Ut-battelle, Inc.

Luke R Scime

Vincent C Paquit

Derek H Siddel

Derek C Rose

Desarae J Goldsby

William H Halsey

Chase B Joslin

Michael D Richardson

Detection and classification of anomalies for powder bed metal additive manufacturing. Anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each layer of the building process. A multi-scaled parallel dynamic segmentation convolutional neural network architecture provides additive manufacturing machine and imaging system agnostic pixel-wise semantic segmentation of layer-wise powder bed image data. Learned knowledge is easily transferrable between different additive manufacturing machines. The anomaly detection can be conducted in real-time and provides accurate and generalizable results.

Systems and methods for powder bed additive manufacturing anomaly detection

Growing community of inventors

Knoxville, TN, United States of America

Luke R Scime