Conductivity inversion of unidirectional CFRP laminate based on ECT using neural network

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1431-1438
Author(s):  
Hongli Ji ◽  
Wei Shen ◽  
Chao Zhang ◽  
Xiaojuan Xu ◽  
Jinhao Qiu
Keyword(s):  
Neural Network ◽  
Eddy Current ◽  
Three Dimensional ◽  
Electrical Anisotropy ◽  
Détection Signal ◽  
Cfrp Laminate ◽  
Electromagnetic Model ◽  
The Neural Network ◽  
Probe Voltage ◽  
Detection Signal

For the electrical anisotropy of carbon fiber reinforced polymer (CFRP), conductivity of unidirectional CFRP laminate in three directions was inverted in this paper. The three-dimensional eddy current electromagnetic model of unidirectional composites was constructed by ANSYS software, and the influence of the electrical conductivity of the material on the detection signal of the probe in the longitudinal, transverse and thickness directions was studied. In order to improve the amplitude of the probe output signal induced by the change of conductivity, the optimal detection angle of the eddy current probe was determined. On this basis, the relationship between the conductivity and the detection signal was studied to estimate the initial values of the conductivity based on the experimental data obtained by the eddy current testing (ECT). According to the forward model, the theoretical probe voltage under the estimated conductivity were calculated. The database consisting of conductivity and corresponding theoretical results was built for the neural network to construct the mapping that can estimate conductivity by experimental results. Using neural network for iteration, the conductivity was inverted quickly and precisely.

scholarly journals A convolutional neural network for defect classification in Bragg coherent X-ray diffraction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Bruce Lim ◽  
Ewen Bellec ◽  
Maxime Dupraz ◽  
Steven Leake ◽  
Andrea Resta ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diffraction Pattern ◽  
Three Dimensional ◽  
X Ray Diffraction ◽  
Coherent Diffraction Imaging ◽  
The Neural Network ◽  
Diffraction Imaging ◽  
Diffraction Patterns ◽  
Defect Recognition

AbstractCoherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials. These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties and functionality of materials. However, their identification in Bragg coherent diffraction imaging remains a challenge and requires significant data mining. The ability to identify defects from the diffraction pattern alone would be a significant advantage when targeting specific defect types and accelerates experiment design and execution. Here, we exploit a computational tool based on a three-dimensional (3D) parametric atomistic model and a convolutional neural network to predict dislocations in a crystal from its 3D coherent diffraction pattern. Simulated diffraction patterns from several thousands of relaxed atomistic configurations of nanocrystals are used to train the neural network and to predict the presence or absence of dislocations as well as their type (screw or edge). Our study paves the way for defect-recognition in 3D coherent diffraction patterns for material science.

scholarly journals DEVELOPMENT OF ALGORITHMIC SUPPORT AND A DEDICATED DEVICE FOR IMAGING THE INTERNAL ORGANS OF THE PATIENT ACCORDING TO MRI AND CT SCANNERS

2019 ◽  
Vol 22 (6) ◽  
pp. 189-197
Author(s):  
E. S. Sirota ◽  
M. I. Truphanov
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Color Difference ◽  
Learning Criterion ◽  
Average Value ◽  
Advantages And Disadvantages ◽  
The Neural Network ◽  
Training Stage ◽  
Algorithmic Support

In work the algorithm of restoration of the images damaged as a result of influence of noise of various nature is considered. The advantages and disadvantages of the existing approaches, as well as the prospects of using artificial neural networks, are noted. A double-layer neural network is used as an image restoration tool, and it is assumed that the location of the damaged pixels is known. A neuron is represented as a 3x3 array, where each element of the array has a pixel color value that corresponds to the value of that color in the palette. The neural network is trained on intact images, while the color difference of pixels acts as a learning criterion. For a more accurate restoration, it is recommended at the training stage to select images similar in color to damaged ones. At the recovery stage, neurons (3x3) are formed around the damaged pixels, so that the damaged pixel is located in the middle of the neuron data array. The damaged pixel is assigned a neuron value depending on the average value of the weights matrix. An algorithm for the restoration of pixels, as well as its software implementation. The simulation was carried out in the RGB palette separately for each channel. To assess the quality of the recovery were selected groups of images with varying degrees of damage. Unlike existing solutions, the algorithm has the simplicity of implementation. The  research results show that regardless of the degree of damage (within 50%), about 70% of damaged pixels are restored. Further studies suggest a modification of the algorithm to restore images with enlarged areas of damage, as well as adapting it to restore three-dimensional images.

scholarly journals Reconstruction of 3D Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3D Models from ShapeNetCore Dataset

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1553 ◽  
Author(s):  
Audrius Kulikajevas ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Sanjay Misra
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
A Priori ◽  
Three Dimensional ◽  
Qualitative Evaluation ◽  
3D Models ◽  
Depth Sensors ◽  
The Neural Network ◽  
Artificial Neural Network Ann ◽  
Commercial Applications

Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.

Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

2012 ◽  
Vol 510 ◽  
pp. 723-728 ◽  
Author(s):  
Liang Cheng ◽  
Hui Chang ◽  
Bin Tang ◽  
Hong Chao Kou ◽  
Jin Shan Li
Keyword(s):  
Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Back Propagation ◽  
Three Dimensional ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Ann Model ◽  
The Neural Network ◽  
Hidden Layer

In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.

scholarly journals Q QUANTITATIVE METHOD OF CALCULATING APPROACHES OF EXERCISES ON THE BASIS OF OUTPUT SIGNAL OF THE NEURAL NETWORK

2020 ◽  
pp. 65-69
Author(s):  
Juliy Broyda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Output Signal ◽  
Three Dimensional ◽  
Test Scheme ◽  
Human Posture ◽  
Professional Activities ◽  
The Neural Network ◽  
Human Joints

Today, artificial intelligence and computer vision systems are developing rapidly in the world; in particular, new architectures of neural networks that assess the three-dimensional human posture on video are produced. Such neural networks require analysis of their output signal in order to obtain useful data for the end user and their subsequent integration into user systems. The author proposes a new method of analysis of the output signal of the neural network, which estimates the position of a person in space that performs the calculation of repetitions of the exercise "squat". This method is based on the state machine, which adds one to the repetition counter at the end of the exercise cycle. The application of this method in the initial stages of the algorithm of exercise analysis will allow further development of systems that test squats and help athletes and coaches during training, as well as scientists in the field of biomechanics during their professional activities. A distinctive feature of this method is resistance to both input signal emissions, i.e. incorrect results of human posture recognition by the neural network, and to human movements that do not belong to the exercise directly. Also, the application of this method to the analysis of the neural network signal allows to combine the positive qualities inherent in neural networks used in computer vision (admissibility of high variability of clothing and background), and the positive qualities of analytical and algorithmic methods (easy interpretability of results, convenient adjustment, possibility to use the experts' subject experience for the selection of parameters). This method is not specific to any particular neural network and therefore can be used at the output of almost any system that determines the position of human joints in space. In addition to the description of the method, the article presents the results of its tests in different conditions. This test scheme can be used not only to apply this method to the exercise "squat", but to any other cyclic exercise.

Study on an Underwater Flexible Manipulator Based on Hydraulic Drive

2020 ◽  
Author(s):  
Xin Li ◽  
He Xu ◽  
Chen Yang ◽  
Haihang Wang ◽  
Fengshu Yu
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Three Dimensional ◽  
Hydraulic Drive ◽  
Flexible Manipulator ◽  
Artificial Muscles ◽  
Flexible Robot ◽  
Hydraulic Power ◽  
The Neural Network ◽  
Real Scene

Abstract The underwater flexible robot is a field of continuous exploration and innovation. An underwater flexible manipulator with the functions of bending and grasping is presented in this paper, which is driven by the water hydraulic. The flexible manipulator is consisted mainly of three sets of transverse and three sets of longitudinal Mckibben artificial muscles (MAM) equidistantly arranged. The motions of the manipulator were driven by accurately controlling the length of each MAM that was changed by controlling the internal pressure, which was provided by the hydraulic power subsystem. The flexible manipulator was controlled remotely by the control subsystem. The inverse kinematics of the flexible manipulator was studied based on the neural network in this paper. The feasibility of the neural network inverse kinematics was proved by the data analysis. The three-dimensional virtual model of the flexible manipulator was projected into the captured real scene by the augmented reality (AR) technology to judge the bending degree of the manipulator operation, which could be seen in the experiment image.

Modeling of Cell Deformation Under External Force Using Neural Network

2010 ◽  
Author(s):  
M. T. Ahmadian ◽  
G. R. Vossoughi ◽  
A. A. Abbasi ◽  
P. Raeissi
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Three Dimensional ◽  
Point Load ◽  
Cell Deformation ◽  
Back Propagation Algorithm ◽  
Three Dimensional Model ◽  
Error Back Propagation ◽  
Injection Process ◽  
The Neural Network

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper the goal is the prediction of cell membrane deformation under a certain force, and to visually estimate the force of indentation on the membrane from membrane geometries. The neural network input and output parameters are associated to a three dimensional model without the assumption of the adherent affects. The neural network is modeled by applying error back propagation algorithm. In order to validate the strength of the developed neural network model, the results are compared with the experimental data on mouse oocyte and mouse embryos that are captured from literature. The results of the modeling match nicely the experimental findings.

A Study of Vision-Based Measurement of Weld Joint Shape Incorporating the Neural Network

1994 ◽  
Vol 208 (1) ◽  
pp. 61-69 ◽  
Author(s):  
K-Y Bae ◽  
S-J Na
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Ccd Camera ◽  
Image Plane ◽  
Welding Distortion ◽  
Surface Geometry ◽  
Visual Sensing ◽  
Laser Stripe ◽  
The Neural Network ◽  
A Charge

Visual sensing of the surface geometry is often necessary to inspect and evaluate the quality of welded joints as well as to sense the transient distortion of a structure during welding for the feedback of its current geometry. This investigation presents a simple and non-contact digitization method of the vision-based system for measuring the three-dimensional surface geometry of the object distorted by welding. Its basic principles are based on the equation derived from the geometric optics, for which the illumination of the laser beam was controlled in the form of the projected plane. This method utilized a 10 mW He-Ne laser for the structured light and a charge coupled device (CCD) camera as the vision sensor. When the laser stripe is projected on to the weldment, a minute deviation from the perfect plane existing on the specimen surface causes a distortion of the stripe. The shape and amount of the weldment distortion can be then calculated by analysing the distorted laser stripe. In this study, a neural network was proposed and implemented for recognizing the laser stripe features from the image plane. A calibration scheme of corresponding an image to the world position was also adopted for determining the sectional features of the welding distortion. The feasibility of determining the welding distortion by the proposed vision-based system was demonstrated through the experiments with various types of specimen.

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