Abstract
AbstractResearch 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 forming near‐net and complex‐shaped ceramic components, challenges remain that inhibit more widespread implementation. In this perspective, we provide a meta‐analysis of recent review articles and highlight a deficiency in two areas of promising future directions to address remaining challenges. The first is incorporation of fiber reinforcements in printed parts to overcome the challenges of poor mechanical performance of monolithic ceramics. Recent work in the area has shown promise incorporating discrete fiber reinforcements as an easier use case given existing equipment limitations, but continuous fibers are needed to reach full toughness potential. Here, we overview some options and future directions bases on success in polymer composites. Second, artificial intelligence (AI) approaches, including machine learning (ML), 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, again inspiration and lessons learned are drawn from the polymer AM community.
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