曹颖, 侯英麟, 张胜传, 丁烁锟, 窦亚萍, 李长安. 基于计算几何的曲轴圆度误差评定[J]. 制造技术与机床, 2023, (8): 174-179. DOI: 10.19287/j.mtmt.1005-2402.2023.08.025
引用本文: 曹颖, 侯英麟, 张胜传, 丁烁锟, 窦亚萍, 李长安. 基于计算几何的曲轴圆度误差评定[J]. 制造技术与机床, 2023, (8): 174-179. DOI: 10.19287/j.mtmt.1005-2402.2023.08.025
CAO Ying, HOU Yinglin, ZHANG Shengchuan, DING Shuokun, DOU Yaping, LI Changan. Evaluation of crankshaft roundness error based on computational geometry[J]. Manufacturing Technology & Machine Tool, 2023, (8): 174-179. DOI: 10.19287/j.mtmt.1005-2402.2023.08.025
Citation: CAO Ying, HOU Yinglin, ZHANG Shengchuan, DING Shuokun, DOU Yaping, LI Changan. Evaluation of crankshaft roundness error based on computational geometry[J]. Manufacturing Technology & Machine Tool, 2023, (8): 174-179. DOI: 10.19287/j.mtmt.1005-2402.2023.08.025

基于计算几何的曲轴圆度误差评定

Evaluation of crankshaft roundness error based on computational geometry

  • 摘要: 为提高测量曲轴圆度误差效率等问题,提出了一种基于凸包的曲轴圆度误差视觉测量的方法。首先,对评定系统进行相机标定与畸变矫正,通过设计的采集系统来实时获取曲轴的旋转图像。然后,通过去噪和亚像素边缘检测等方法提取边缘坐标,利用三维重构手段获取圆周离散数据点。最后,提出一种基于计算几何技术的圆度评定模型,利用文献中数据进行仿真验证;以三坐标测量机的曲轴圆度误差结果作为标准值,对文章所提出的方法进行比较验证。结果表明:机器视觉检测结果较三坐标测量机绝对误差均值为3.4 μm,证明了圆度误差视觉评定方法有较高的准确度,可应用于曲轴圆度的评定。

     

    Abstract: Aiming at the problem in improving the efficiency of crankshaft roundness error measurement, a method of visual measurement which was used in the detection of crankshaft roundness error based on convex hull was proposed. Firstly, the camera calibration and distortion correction of roundness visual evaluation system were carried out, and the designed acquisition system was used to obtain the rotating image of the crankshaft in real time. Then, the edge coordinates were extracted by noise removal and sub-pixel edge detection, and the discrete data points were obtained by three-dimensional reconstruction. Finally, a roundness evaluation model based on computational geometry technology was proposed, and the data in literature were used for simulation verification. The results of crankshaft roundness error of CMM are taken as standard values to compare and verify the proposed methods. The results show that the mean absolute error of machine vision inspection results compared with CMM is 3.39 μm. It proves that the visual evaluation method of roundness error has high accuracy, and can be applied to the evaluation of crankshaft roundness.

     

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