Additive manufacturing has enabled the production of intricate lattice structures that meet stringent design requirements with minimal mass. While many methods such as lattice-based topology optimization are being developed to enable engineers to design lightweight structures for static loading, there is a need for design tools for achieving dynamic loading requirements. Lattice structures have shown particular promise as low-mass energy absorbers, but the computational expense of nonlinear finite element analysis (FEA) and the difficulty of obtaining objective gradient information has made their optimization for impact loading particularly challenging. This study proposes a Bayesian optimization framework that enables designers to determine the lattice structure design that provides the best performance under a specified impact, while managing the structure’s mass. A two-dimensional nonlinear explicit finite element model is constructed for multiple lattice unit cell types under impact, and parameterized with respect to geometric attributes such as height, width, and strut thickness. These parameters are considered design variables in a minimization problem. The objective function is a weighted sum of the part mass and a common injury metric, the head injury criterion (HIC), at the projectile-lattice interface. Penalty values are assigned to designs that fail to absorb the entire impact. Design for additive manufacturing (DFAM) constraints including minimum feature thickness and maximum overhang angle are applied to ensure that the optimal design can be manufactured without subsequent manual refinement or post-processing. The algorithm begins by evaluating a small number of randomly selected designs, and using the results to train a surrogate model to represent the true objective function, which is expensive to evaluate directly since it involves nonlinear FEA. In this study, a Gaussian process with a Matérn family covariance function having user-tuned hyper-parameters is used as the surrogate model. An acquisition function, which incorporates both the mean and uncertainty of the surrogate model at every point throughout the design space, is used to determine which design to evaluate next, according to a user-tuned tradeoff between exploration and exploitation. The hyper-parameters and exploration-exploitation tradeoff are tuned for each type of unit cell in a set of lower-dimensional problems, then carried over to higher-dimensional problems involving lattice structures. Results reveal the effects of the additive manufacturing constraints and the importance of the weights assigned to the performance and mass objectives. Future work can include expanding this framework to allow for lattice structure designs with non-rectangular boundaries, possibly by incorporating it into a sequence involving traditional design optimization algorithms. Opportunities also exist for leveraging graded lattice structures.


Bayesian Optimization of Energy-Absorbing Lattice Structures for Additive Manufacturing

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

IMECE2020 - Virtual Conference

Supporting the need among researchers worldwide for additional online and multimedia content, ASME now offers the ASME Conference Video Collection. This platform will collect and provide long-term access to conference presentation videos, encompassing all technology areas within ASME's technical conference and events program. About 30,000-40,000 engineers across all mechanical engineering and related disciplines participate in ASME conferences each year. This includes authors of approximately 6,000 to 8,000 technical papers published by ASME, the majority of whom also submit video presentations for our virtual conferences. The video collection will be available as an add-on to existing product packages or as a separate subscription.