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.

我们提出了一种将增材制造（AM）工艺参数和给定工具路径映射到制成品表示的方法捕获机器特定制造不确定性的形状。解决了以最小制造规模捕获沉积过程的多物理场模型，以准确模拟局部材料的积累。用于多物理场模拟的替代模型用于通过沿工具路径局部改变材料的空间分布来实际模拟材料累积。这将生成一个训练集，表示制造出的形状的变体类别。然后，将特定于机器的制造不确定性表示为3D内核，该3D内核是通过将模拟的印刷形状与刀具路径解卷积而获得的。该内核可以很好地估计局部材料累积的可能性，而与所选零件和刀具路径无关。内核与工具路径的卷积以及结果字段的适当超级集相结合，提供了一种计算有效的方式来估计AM零件的制造形状。效率来自GPU上卷积的高度并行化实现。我们演示了高分辨率形状估计和使用此方法构造的印刷零件的可视化。我们通过模拟基于液滴的AM的构建过程，通过对3D Navier-Stokes多相流与传热和相变相结合的偏微分方程组进行模型阶数缩减，来验证该方法的有效性。效率来自GPU上卷积的高度并行化实现。我们演示了高分辨率形状估计和使用此方法构造的印刷零件的可视化。我们通过模拟基于液滴的AM的构建过程，通过对3D Navier-Stokes多相流与传热和相变相结合的偏微分方程组进行模型阶数缩减，来验证该方法的有效性。效率来自GPU上卷积的高度并行化实现。我们演示了高分辨率形状估计和使用此方法构造的印刷零件的可视化。我们通过模拟基于液滴的AM的构建过程，通过对3D Navier-Stokes多相流与传热和相变相结合的偏微分方程组进行模型阶数缩减，来验证该方法的有效性。