A Data Pipeline to Classify PCB Welding Defects on Noisy Data

In today's electronics manufacturing, Printed Circuit Board (PCB) component welding faults are a critical issue that can significantly impact the reliability and functionality of devices. Accurate detection of anomalies in welded components not only improves the quality and efficiency of the manufacturing process but also helps reduce economic waste.This paper presents a data pipeline that combines classical machine learning and deep learning techniques with computer vision to identify potential soldering defects in PCB assemblies. The proposed method involves cleaning the data obtained from Automated Optical Inspection (AOI) systems to create a dataset suitable for machine learning tasks, such as classification and anomaly detection.We then use this cleaned data to develop two classification models: a Random Forest (RF) model and a Convolutional Neural Network (CNN). These models are applied to perform both binary classification-distinguishing between defective and non-defective components-and multi-class classification to identify specific types of defects. The models are also tested on noisy data to assess their reliability when exposed to data differing from the training set.The results show good accuracy across almost all tests, with the CNN model demonstrating greater resilience to noise in the test set.