| Organizer(s) |
Xuan Song, University of Iowa
Corson L. Cramer, Oak Ridge National Laboratory
Lei Chen, University of Michigan-Dearborn
Lisa M. Rueschhoff, Air Force Research Laboratory
Yiquan Wu, Alfred University
David J. Mitchell, University of Central Florida
Paolo Colombo, University of Padova
Beth L. Armstrong, Oak Ridge National Laboratory
Rajendra K. Bordia, Clemson University
Long-Qing Chen, Pennsylvania State University |
| Scope |
The wide-use of advanced ceramic-based materials depends on the availability of industrial processing routes to fabricate parts with required geometries. However, it is still challenging for current manufacturing methods to produce complex shaped ceramic parts with the desired microstructures and properties. Additive Manufacturing (AM) or 3D printing is a fast-growing technique for fabricating parts layer-by-layer directly from 3D digital models. AM has tremendous potential for producing high-value, complex, individually customized parts. While AM technologies for ceramics have been extensively explored in the past decade, additively shaping ceramic materials with high performance and high reliability remains challenging, due to their inherent brittleness and stringent processing requirements (feedstock and/or sintering). As more technological progress continues in the field, it can be expected that AM technologies will have an extraordinary impact on the industrial production of ceramic components and will open up new possibilities for ceramics uses and markets.
This symposium will highlight recent advances in additive manufacturing of ceramic-based materials. The topics of presentations are sought to include but not limited to:
1. Hybridized processes for ceramic-based materials based on established technologies (Sheet Lamination, Powder Bed Fusion, Directed Energy Deposition, Material Jetting, Binder Jetting, Stereolithography techniques, Material Extrusion techniques), and novel approaches;
2.Computational and experimental investigations of process-microstructure-property relations from feedstock to green forming, drying/debinding, and sintering/infiltration, including but not limited to multiscale multi-physics computational modelling, and in-situ and ex-situ characterization of grain structures, porosity, surface roughness, structural and functional properties for AM-fabricated ceramics and ceramics composites;
3. Data-driven process optimization and quality control of AM ceramic-based components, e.g., based on surrogate models, uncertainty quantification, machine learning and deep learning technologies;
4. Novel applications and testing of AM ceramic-based components for energy and defense. |