Adaptive Data Mining Approach for PCB Defect Detection and Classification

Objective: To develop a model for PCB defect detection and classification with the help of soft computing technique. Methodology: To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. The proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, pre-processing, feature selection and reduction is carried out by measuring of confidence with the adaptive genetic algorithm. Prediction and classification is carried out by using neural network classifier. A genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. Findings: The system is implemented using MatLab 2013b. The resulting analysis shows that the proposed approach is capable of detecting and classifying defects in PCB board.