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Classifying the Dimensional Variation in Additive Manufactured Parts From…

- - - - Low dimensional variation results in a sparse graph (fewer edges), whereas severe variation leads to a dense network.
    - - Laplacian eigenvalues capture intrinsic patterns in the data, similar to how a Fourier transform analyzes signals in the frequency domain.
- - - - Transitioning to anomaly detection to identify unknown defect types without labeled training data.
    - - Implementing algorithms that automatically identify optimal Regions of Interest (ROI) based on CAD geometry.