# Anomaly Detection for Solder Joints Using β-VAE

arXiv: https://arxiv.org/abs/2104.11927 · HTML: https://mlsanity.com/paper/2104.11927/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 24 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Unsupervised Anomaly Localization using Variational Auto-Encoders](https://mlsanity.com/paper/1907.02796/) (2019)
- [Backpropagated Gradient Representations for Anomaly Detection](https://mlsanity.com/paper/2007.09507/) (2020)
- [Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems](https://mlsanity.com/paper/2012.03448/) (2020)
- [Anomaly Detection for Skin Disease Images Using Variational Autoencoder](https://mlsanity.com/paper/1807.01349/) (2018)

## Built on by (most cited first, 3 of 3)

- [Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations](https://mlsanity.com/paper/2307.07893/) (2023)
- [VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface](https://mlsanity.com/paper/2203.01193/) (2022)
- [Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence](https://mlsanity.com/paper/2209.09857/) (2022)

## Abstract (arXiv)

In the assembly process of printed circuit boards (PCB), most of the errors are caused by solder joints in Surface Mount Devices (SMD). In the literature, traditional feature extraction based methods require designing hand-crafted features and rely on the tiered RGB illumination to detect solder joint errors, whereas the supervised Convolutional Neural Network (CNN) based approaches require a lot of labelled abnormal samples (defective solder joints) to achieve high accuracy. To solve the optical inspection problem in unrestricted environments with no special lighting and without the existence of error-free reference boards, we propose a new beta-Variational Autoencoders (beta-VAE) architecture for anomaly detection that can work on both IC and non-IC components. We show that the proposed model learns disentangled representation of data, leading to more independent features and improved latent space representations. We compare the activation and gradient-based representations that are used to characterize anomalies; and observe the effect of different beta parameters on accuracy and on untwining the feature representations in beta-VAE. Finally, we show that anomalies on solder joints can be detected with high accuracy via a model trained on directly normal samples without designated hardware or feature engineering.