CAREER: Toward RIPEST Photopolymer Additive Manufacturing (PAM): A Cyber-Physical System of Novel Dual-wavelength Photoinhibition aided PAM

Photopolymer additive manufacturing (PAM) uses light to cure photosensitive materials and is one of the most versatile technologies to fabricate components with higher resolution compared to existing polymer manufacturing technologies. However, the state-of-the-art photopolymer additive manufacturing technologies still suffer an intractable issue of overcuring, which is detrimental to the structural integrity of printed parts. This Faculty Early Career Development (CAREER) grant supports research to elucidate the fundamental science of light propagation and photochemistry reactions for establishing a smart dual-wavelength photoinhibition aided PAM process. This new process allows to manipulate cure and curb exposures of the PAM processing so that the geometrical fidelity and functional integrity can be enhanced. If successful, this project will impact numerous applications including biochips, electrodes, soft robots, metamaterials, and others that demand precision manufacturing of parts with complex shapes and strengthened performance. This project will provide educational activities aimed to train future leaders to pursue manufacturing-related careers and improve the diversity of the STEM workforce by reaching out to historically underrepresented communities. The overarching goal of this CAREER project is to realize Rapid, Intelligent, Precise, Extensive, Sustainable, and Transformative (RIPEST) PAM. The primary research objective is to establish a novel digital light processing method - DLP2Curb that capitalizes on photoinhibition induced by a second wavelength light to curb curing parts, via a holistic cyber-physical approach that combines physics-guided surrogate modeling with real-time sensing and control. Thrust 1 elaborates the DLP2Curb process dynamics and materials behavior through multiple physical regimes, temporal stages, and spatial scales to unravel the causes of overcuring and the curb reaction paths by constructing a digital twin. Thrust 2 aims to achieve non-contact, full-field operando characterization of PAM in real time. In-process part properties will be measured by deploying in-situ interferometric and ultrasonic monitoring systems along with physical sensor models and data analytics methods. Thrust 3 develops a real-time control method via deep reinforcement learning of model predictions and in-situ measurements feedback. With case studies such as micropillars and lattices, this project will demonstrate the potential ability of DLP2Curb to unlock efficient precision manufacturing of sophisticated parts with exact geometry and exquisite details. 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.