scholarly journals Extraction of Load Information From Photoelastic Images Using Neural Networks

2006 ◽  
Author(s):  
Venketesh N. Dubey ◽  
Gurtej S. Grewal ◽  
Denzil J. Claremont
Keyword(s):  
Neural Networks ◽  
Finite Element Analysis ◽  
Biomedical Engineering ◽  
Stress Pattern ◽  
Field Analysis ◽  
Element Analysis ◽  
Sensing Applications ◽  
Fringe Patterns ◽  
Theoretical Results ◽  
And Robotics

Photoelastic materials develop colored fringes under white light when subjected to mechanical stresses which can be viewed through a polariscope. This technique has traditionally been used for stress analysis of loaded components, however, this can also be potentially used in sensing applications where the requirement may be measurement of the stimulating forces causing the generation of the fringes. This leads to inverse photoelastic problem where the developed image can be analyzed for the input forces. However, there could be infinite number of possible solutions which cannot be obtained by conventional techniques. This paper presents neural networks based approach to solve this problem. Experiments conducted to prove the principle have been verified with theoretical results and finite element analysis of the loaded specimens. The technique, if fully developed, can be implemented for any generalized case involving complex fringe patterns under different loading conditions for whole-field analysis of the stress pattern, which may find application in a variety of specialized areas including biomedical engineering and robotics.

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.

Finite-Element Analysis of the Magnetostatic Field of a Coaxial Magnetic Gear Device

2015 ◽  
Vol 764-765 ◽  
pp. 289-293
Author(s):  
Yi Chang Wu ◽  
Han Ting Hsu
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Magnetic Flux ◽  
High Speed ◽  
Field Analysis ◽  
Element Analysis ◽  
Magnetostatic Field ◽  
Speed Reduction ◽  
Dimensional Finite Element ◽  
Magnetic Gear

This paper presents the magnetostatic field analysis of a coaxial magnetic gear device proposed by Atallah and Howe. The structural configuration and speed reduction ratio of this magnetic gear device are introduced. The 2-dimensional finite-element analysis (2-D FEA), conducted by applying commercial FEA software Ansoft/Maxwell, is performed to evaluate the magnetostatic field distribution, especially for the magnetic flux densities within the outer air-gap. Once the number of steel pole-pieces equals the sum of the pole-pair numbers of the high-speed rotor and the low-speed rotor, the coaxial magnetic gear device possesses higher magnetic flux densities, thereby generating greater transmitted torque.

Forming and springback prediction in press brake air bending combining finite element analysis and neural networks

2018 ◽  
Vol 53 (8) ◽  
pp. 584-601 ◽  
Author(s):  
Sara S Miranda ◽  
Manuel R Barbosa ◽  
Abel D Santos ◽  
J Bessa Pacheco ◽  
Rui L Amaral
Keyword(s):  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Sheet Metal ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Forming Process ◽  
Element Analysis ◽  
Air Bending ◽  
Springback Prediction

Press brake air bending, a process of obtaining products by sheet metal forming, can be considered at first sight a simple geometric problem. However the accuracy of the obtained geometries involves the combination of multiple parameters directly associated with the tools and the processing parameters, as well as with the sheet metal materials and dimensions. The main topic herein presented deals with the capability of predicting the punch displacement process parameter that enables the product to be accurately shaped to a desired bending angle, in press brake air bending. In our approach, it is considered separately the forming process and the elastic recovery (i.e. the springback effect). Current solutions in press brake numerical control (computer numerical control) are normally configured by analytical models developed from geometrical analysis and including correcting factors. In our approach, it is proposed to combine the use of a learning tool, artificial neural networks, with a simulation and data generation tool (finite element analysis). This combination enables modeling the complex nonlinear behavior of the forming process and springback effect, including the validation of results obtained. A developed model taking into account different process parameters and tool geometries allow extending the range of applications with practical interest in industry. The final solution is compatible with its incorporation in a computer numerical control press brake controller. It was concluded that, using this methodology, it is possible to predict efficient and accurate final geometries after bending, being also a step forward to a “first time right” solution. In addition, the developed models, methodologies and obtained results were validated by comparison with experimental tests.

The Steady-State Temperature Field Analysis of the 3D Rolling Tire Based on Adina FEA Software

2009 ◽  
Vol 87-88 ◽  
pp. 518-523 ◽  
Author(s):  
Jing Li ◽  
Yan He ◽  
Zhen Chao Chen
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Temperature Field ◽  
Steady State ◽  
Field Analysis ◽  
Analysis Model ◽  
Element Analysis ◽  
Finite Element Analysis Model ◽  
Steady State Temperature ◽  
Steady Temperature Field

Based on the Adina finite element analysis software, 3D axisymmetric finite element analysis model of the 205/75R15 PCR tire was established, the steady temperature field of rolling tire was simulated, and the thermal distribution colored cloud diagram of steady-state temperature field of 3D rolling tire which directly shows the temperature distribution of each section of tire was analyzed to offer certain guidance to the improvement of tire structure and rubber formula.

Triangulation Based Digitizing of Tooling and Sheet Metal Part Surfaces - Measuring Technique, Analysis of Deviation to CAD and Remarks on Use of 3D-Coordinate Fields for the Finite Element Analysis

2007 ◽  
Vol 344 ◽  
pp. 847-853 ◽  
Author(s):  
J. Hecht ◽  
K. Lamprecht ◽  
Marion Merklein ◽  
Konstantin Galanulis ◽  
J. Steinbeck
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Element Analysis ◽  
Discrete State ◽  
Full Field ◽  
The Finite Element Analysis ◽  
Hydroforming Process ◽  
Fringe Patterns ◽  
Formed Part ◽  
The Impact

The dynamic development of highly accurate optical measuring machines within the last years pushed the introduction of digitizing techniques to many applications in the fields of quality control, reverse engineering and rapid prototyping. By projecting fringe patterns onto the object's surface and recording pictures of the curvature dependant deformation of the pattern, 3D coordinates for each camera pixel are calculated on the basis of the principle of triangulation. The generation of a polygon mesh can be used for the analysis of the deviation of a die or a formed part to the initial CAD data, i.e. by means of full field or section based comparison. This paper presents the application of the above mentioned techniques on a double sheet hydroforming process. The gathered 3D data of the clam-shell part as well as of the tooling dies served for the calculation of the deviation to the respective reference geometry. With respect to the utilization of digitized tooling data within the finite element analysis, further investigations were performed on the impact of data reduction strategies. Aiming on the minimization of the necessary number of elements, representing the tooling surface in a discrete state, and on the request for a sufficient degree of accuracy, these strategies have to be considered of high priority.

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