According to data provided by the U.S. Department of Health and Human Services, the waiting list of organ transplantation as of April 2021 is approximately 107,550 out of which 90,908 patients are waiting for a kidney and 11,871 are waiting for a liver. In 2020, only 39,000 transplants were performed. A promising potential solution to this organ shortage crisis is rapid development of drugs for end-stage kidney and liver failure and the fabrication of organs using additive biomanufacturing (Bio-AM) processes. While progress toward industrial-scale production of 3D-bioprinted tissue models and organs remains hindered by various biological and tissue engineering challenges, such as vascularization and innervation, quality Bio-AM is impeded by lack of integrated process monitoring and control strategies. This dissertation aims to address the compelling need to incorporate sensing and control with Bio-AM processes, which are currently open-loop processes and improve the scalability and reliability of additively biomanufactured products. The specific aim is to develop a closed loop-controlled additive biomanufacturing process capable of fabricating 3D-bioprinted biological constructs (mini-tissues) of controlled mechanical properties. The proposed methodology is based on the use of embedded sensors and real-time material property sensing for feedback control of the bioprinted constructs mechanical property. There are three objectives of this dissertation: (1) experimenting and modeling the processes to understand the causal effect of process-material interactions on Bio-AM defects, (2) use of sensors to detect defects during printing, (3) prevention of the propagation of defects through closed-loop process control. This will help us understand the fundamentals of the bio-physical process interactions that govern the quality of printed biological tissue through empirical investigation of the sensor-based data This will also provide us with a real-time monitoring, closed-loop quality control strategy to prevent the propagation of quality defects by executing corrective actions during the whole duration of the printing process. / Doctor of Philosophy / As of April 2021, there are 107,550 patients on the national transplant list out of which approximately 39,000 patients received a transplant. Simultaneously, drug development remains an expensive and time-consuming endeavor. These burden on the public and healthcare system are expected to further increase compounded by the rapidly aging population in the United States with 80 million people expected to be older than 65 years old by 2040. Additive biomanufacturing processes, commonly referred to as 3D bioprinting processes, are automated biofabrication processes that offer great potential toward manufacturing future therapeutics and models for drug discovery. Despite all the benefits and the versatility that 3D printing provides, it does not come without its own shortcomings. Additive biomanufacturing is traditionally an open-loop process, meaning the process parameters are not adjusted during the biofabrication process making it challenging to detect and correct defects during processing and achieve high reproducibility and product quality. While the dimensional characteristics and material properties are important quality signatures of a cell-based products, there are additional signatures associated with the cell quality. Some of these quality attributes include cell viability, cell proliferation, metabolic activity, morphology, and gene expression profile. Given the clinical importance and invasive nature of bio-products such as scaffolds for tissue regeneration and stem cell therapy, rigorous approaches for characterization, monitoring, and control of quality are critical to future additive bio-manufacturing paradigms. In particular, the elastic modulus of the extracellular matrix has been found to have an influence on the cell morphology, proliferation, and differentiation process. Hence, it is an excellent parameter to monitor as a measure of tissue quality. However, the traditional techniques used to characterize tissue elastic modulus are low-throughput, offline techniques and face challenges with tissue integration. Thus, there is a need for integrated sensors that can measure the modulus of tissues during 3D bioprinting. This dissertation aims to address some of these issues by developing a multi-material 3D printing and pick-and-place approach to develop smart tissue cultureware and designing a tissue integrated closed-loop feedback sensor system for polymerization of hydrogels.