relation: https://khub.utp.edu.my/scholars/18105/
title: Empirical modeling of stress concentration factors using finite element analysis and artificial neural networks for the fatigue design of tubular KT-joints under combined loading
creator: Iqbal, M.
creator: Karuppanan, S.
creator: Perumal, V.
creator: Ovinis, M.
creator: Nouman, H.
description: The hotspot stress (HSS) approach for the fatigue design of tubular joints requires that peak HSS be known. Peak HSS in tubular joints is usually determined based on the stress concentration factor (SCF) estimated from empirical models developed through extensive experimental investigations and finite element analysis. While peak HSS usually occurs at a KT-joint's crown and saddle points, its location may change if the tubular joint is subjected to a combination of axial, in-plane bending, or out-of-plane bending loads. This study investigated the peak HSS and its location in a typical KT-joint subjected to the combined loading. Specifically, empirical models to determine the SCF around the brace axis have been developed using extensive finite element analysis and artificial neural networks (ANN) simulations. Less than 3 error was noticed between peak HSS determined through developed models and FEA. Hence, the ANN-based SCF equations and principle of superposition can be used to calculate peak HSS rapidly for fatigue design of tubular joints. This methodology is applicable for developing empirical models for SCF in other tubular joints and boundary conditions. © 2023 John Wiley & Sons Ltd.
date: 2023
type: Article
type: PeerReviewed
identifier:   Iqbal, M. and Karuppanan, S. and Perumal, V. and Ovinis, M. and Nouman, H.  (2023) Empirical modeling of stress concentration factors using finite element analysis and artificial neural networks for the fatigue design of tubular KT-joints under combined loading.  Fatigue and Fracture of Engineering Materials and Structures, 46 (11).  pp. 4333-4349.      
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167398634&doi=10.1111%2fffe.14122&partnerID=40&md5=f6b95ded127e9668360afda22fd699cf
relation: 10.1111/ffe.14122
identifier: 10.1111/ffe.14122