Todd Weisgraber (14-ERD-087)
Abstract
Additive manufacturing is a revolutionary technology that offers the ability to fabricate, one layer at a time, the complex three-dimensional structures in a digital model through photochemical, electronic, or thermal manipulation of a feeder stock of material such as a resin or metallic powder. By tightly coupling design and manufacturing, it is spurring innovation across diverse applications in aerospace, biomedicine, and high explosives. The efficacy of each process is governed by several parameters, which traditionally have been adjusted in the laboratory to achieve robust and repeatable structures. This experimental approach towards optimization is time intensive, so there is a need for a more rigorous approach to determine if a given structure can be built, and if so, select the fabrication parameters that will produce repeatable parts with the desired material properties and functionality. Therefore, we will pursue the development of an optimization framework for our electrophoretic deposition and stereolithography models and apply this computational capability to improve our understanding of and streamline the manufacturing processes.
We expect to develop a new computational capability by incorporating our improved and experimentally validated process models for stereolithography optical additive manufacturing and electrophoretic coating deposition into a highly parallel optimization framework. By exercising this framework, we will provide a systematic and robust approach to determine the best processing parameters to manufacture a variety of materials and structures that could serve as a set of standards and guidelines previously unobtainable by experiments alone. These optimization capabilities would enable more reliable builds, shorter build times, finer control over the microstructure, and aid in the design of scaled-up implementations of these processes. The outcome of our work will help establish an engineering competency in fabrication optimization.
Mission Relevance
Our effort is directly aligned with the Laboratory's strategic core competencies in advanced materials and manufacturing and high-performance computing, simulation, and data science to address the scientific and engineering challenges of accelerating the design, fundamental understanding, and development of new materials and manufacturing processes with the aid of a new computational capability. If successful, improvements in lithography and electrophoretic deposition processes enabled by this project would impact other Livermore efforts currently utilizing those fabrication methods for high explosives, target development, and other NNSA and defense applications.
FY15 Accomplishments and Results
In FY15 our accomplishments included (1) developing a three-dimensional stereolithography model, (2) incorporating light scattering and multiple stereolithography layer exposures into the model, (3) optimizing the experimental parameters for larger cure depths, (4) developing an electrophoretic deposition model (see figure), and (5) detailing the electrophoretic validation simulations.
Publications and Presentations
- Giera, B., et al., "Mesoscale particle-based model of electrophoresis." J. Electrochem. Soc. 162(11), D3030 (2015). LLNL-JRNL-669534.
