Mao Yihao, Tu Jun, Wang Huizhen, Zhou Yangfan, Wu Qiao, Zhang Xu, Song Xiaochun
School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.
Modern Manufacturing Quality Engineering Hubei Key Laboratory, Wuhan 430068, China.
Sensors (Basel). 2025 May 4;25(9):2907. doi: 10.3390/s25092907.
To address the challenges of automatic detection caused by the variation of surface normal vectors in automotive steering knuckles, an automatic detection method based on ultrasonic phased array technology is herein proposed. First, a point cloud model of the workpiece was constructed using ultrasonic distance measurement, and Gaussian-weighted principal component analysis was used to estimate the normal vectors of the point cloud. By utilizing the normal vectors, water layer thickness during detection, and the incident angle of the sound beam, the probe pose information corresponding to the detection point was precisely calculated, ensuring the stability of the sound beam incident angle during the detection process. At the same time, in the trajectory planning process, piecewise cubic Hermite interpolation was used to optimize the detection trajectory, ensuring continuity during probe movement. Finally, an automatic detection system was set up to test a steering knuckle specimen with surface circumferential cracks. The results show that the point cloud data of the steering knuckle specimen, obtained using phased array ultrasound, had a relative measurement error controlled within 1.4%, and the error between the calculated probe angle and the theoretical angle did not exceed 0.5°. The probe trajectory derived from these data effectively improved the B-scan image quality during the automatic detection of the steering knuckle and increased the defect signal amplitude by 5.6 dB, demonstrating the effectiveness of this method in the automatic detection of automotive steering knuckles.
为解决汽车转向节表面法向矢量变化导致的自动检测难题，本文提出一种基于超声相控阵技术的自动检测方法。首先，利用超声测距构建工件的点云模型，并采用高斯加权主成分分析估计点云的法向矢量。通过利用法向矢量、检测过程中的水层厚度和声束入射角，精确计算出与检测点对应的探头姿态信息，确保检测过程中声束入射角的稳定性。同时，在轨迹规划过程中，采用分段三次埃尔米特插值优化检测轨迹，确保探头移动过程中的连续性。最后，搭建自动检测系统对带有表面周向裂纹的转向节试件进行检测。结果表明，使用相控阵超声获取的转向节试件点云数据相对测量误差控制在1.4%以内，计算得到的探头角度与理论角度之间的误差不超过0.5°。基于这些数据得出的探头轨迹有效提高了转向节自动检测过程中的B扫描图像质量，缺陷信号幅值提高了5.6 dB，证明了该方法在汽车转向节自动检测中的有效性。
