Materials Design and Processing Optimization for Advanced Manufacturing: From Fundamentals to Application: On-Demand Poster Session
Sponsored by: TMS Structural Materials Division, TMS: Alloy Phases Committee
Program Organizers: Wei Xiong, University of Pittsburgh; Dana Frankel, Apple Inc; Gregory Olson, Massachusetts Institute of Technology
Monday 8:00 AM
March 14, 2022
Room: Materials Design
Location: On-Demand Poster Hall
Friction Assisted Dissimilar Solid State Joining of Aluminum and Copper Pipes: Ji-Won Kang1; Tu-Anh Bui-Thi1; Sung-Tae Hong1; Heung-Nam Han2; 1University of Ulsan; 2Seoul National University
The friction assisted solid state joining process of dissimialr metal pipes is proposed. The joining process is designed to have two stages. In the first stage, a copper pipe rotating with a mandrel breaks the oxide lalyers at the interface of copper and aluminum pipes. In the second state, the madrel rotate alone against the stationary copper pipe to induce diffusion bonding between the copper and aluminum pipes. The result of microstructural anlysis and mechanical testing shows that solid state joining between the copper and aluminum pipes for a light-weithg heat exchanging component is successfully achieved.
New Methodologies for Grain Boundary Detection in EBSD Data of Microstructures: Richard Catania1; 1Virginia Tech
This work discusses new methodologies for identifying the grain boundaries in color images of metallic microstructures and the quantification of their grain topology. This work employs the experimental microstructure data of Titanium-Aluminum alloys, which are used for various aerospace components, owing to their mechanical performance in elevated temperatures. The grain topology of these metallic microstructures is quantified using the concept of shape moment invariants (SMI). First, it is necessary to identify the grain boundaries and separate them from their respective grains. The two methods this work investigates are tolerance-based neighbor analysis and Euclidean distance analysis to separate different grains. Additionally, since the grain boundaries do not possess the same material properties as the grain itself, this work investigates the effect of including the grain boundaries when determining the overall microstructure properties. Once the topology is quantified, a machine learning algorithm will be used to obtain material properties from the SMI.