The inspection of solder joints on printed circuit boards is a difficult task because defects inside the joints cannot be observed directly. In addition, because anomalous samples are rarely obtained in a general anomaly detection situation, many methods use only normal samples in the learning phase. However, sometimes a small number of anomalous samples are available for learning. We propose a method to improve performance using a small number of anomalous samples for training in such situations. Specifically, our proposal is an anomaly detection method using an adversarial autoencoder (AAE) and Hotelling’s T-squared distribution. First, the AAE learns features of the solder joint following the standard Gaussian distribution from a large number of normal samples and a small number of anomalous samples. Then, the anomaly score of a solder joint is calculated by Hotelling’s T-squared method from the features learned by the AAE. Finally, anomaly detection is performed by thresholding using this anomaly score. In experiments, we show that our method performs anomaly detection with few false positives in such situations. Moreover, we confirmed that our method outperforms the conventional method using handcrafted features and a one-class support vector machine.
We propose a defect detection method of solders on a printed circuit board using X-ray CT inspection system and Adversarial Autoencoder (AAE) . We obtain sliced images of the solder using X-ray CT and extract their features that follow the standard normal distribution by using AAE. Then, the solder defects are detected by Hotelling's T square. As a result of experiments, we show that we can classify normal and anomalous data samples completely on the condition of training with large normal samples and small anomalous samples.