An official website of the United States government
Abstract Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35% higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.
more »
« less
Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework
Cellular materials have been widely used in load carrying lightweight structures. Although lightweight increases natural frequency, low stiffness of cellular structures reduces natural frequency. Designing structures with higher natural frequency can usually avoid resonance. In addition, because of the less amount of materials used in cellular structures, the energy absorption capability usually decreases such as under impact loading. Therefore, designing cellular structures with higher natural frequency and higher energy absorption capability is highly desired. In this study, machine learning and novel inverse design techniques enable to search a huge space of unexplored structural designs. In this study, machine learning regression and Generative Neural Networks (GANs) were used to form an inverse design framework. Optimal cellular unit cells that surpass the performance of biomimetic structures inspired from honeycomb, plant stems and trabecular bone in terms of natural frequency and impact resistance were discovered using machine learning. The discovered optimal cellular unit cells exhibited 30–100% higher natural frequency and 300% higher energy absorption than those of the biomimetic counterparts. The discovered optimal unit cells were validated through experimental and simulation comparisons. The machine learning framework in this study would help in designing load carrying engineering structures with increased natural frequency and enhanced energy absorption capability.
more »
« less
- Award ID(s):
- 1946231
- PAR ID:
- 10319515
- Date Published:
- Journal Name:
- Frontiers in Mechanical Engineering
- Volume:
- 7
- ISSN:
- 2297-3079
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract High-performance lightweight architectures, such as metallic microlattices with excellent mechanical properties have been 3D printed, but they do not possess shape memory effect (SME), limiting their usages for advanced engineering structures, such as serving as a core in multifunctional lightweight sandwich structures. 3D printable self-healing shape memory polymer (SMP) microlattices could be a solution. However, existing 3D printable thermoset SMPs are limited to either low strength, poor stress memory, or non-recyclability. To address this issue, a new thermoset polymer, integrated with high strength, high recovery stress, perfect shape recovery, good recyclability, and 3D printability using direct light printing, has been developed in this study. Lightweight microlattices with various unit cells and length scales were printed and tested. The results show that the cubic microlattice has mechanical strength comparable to or even greater than that of metallic microlattices, good SME, decent recovery stress, and recyclability, making it the first multifunctional lightweight architecture (MLA) for potential multifunctional lightweight load carrying structural applications.more » « less
-
Tensegrity structures made from rigid rods and elastic cables have unique characteristics, such as being lightweight, easy to fabricate, and high load-carrying to weight capacity. In this article, we leverage tensegrity structures as wheels for a mobile robot that can actively change its shape by expanding or collapsing the wheels. Besides the shape-changing capability, using tensegrity as wheels offers several advantages over traditional wheels of similar sizes, such as a shock-absorbing capability without added mass since tensegrity wheels are both lightweight and highly compliant. We show that a robot with two icosahedron tensegrity wheels can reduce its width from 400 to 180 mm, and simultaneously, increase its height from 75 to 95 mm by changing the expanded tensegrity wheels to collapsed disk-like ones. The tensegrity wheels enable the robot to overcome steps with heights up to 110 and 150 mm with the expanded and collapsed configuration, respectively. We establish design guidelines for robots with tensegrity wheels by analyzing the maximum step height that can be overcome by the robot and the force required to collapse the wheel. The robot can also jump onto obstacles up to 300-mm high with a bistable mechanism that can gradually store but quickly release energy. We demonstrate the robot's locomotion capability in indoor and outdoor environments, including various natural terrains, like sand, grass, rocks, ice, and snow. Our results suggest that using tensegrity structures as wheels for mobile robots can enhance their capability to overcome obstacles, traverse challenging terrains, and survive falls from heights. When combined with other locomotion modes (e.g., jumping), such shape-changing robots can have broad applications for search-and-rescue after disasters or surveillance and monitoring in unstructured environments.more » « less
-
Various factors are responsible for injuries that occur in the U.S. Army soldiers. In particular, rucksack load carriage equipment influences the stability of the lower extremities and possibly affects gait balance. The objective of this investigation was to assess the gait and local dynamic stability of the lower extremity of five subjects as they performed a simulated rucksack march on a treadmill. The Motek Gait Real-time Interactive Laboratory (GRAIL) was utilized to replicate the environment of the rucksack march. The first walking trial was without a rucksack and the second set was executed with the All-Purpose Lightweight Individual Carrying Equipment (ALICE), an older version of the rucksack, and the third set was executed with the newer rucksack version, Modular Lightweight Load Carrying Equipment (MOLLE). In this experiment, the Inertial Measurement Unit (IMU) system, Dynaport was used to measure the ambulatory data of the subject. This experiment required subjects to walk continuously for 200 seconds with a 20kg rucksack, which simulates the real rucksack march training. To determine the dynamic stability of different load carriage and normal walking condition, Local Dynamic Stability (LDS) was calculated to quantify its stability. The results presented that comparing Maximum Lyapunov Exponent (LyE) of normal walking was significantly lower compared to ALICE (P=0.000007) and MOLLE (P=0.00003), however, between ALICE and MOLLE rucksack walking showed no significant difference (P=0.441). The five subjects showed significantly improved dynamic stability when walking without a rucksack in comparison with wearing the equipment. In conclusion, we discovered wearing a rucksack result in a significant (P < 0.0001) reduction in dynamic stability.more » « less
-
null (Ed.)Abstract Automated inverse design methods are critical to the development of metamaterial systems that exhibit special user-demanded properties. While machine learning approaches represent an emerging paradigm in the design of metamaterial structures, the ability to retrieve inverse designs on-demand remains lacking. Such an ability can be useful in accelerating optimization-based inverse design processes. This paper develops an inverse design framework that provides this capability through the novel usage of invertible neural networks (INNs). We exploit an INN architecture that can be trained to perform forward prediction over a set of high-fidelity samples and automatically learns the reverse mapping with guaranteed invertibility. We apply this INN for modeling the frequency response of periodic and aperiodic phononic structures, with the performance demonstrated on vibration suppression of drill pipes. Training and testing samples are generated by employing a transfer matrix method. The INN models provide competitive forward and inverse prediction performance compared to typical deep neural networks (DNNs). These INN models are used to retrieve approximate inverse designs for a queried non-resonant frequency range; the inverse designs are then used to initialize a constrained gradient-based optimization process to find a more accurate inverse design that also minimizes mass. The INN-initialized optimizations are found to be generally superior in terms of the queried property and mass compared to randomly initialized and inverse DNN-initialized optimizations. Particle swarm optimization with INN-derived initial points is then found to provide even better solutions, especially for the higher-dimensional aperiodic structures.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fmech.2021.779098
