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An Automated Optical Inspection System for PIP Solder Joint Classification Using Convolutional Neural Networks

机译:利用卷积神经网络的PIP焊接关节分类自动化光学检测系统

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In the fields of electronics manufacturing, the application of through-hole devices is still required, as heat dissipation and high current carrying capacity plays an important role. To ensure the highest quality standards, these electronics production processes take a multitude of inspection processes into account. For the detection of error patterns regarding the quality of the solder connections, usually, high-end inspection machines are utilized in the industrial application. The Automated Optical Inspection is a commonly conducted process, using visible light and rule-based inspection routines, setup by process experts for the evaluation of the Region of Interest. The high overhead of creating and maintaining product-specific checking routines and machine acquisition leads to increased costs and severe dependency on expert know-how. A flexible inspection algorithm, implemented into low-cost equipment for image generation is expected to reduce acquisition and optimization costs, and lower dependency on expert knowledge and high-end machinery. In this contribution, we present a novel framework for the automatic, near real-time solder joint classification based on Convolutional Neural Networks, flexibly detecting, and classifying solder connections. We utilize existing Deep Learning architectures for detection and classification. The localization model utilizes a YOLO-architecture (you-only-look-once), learning feature inputs based on a supervised learning approach. Pseudo-labeling is carried out automatically by an anomaly detection model. The image generation is executed by an industrial low-cost camera and an industrial rack-PC. The developed prototype is integrated into the existing production infrastructure. The results indicate a satisfactory detection and classification of the investigated solder connections with the proposed system. Hence, this system represent an alternative to commercially available high-end inspection systems being used for an inline control of Pin-in-Paste and through-hole device solder connections.
机译:在电子制造领域,仍然需要应用通孔装置,因为散热和高电流承载能力起着重要作用。为确保最高质量的标准,这些电子生产过程考虑了众多的检查流程。为了检测关于焊料连接的质量的误差模式,通常,在工业应用中使用高端检查机器。自动化光学检查是一种通常进行的过程,使用可见光和规则的检测例程,由过程专家设置,用于评估感兴趣的区域。创建和维护产品特定的检查例程和机器采集的高度开销导致成本和严重依赖于专家专业知识。预计柔性检查算法,用于图像生成的低成本设备,以降低采集和优化成本,以及对专家知识和高端机械的依赖依赖。在这一贡献中,我们为自动提供了一种新颖的框架,即基于卷积神经网络,灵活检测和分类焊料连接的实时焊接联合分类。我们利用现有的深度学习架构进行检测和分类。本地化模型利用Yolo-Architecture(仅关注一次),基于监督学习方法的学习功能输入。伪标记由异常检测模型自动进行。图像生成由工业低成本相机和工业机架PC执行。开发的原型集成到现有的生产基础设施中。结果表明,与所提出的系统的研究焊接连接的令人满意的检测和分类。因此,该系统代表了用于市售的高端检查系统的替代方案,用于粘贴粘贴和通孔装置焊接连接的内联控制。

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