ANALYSIS OF ELASTO-PLASTIC STRESS WAVES BY A TIME-DISCONTINUOUS VARIATIONAL INTEGRATOR OF HAMILTONIAN

2008 ◽  
Vol 22 (31n32) ◽  
pp. 6259-6264
Author(s):  
SANG-SOON CHO ◽  
HOON HUH ◽  
KWANG-CHUN PARK
Keyword(s):  
Finite Element Analysis ◽  
Plastic Deformation ◽  
Numerical Algorithm ◽  
Stress Waves ◽  
Element Analysis ◽  
One Dimensional ◽  
Variational Integrator ◽  
The Stability ◽  
Elastic Unloading ◽  
Plastic Stress

This paper proposes a numerical algorithm of a time-discontinuous variational integrator based on the Hamiltonian in order to obtain more accurate results in the analysis of elasto-plastic stress wave. The algorithm proposed adopts both a time-discontinuous variational integrator and space-continuous Hamiltonian so as to capture discontinuities of stress waves. The algorithm also adopts the limited kinetic energy to enhance the stability of the numerical algorithm so as to solve the discontinuities such as elastic unloading and internal reflection in plastic deformation. Finite element analysis of one dimensional elasto-plastic stress waves is carried out in order to demonstrate the accuracy of the algorithm proposed.

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 A DENTAL IMPLANT

2003 ◽  
Vol 15 (02) ◽  
pp. 82-85 ◽  
Author(s):  
SHYH-CHOUR HUANG ◽  
CHANG-FENG TSAI
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Stress Distribution ◽  
Element Model ◽  
Element Analysis ◽  
3 Dimensional ◽  
Dimensional Finite Element ◽  
The Stability ◽  
Stress Concentration Effect ◽  
Implant System

This paper presents results from using a 3-dimensional finite element model to assess the stress distribution in the bone, in the implant and in the abutment as a function of the implant's diameter and length. Increasing implant diameter and length increases the stability of the implant system. By using a finite element analysis, we show that implant length does not decrease the stress distribution of either the implant or the bone. Alternatively, however implant diameter increases reduce the stresses. For the latter case, the contact area between implant and bone is increased thus the stress concentration effect is decreased. Also, with increased implant diameter the bone loss is decreased and as a consequence the success rate is improved.

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