Defect detection in PCBs using convolutional neural network

Abstract

Printed circuit boards(PCBs) are used in a majority of electronic products in Indian Space Research Org anisation(ISRO. It can have manufacturing defects, timely detection of which in early stages can prove to be a major advantage during the manufacturing process of a PCB, which can speed up the time in which delivery of electronic products takes place at ISRO. Defect detection in PCBs is majorly a manual process. This process can be automated to save time. A convolution neural network is proposed for the same. It consists of four layers. First layer is the convolution layer, followed by the ReLU activation function in the second layer. Third layer consists of the pooling function and finally the fourth layer is the fully connected layer. This model provides a fair accuracy for detecting defective PCB images, thus proving as a major time saver for ISRO.