Pseudo vs. True Defect Classification in Printed Circuits Boards using Wavelet Features
This addresses quality control in PCB manufacturing for consumer electronics, but appears incremental as it combines existing techniques.
The paper tackled the problem of classifying defects in printed circuit boards as true or pseudo to decide on re-manufacturing, using wavelet features and kernel SVM, and demonstrated efficacy on a real-world dataset.
In recent years, Printed Circuit Boards (PCB) have become the backbone of a large number of consumer electronic devices leading to a surge in their production. This has made it imperative to employ automatic inspection systems to identify manufacturing defects in PCB before they are installed in the respective systems. An important task in this regard is the classification of defects as either true or pseudo defects, which decides if the PCB is to be re-manufactured or not. This work proposes a novel approach to detect most common defects in the PCBs. The problem has been approached by employing highly discriminative features based on multi-scale wavelet transform, which are further boosted by using a kernalized version of the support vector machines (SVM). A real world printed circuit board dataset has been used for quantitative analysis. Experimental results demonstrated the efficacy of the proposed method.