U-shaped Part Springback Forecasting Based on the Finite Element Method and the Neural Network

2010 ◽  
Author(s):  
Wu Jianxin ◽  
Yang Xiaojun
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Finite Element ◽  
The Finite Element Method ◽  
The Neural Network ◽  
Shaped Part ◽  
Element Method

scholarly journals Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures

Nanomaterials ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 170
Author(s):  
Sneha Verma ◽  
Sunny Chugh ◽  
Souvik Ghosh ◽  
B. M. Azizur Rahman
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Plasmonic Nanostructures ◽  
Optical Parameters ◽  
Complex Data ◽  
The Neural Network ◽  
Artificial Neural ◽  
Element Method

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.

scholarly journals Development and Optimization for a New Planar Spring Using Finite Element Method, Deep Feedforward Neural Networks, and Water Cycle Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Ngoc Le Chau ◽  
Hieu Giang Le ◽  
Van Anh Dang ◽  
Thanh-Phong Dao
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Finite Element ◽  
Strain Energy ◽  
Water Cycle ◽  
Feedforward Neural Network ◽  
The Finite Element Method ◽  
Water Cycle Algorithm ◽  
Balance Mechanism ◽  
Element Method

The gravity balance mechanism plays a vital role in maintaining the equilibrium for robots and assistive devices. The purpose of this paper was to optimize the geometry of a planar spring, which is an essential element of the gravity balance mechanism. To implement the optimization process, a hybrid method is proposed by combining the finite element method, the deep feedforward neural network, and the water cycle algorithm. Firstly, datasets are collected using the finite element method with a full experiment design. Secondly, the output datasets are normalized to eliminate the effects of the difference of units. Thirdly, the deep feedforward neural network is then employed to build the approximate models for the strain energy, deformation, and stress of the planar spring. Finally, the water cycle algorithm is used to optimize the dimensions of the planar spring. The results found that the optimal geometries of the spring include the length of 45 mm, the thickness of 1.029 mm, the width of 9 mm, and the radius of 0.3 mm. Besides, the predicted results determined that the strain energy, the deformation, and the stress are 0.01123 mJ, 33.666 mm, and 79.050 MPa, respectively. The errors between the predicted result and the verifying results for the strain energy, the deformation, and the stress are about 1.87%, 1.69%, and 3.06%, respectively.

scholarly journals Multiaxis Capacitive Force Sensor and its Measurement Principle Using Neural Networks

2006 ◽  
Vol 18 (4) ◽  
pp. 442-449 ◽  
Author(s):  
Seiji Aoyagi ◽  
◽  
Masaru Kawanishi ◽  
Daiichiro Yoshikawa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Full Range ◽  
Force Sensor ◽  
The Finite Element Method ◽  
Electrical Field ◽  
The Neural Network ◽  
Measurement Principle

We propose a multiaxis capacitive force sensor consisting of one movable upper electrode on a plate and fixed lower electrodes on a substrate. The plate moves both vertically and horizontally when force is applied, and capacitance between upper and lower electrodes changes. This sensor uses the main electrical field between two directly facing electrodes and the fringe electrical field between diagonally opposed electrodes, making capacitance difficult to analyze. We simulated changes in nonlinear capacitance based on the upper electrode’s movement using the finite element method (FEM) and proved that capacitance is a function of the upper electrode’s displacement. We used a neural network to calculate the upper electrode’s displacement from capacitance. The neural network operates appropriately and calculated displacement error is within 0.5% of the full range. We proposed fabricating a practical force sensor consisting of planar capacitors making it compatible with surface micromachining and not requiring 3-D bulk micromachining, which simplifies fabrication, making it economical.

scholarly journals A Novel Method for Establishing an Efficiency Map of IPMSMs for EV Propulsion Based on the Finite-Element Method and a Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1049
Author(s):  
Sung-Bae Jun ◽  
Chan-Ho Kim ◽  
JuKyung Cha ◽  
Jin Hwan Lee ◽  
Yong-Jae Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Finite Element ◽  
The Finite Element Method ◽  
Permanent Magnet Synchronous Motors ◽  
Novel Method ◽  
Electro Magnetic ◽  
Time Required ◽  
Efficiency Map ◽  
Element Method

In this paper, we introduce a novel method for establishing an efficiency map of interior permanent-magnet synchronous motors that are used for electric vehicle propulsion, by employing the finite-element method (FEM) and a neural network (NN) to reduce the analysis time. The electro-magnetic analysis of motors using the FEM, particularly iron loss analysis, is significantly time-consuming owing to the nonlinearity and the post-processing. Moreover, to obtain an efficiency map, a data map of the d-q flux linkages based on the d-q currents should be established. At this stage, we compute the flux densities in all the elements, and they are learned by the NN to obtain a function of the d-q currents. Subsequently, the iron losses at all operating points are calculated using the learned data via the harmonic loss method. The results of the proposed method indicate that the time required to obtain the efficiency map is reduced; furthermore, the results are validated via a comparison with the FEM results.

Simulation of tuning graphene plasmonic behaviors by ferroelectric domains for self-driven infrared photodetector applications

Nanoscale ◽  
2019 ◽  
Vol 11 (43) ◽  
pp. 20868-20875 ◽  
Author(s):  
Junxiong Guo ◽  
Yu Liu ◽  
Yuan Lin ◽  
Yu Tian ◽  
Jinxing Zhang ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Interfacial Effect ◽  
Infrared Photodetector ◽  
The Finite Element Method ◽  
Ferroelectric Domains ◽  
Element Method

We propose a graphene plasmonic infrared photodetector tuned by ferroelectric domains and investigate the interfacial effect using the finite element method.

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