Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.

In this study, the effect on the conductance of polymer nanocomposites considering quantum tunneling resistance is investigated with respect to the chirality of carbon nanotubes (CNTs) and uncertainties in the geometric parameters of CNTs by using Monte Carlo simulations. The random spatial placement for CNTs was accomplished with a one-dimensional line segment and the periodic boundary conditions were applied to CNTs in the two-dimensional representative volume element. Intersection points between each CNT were calculated to obtain connectivity lists of the connected network path. Both the intrinsic resistance of the CNT and the inter-CNT tunneling resistance were considered in this model. In addition, the in-house code developed was validated by comparison with several experimental datasets from the literature. Unlike past studies, uncertainties in the chiral index of single-wall CNTs concerning armchair and zig–zag structures have been considered here and the electrical conductivity and percolation threshold are predicted.