(V) Deep Learning Microstructural Characterization in Friction Stir Welded Al7175 joining

Wednesday, September 15, 2021: 1:20 PM
242 (America's Center)
Ms. Amra Peles , PNNL - Pacific Northwest National Laboratory, Richland, WA
N/A Reza-E-Rabby Md , PNNL - Pacific Northwest National Laboratory, Richland, WA
Ms. Nicole Overman , PNNL - Pacific Northwest National Laboratory, Richland, WA
N/A Mageshwari Komarasamy , PNNL - Pacific Northwest National Laboratory, Richland, WA
Mr. Anthony Guzman , PNNL - Pacific Northwest National Laboratory, Richland, WA
Mr. Glenn Grant , Pacific North West National Lab, Washington, WA
Dr. Scott Whalen , Pacific Northwest National Laboratory, Richland, WA
Deep learning and image analysis are proving a powerful technique to enhance our understanding and knowledge of materials structure property relationships and how they relate to materials processing. The library of SEM micrographs for friction Stir Welded AI7175 joining was collected to study effects of friction stir welding (FSW) induced plastic deformation at elevated temperatures and provide deeper insights in resultant microstructural changes. A quantitative analysis of secondary phases in alloy matrix and classification of FSW zones, based on the machine learning analysis will be present in this contribution.
See more of: Quantification and Simulation of Microstructures
See more of: Characterization of Materials and Microstructure through Metallography, Image Analysis, and Mechanical Testing - Fundamental and Applied Studies