【摘要】 波峰焊在线质量检测是检验不良产品的重要方法，高精度的质量检测能够降低生产成本并为波峰焊过程中的质量预警提供支持。常规的质量检测方法能够较好地检测波峰焊产线中的漏焊、虚焊和连焊等质量缺陷。但波峰焊产线中还会发生其他异常，这些异常往往具有突发性、稀有性、未知性与异构性。常规检测手段难以检测这些突发异常，因此提出一种基于重建误差度量的异常检测方法。该方法通过对现有质量检测模型生成的潜在特征向量进行重构，并利用重建误差判定样本是否为异常，以在对常见缺陷高精度检测的前提下完成对生产异常的在线检测。此外，进一步构建了融合空间-通道注意力模块(Convolutional Block Attention Module, CBAM)与EfficientNet架构的特征提取模型，以及门控集成生成对抗网络(Gated ensemble Generative Adversarial Network, GGAN)模型，以进一步提升异常检测性能。针对南京某电子企业生产数据的实例验证表明，所提方法与构建的模型可实现高达98.5%的异常检出率。与现有方法相比，所提方法显著提升了对波峰焊产线中异常样本的检测能力，同时保证了对常见缺陷的高精度检测。更多还原

【Abstract】 Wave soldering online quality inspection serves as a crucial method for identifying defective products. High-precision inspection can reduce production costs and support early warning mechanisms during the wave soldering process.Conventional inspection techniques are effective in detecting common defects such as solder skips, cold solder joints, and solder bridging.However, various other anomalies may also occur on the production line, characterized by their suddenness, rarity, unpredictability, and heterogeneity. These anomalies are difficult to detect using standard inspection approaches. To address this challenge, an anomaly detection method based on reconstruction error metrics was proposed.Latent feature vectors generated by existing quality inspection models are reconstructed, and reconstruction errors were used to determine whether a sample was anomalous. This enabled online detection of production anomalies while maintaining high-accuracy detection of common defects. Furthermore, a feature extraction model was developed by integrating the Convolutional Block Attention Module(CBAM) with the EfficientNet architecture, and a Gated ensemble Generative Adversarial Network(GGAN) was introduced to further enhance anomaly detection performance. Empirical verification using production data from an electronics enterprise in Nanjing demonstrated that an anomaly detection rate of up to 98.5 % was achieved.Compared to existing methods, the proposed method significantly improved the detection of anomalous samples in wave soldering production lines while ensuring high-precision identification of typical defects.更多还原