With advances in additive manufacturing, the technology has significantly increased the applications in a wide range of industrial sectors. For example, stereolithography has become a promising candidate for the mass production of energy absorption architected cellular materials due to its capability to fabricate complex material designs with advantageous characteristics. As stereolithography is being applied in different industrial settings, uncertainties become a critical factor that influences the performance of the products. As a solution, uncertainty quantification is needed to understand the impact of uncertainties on the overall performance variability of the design and inform decision-makers to enhance system robustness and reliability better. This paper presented a novel framework for accelerated uncertainty quantification based on integrating physics-based computational modeling and data-driven surrogate models. The high-fidelity finite element model can be built and validated based on experimental tests. With an adaptive sampling technique, the surrogate model can be built with fewer expensive simulation runs while achieving a desirable modeling accuracy, saving the computational cost. Then, uncertainty quantification can be conducted accordingly using the developed surrogate model, which provides insights for the design and manufacturing decision-making processes of the architected cellular materials utilizing the additive manufacturing technology.

• Published 1 paper as the 1st author on ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering