scholarly journals Predicting Nonlinear and Anisotropic Mechanics of Metal Rubber Using a Combination of Constitutive Modeling, Machine Learning, and Finite Element Analysis

Materials ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5200
Author(s):  
Yalei Zhao ◽  
Hui Yan ◽  
Yiming Wang ◽  
Tianyi Jiang ◽  
Hongyuan Jiang
Keyword(s):  
Finite Element ◽  
Constitutive Modeling ◽  
Constitutive Models ◽  
Finite Element Method Simulation ◽  
Ann Model ◽  
The Finite Element Method ◽  
Functional Material ◽  
Element Analysis ◽  
Metal Rubber ◽  
Artificial Neural Network Ann

Metal rubber (MR) is an entangled fibrous functional material, and its mechanical properties are crucial for its applications; however, numerical constitutive models of MR for prediction and calculation are currently undeveloped. In this work, we provide a numerical constitutive model to express the mechanics of MR materials and develop an efficient finite elements method (FEM) to calculate the performance of MR components. We analyze the nonlinearity and anisotropy characteristics of MR during the deformation process. The elasticity matrix is adopted to express the nonlinearity and anisotropy of MR. An artificial neural network (ANN) model is built, trained, and tested to output the current elastic moduli for the elasticity matrix. Then, we combine the constitutive ANN model with the finite element method simulation to calculate the mechanics of the MR component. Finally, we perform a series of static and shock experiments and finite element simulations of an MR isolator. The results demonstrate the feasibility and accuracy of the numerical constitutive MR model. This work provides an efficient and convenient method for the design and analysis of MR components.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

Neural Network Algorithms for the Correction of Concrete Slab Stresses from Linear Elastic Layered Programs

1997 ◽  
Vol 1568 (1) ◽  
pp. 44-51 ◽  
Author(s):  
Louis D. Haussmann ◽  
Erol Tutumluer ◽  
Ernest J. Barenberg
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Load Transfer ◽  
Concrete Slab ◽  
Loading Conditions ◽  
Ann Model ◽  
Portland Cement Concrete ◽  
Element Analysis ◽  
Wide Range ◽  
Artificial Neural Network Ann

Elastic layered programs (ELPs) are currently being used in mechanistic-based pavement design procedures for the analysis of jointed portland cement concrete pavements. Corrections must be made to stresses obtained from ELP solutions to account for the effects of finite slab size, load location on the slab, and load transfer efficiency of the joints. A preliminary artificial neural network (ANN) model is trained and used as a tool to predict the results of a finite-element analysis program for a standard pavement section. Under identical loading conditions, the trained neural network produces stresses within 1.2 percent of those obtained from finite-element analyses. The trained ANN model is found to be very effective for correcting ELP stresses, practically in the blink of an eye, with no requirements of complicated finite-element inputs. The preliminary ANN algorithm is currently being expanded to handle more general input conditions covering a wide range of slab sizes, slab thicknesses, subgrade supports, and loading conditions. Design curves created from these ANN algorithms will eventually enable pavement engineers to easily incorporate sophisticated state-of-the-art technology into routine practical design.

scholarly journals Force Prediction in Robotized Ultrasonic Sheet Metal Forming Based on Finite Element Analysis and Artificial Neural Networks

2021 ◽  
Author(s):  
Vytautas Ostasevicius ◽  
Agne Paulauskaite-Taraseviciene ◽  
Ieva Paleviciute ◽  
Vytautas Jurenas ◽  
Paulius Griskevicius ◽  
...  
Keyword(s):  
Finite Element ◽  
Sheet Metal ◽  
Single Point ◽  
The Finite Element Method ◽  
Element Analysis ◽  
The Coefficient Of Friction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Implicit Data ◽  
Workpiece Interaction

Abstract The forces acting in the process of single point incremental forming (SPIF) change the geometry of the sheet metal. The tool-workpiece interaction process is non-linear due to the large deformations of the sheet metal, which determine the plastic behaviour, as well as the evolutionary boundary conditions resulting from the contact between the tool and the sheet. Instead of lubricating the contact surface of the forming tool and the sheet metal, an innovative environmentally friendly method to reduce the coefficient of friction by vibrating the sheet has been proposed. The finite element method (FEM) allowed a virtual evaluation of the deformation parameters of the SPIF process in order to determine the destructive loads. The FEM was chosen as a deterministic numerical tool to evaluate the set of defect parameters induced by forming forces. The paper also proposes a method for predicting the formation force using an artificial neural network (ANN), assuming that such a model is generalized to implicit data. In this context, an empirical analysis of the implementation of the ANN technique is performed.

scholarly journals Failure Pressure Prediction of a Corroded Pipeline with Longitudinally Interacting Corrosion Defects Subjected to Combined Loadings Using FEM and ANN

2021 ◽  
Vol 9 (3) ◽  
pp. 281
Author(s):  
Michael Lo ◽  
Saravanan Karuppanan ◽  
Mark Ovinis
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Training Data ◽  
Percentage Error ◽  
Learning Tools ◽  
Ann Model ◽  
Element Analysis ◽  
Failure Pressure ◽  
Corrosion Defects ◽  
Artificial Neural Network Ann

Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging from −9.39% to 4.63%, when compared with FEA results.

Towards Predicting Relative Belt Edge Endurance With the Finite Element Method

1990 ◽  
Vol 18 (4) ◽  
pp. 216-235 ◽  
Author(s):  
J. De Eskinazi ◽  
K. Ishihara ◽  
H. Volk ◽  
T. C. Warholic
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Three Dimensional ◽  
The Finite Element Method ◽  
Shear Deformations ◽  
Element Analysis ◽  
Vertical Loading ◽  
Predicted Values ◽  
Element Method

Abstract The paper describes the intention of the authors to determine whether it is possible to predict relative belt edge endurance for radial passenger car tires using the finite element method. Three groups of tires with different belt edge configurations were tested on a fleet test in an attempt to validate predictions from the finite element results. A two-dimensional, axisymmetric finite element analysis was first used to determine if the results from such an analysis, with emphasis on the shear deformations between the belts, could be used to predict a relative ranking for belt edge endurance. It is shown that such an analysis can lead to erroneous conclusions. A three-dimensional analysis in which tires are modeled under free rotation and static vertical loading was performed next. This approach resulted in an improvement in the quality of the correlations. The differences in the predicted values of various stress analysis parameters for the three belt edge configurations are studied and their implication on predicting belt edge endurance is discussed.

Tire Cornering Simulation Using an Explicit Finite Element Analysis Code

1998 ◽  
Vol 26 (2) ◽  
pp. 109-119 ◽  
Author(s):  
M. Koishi ◽  
K. Kabe ◽  
M. Shiratori
Keyword(s):  
Finite Element Method ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Test System ◽  
Experimental Results ◽  
The Finite Element Method ◽  
Element Analysis ◽  
Explicit Finite Element ◽  
Explicit Finite Element Analysis ◽  
Element Method

Abstract The finite element method has been used widely in tire engineering. Most tire simulations using the finite element method are static analyses, because tires are very complex nonlinear structures. Recently, transient phenomena have been studied with explicit finite element analysis codes. In this paper, the authors demonstrate the feasibility of tire cornering simulation using an explicit finite element code, PAM-SHOCK. First, we propose the cornering simulation using the explicit finite element analysis code. To demonstrate the efficiency of the proposed simulation, computed cornering forces for a 175SR14 tire are compared with experimental results from an MTS Flat-Trac Tire Test System. The computed cornering forces agree well with experimental results. After that, parametric studies are conducted by using the proposed simulation.

Analysis of Hydroelectric Unit’s Upper Bracket Based on Test and FEM

2014 ◽  
Vol 721 ◽  
pp. 131-134
Author(s):  
Mi Mi Xia ◽  
Yong Gang Li
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Element Model ◽  
The Finite Element Method ◽  
Ansys Software ◽  
Element Analysis ◽  
Hydroelectric Unit ◽  
The Finite Element Analysis ◽  
Strain Rosette ◽  
The Finite Element Model

To research the load upper bracket of Francis hydroelectric unit, then established the finite-element model, and analyzed the structure stress of 7 operating condition points with the ANSYS software. By the strain rosette test, acquired the data of stress-strain in the area of stress concentration of the upper bracket. The inaccuracy was considered below 5% by analyzing the contradistinction between the finite-element analysis and the test, and match the engineering precision and the test was reliable. The finite-element method could be used to judge the stress of the upper bracket, and it could provide reference for the Structural optimization and improvement too.

A FINITE ELEMENT ANALYSIS OF EFFECTIVE THERMOELECTROELASTIC PROPERTIES OF COMPOSITES WITH A DOUBLY PERIODIC ARRAY OF PIEZOELECTRIC FIBERS

2009 ◽  
Vol 23 (06n07) ◽  
pp. 1689-1694 ◽  
Author(s):  
PENG YAN ◽  
CHIPING JIANG
Keyword(s):  
Finite Element ◽  
Effective Properties ◽  
Cell Model ◽  
Periodic Array ◽  
The Finite Element Method ◽  
Element Analysis ◽  
Uniform Temperature Field ◽  
Periodic Microstructures ◽  
Doubly Periodic Array ◽  
Electrical Loads

This work deals with modeling of 1-3 thermoelectroelastic composites with a doubly periodic array of piezoelectric fibers under arbitrary combination of mechanical, electrical loads and a uniform temperature field. The finite element method (FEM) based on a unit cell model is extended to take into account the thermoelectroelastic effect. The FE predictions of effective properties for several typical periodic microstructures are presented, and their influences on effective properties are discussed. A comparison with the Mori-Tanaka method is made to estimate the application scope of micromechanics. The study is useful for the design and assessment of composites.

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