Additive Manufacturing and Innovative Powder Processing of Functional and Magnetic Materials: Unique Consolidation and Computational Processing
Sponsored by: TMS Functional Materials Division, TMS Materials Processing and Manufacturing Division, TMS: Additive Manufacturing Committee, TMS: Magnetic Materials Committee, TMS: Powder Materials Committee
Program Organizers: Emily Rinko, Iowa State University; Iver Anderson, Iowa State University Ames Laboratory; Markus Chmielus, University of Pittsburgh; Emma White, DECHEMA Forschungsinstitut; Deliang Zhang, Northeastern University; Andrew Kustas, Sandia National Laboratories; Kyle Johnson, Sandia National Laboratories
Thursday 8:30 AM
March 3, 2022
Location: Anaheim Convention Center
Session Chair: Emily Rinko, Iowa State University
8:30 AM Introductory Comments
8:35 AM Invited
Iron Nitride Based Soft Magnets through Spark Plasma Sintering: Todd Monson1; Tyler Stevens1; Stanley Atcitty1; Baolong Zheng2; Calvin Belcher2; Yizhang Zhou2; Enrique Lavernia2; 1Sandia National Laboratories; 2University of California, Irvine
The soft magnetic phase of iron nitride (γ’-Fe4N) can extend soft magnetic performance boundaries within a broad set of applications. With a magnetization slightly greater than silicon steel and an electrical resistivity at least an order of magnitude higher than Si steel, iron nitride can increase overall efficiency while maintaining or even improving power density in a wide range of devices. Taking advantage of the low consolidation temperatures of spark plasma sintering (SPS), we have demonstrated a path for consolidating raw powders of iron nitride into both bulk and composite parts. Iron nitride powder processing and preparation methods will be discussed in addition to the details of SPS consolidation. The fabrication of iron nitride based soft magnetic composites leveraging the same powder processing methods as for SPS will also be covered.
Additive Manufacturing as a Hybrid Synthesis-joining Method to Optimize Magnetic and Mechanical Properties of Dissimlar Alloys: Donald Susan1; Andrew Kustas1; Rick Kellogg1; Dale Cillessen1; Bradley Salzbrenner1; 1Sandia National Laboratories
High-strength stainless steel alloy PH13-8Mo was additively manufactured (AM) on Fe-Co-2V soft magnetic alloy substrates using laser powder bed fusion (LPBF). Hybrid AM processing and dissimilar alloy joining enabled optimization of both mechanical and soft magnetic properties, desired for small components. The PH13-8 AM build material was tensile tested in H950, H1025, and H1100 heat-treat conditions and resulted in comparable strength levels to wrought and cast PH stainless steels. The ductility of the AM 13-8PH builds is negatively affected by porosity and other AM defects. Composite specimens of PH13-8/Fe-Co-2V were also tested. As expected, failure occurred in the weaker Fe-Co-2V substrate, away from the interface, indicating strong metallurgical bonding between the dissimilar alloys. Two-step heat treatments were selected for simultaneous optimization of mechanical properties in the PH13-8Mo material and soft magnetic performance in the Fe-Co-2V substrate alloy.SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525
Selective Laser Melting of NiZnCu-ferrite Soft Magnetic Composites: Process-property Relationships: Joseph Sopcisak1; Caleb Andrews2; Li Ma1; Ryan Carter1; Mitra Taheri2; 1Johns Hopkins University Applied Physics Laboratory; 2Johns Hopkins University
Soft magnetic composites (SMCs), which provide high electrical resistivity with high magnetic permeability, have the potential to create lighter and more efficient electronic devices. With the increasing complexity of these devices, conventional manufacturing methods limit their application. Recent advances in multi-material laser powder bed fusion (L-PBF) additive manufacturing (AM) have enabled the production of more complex SMCs. Prior research has demonstrated the ability to manufacture metal-on-metal SMCs with high permeability and acceptable core loss, but with poor properties for high frequency applications and high porosity. In this work, we demonstrate an optimization study combining design of experiments, multiscale material modeling, CT analysis, SEM/EBSD, and VSM analysis. This research has yielded a pathway for producing highly dense, higher performance SMCs.
9:45 AM Break
The Development of a Machine Learning Guided Process for the Additive Manufacturing of Thermoelectric Materials: Connor Headley1; Roberto Herrera del Valle1; Ji Ma1; Prasanna Balachandran1; Vijayabarathi Ponnambalam2; Dylan Kirsch3; Saniya LeBlanc2; Joshua Martin3; 1University of Virginia; 2George Washington University; 3National Institute for Standards and Technology
The implementation of additive manufacturing promises to create thermoelectric devices with increased efficiency and lowered production costs. However, the optimal additive manufacturing processing parameters for any thermoelectric material are currently unknown, and the development of an additive manufacturing process for a new material is traditionally an arduous task that requires numerous rounds of experimental trial-and-error. Through the integration of machine learning techniques alongside well-curated additive manufacturing experimentation, we quickly draw vital connections between processing parameters, melt pool geometries, and defects while significantly reducing experimental burden. We rapidly developed process parameters for laser powder bed fusion that created highly dense, geometrically complex bismuth telluride parts through additive manufacturing. The thermal conductivities, electrical conductivities, and Seebeck coefficients of these parts were also measured for comparison to traditional thermoelectric devices. Finally, microstructure characterization was carried out to make connections between the additive manufacturing process and the resulting thermoelectric properties.
Deep Learning with Generative Adversarial Network for Ti-6Al-4V Surface Roughness Improvement in Direct Energy Deposition Process: Im Doo Jung1; Tae Kyeong Kim1; Hyo Kyung Sung1; Jung Gi Kim1; Hyung Sub Kim1; 1UNIST
The Direct Energy Deposition (DED) process is one of the additive manufacturing (AM) that sprays metal powder directly into a high-power laser and is useful for manufacturing large metal parts. However, there is a bottleneck that surface roughness is relatively low compared to other AM methods. In this work, we controlled three process variables that affect surface illumination during the DED process with titanium powder material and conducted convolutional neural network-based machine learning based on 2D scanned images of the sculptural surface manufactured accordingly. We predicted the process conditions of images with accuracy of more than 80% of MAPE based on test sets. In addition, we visually observed surface roughness with images generated by DC-GAN. It is expected that artificial intelligence will be actively used to improve surface roughness during the DED process for titanium alloy powder materials.