scholarly journals Neural Network and Genetic Algorithm Based Finite Element Model for Optimal Die Shape Design in Al-1100 Cold Forward Extrusion

2016 ◽  
Vol 4 (2) ◽  
pp. 25-33 ◽  
Author(s):  
Abdul Kareem Flaih Hassan ◽  
Raad Jamal Jassim ◽  
Mustafa Muneam Jafaar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
Shape Design ◽  
Forward Extrusion

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.

Reliability-Based Assessment Method of Buried Pipeline at Fault Crossings

2020 ◽  
Author(s):  
Qian Zheng ◽  
Xiaoben Liu ◽  
Hong Zhang ◽  
Samer Adeeb
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Limit State ◽  
Element Model ◽  
Normal Operation ◽  
Operating Pressure ◽  
Peak Strain ◽  
Highly Nonlinear ◽  
Fault Displacement

Abstract The tectonic fault, which is one of the most common geohazards in field, poses great threat to buried pipe segments. Pipes will process to buckling or fracture due to large strain induced by continuously increasing ground displacements during earthquakes. Therefore, it is imperative to conduct safety analysis on pipes which are buried in seismic areas for the sake of ensuring normal operation. However, the highly nonlinearity of pipe response restricts the proceeding of reliability assessment. In this study, a hybrid procedure combining finite element method and artificial neural network is proposed for reliability-based assessment. First of all, the finite element model is developed on ABAQUS platform to simulate pipe response to strike-slip fault displacements. Thus, the strain demand value (the peak strain value obtained by finite element model in each design case) can be collected for database establishment, which is the preparation for neural network training. Thoroughness of the strain demand database can be achieved by a fully comprehensive calculation with consideration of influencing factors involving pipe diameter and wall thickness, operating pressure, magnitude of fault displacement, intersection angle between pipeline and fault plane, and characteristic value of backfill mechanics. Sequentially, Back Propagation Neural Network (BPNN) with double hidden layers is trained based on the developed database, and the surrogate strain demand prediction model can be obtained after accuracy verification. Hence, the strain-based limit state function can be respectively determined for tensile and compressive conditions. The strain capacity term is simply assumed based on published papers, the strain demand term is naturally superseded by the surrogate BPNN model, and Monte Carlo Simulation is employed to compute the probability of failure (POF). At last, the workability of the proposed approach is tested by a case study in which basic variables are referred to the Second West-to-East natural gas transmission pipeline project. It indicates that ANN is a good solver for reliability problems with implicit limit state functions especially for highly nonlinear problems. The proposed method is capable of computing POFs, which is an exploratory application for reliability research on pipes withstanding fault displacement loads.

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.

Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

2020 ◽  
pp. 147592172092748 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Data Driven ◽  
Finite Element Model Updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

scholarly journals Finite Element Model Fractional Steps Updating Strategy for Spatial Lattice Structures Based on Generalized Regression Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Caiwei Liu ◽  
Jijun Miao ◽  
Changyong Zhao
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Element Model ◽  
Engineering Practice ◽  
Measuring Point ◽  
Stiffness Reduction ◽  
Spatial Lattice ◽  
Ball Joints

In order to get a more accurate finite element model of a spatial lattice structure with bolt-ball joints for health monitoring, a method of modifying the bolt-ball joint stiffness coefficient was proposed. Firstly, the beam element with adjustable stiffness was used in the joint zone in this paper to reveal the semirigid characteristic of the joint. Secondly, the value of stiffness reduction factor (ar) was limited in the range of[0.2,0.8]and the reference value (ar0) of it was suggested to be 0.5 based on referenced literatures. Finally, the finite element model fractional steps updating strategy based on neural network technique was applied and the limited measuring point information was used to form the network input parameter. A single-layer latticed cylindrical shell model with 157 joints and 414 tubes was used in a shaking TABLE test. Based on the measured modal data, the presented method was verified. The results show that this model updating technique can reflect the true dynamic characters of the shell structure better. Moreover, the neural network can be simplified considerably by using this algorithm. The method can be used for model updating of a latticed shell with bolt-ball joints and has great value in engineering practice.

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