Embedding convolution neural network-based defect finder for deployed vision inspector in manufacturing company frontec

Abstract

In collaboration with Frontec, which produces parts such as bolts and nuts for the automobile industry, Kyung Hee Uni-versity and Benple Inc. develop and deploy AI system for automatic quality inspection of weld nuts. Various con-straints to consider exist in adopting AI for the factory, such as response time and limited computing resources available. Our convolutional neural network (CNN) system using large-scale images must classify weld nuts within 0.2 se-conds with accuracy over 95%. We designed Circular Hough Transform based preprocessing and an adjusted VGG (Visual Geometry Group) model. The system showed accuracy over 99% and response time of about 0.14 sec. We use TCP / IP protocol to communicate the embedded classi-fication system with an existing vision inspector using Lab-VIEW. We suggest ways to develop and embed a deep learning framework in an existing manufacturing environ-ment without a hardware change.