THE APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY PAINT DEFECTS

Author

Sherri F. Houmadi, Southern Illinois University CarbondaleFollow

Date of Award

5-1-2020

Degree Name

Doctor of Philosophy

Department

Engineering Science

First Advisor

Dunston, Julie

Abstract

AN ABSTRACT OF THE DISSERTATION OFSherri Houmadi, for the Doctor of Philosophy degree in Engineering Science, presented on March 27, 2020, at Southern Illinois University Carbondale. TITLE: THE APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY PAINT DEFECTSMAJOR PROFESSOR: Dr. Julie DunstonDespite all of the technological advancements in computer vision, many companies still utilize human visual inspection to determine whether parts are good or bad. It is particularly challenging for humans to inspect parts in a fast-moving manufacturing environment. Such is the case at Aisin Manufacturing Illinois where this study will be testing the use of convolutional neural networks (CNNs) to classify paint defects on painted outside door handles and caps for automobiles. Widespread implementation of vision systems has resulted in advancements in machine learning. As the field of artificial intelligence (AI) evolves and improvement are made, diverse industries are adopting AI models for use in their applications. Medical imaging classification using neural networks has exploded in recent years. Convolutional neural networks have proven to scale very well for image classification models by extracting various features from the images. A goal of this study is to create a low-cost machine learning model that is able to quickly classify paint defects in order to identify rework parts that can be repaired and shipped. The central thesis of this doctoral work is to test a machine learning model that can classify the paint defects based on a very small dataset of images, where the images are taken with a smartphone camera in a manufacturing setting. The end goal is to train the model for an overall accuracy rate of at least 80%. By using transfer learning and balancing the class datasets, the model was trained to achieve an overall accuracy rate of 82%.