About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
A Quantitative Approach to Explainable AI in DIW AM |
Author(s) |
Jennifer Ruddock, Robert Weeks, Ezra Ameperosa, James Hardin, Jennifer Lewis |
On-Site Speaker (Planned) |
Jennifer Ruddock |
Abstract Scope |
Machine learning is an increasingly prevalent tool in automated manufacturing. However, having an understanding of the uncertainty in a prediction, or an understanding of the likelihood the model is over- or underestimating a value, and understanding how the algorithm came up with a given prediction is important. Here, we use Layerwise Relevance Propagation (LRP) to examine the results of convolutional neural networks used to estimate the rheological properties of inks printed by direct ink write 3D printing using image regression analysis. In particular, ink properties such as the yield stress, flow index, and consistency index of an ink can be determined from the sharpness of printed corners and the width of deposited filaments. We determine how well the model predicts these properties, while also drawing relations to the LRP pixel relevance values and their locations. We bring a quantitative approach to LRP in understanding print morphologies in AM. |
Proceedings Inclusion? |
Planned: |
Keywords |
Other, Machine Learning, Additive Manufacturing |