About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Feature Anomaly Detection System (FADS) for Intelligent Manufacturing |
Author(s) |
Anthony Garland, Kevin Potter, Matthew Smith |
On-Site Speaker (Planned) |
Anthony Garland |
Abstract Scope |
Anomaly detection is important for industrial automation and part quality assurance, and while humans are able to easily detect anomalies in components given a few examples, designing a generic automated system that can perform at human or above human capabilities remains a challenge. In this work, we present a simple new anomaly detection algorithm called FADS (feature based anomaly detection system) which leverages a pretrained convolutional neural network (CNN) to generate a statistical model of what a normal part should look like. By using a pretrained network, FADS demonstrates excellent performance similar to or better than other machine learning approaches to anomaly detection while at the same time FADS requires no tuning of the CNN weights. We demonstrate FADS’ ability by detecting process parameter changes on a custom dataset of additively manufactured lattices. In addition, we test FADS on benchmark datasets, such as MVTec, and report good results. |
Proceedings Inclusion? |
Undecided |