高精度锻造磨球的不良圆度和飞边视觉检测研究
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1.广州先进技术研究所;2.粤港澳人机智能协同系统联合实验室;3.安徽铜冠智能科技有限责任公司

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High-Precision Visual Inspection of Defective Roundness and Burrs in Forged Grinding Balls
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1.Guangzhou Institute of advanced technology;2.China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences;3.Anhui Tongguan Intelligent Technology Co., Ltd.;4.Guangzhou Institute of Advamced Technology

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    摘要:

    锻造生产的耐磨钢球经常出线圆度不良和飞边缺陷,严重影响它的碾磨性能,为了解决这一问题,本文提出了一种针对高温耐磨球的在线视觉检测方法,首先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,以此量化表示圆度,完成对不良圆度磨球的筛选。对于飞边的检测我们采用了深度学习策略,按一定规则有效地标识飞边,以区分背景区域的复杂纹理,使模型有效地训练。另外,采用数字滤波成像方式拍摄处于高温状态的磨球,能有效去除热辐射噪声,获得清晰的磨球图像,采用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。

    Abstract:

    The forged wear-resistant steel balls in production often exhibit poor roundness and burrs, which significantly affect their grinding performance. To solve this problem, an industrial vision inspection method and system is proposed. Roundness of the ball is calculated by the maximum difference between the distances from the ball''s center to its contour. For the default of burr detection, a deep learning detection model is employed. Certain rules to distinguish burrs from the complex textures of the background regions are regulated, which enables the model to be trained effectively. Through analysis of burr features, it is found that burrs often appear as protrusions at the contours and exhibit stripe patterns in terms of brightness and slope. Additionally, capturing images of the high-temperature steel balls using digital filtering imaging effectively removes thermal radiation noise and obtains clear ball images. These images are applied to the YOLOv5 instance segmentation model, resulting in a burr detection rate of 95.3%.

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引用本文

王卫军,徐川,黄晨,等.高精度锻造磨球的不良圆度和飞边视觉检测研究 [J].集成技术,

Citing format
Wang Weijun, Xu Chuan, Huang Chen, et al. High-Precision Visual Inspection of Defective Roundness and Burrs in Forged Grinding Balls[J]. Journal of Integration Technology.

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  • 收稿日期:2023-07-27
  • 最后修改日期:2023-07-27
  • 录用日期:2024-01-30
  • 在线发布日期: 2024-01-30
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