Intelligent defect classification system based on deep learning

• Main Authors: Ruifang Ye, Chia-Sheng Pan, Ming Chang, Qing Yu
• Format: Article
• Language: English
• Published: SAGE Publishing 2018-03-01
• Series: Advances in Mechanical Engineering
• Online Access: https://doi.org/10.1177/1687814018766682

A fast and intelligent defect classification system for distinguishing defect features is developed in this study. Defect images obtained from an automated optical inspection instrument are first trained utilizing a deep learning approach based on the convolutional neural network. The detailed features of defects, such as flaws in the inclination, size, quantity, and settlement, can then be characterized with the developed system. The obtained defect characteristics can be provided as a reference to evaluate the manufacturing process. A graphics processing unit card is used to build the parallel computing architecture for fast data computation both in the training and classification processes. The experimental results show that the defect classification for a touch panel glass surface of size 43 × 229.4 mm 2 that yields 805 million pixel data points was completed in 2 s. The classification accuracy was above 96%. Therefore, an automated and fast defect analysis catalog integrated to an optical inspection result for the evaluation and adjustment of manufacturing operations can be expected with the proposed approach.