CAREER: Enabling Single-Step Additive Manufacturing of Ceramics via Laser-Triggered Flash Sintering and Scientific Artificial Intelligence-based Multiscale Modeling

About This Grant

Ceramics possess exceptional resistance to heat, wear, radiation and corrosion, yet their widespread adoption is limited because direct manufacturing complex ceramic parts requires extremely high temperatures and often leads to cracking, defects, and long processing times. This Faculty Early Career Development Program (CAREER) award supports research in additive manufacturing (AM) of high-performance ceramics to enable faster, more reliable production of components used in aerospace, nuclear energy, electronics, and biomedical systems. Current AM methods either rely on multi-step processes that are slow and prone to distortion or single-step methods that generate severe cracking and poor material quality. Research enabled by this award seeks to overcome these limitations by developing a new AM approach that enables rapid, defect-resistant fabrication of complex components. By advancing reliable manufacturing of high-performance ceramics, the award is expected to accelerate innovations in energy efficiency, advanced transportation, and resilient infrastructure, strengthening U.S. technological leadership, economic competitiveness, and national security.    This CAREER award aims to establish the scientific foundation for a transformative single-step ceramic AM process based on laser-triggered flash sintering (LTFS). A central challenge is the lack of fundamental understanding of how coupled laser heating and electric-field stimulation initiate flash sintering, govern densification kinetics, and influence microstructure evolution, defect formation, and process reliability. To address this gap, research is planned to develop an integrated experimental, computational, and data-driven framework. Specifically, the research tasks include (1) design and construct an LTFS-enabled AM testbed with in-situ monitoring for real-time process characterization; (2) investigate flash-sintering initiation, stability, and microstructure evolution through coordinated experiments and multiphysics microscale modeling; (3) establish a multiscale electro-thermal-mechanical modeling framework to quantify how manufacturing parameters influence densification, shrinkage, and resulting material properties; (4) develop a Scientific Artificial Intelligence (Sci-AI) framework that integrates in-situ data with physics-based models to capture process stochasticity, improve predictive accuracy, and enable intelligent process control; and (5) demonstrate manufacturing capability through fabrication and evaluation of complex, high-performance ceramic components. The outcomes are expected to establish quantitative process–structure–property relationships for LTFS-based ceramic manufacturing, enabling defect-controlled fabrication of advanced ceramics and advancing smart, data-driven manufacturing of high-performance materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.