Support vector machine and convolutional neural network based approaches for defect detection in fused filament fabrication

Barath Narayanan Narayanan; Kelly Beigh; Gregory Loughnane; Nilesh Powar

doi:10.1117/12.2524915

6 September 2019 Support vector machine and convolutional neural network based approaches for defect detection in fused filament fabrication

Barath Narayanan Narayanan, Kelly Beigh, Gregory Loughnane, Nilesh Powar

Author Affiliations +

Proceedings Volume 11139, Applications of Machine Learning; 1113913 (2019) https://doi.org/10.1117/12.2524915
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

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.

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Barath Narayanan Narayanan, Kelly Beigh, Gregory Loughnane, and Nilesh Powar "Support vector machine and convolutional neural network based approaches for defect detection in fused filament fabrication", Proc. SPIE 11139, Applications of Machine Learning, 1113913 (6 September 2019); https://doi.org/10.1117/12.2524915

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