Deep Neural Network Modeling for Metallic Component Defects Using the Finite Element Model

2019 ◽  
pp. 259-270
Author(s):  
Liyana Isamail ◽  
Nor Liyana Maskuri ◽  
Neil Jeremy Isip ◽  
Siti Farhana Lokman ◽  
Muhamad Husaini Abu Bakar
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Element Model ◽  
Neural Network Modeling ◽  
Metallic Component ◽  
The Finite Element Model

Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure

2020 ◽  
pp. 147592172093261 ◽  
Author(s):  
Zohreh Mousavi ◽  
Sina Varahram ◽  
Mir Mohammad Ettefagh ◽  
Morteza H. Sadeghi ◽  
Seyed Naser Razavi
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Damage Detection ◽  
Deep Neural Network ◽  
Element Model ◽  
Frequency Data ◽  
Vibration Signals ◽  
Intact State ◽  
The Finite Element Model

Structural health monitoring of mechanical systems is essential to avoid their catastrophic failure. In this article, an effective deep neural network is developed for extracting the damage-sensitive features from frequency data of vibration signals to damage detection of mechanical systems in the presence of the uncertainties such as modeling errors, measurement errors, and environmental noises. For this purpose, the finite element method is used to analyze a mechanical system (finite element model). Then, vibration experiments are carried out on the laboratory-scale model. Vibration signals of real intact system are used to updating the finite element model and minimizing the disparities between the natural frequencies of the finite element model and real system. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition technique. Frequency domain decomposition method is used to extract frequency data. The proposed deep neural network is trained using frequency data of the finite element model and real intact state and then is tested using frequency data of the real system. The proposed network is designed in two stages, namely, the pre-training classification based on deep auto-encoder and Softmax layer (first stage), and the re-training classification based on backpropagation algorithm for fine tuning of the network (second stage). The proposed method is validated using a lab-scale offshore jacket structure. The results show that the proposed method can learn features from the frequency data and achieve higher accuracy than other comparative methods.

Tail Deviation Mechanism and Neural Network Control of the Tandem Rolling Strap

2011 ◽  
Vol 103 ◽  
pp. 488-492
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yi Lun Liu
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Control Policy ◽  
Element Model ◽  
Rolling Process ◽  
Network Control ◽  
Neural Network Control ◽  
The Neural Network ◽  
The Finite Element Model

In the rolling process, deviation is the phenomenon that the strap width direction's centerline deviates from rolling system setting centerline,serious deviation will cause product quality drop and rolling equipment fault. This paper has established the finite element model to the hot tandem rolling aluminum strap, analyzed the strap’s deviation rule under four kinds of incentives,obtained the neural network predictive model and the control policy of the tail deviation.The result to analyze a set of fact deviation data shows this method may control tail deviation in preconcerted permission range.

A Real-Time Neural Network Estimator for Workpiece Thermal Expansion Errors

2000 ◽  
Author(s):  
A. D. Yoder ◽  
R. N. Smith
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Thermal Expansion ◽  
Finite Element Model ◽  
Real Time ◽  
Element Model ◽  
Precision Machining ◽  
Machining Processes ◽  
The Neural Network ◽  
The Finite Element Model

Abstract The importance of predicting and reducing thermal expansion errors in workpieces is becoming greater as better precision machining processes are developed. An artificial neural network model to estimate the workpiece thermal expansion errors in real-time during precision machining operations is developed and compared with experimental results. A finite element model of workpiece thermal expansion has been created to predict expansions in a thin cylinder undergoing a turning process. The neural network has been trained using finite element model solutions over a range of conditions to allow for changing machining parameters. To realize “on-line” capability, the measurable values of heat flux into the workpiece, surface heat transfer coefficient, and tool location are used as inputs and the expansion as the output for the neural network. The estimations of the network are compared with experimental results from a turning process on a large diameter aluminum cylinder. There is reasonable agreement between measured and estimated expansions with an average error of 18%. The neural network has not been trained at the cutting conditions used during the experiment. The speed of the neural network estimation is much greater than the solution to the finite element model. The finite element model required over 15 minutes to solve on a Pentium 133Mhz computer. The neural network calculated the expansions easily at 1 Hz during the experiment on the same computer. With real-time estimation using measurable data, compensation can be made in the tool path to correct for these errors. The application of this method to precision machining processes has the capability of greatly reducing the error caused by workpiece thermal expansions.

scholarly journals Finite Element Model Modification of Arch Bridge Based on Radial Basis Function Neural Network

2019 ◽  
Vol 136 ◽  
pp. 04033
Author(s):  
Tongqing Chen ◽  
Lei Wang ◽  
Xijuan Jiang ◽  
Yubin Wang ◽  
Kai Yan
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Element Model ◽  
Arch Bridge ◽  
Hidden Layer ◽  
Tied Arch Bridge ◽  
The Finite Element Model

Compared with other neural networks, Radial Basis Function (RBF) neural network has the advantages of simple structure and fast convergence. As long as there are enough hidden layer nodes in the hidden layer, it can approximate any non-linear function. In this paper, the finite element model of a through tied arch bridge is modified based on Neural Network. The approximation function of RBF neural network is utilized to fit the implicit function relationship between the response of the bridge and its design parameters. Then the finite element model of the bridge structure is modified. The results show that RBF neural network is efficient to modify the model of a through tied arch bridge.

scholarly journals Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

2017 ◽  
Vol 17 (3) ◽  
pp. 135-144 ◽  
Author(s):  
Gaiyun He ◽  
Can Huang ◽  
Longzhen Guo ◽  
Guangming Sun ◽  
Dawei Zhang
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Bp Neural Network ◽  
Stress Test ◽  
Element Model ◽  
Geometric Errors ◽  
Guide Rail ◽  
Test Plate ◽  
The Finite Element Model

AbstractThe relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.

Finite Element and Neural Network Modeling of Extreme Overbalance Perforating

1999 ◽  
Author(s):  
Mehdi Azari ◽  
Mahmoud Asadi ◽  
Roger Schultz ◽  
Ali Ghalambor
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Network Modeling ◽  
Neural Network Modeling

scholarly journals Sensitivity Analysis of the Influence of Structural Parameters on Dynamic Behaviour of Highly Redundant Cable-Stayed Bridges

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
B. Asgari ◽  
S. A. Osman ◽  
A. Adnan
Keyword(s):  
Sensitivity Analysis ◽  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Structural Parameters ◽  
Element Model ◽  
Structural Behavior ◽  
Model Tuning ◽  
The Finite Element Model ◽  
Cable Stayed Bridges

The model tuning through sensitivity analysis is a prominent procedure to assess the structural behavior and dynamic characteristics of cable-stayed bridges. Most of the previous sensitivity-based model tuning methods are automatic iterative processes; however, the results of recent studies show that the most reasonable results are achievable by applying the manual methods to update the analytical model of cable-stayed bridges. This paper presents a model updating algorithm for highly redundant cable-stayed bridges that can be used as an iterative manual procedure. The updating parameters are selected through the sensitivity analysis which helps to better understand the structural behavior of the bridge. The finite element model of Tatara Bridge is considered for the numerical studies. The results of the simulations indicate the efficiency and applicability of the presented manual tuning method for updating the finite element model of cable-stayed bridges. The new aspects regarding effective material and structural parameters and model tuning procedure presented in this paper will be useful for analyzing and model updating of cable-stayed bridges.

Biodynamics of Human Thorax With Body Armors Subject to Bullet Impact

2001 ◽  
Author(s):  
Y. W. Kwon ◽  
J. A. Lobuono
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
Body Armor ◽  
Projectile Impact ◽  
Human Thorax ◽  
Trachea And Bronchi ◽  
Armor System ◽  
The Finite Element Model

Abstract The objective of this study is to develop a finite element model of the human thorax with a protective body armor system so that the model can adequately determine the thorax’s biodynamical response from a projectile impact. The finite element model of the human thorax consists of the thoracic skeleton, heart, lungs, major arteries, major veins, trachea, and bronchi. The finite element model of the human thorax is validated by comparing the model’s results to experimental data obtained from cadavers wearing a protective body armor system undergoing a projectile impact.

Dynamic Analysis of a Turbocharger Centrifugal Compressor Impeller

1991 ◽  
Author(s):  
V. Ramamurti ◽  
D. A. Subramani ◽  
K. Sridhara
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Centrifugal Compressor ◽  
Element Model ◽  
Simplified Model ◽  
Cyclic Symmetry ◽  
Compressor Impeller ◽  
Cyclic Symmetric ◽  
The Finite Element Model

Abstract Stress analysis and determination of eigen pairs of a typical turbocharger compressor impeller have been carried out using the concept of cyclic symmetry. A simplified model treating the blade and the hub as isolated elements has also been attempted. The limitations of the simplified model have been brought out. The results of the finite element model using the cyclic symmetric approach have been discussed.

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