Fabricated shape estimation for additive manufacturing processes with uncertainty

We present an approach to map Additive Manufacturing (AM) process parameters and a given tool path to a representation of the as-manufactured shape that captures machine-specific manufacturing uncertainty. Multi-physics models that capture the deposition process at the smallest manufacturing scale are solved to accurately simulate local material accumulation. A surrogate model for the multiphysics simulation is used to practically simulate the material accumulation by locally varying the spatial distribution of material along the tool path. This generates a training set representing a variational class of as-manufactured shapes. Machine specific manufacturing uncertainty is then represented as a 3D kernel obtained by deconvolving the simulated as-printed shape with the tool path. This kernel provides a good estimate of the probability of local material accumulation independent of the chosen part and tool-path. Convolution of the kernel with a tool-path combined with an appropriate super-level-set of the resulting field provides a computationally efficient way to estimate the as-manufactured shape of AM parts. The efficiency results from the highly parallelized implementation of convolution on the GPU. We demonstrate high-resolution shape estimation and visualization of as-printed parts constructed using this approach. We validate the method using data generated by simulating a build process for droplet-based AM, by performing model order reduction of a system of partial differential equations for the 3D Navier-Stokes multiphase flows coupled with heat-transfer and phase change. (C) 2020 Elsevier Ltd. All rights reserved.