Advancing Additive Manufacturing of Biomedical Sensing Devices through Hybrid Printing and Artificial Intelligence

Additive manufacturing (AM) has emerged as a transformative manufacturing process in modern production, enabling the creation of complex 3D structures with minimal waste. Despite recent advancements, AM still encounters significant challenges, including limited material options and the trade-offs between feature sizes and resolution that can be achieved by a single AM technology, extensive post-processing that compromises efficiency and material compatibility, and the time-consuming printing parameter optimizations. This dissertation introduces innovative solutions to address these challenges by developing hybrid printing techniques and artificial intelligence enabled autonomous AM processes. Central to this research is the development of a hybrid printing system that integrates a novel high-throughput combinatorial aerosol jet printing and extrusion printing on a single platform. This integration facilitates the streamlined manufacturing of complex devices that incorporate a diverse range of materials and feature sizes, achieving high performance, high efficiency, and high resolution. The effectiveness of this hybrid system is demonstrated through the fabrication and characterization of novel wearable devices and biosensors. These include a tellurium nanowire-based wearable piezoelectric sensor, which offers high sensitivity and stretchability for monitoring physiological signals such as heart rate, and a microfluidic sweat sensor that enables the amperometric sensing of lactate in sweat. Moreover, the development of an in-situ plasma jet sintering technique eliminates the need for post-processing and enhances process compatibility through ambient temperature processings. To further enhance AM's capabilities, this work integrates a hybrid machine learning algorithm to autonomously optimize printing parameters based on user-defined target output properties in a data-efficient manner. Additionally, the dissertation explores the application of an environment-adaptive robotic printer equipped with environmental perception capabilities, enabling precise printing on irregular and dynamically changing surfaces. These advancements have the potential to revolutionize personalized healthcare by allowing the direct printing of biomedical devices on living biological materials.