scholarly journals An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

Actuators ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Seongkyu Chang ◽  
Sung Gook Cho
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Parameters ◽  
Internal Diameter ◽  
Displacement Curve ◽  
Element Analysis ◽  
Steel Coil ◽  
Artificial Neural ◽  
Load Displacement ◽  
Plastic Steel

This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

A Multidisciplinary Design and Optimization Methodology in Electronics Packaging: Application to BGA Design

2003 ◽  
Author(s):  
Hamid Hadim ◽  
Tohru Suwa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Objective Function ◽  
Thermal Strain ◽  
Design Parameters ◽  
Design And Optimization ◽  
Trade Offs ◽  
Artificial Neural ◽  
Optimization Methodology ◽  
The Individual

In this manuscript a systematic multidisciplinary electronic packaging design and optimization methodology based on the artificial neural networks technique is presented. This method is applied to a Ball Grid Array (BGA) package design as an example. Multidisciplinary criteria including thermal, structural (thermal strain), electromagnetic leakage, and cost are optimized simultaneously. A simplified routability criterion is also considered as a constraint. The artificial neural networks technique is used for thermal and structural performance predictions. Large calculation time reduction is achieved using the artificial neural networks, which also provide enough information to specify the individual weights for each design discipline within the objective function used for optimization. This methodology is able to provide the designers a clear view of the design trade-offs, which are represented in the objective function using various design parameters. This methodology can be applied to any electronic product design at any packaging level.

An Optimal Design Method for Artificial Neural Networks by Using the Design of Experiments

2007 ◽  
Vol 11 (6) ◽  
pp. 593-599 ◽  
Author(s):  
Eiichi Inohira ◽  
◽  
Hirokazu Yokoi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimal Design ◽  
Design Method ◽  
Feedforward Neural Networks ◽  
Learning Rate ◽  
Design Parameters ◽  
Approximation Accuracy ◽  
Artificial Neural ◽  
Trained Neural Network

This paper presents a method to optimally design artificial neural networks with many design parameters using the Design of Experiment (DOE), whose features are efficient experiments using an orthogonal array and quantitative analysis by analysis of variance. Neural networks can approximate arbitrary nonlinear functions. The accuracy of a trained neural network at a certain number of learning cycles depends on both weights and biases and its structure and learning rate. Design methods such as trial-and-error, brute-force approaches, network construction, and pruning, cannot deal with many design parameters such as the number of elements in a layer and a learning rate. Our design method realizes efficient optimization using DOE, and obtains confidence of optimal design through statistical analysis even though trained neural networks very due to randomness in initial weights. We apply our design method three-layer and five-layer feedforward neural networks in a preliminary study and show that approximation accuracy of multilayer neural networks is increased by picking up many more parameters.

Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

2016 ◽  
Vol 231 (11) ◽  
pp. 2005-2016 ◽  
Author(s):  
Adil Baykasoğlu ◽  
Cengiz Baykasoğlu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Functionally Graded ◽  
Design Parameters ◽  
Multiple Objective ◽  
Circular Tubes ◽  
Crashworthiness Optimization ◽  
Artificial Neural ◽  
Graded Thickness

The objective of this paper is to develop a multiple objective optimization procedure for crashworthiness optimization of circular tubes having functionally graded thickness. The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; artificial neural networks for predicting objective functions values (peak crash force and specific energy absorption) for design parameters; and genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The proposed approach seaminglesly integrates artificial neural networks and genetic algorithms. Artificial neural network acts as an objective function evaluator within the multiple objective genetic algorithms. We have shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the finite element results.

scholarly journals Optimization of Running Blade Prosthetics Utilizing Crow Search Algorithm Assisted by Artificial Neural Networks

2021 ◽  
Vol 67 (3) ◽  
pp. 88-100
Author(s):  
Rosel Solís Manuel Javier ◽  
José Omar Dávalos Ramírez ◽  
Javier Molina Salazar ◽  
Juan Antonio Ruiz Ochoa ◽  
Antonio Gómez Roa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Carbon Fibre ◽  
Failure Criterion ◽  
Search Algorithm ◽  
Acrylonitrile Butadiene Styrene ◽  
Sensitivity Study ◽  
Design Parameters ◽  
Artificial Neural ◽  
Important Design

A crow search algorithm (CSA) was applied to perform the optimization of a running blade prosthetics (RBP) made of composite materials like carbon fibre layers and cores of acrylonitrile butadiene styrene (ABS). Optimization aims to increase the RBP displacement limited by the Tsai-Wu failure criterion. Both displacement and the Tsai-Wu criterion are predicted using artificial neural networks (ANN) trained with a database constructed from finite element method (FEM) simulations. Three different cases are optimized varying the carbon fibre layers orientations: –45°/45°, 0°/90°, and a case with the two-fibre layer orientations intercalated. Five geometric parameters and a number of carbon fibre layers are selected as design parameters. A sensitivity analysis is performed using the Garzon equation. The best balance between displacement and failure criterion was found with fibre layers oriented at 0°/90°. The optimal candidate with –45°/45° orientation presents higher displacement; however, the Tsai-Wu criterion was less than 0.5 and not suitable for RBP design. The case with intercalated fibres presented a minimal displacement being the stiffer RBP design. The damage concentrates mostly in the zone that contacts the ground. The sensitivity study found that the number of layers and width were the most important design parameters.

scholarly journals A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6135
Author(s):  
Suria Devi Vijaya Kumar ◽  
Michael Lo Yin Kai ◽  
Thibankumar Arumugam ◽  
Saravanan Karuppanan
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Structural Integrity ◽  
The Finite Element Method ◽  
Element Analysis ◽  
Strength Assessment ◽  
Failure Pressure ◽  
Prediction Tools ◽  
Artificial Neural

This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.

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