Identifying defective builds early on during Additive Manufacturing (AM) processes is a cost-effective way to reduce scrap and ensure that machine time is utilized efficiently. In this paper, we present an automated method to classify 3Dprinted polymer parts as either good or defective based on images captured during Fused Filament Fabrication (FFF), using independent machine learning and deep learning approaches. Either of these approaches could be potentially useful for manufacturers and hobbyists alike. Machine learning is implemented via Principal Component Analysis (PCA) and a Support Vector Machine (SVM), whereas deep learning is implemented using a Convolutional Neural Network (CNN). We capture videos of the FFF process on a small selection of polymer parts and label each frame as good or defective (2674 good frames and 620 defective frames). We divide this dataset for holdout validation by using 70% of images belonging to each class for training, leaving the rest for blind testing purposes. We obtain an overall accuracy of 98.2% and 99.5% for the classification of polymer parts using machine learning and deep learning techniques, respectively.

Date Published: 6 September 2019
PDF: 9 pages
Proc. SPIE 11139, Applications of Machine Learning, 1113913 (6 September 2019); doi: 10.1117/12.2524915

Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)