Rotation-Angle-Based Principal Feature Extraction and Optimization for PCB Defect Detection Under Uncertainties

In printed circuit board (PCB) production, uncertainty in the manufacturing stage and sampling uncertainty in the inspection stage have a great impact on product quality. This article proposes rotating angles based on the optimal detection of PCB defects under uncertainties. First, the principal features are extracted and factors of information concentration are designed to construct the reference template from the preprocessed clean dataset. After that, a rotation-angle-based feature extraction and concentration optimization are performed to select the better representative angles. Then, all optimized features are probabilistically synthesized and compared with the reference template for defect detection. Finally, extensive experiments are conducted on the bare PCB and high-resolution integrated PCB to demonstrate the proposed method is efficient and faster and requires fewer model parameters.