An official website of the United States government
Metamaterials with functional responses can exhibit varying properties under different conditions (e.g., wave‐based responses or deformation‐induced property variation). This work addresses rapid inverse design of such metamaterials to meet target qualitative functional behaviors, a challenge due to its intractability and nonunique solutions. Unlike data‐intensive and noninterpretable deep‐learning‐based methods, this work proposes the random‐forest‐based interpretable generative inverse design (RIGID), a single‐shot inverse design method for fast generation of metamaterials with on‐demand functional behaviors. RIGID leverages the interpretability of a random forest‐based “design → response” forward model, eliminating the need for a more complex “response → design” inverse model. Based on the likelihood of target satisfaction derived from the trained random forest, one can sample a desired number of design solutions using Markov chain Monte Carlo methods. RIGID is validated on acoustic and optical metamaterial design problems, each with fewer than 250 training samples. Compared to the genetic algorithm‐based design generation approach, RIGID generates satisfactory solutions that cover a broader range of the design space, allowing for better consideration of additional figures of merit beyond target satisfaction. This work offers a new perspective on solving on‐demand inverse design problems, showcasing the potential for incorporating interpretable machine learning into generative design under small data constraints.
more »
« less
Parameter Space Exploration of Cellular Mechanical Metamaterials Using Genetic Algorithms
Cellular materials widely exist in natural biologic systems such as honeycombs, bones, and woods. With advances in additive manufacturing, research on cellular metamaterials is emerging due to their unique mechanical performance. However, the design of on-demand cellular metamaterials usually requires solving a challenging inverse design problem for exploring complex structure–property relations of microstructured representative volume elements (RVEs) in the design domain. Here, we propose an experience-free and systematic methodology for exploring a parametrized system for microstructures of cellular mechanical metamaterials using a multiobjective genetic algorithm (GA). Globally, by considering the importance of the initial population selection for a population-based heuristic optimization method, we study the impact of the populations initialized by the different sampling methods on the optimal solutions. Locally, we develop our method by using a micro-GA with a new searching strategy, which requires the standard genetic algorithm to be conditionally run for a sufficient number of times with a small population size during the global searching process. We have applied our method to explore optimal solutions for applications mapped on two different parameter spaces of the cellular mechanical metamaterials with periodic and nonperiodic RVEs effectively and accurately.
more »
« less
- Award ID(s):
- 2053840
- PAR ID:
- 10418301
- Date Published:
- Journal Name:
- AIAA Journal
- ISSN:
- 0001-1452
- Page Range / eLocation ID:
- 1 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Recent advancements in manufacturing, finite element analysis (FEA), and optimization techniques have expanded the design possibilities for metamaterials, including isotropic and auxetic structures, known for applications like energy absorption due to their unique deformation mechanism and consistent behavior under varying loads. However, achieving simultaneous control of multiple properties, such as optimal isotropic and auxetic characteristics, remains challenging. This paper introduces a systematic design approach that combines modeling, FEA, genetic algorithm, and optimization to create tailored mechanical behavior in metamaterials. Through strategically arranging 8 distinct neither isotropic nor auxetic unit cell states, the stiffness tensor in a 5 × 5 × 5 cubic symmetric lattice structure is controlled. Employing the NSGA-II genetic algorithm and automated modeling, we yield metamaterial lattice structures possessing both desired isotropic and auxetic properties. Multiphoton lithography fabrication and experimental characterization of the optimized metamaterial highlights a practical real-world use and confirms the close correlation between theoretical and experimental data.more » « less
-
To maximize indoor daylight, design projects commonly use commercial optimization tools to find optimum window configurations. However, experiments show that such tools either fail to find the optimal solution or are very slow to compute in certain conditions.This paper presents a comparative analysis between a gradient-free optimization technique, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and the widely used Genetic Algorithm (GA)-based tool, Galapagos, to optimize window parameters to improve indoor daylight in six locations across different latitudes. A novel combination of daylight metrics, sDA, and ASE, is proposed for single-objective optimization comparison. Results indicate that GA in Galapagos takes progressively more time to converge, from 11 minutes in southernmost to 11 hours in northernmost latitudes, while runtime for CMA-ES is consistently around 2 hours. On average, CMA-ES is 1.5 times faster than Galapagos, while consistently producing optimal solutions. This paper can help researchers in selecting appropriate optimization algorithms for daylight simulation based on latitudes, runtime, and solution quality.more » « less
-
Optical sensing has the potential to be an important tool in the automated monitoring of food quality. Specifically, hyperspectral imaging has enjoyed success in a variety of tasks ranging from plant species classification to ripeness evaluation in produce. Although effective, hyperspectral imaging is prohibitively expensive to deploy at scale in a retail setting. With this in mind, we develop a method to assist in designing a low-cost multispectral imager for produce monitoring by using a genetic algorithm (GA) that simultaneously selects a subset of informative wavelengths and identifies effective filter bandwidths for such an imager. Instead of selecting the single fittest member of the final population as our solution, we fit a univariate Gaussian mixture model to the histogram of the overall GA population, selecting the wavelengths associated with the peaks of the distributions as our solution. By evaluating the entire population, rather than a single solution, we are also able to specify filter bandwidths by calculating the standard deviations of the Gaussian distributions and computing the full-width at half-maximum values. In our experiments, we find that this novel histogram-based method for feature selection is effective when compared to both the standard GA and partial least squares discriminant analysis.more » « less
-
Additive manufacturing (AM) has the potential to improve productivity especially processing time, cost and surface roughness. In similar lines, part separation for assembly-based design in additive manufacturing can help in improving productivity. This paper discusses an optimization technique for part separation in assembly based part design in additive manufacturing. The technique improves productivity by decreasing the processing time of printed parts, which is the sum of the build time and the assembly time. The technique uses optimal cutting planes for part separation that has distinct advantages compared to random cutting planes. The work discusses a Genetic Algorithm (GA) technique for part separation using planar cuts. The optimization technique provides the optimal number of parts for assembly and their corresponding build orientations for the minimum processing time. Three examples have been provided to demonstrate the application of the proposed method. Finally, the results from two examples are compared to the already established hill climbing optimization method for part separation.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.2514/1.J062864
