Reduced-order process-aware design of multiscale structures with architected materials
Creators: name: Sharpe, Conner Terry
Publisher: name: The University of Texas at Austin
Year: 2020
Subjects: Lattice
Optimization

description: Advances in additive manufacturing (AM) techniques have enabled the production of parts with ever increasing geometric complexity across multiple length scales. However, while expanding the space of possible designs is exciting from an engineering perspective, it also presents new challenges that the design community must address to fully take advantage of these capabilities. Analyzing and searching through potential designs in a multiscale setting can quickly become intractable. This work seeks to alleviate these concerns by decoupling the design process across hierarchical length scales and developing surrogate models that can link these scales in a computationally efficient manner. Reduced-order design representations are used at each length scale to render a more tractable search space. Surrogate optimization combined with geometric projection approaches are used to yield architected materials (or unit cell geometries) with desirable homogenized mechanical properties. The macroscale material density field is parameterized using B-Spline surfaces to design lattice structures for stiffness and strength. Many of the most popular AM processes also produce inherently anisotropic properties that depend on geometric design parameters such as feature orientation or size. This factor is often ignored in the design process and consequently causes deviations in manufactured performance from desired outcomes, thus limiting the adoption of AM. This work builds a data-driven method to incorporate design-dependent properties produced by particular AM processes into a multiscale design optimization workflow. A study of the mechanical properties of struts built at various orientations and sizes in metal powder bed fusion is used to build a process-aware material model. Accounting for these manufacturing concerns through design is shown to result in significant improvements in the effective properties of architected materials and lattice structures. Finally, AM allows for an unprecedented freedom in the topology or material layout within a given design domain. However, it is often difficult for engineers to utilize this freedom by fully considering design possibilities as there is a dearth of tools that allow for efficient topology exploration. This work explores the use of deep learning algorithms as generative design tools, allowing for quick visual exploration of design options in the early stages of design. Specifically, conditional generative adversarial networks are used to encode a diverse suite of complex topologies derived from structural optimization in a compact latent space to enable many design options to be considered for the problem at hand. Design factors such as type of architected material, allowable mass, and loading conditions can be included in the exploration. These tools can also be used as reduced-order design parameterizations in themselves to enable further directed design when traditional optimization approaches may be difficult to implement effectively.