Adaptively sampled distance functions: A unifying digital twin representation for advanced manufacturing

Abstract

Digital twin tools for additive manufacturing (AM) are constrained by the underlying representations of component geometry that are currently in wide use. Mesh, voxel, and parametric surface representations require numerous conversions to intermediate representations at multiple points throughout the processing chain. Each conversion introduces additional error in the geometric representation and complicates comparison of in-situ process sensor data to the as-designed component. Additionally, the limited interoperability of the various representations produced throughout the process chain limit the insights available from current digital twin tools. We introduce a novel framework based on a unifying geometric representation that serves the complete AM digital thread. The presented GPU-accelerated, adaptively sampled distance function (ASDF) framework serves as a foundation for component design and path planning tools, especially for real-time path planning in AM, as well as provides a baseline representation of geometry from control systems, and enables rapid comparison of in-situ sensor data to the as-designed model without intermediate conversion, greatly reducing the burden of reducing such data to usable process insights.