skip to main content

Title: 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 more » energy absorption capability. « less
; ; ;
Award ID(s):
Publication Date:
Journal Name:
Frontiers in Mechanical Engineering
Sponsoring Org:
National Science Foundation
More Like this
  1. 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%more »higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.« less
  2. 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.

  3. The characteristics of metal and materials are very important to design any component so that it should not fail in the life of the service. The properties of the materials are also an important consideration while setting the manufacturing parameters which deforms the raw material to give the design shape without providing any defect or fracture. For centuries the commonly used method to characterize the material is the traditional uniaxial tension test. The standard has been created for this test by American Standard for Testing Materials (ASTM) – E8. This specimen is traditionally been used to test the materials and extract the properties needed for designing and manufacturing. It should be noted that the uniaxial tension test uses one axis to test the material i.e., the material is pulled in one direction to extract the properties. The data acquired from this test found enough for manufacturing operations of simple forming where one axis stretching is dominant. Recently a sudden increase in the usage of automotive vehicles results in sudden increases in fuel consumption which results in an increase in air pollution. To cope up with this challenge federal government is implying the stricter environmental regulation to decrease air pollution. Tomore »save from the environmental regulation penalty vehicle industry is researching innovation which would reduce vehicle weight and decrease fuel consumption. Thus, the innovation related to light-weighting is not only an option anymore but became a mandatory necessity to decrease fuel consumption. To achieve this target, the industry has been looking at fabricating components from high strength to ultra-high strength steels or lightweight materials. This need is driven by the requirement of 54 miles per gallon by 2025. In addition, the complexity in design increased where multiple individual parts are eliminated. This integrated complex part needs the complex manufacturing forming operation as well as the process like warm or hot forming for maximum formability. The complex forming process will induce the multi-axial stress states in the part, which is found difficult to predict using conventional tools like tension test material characterization. In many pieces of literature limiting dome height and bulge tests were suggested analyzing these multi-axial stress states. However, these tests limit the possibilities of applying multi-axial loading and resulting stress patterns due to contact surfaces. Thus, a test machine called biaxial test is devised which would provide the capability to test the specimen in multi-axial stress states with varying load. In this paper, two processes, limiting dome test and biaxial test were experimented to plot the forming limit curve. The forming limit curve serves the tool for the design of die for manufacturing operation. For experiments, the cruciform test specimens were used in both limiting dome test and biaxial test and tested at elevated temperatures. The forming limit curve from both tests was plotted and compared. In addition, the strain path, forming, and formability was investigated and the difference between the tests was provided.« less
  4. The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, andmore »tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.« less
  5. 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 foundmore »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.« less