|About this Abstract
||MS&T23: Materials Science & Technology
||Additive Manufacturing of Ceramic-based Materials: Process Development, Materials, Process Optimization and Applications
||Challenges and Future Directions for Ceramic Additive Manufacturing in Incorporation of Fiber Reinforcements and Machine Learning Strategies
||Lisa M. Rueschhoff, Luke Baldwin, James Hardin, Jonathan Kaufman
|On-Site Speaker (Planned)
||Lisa M. Rueschhoff
Research in the field of ceramic additive manufacturing (AM) has been rapidly accelerating, resulting in hundreds of publications and review articles in recent years. While strides have been made in utilizing these AM techniques for near-net and complex-shaped ceramic components, challenges remain that inhibit more widespread implementation. Here, a meta-analysis of recent review articles on the topic, along with focus on two future directions as prominent opportunities to address existing challenges in ceramic AM, will be provided. The first is incorporation of fiber reinforcements in to overcome the challenges of poor mechanical performance of monolithic ceramics. Recent work in the area has shown promise in incorporating discrete fiber phases as an easier barrier to entry given existing equipment limitations, but continuous fibers are needed to reach full toughness potential. Suggested pathways and examples from literature will be presented. Second, artificial intelligence and machine learning (AI/ML) type approaches are suggested in order to accelerate feedstock development and process optimization. While there has been very limited work to date in utilizing AI/ML techniques for ceramic AM, inspiration and lessons learned can be drawn from other fields.