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Improved Fatigue Strength of a Dissimilar Metal T-Lap Joint Fabricated by Controlled Double-Pass Friction Stir Welding Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Reconstruction of Defect Configuration in Plane Construction Using Numerical Modeling and Neural Networks at Positional Scanning

Authors : A. N. Soloviev, I. A. Parinov, A. V. Cherpakov

Published in: Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)

Publisher: Springer International Publishing

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Abstract

The results of modeling a plane construction with defect are presented. The aim of the work is to identify the parameters of the defect based on the use of finite element modeling (FEM) and artificial neural network (ANN). The properties and configuration of the defect differ from the layer in which it is located. The calculation of construction oscillations is carried out during its impulse loading at a certain point of the construction surface. The shape of the defect is considered as identifiable parameter of the defect. Neural networks are used as a tool for analysis. Surface waves excited by impulse loading are considered as input parameters. The results of the restoration of defect parameters obtained on the base of a combination of FEM and ANN are presented.

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previous chapter Adaptive Variable Stiffness Vibration Absorber Using Magnetorheological Material
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Metadata
Title
Reconstruction of Defect Configuration in Plane Construction Using Numerical Modeling and Neural Networks at Positional Scanning
Authors
A. N. Soloviev
I. A. Parinov
A. V. Cherpakov
Copyright Year
2021
Book
Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)

Print ISBN: 978-3-030-69609-2

Electronic ISBN: 978-3-030-69610-8

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-69610-8_32