Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2019 Feb 12;19(3):744. doi: 10.3390/s19030744.
Periodic health checks of contouring errors under unloaded conditions are critical for machine performance evaluation and value-added manufacturing. Aiming at breaking the dimension, range and speed measurement limitations of the existing devices, a cost-effective knowledge-driven approach for detecting error motions of arbitrary paths using a single camera is proposed. In combination with the PNP algorithm, the three-dimensional (3D) evaluation of large-scale contouring error in relatively high feed rate conditions can be deduced from a priori geometrical knowledge. The innovations of this paper focus on improving the accuracy, efficiency and ability of the vision measurement. Firstly, a camera calibration method considering distortion partition of the depth-of-field (DOF) is presented to give an accurate description of the distortion behavior in the entire photography domain. Then, to maximize the utilization of the decimal involved in the feature encoding, new high-efficient encoding markers are designed on a cooperative target to characterize motion information of the machine. Accordingly, in the image processing, markers are automatically identified and located by the proposed decoding method based on finding the optimal start bit. Finally, with the selected imaging parameters and the precalibrated position of each marker, the 3D measurement of large-scale contouring error under relatively high dynamic conditions can be realized by comparing the curve that is measured by PNP algorithm with the nominal one. Both detection and verification experiments are conducted for two types of paths (i.e., planar and spatial trajectory), and experimental results validate the measurement accuracy and advantages of the proposed method.
定期检查非承载条件下的轮廓误差对于机器性能评估和增值制造至关重要。针对现有设备在尺寸、范围和速度测量方面的局限性，提出了一种经济有效的基于知识的方法，使用单个相机检测任意路径的误差运动。结合 PNP 算法，可以从先验几何知识中推导出在较高进给速度条件下对大规模轮廓误差的三维（3D）评估。本文的创新点主要在于提高视觉测量的准确性、效率和能力。首先，提出了一种考虑景深（DOF）失真分区的相机标定方法，以准确描述整个摄影域的失真行为。然后，为了最大限度地利用特征编码中涉及的十进制数，在协作目标上设计了新的高效编码标记来表征机器的运动信息。相应地，在图像处理中，通过基于找到最佳起始位的提议解码方法自动识别和定位标记。最后，通过比较 PNP 算法测量的曲线与标称曲线，利用选定的成像参数和每个标记的预校准位置，可实现相对较高动态条件下的大规模轮廓误差的 3D 测量。针对两种类型的路径（即平面和空间轨迹）进行了检测和验证实验，实验结果验证了所提出方法的测量精度和优势。
