Extracted motion was benchmarked against twice integrated measured tool point accelerations, and motion was generally observed to compare well. Modal parameters extracted from vision-based measurements were also observed to agree with those extracted using more traditional experimental modal analysis procedures using a contact type accelerometer as the transducer. Since methods presented are generalized, they can suitably be adapted for other applications of interest.

本文介绍了基于视觉的方法在刀具运动配准和模态分析中的应用。使用具有足够高分辨率的低速和高速相机记录了三个说明性工具的运动。该工具自身的功能用于记录运动。振动工具录制的图像中的像素被视为非接触式运动传感器。提出了三种不同的运动配准方法的比较分析，以评估它们对感兴趣的应用的适用性。这些包括扩展边缘检测和跟踪方案，基于光流的扩展方案和已建立的数字图像相关方法的变体。观察到不同方法的性能受工具自身功能，照明条件，噪声和图像采集参数的支配。提取的运动是针对两次积分的测量工具点加速度进行基准测试的，通常观察到运动可以很好地进行比较。还观察到从基于视觉的测量中提取的模态参数与使用更传统的实验模态分析程序（使用接触式加速度计作为传感器）提取的模态参数一致。由于给出的方法是通用的，因此它们可以适当地适用于其他感兴趣的应用。