一种基于AI的Chip类元件AOI自动复判方法
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1.东南大学生物科学与医学工程学院 南京 210096;2.明锐理想科技有限公司 深圳 518000

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TP23;TP391

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An AI-based AOI automatic reassessment method for Chip components
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1. School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; 2. Magic Ray Technology Co., Ltd, Shenzhen 518000, China

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    摘要:

    贴片元件AOI自动复判可提高检测的直通率和精度,降低人工目检带来的时间与经济成本。Chip元件作为PCB电路贴片元件中的最常用元件,其错误类型繁多,包括焊接的少锡、多锡、缺焊、水平与垂直偏移,以及本体的缺件、错件等问题。针对Chip元件,本文提出了一种基于AI的AOI复判检测方法,包括了本体与焊盘的定位、本体颜色或丝印的检测和元件焊接检测等三个步骤,每个步骤根据需求不同设计了相应的网络模型,并对上述模型分别进行了训练和测试。测试结果表明,本体与焊盘检测网络在IOU阈值为0.7,置信度阈值0.6的条件下,mAP为0.93,检测准确率94.94%;颜色判断网络平均准确率为99.41%;丝印字符检测网络检测准确率为97.6%;焊接问题检测网络准确率为92.27%。最后,跟据收集到233幅NG数据以及3万幅OK数据对整体流程进行测试,在保证233幅NG数据不出现漏判的前提下,该方法整体的误判率为11.2%,平均每幅运行耗时97.5ms。目前,该方法已在生产线上进行了试用,初步结果表明,该方法能够有效降低生产线上元件的误报率,降低人工成本,具有较好的应用前景。

    Abstract:

    SMD components AOI automatic re-judgment can improve the detection of straight-through rate and accuracy, reduce the time and economic costs of manual visual inspection. Chip components as the most commonly used PCB circuit SMD components, its error types are numerous, including solder insufficient, more tin, open solder, horizontal and vertical offset, and the body of the missing parts, wrong parts and other issues. For Chip components, this paper proposes an AI-based AOI re-judgment detection method, including the body and solder positioning, body color or silk screen detection and component soldering detection, each step according to the different needs of the design of the corresponding network model, and the above models were trained and tested. The test results show that the body and solder detection network has a mAP of 0.93 and a detection accuracy of 94.94% under the condition that the IOU threshold is 0.7 and the confidence threshold is 0.6; the average accuracy of the color judgment network is 99.41%; the detection accuracy of the screen-print character detection network is 97.6%; and the accuracy of the welding problem detection network is 92.27%. Finally, the overall process was tested according to the 233 NG data collected and 30,000 OK data, and the overall false alarm rate of the method was 11.2%, with an average run time of 97.5ms per frame, under the premise of ensuring that 233 NG data were not missed. currently, the method has been tried on the production line, and the preliminary results show that the method can effectively reduce the component false alarm rate and reduce the labor cost, which has a good application prospect.

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王瑞丰,魏嘉莉,周静,冀运景,罗守华.一种基于AI的Chip类元件AOI自动复判方法[J].电子测量技术,2021,44(15):114-121

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  • 在线发布日期: 2024-08-26
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