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1 August 2021 Explaining neural network predictions of material strength
Nathan Mundhenk, Ian Palmer, Brian J. Gallagher, T. Yong Han
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Proceedings Volume 11843, Applications of Machine Learning 2021; 1184302 (2021) https://doi.org/10.1117/12.2594295
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States
Abstract
We recently developed a deep learning method that can determine the critical peak stress of a material by looking at scanning electron microscope (SEM) images of the material’s crystals. However, it has been somewhat unclear what kind of image features the network is keying off of when it makes its prediction. It is common in computer vision to employ an explainable AI saliency map to tell one what parts of an image are important to the network’s decision. One can usually deduce the important features by looking at these salient locations. However, SEM images of crystals are more abstract to the human observer than natural image photographs. As a result, it is not easy to tell what features are important at the locations which are most salient. To solve this, we developed a method that helps us map features from important locations in SEM images to non-abstract textures that are easier to interpret.
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Nathan Mundhenk, Ian Palmer, Brian J. Gallagher, and T. Yong Han "Explaining neural network predictions of material strength", Proc. SPIE 11843, Applications of Machine Learning 2021, 1184302 (1 August 2021); https://doi.org/10.1117/12.2594295
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KEYWORDS
Scanning electron microscopy
Neural networks
Crystals
Artificial intelligence
Computer vision technology
Electron microscopes
Machine vision
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