Learned region selection and deep learning for generalized source identification of additively manufactured parts

Additive manufacturing (AM) imparts machine-specific fingerprints into the surface texture of printed parts, which can be used to identify the machine or factory of origin. Deep learning methods can detect these fingerprints even when they are not detectable by humans; however, these methods suffer from poor data efficiency and have not been shown to generalize to diverse camera views and other practical imaging conditions. This study develops a novel deep learning network referred to as a Learned Region Fingerprint Model (LRFM) that combines a Differentiable Patch Selection (DPS) module that identifies key textural features, a Fingerprinting (FP) module that extracts latent identifiable features from the image patches, and a consolidation network that aggregates extracted features and makes source predictions. To develop the LRFM, we designed and produced a total of 1,620 AM parts with nine different designs from six contract manufacturers. Data collection is enabled by a unique custom robotic imaging system which photographed each part from 132 unique view angles, creating a dataset of 213,840 images with highly varied appearance due to diverse lighting and shadows. The LRFM predicts the manufacturing source with 98% accuracy, and generalizes to part designs and view angles not present in the training dataset. The LRFM achieves exceptional data efficiency, showing 84% prediction accuracy when trained with only one part per supplier – a total of six parts. Finally, the LRFM evaluates photographs and videos captured using smartphone cameras in realistic environments and unseen view angles, achieving 79% accuracy. This research demonstrates the applicability of AM fingerprinting in inspection and verification workflows and highlights the potential of smartphone imaging to rapidly authenticate parts.