1101 Advances in Multiscale Computational Methods for the Process and Performance Modeling of Metal-based Additive Manufacturing

Deepankar Pal, 3DSIM
Joe Bishop, Sandia NL
Additive manufacturing (AM) holds the promise of revolutionizing current paradigms in engineering design, manufacturing, and material and structural performance. However, for metal-based AM, numerous challenges exist in material quality and structural reliability. To fully realize the possibilities of AM requires significant advances in both the process and performance modeling of additively manufactured materials and structures. The modeling and simulation of AM processes is multidisciplinary in nature, involving multiple physics as well as multiple time and space scales, thus requiring significant advances in multiscale computational methods that are tailored for metal-based additive manufacturing. The purpose of this minisymposium is to bring together researchers who are developing and applying novel multiscale computational methods for both the process and performance modeling of metal-based additive manufacturing.

Contributions to this mini-symposium are solicited that emphasize multiscale multiphysics modeling in both space and time. While contributions in all aspects related to the process and performance modeling of metal-based additive manufacturing are invited, the featured topics will include:

· upscaling of melt-pool physics and spatiotemporal gradients for use in part-scale modeling
· residual stress modeling
· defect indicators including lack of fusion, keyhole porosity and balling.
· part distortion
· microstructure predictions and optimization
· multiscale non-linear topology optimization for AM
· multiscale design for AM
· Build Volume Optimization with residual stress constraints for reduced delamination
· rapid qualification methodologies
· errors in homogenization methodologies and their quantification
· incorporation of material texture and anisotropy
· performance modeling
· feedback control and process optimization
· integration of modeling and in-situ experimental techniques for enhanced simulation fidelity
· uncertainty quantification for AM
· incorporation of x-ray computed tomography data into performance predictions