高精度锻造磨球的不良圆度和飞边视觉检测研究

High-Precision Visual Inspection of Defective Roundness and Burrs inForged Grinding Balls

锻造生产的耐磨钢球经常出现圆度不良和飞边缺陷,严重影响其碾磨性能。为解决这一问题,作者提出一种针对高温耐磨球的在线视觉检测方法。通过计算图像中磨球圆心到轮廓的最大距离与最小距离之差,可量化圆度,完成对不良圆度磨球的筛选。针对飞边检测,作者利用深度学习策略,按一定规则有效识别飞边,以区分背景区域的复杂纹理,使模型有效地训练。此外,采用数字滤波成像方式拍摄处于高温状态的磨球可有效去除热辐射噪声,获得清晰的磨球图像。作者利用YOLOv5实例分割模型实现了95.3%的飞边检出率,达到了在线检测技术指标要求。

Wear-resistant steel balls produced by forging often exhibit poor roundness and flash defects,which severely impact their grinding performance.To address this issue,this paper proposes an online visual 109inspection method for high-temperature wear-resistant balls. By calculating the difference between the maximum and minimum distances from the center of the grinding ball to the contour in the image, roundness is quantitatively represented, allowing for the selection of grinding balls with poor roundness. For flash detection, this paper utilizes a deep learning strategy to effectively identify flash according to certain rules, distinguishing the complex textures of the background area and enabling effective model training. Moreover, capturing the grinding balls at high temperatures using digital filtering imaging techniques effectively removes thermal radiation noise, resulting in clear images of the grinding balls. This paper achieves a 95.3% detection rate of flash using the YOLOv5 instance segmentation model, meeting the technical requirements for online inspection.