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Accelerating Robustness Prediction

The falling prices of 3D printers and 3D printing services have made rapid prototyping technologies increasingly accessible. Artists and engineers skilled with a 3D modeling program can design shapes and have them cheaply, quickly, and easily fabricated in a variety of materials. As this process becomes widespread, however, it is essential to develop tools to predict the strength of the fabricated model; insufficiently reinforced models will break in the customer’s hands and might not even survive the printing process. Such a tool should run fast enough not to discourage its use and ideally would provide real-time feedback while the user is modeling. Although such interactive feedback is still out of reach, this paper discusses how to accelerate various stages of an existing robustness prediction pipeline currently under development by James Zhou and Denis Zorin.