Impact Ice Microstructure Segmentation Using Transfer Learned Model NASA Glenn Research Center

Format:

Book

Conference/Event

Author/Creator:

Chen, Ru-Ching, author.

Contributor:

Giuffre, Christopher

Stuckner, Joshua

Conference Name:

International Conference on Icing of Aircraft, Engines, and Structures (2023-06-20 : Vienna, Austria)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The microstructure of impact ice has been shown to correlate with the adhesion strength of ice. Machine vision techniques are explored as a method of decreasing analysis time. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn's Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods. Results show promise in accuracy as well as a possible improvement in repeatability and consistency. Next steps for improving the model are suggested

Notes:

Vendor supplied data

Publisher Number:

2023-01-1410

Access Restriction:

Restricted for use by site license