scholarly journals Discovery of Cellular Unit Cells With High Natural Frequency and Energy Absorption Capabilities by an Inverse Machine Learning Framework

2021 ◽  
Vol 7 ◽  
Author(s):  
Adithya Challapalli ◽  
John Konlan ◽  
Dhrumil Patel ◽  
Guoqiang Li
Keyword(s):  
Machine Learning ◽  
Natural Frequency ◽  
Energy Absorption ◽  
Impact Resistance ◽  
Inverse Design ◽  
Cellular Structures ◽  
Engineering Structures ◽  
Learning Framework ◽  
Load Carrying ◽  
Unit Cells

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.

Impact Resistance and Energy Absorption of Functionally Graded Cellular Structures

2011 ◽  
Vol 69 ◽  
pp. 73-78 ◽  
Author(s):  
Xiao Kai Wang ◽  
Zhi Jun Zheng ◽  
Ji Lin Yu ◽  
Chang Feng Wang
Keyword(s):  
Energy Absorption ◽  
Impact Resistance ◽  
Functionally Graded ◽  
Cellular Structures ◽  
Stress Wave Propagation ◽  
Initial Kinetic Energy ◽  
Gradual Change ◽  
Two Dimensional ◽  
The One ◽  
The Impact

The dynamic response of functionally graded cellular structures subjected to impact of a finite mass was investigated in this paper. Compared to a cellular structure with a uniform cell size, the one with gradually changing cell sizes may improve many properties. Based on the two-dimensional random Voronoi technique, a two-dimensional topological configuration of cellular structures with a linear density-gradient in one direction was constructed by changing the cell sizes. The finite element method using ABAQUS/Explicit code was employed to investigate the energy absorption and the influence of gradient on stress wave propagation. Results show that functionally graded cellular structures studied are superior in energy absorption to the equivalent uniform cellular structures under low initial kinetic energy impacts, and the performance of such structures can be significantly improved when the density difference is enlarged. The stress levels at the impact and support ends may be reduced by introducing a gradual change of density in cellular structures when the initial impact velocity is low.

A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades

2021 ◽  
Author(s):  
Sayan Ghosh ◽  
Valeria Andreoli ◽  
Govinda A. Padmanabha ◽  
Cheng Peng ◽  
Steven Atkinson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Turbine Blades ◽  
Inverse Design ◽  
Learning Framework ◽  
Industrial Gas Turbines ◽  
Critical Components ◽  
Turbine Blade Design ◽  
The Individual ◽  
Probabilistic Machine Learning

Abstract One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from a several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely Pro-ML IDeAS, to carry out an explicit inverse design. Pro-ML IDeAS calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of 2D airfoil of turbine blades.

scholarly journals Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adithya Challapalli ◽  
Guoqiang Li
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Sandwich Structures ◽  
Unit Cell ◽  
Machine Learning Algorithms ◽  
Compression Tests ◽  
Load Carrying ◽  
Unit Cells ◽  
Lattice Unit ◽  
Lattice Core

AbstractHerein 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.

scholarly journals Inverting the structure–property map of truss metamaterials by deep learning

2022 ◽  
Vol 119 (1) ◽  
pp. e2111505119
Author(s):  
Jan-Hendrik Bastek ◽  
Siddhant Kumar ◽  
Bastian Telgen ◽  
Raphaël N. Glaesener ◽  
Dennis M. Kochmann
Keyword(s):  
Deep Learning ◽  
Design Space ◽  
Cellular Structures ◽  
Patient Specific ◽  
Structure Property ◽  
Physical Constraints ◽  
Lightweight Structures ◽  
Learning Framework ◽  
Unit Cells ◽  
Spatially Varying

Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.

scholarly journals A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6847
Author(s):  
Thomas Fleet ◽  
Khangamlung Kamei ◽  
Feiyang He ◽  
Muhammad A. Khan ◽  
Kamran A. Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dynamic Response ◽  
Natural Frequency ◽  
Learning Approach ◽  
Engineering Structures ◽  
Machine Learning Approach ◽  
Damage Data ◽  
Simultaneous Prediction ◽  
Different Temperatures ◽  
Non Destructive

Accurate damage detection in engineering structures is a critical part of structural health monitoring. A variety of non-destructive inspection methods has been employed to detect the presence and severity of the damage. In this research, machine learning (ML) algorithms are used to assess the dynamic response of the system. It can predict the damage severity, damage location, and fundamental behaviour of the system. Fatigue damage data of aluminium and ABS under coupled mechanical loads at different temperatures are used to train the model. The model shows that natural frequency and temperature appear to be the most important predictive features for aluminium. It appears to be dominated by natural frequency and tip amplitude for ABS. The results also show that the position of the crack along the specimen appears to be of little importance for either material, allowing simultaneous prediction of location and damage severity.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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