J. Mech. Des. Oct 2019, 141(10): 101101
The capabilities of additive manufacturing for creating products that are rich in shape, material, hierarchical, and functional complexities offer high potentials to revolutionize existing product development processes. However, searching design solutions in such a multidimensional design space is a challenging task. 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 stages of product design. A 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 tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses in different directions.