Predictive model building across different process conditions and shapes in 3D printing

摘要

Predictive models for geometric shape deformation constitute an important component in geometric fidelity control for three-dimensional (3D) printing. However, model building is made difficult by the wide variety of possible process conditions and shapes. A methodology that can make full use of data collected on different shapes and conditions, and reduce the haphazard aspect of traditional statistical model building techniques, is necessary in this context. We develop a new Bayesian procedure based on the effect equivalence and modular deformation features concepts that incorporates all available data for the systematic construction of predictive deformation models. Our method is applied to dramatically facilitate modeling of the multiple deformation profiles that exist in flat cylinders with different types of cavities. Ultimately, our Bayesian approach connects different process conditions and shapes to provide a unified framework for geometric fidelity control in 3D printing.