Dr. Ju received funding from DOE on large scale additive manufacturing optimization, in collaboration with LM Industries and ORNL.


Summary: LM Industries, a digital OEM, utilizes large scale additive manufacturing (AM) to produce vehicles, resulting in reduced design to manufacture times (50%), decreased embodied energy (37%), and lowered carbon dioxide emissions (52%), when compared to traditional manufacturing methods. This work proposes to optimize the AM process to further reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that a regression-based layer time control model, using thermal images, could result in up to 30% build time reduction for simple geometries. This proposed work would use HPC to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost.

automotive component that would be benefit from thermo-mechanical formin