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Licensed Unlicensed Requires Authentication Published by De Gruyter May 23, 2021

Notch (stress concentration) factor estimation of a cylinder under internal pressure using different approaches

Osman Atalay and Ihsan Toktas
From the journal Materials Testing

Abstract

Today, fluid transportation via pipes can be found in many sectors. Therefore, safe fluid transportation possesses critical importance. While working, transportation pipes are exposed to unwanted loads that culminate in stresses which cause deformation on the part geometry especially in sharp corners, holes or sudden cross-section change areas considered as notched. The notch effect parameter is considered in the mechanical design formulas. This study is interested in the notch factor that is estimated for a cylinder which undergoes an inner pressure. Some users can use false numerical values due to misreading or lack of attention. Because of this reason, graphs were converted to the numerical value by using computer software. In this study, Peterson’s chart was accepted as scientifically valid. Stress concentration factors were obtained by using four other approaches. These are regression, analytical, artificial neural network and finite element analysis. Among these models, high accuracy values were given by the artificial neural network model.


Osman Atalay Graduate School of Natural Sciences University of Ankara Yildirim Beyazit Ankara, Turkey

Nomenclature

α:

radius proportion, Ri/Ro

σ max:

maximum stress, MPa

ANN:

artificial neural network

FEA:

finite element analysis

FEM:

finite element method

Kt:

Peterson’s empirical stress concentration factor

MEP:

mean error percentage

n:

the number of processing elements in the preceding layer.

NETi:

weighted sum of input data corresponding to the ith processing element

o:

output value

p:

applied inner pressure

R2:

absolute fraction of variance

Ri:

inner radius of the part

R o:

outer radius of the part

REGA:

regression analysis

RMSE:

root mean square error

t:

target value

wij:

weights of the connections between ith and jth processing elements

wbi:

weights of the biases between layers

xj:

output of the jth processing element

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Published Online: 2021-05-23
Published in Print: 2021-05-26

© 2021 Walter de Gruyter GmbH, Berlin/Boston, Germany

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