Fabricated shape estimation for additive manufacturing processes with uncertainty. (October 2020)

• Record Type: Journal Article
• Authors: Korneev, Svyatoslav; Wang, Ziyan; Thiagarajan, Vaidyanathan; Nelaturi, Saigopal
• Is Part Of: Computer aided design. Volume 127(2020)
• Publication Date: 2020-10
• Subjects: Additive manufacturing -- Multiphysics -- Shape estimation -- Machine learning -- Visualization; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2020.102852
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: 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. Graphical abstract: Highlights: Estimate as-manufactured shape that captures manufacturing uncertainty. Uncertainty is represented as a 3D kernel obtained by deconvolving. Convolution of the kernel with a tool-path estimates the as-printed shape.