Powder Materials Processing and Fundamental Understanding: Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Powder Materials Committee
Program Organizers: Elisa Torresani, San Diego State University; Kathy Lu, University of Alabama Birmingham; Eugene Olevsky, San Diego State University; Ma Qian, Royal Melbourne Institute of Technology; Diletta Giuntini, Eindhoven University of Technology; Paul Prichard, Kennametal Inc.; Wenwu Xu, San Diego State University
Monday 5:30 PM
March 20, 2023
Room: Exhibit Hall G
Location: SDCC
Combustion Synthesis of B4C-TiB2 Composite Nanoparticle by Self-propagating High Temperature Synthesis (SHS) in B2O3 – TiO2 – Mg - C System: Ozan Coban1; Mehmet Bugdayci2; Serkan Baslayici3; Ercan Acma1; 1Istanbul Technical University; 2Yalova University; 3İstanbul Medipol University
In this study, B4C-TiB2 nanocomposite powder was synthesized from oxide raw materials with the principle of magnesiothermic reduction in B2O3 - TiO2 - Mg - C system by SHS method. For the SHS process, Mg and C stoichiometries were optimized with thermochemical simulation, and composite charge stoichiometry and Mg particle size were optimized with XRD, BET and SEM analyzes. Optimization of acid concentration, leaching temperature and leaching time parameters has been provided for the HCl leaching processes carried out to remove undesired by-products after SHS. In addition, pH and temperature changes during leaching were analyzed and an innovative application of modified leaching with H2O2 and carbonic acid addition was investigated. The results showed that by optimizing the process steps for the synthesis of B4C-TiB2 composite nanoparticle by the SHS method, a commercial grade product with a surface area of 30.6 m2/g and a particle size of 193 nm was obtained.
A-57: Efficient Production of Y2O3-Decorated Ti4822 Powder as an Oxide Dispersion Strengthened Material for Powder-Bed-Based Additive Manufacturing: Saeid Alipour Masoumabad1; Arezoo Emdadi1; 1Missouri University of Science & Technology
The need for lightweight, high-melting temperature materials with the structural application that can function at elevated temperatures is still being researched on a global scale, and TiAl-based alloys as a potential candidate are now in the spotlight. Among the titanium aluminide alloys, Ti4822 alloy with nano-scale oxide reinforcements have gained a great deal of attention due to their enhanced mechanical properties in high temperatures. This research aims to produce Ti4822 powder decorated by nano-scale Y2O3 to maintain the primary spherical morphology of the Ti4822 powders while have a well distribution of nano oxides onto the Ti4822 powders. To this end, an innovative mixing process is employed and the XRD, SEM, and TEM of synthesized powder compared with the ones produced by the conventional ball milling technique.
A-58: Instance Segmentation for the Characterization of Metal Powders Using Synthetic Datasets: Kyle Farmer1; Ryan Cohn2; Elizabeth Holm2; 1KCNSC/Carnegie Mellon University; 2Carnegie Mellon University
Powder characteristics can significantly impact the manufacturing and performance of powder based additive manufactured materials. Instance segmentation, using Mask R-CNN with transfer learning, has shown success in characterizing the size distribution of gas atomized powders. However, the manual annotation process to provide ground truth labels for training is expensive and difficult. In this work, we propose training Mask R-CNN with synthetic datasets which will help alleviate the time cost and increase the accuracy of ground truth labels while yielding high accuracy during evaluation of real gas atomized powders. The synthetic datasets consist of eight closely related particle size distributions. The resulting particle size measurements agree closely with their underlying distributions. The model also showed success in predicting the overlapping regions of particles, despite their being hidden from view. Honeywell Federal Manufacturing & Technologies, LLC operates the Kansas City National Security Campus for the United States Department of Energy / National Nuclear Security Administration under Contract Number DE-NA0002839 NSC-614-4681 dated 07/2022 Unclassified Unlimited Release.
A-59: The Characterization of CoCrFeMnCu High Entropy Alloy Powders Produced by Gas Atomization for Powder-based Additive Manufacturing Processes: Sertaç Altınok1; Yunus Kalay2; 1TAI; 2Middle East Technical University
The interest in high entropy alloys has leaped with the improvements in additive manufacturing (AM) maturity due to the local performing process that leads to an increased cooling rate enhancing the stability of high entropy alloys and reducing the segregation. Powder bed fusion (PBF) is the most widely used metal AM process in the aerospace industry in order to promote high design flexibility resulting in maximizing the mass savings for parts. However, PBF AM needs to have spherical powder feedstock with definite particle requirements to build high-performance parts. In this study, the empirical and thermodynamic-based prediction models were used to determine CoCrFeMnCu high entropy alloy and its powders were produced by gas atomization followed by sieving to obtain four different particle size ranges. In order to reveal the effect of undercooling and solidification rates, XRD and TEM were used for phase analysis and microstructural investigation at various particle sizes.
A-60: Understanding Surface Roughness on Vertical Surfaces via Computational Simulations in Laser Powder Bed Fusion Additive Manufacturing: Zilong Zhang1; Tianyu Zhang1; Lang Yuan1; 1University of South Carolina
The surface roughness of additively printed components via laser powder bed fusion is contributed by partially melted particles, melt tracks, spatters and the dross formation. In this study, different processing parameters corresponding to different melt pool instability and the solidification behaviors on top and vertical surfaces. Cubic samples were printed to observe the correlations of the melt pool size, solidification microstructure and surface roughness. The same conditions were simulated by the commercial software package, Flow3d, to interpret the experimental results. The model solves the general physics of heat transfer, solidification, vaporization, and fluid dynamics. Representative regions consisted of multiple tracks and multiple layers were simulated to predict the melt pool behaviors, including balling, and the thermal history on the surfaces. The simulation results explain the formation of the observed dominant surface features, revealing the underlying mechanisms that drive surface roughness.