Numerical Calculation of Stress Intensity Factors for Semi-Elliptical Surface Cracks in Buried-Arc Welded Thick Plates

Jukić, Krešimir; Perić, Mato; Tonković, Zdenko; Skozrit, Ivica; Jarak, Tomislav

doi:10.3390/met11111809

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…where {dF} e represents the node force increment generated by any element of the titanium alloy plate; in the process of titanium alloy line heating forming, the heat source has an effect on the plate temperature. {dR} e represents the equivalent increment of any element caused by the change in temperature gradient, which can be calculated through Formula (7).…”

Section: Thermal-elastic-plastic Theory Of Titanium Alloy Line Heatingmentioning

confidence: 99%

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Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

Wang,

et al. 2024

JMSE

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The application of titanium alloy in shipbuilding can reduce ship weight and carbon emissions. To solve the problem of titanium alloy forming, the deformation prediction of titanium alloy line heating based on a backpropagation (BP) neural network and sparrow search algorithm (SSA) was researched. Based on the thermal–elastic–plastic finite element method, the numerical calculation model of TA5 titanium alloy overlapping heating forming was established. The feasibility of the model was verified by comparing it with the numerical calculation and experiment of low-carbon steel. Considering the characteristics of the titanium alloy-forming process, 73 groups of titanium alloy-forming schemes were obtained by the Latin hypercube sampling method. The deformation data of the samples were obtained by using the numerical calculation model of titanium alloy forming. The prediction methods of titanium alloy-forming deformation based on BP, genetic algorithm–backpropagation (GA-BP), and SSA-BP were proposed. The accuracy of different neural network prediction models was analyzed. The mean absolute percentage errors (MAPEs) of BP, GA-BP, and SSA-BP in shrinkage prediction were 7.45%, 4.08%, and 2.96%, respectively. The MAPEs of BP, GA-BP, and SSA-BP in deflection prediction were 8.44%, 4.73%, and 2.64%, respectively. The goodness of fit (R2) of SSA-BP is closest to 1 among the three models. The calculation results show that SSA-BP is better than BP and GA-BP in predicting the forming deformation of titanium alloy. The maximum prediction error of SSA-BP is 4.95%, which is within the allowable range of engineering error. The SSA-BP prediction model is suitable for the rapid and accurate prediction of the deformation of titanium alloy line heating forming. The intelligent prediction model provides data support for intelligent decisions for titanium alloy forming.

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Section: Thermal-elastic-plastic Theory Of Titanium Alloy Line Heatingmentioning

confidence: 99%

“…The efficiency and accuracy of hot stamping of high-strength steel have been improved. Based on the finite element method, Jukić K et al [7] established a finite element model for the welding process. The influence of submerged arc welding on the strength of the welding area of the plate was researched.…”

Section: Introductionmentioning

confidence: 99%

Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

Wang,

et al. 2024

JMSE

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“…M Steel Statistical Yearbook 2017). When analyzing structures under repeated loads, existing methods consider the impact of stress concentration on the development of fatigue phenomena (Jukić et al, 2021). However, in tubular assemblies, geometric discontinuities arise due to construction requirements, leading to stress concentrations, particularly near weld beads.…”

Section: Introductionmentioning

confidence: 99%

Using finite element modeling to predict stress concentration factors in tubular T, Y and K joints

Berkia,

Rebai,

Mansouri

et al. 2024

10.5267/j.esm

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Tubular offshore structures are commonly assembled using welded joints, creating areas of stress concentration and potential fatigue failure. This study focuses on tubular T, Y and K joints, a common offshore structural component. Finite element modeling is used to predict stress concentration factors (Kt) for various loading conditions on the T, Y and K joints. The goal is to calculate Kt values and compare them to existing theoretical solutions from literature. Additionally, the influence of different loading modes (tension, bending) on the Kt values is investigated. By using advanced modeling techniques, this work aims to provide new insight into the behavior of tubular T, Y and K joints connections under realistic offshore loading conditions. The results can help improve design standards and fatigue life predictions for these critical structural joints.

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Formation cause and oxidation behaviour of surface crack on the hot-rolled thick plate of high strength low alloy steel

Shen,

Cheng,

Zhang

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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The formation causes and oxidation behaviour of two types of small-sized and irregular morphological surface cracks on S355 hot-rolled thick plate were investigated. The study employed surface pickling, microstructure analysis and inclusion observation to characterise the cracks. It was observed that the surface morphology of two types of cracks exhibited similarities, while their subsurface morphology differed. The Type-A cracks displayed a bulge shape with a rounded tail, while the Type-B cracks had a V-shaped morphology with a slender tail. The Type-A cracks predominantly consisted of FeO. The surface part of the Type-B cracks contained unusual large-sized carbon particles and Al2O3 inclusions, and the iron oxides within the subsurface part followed the sequence of FeO–Fe2O3–Fe from the centre of the cracks to the steel matrix. It was observed that the Type-A cracks originated from subsurface bubbles in the continuous casting slab, which deformed and developed into surface cracks during the rolling process. The iron oxide inside these cracks was formed through a single oxidation process, and the bulge shape in the subsurface part was a distinguishing characteristic. The Type-B cracks were attributed to surface microcracks on the continuous casting slab, which were linked to the presence of unusual large-sized inclusions. The iron oxides within these cracks underwent two oxidation processes. The subsurface morphology and oxide characteristics are the significant features to identify these small-sized and irregular morphological cracks.

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Numerical Calculation of Stress Intensity Factors for Semi-Elliptical Surface Cracks in Buried-Arc Welded Thick Plates

Cited by 7 publications

References 33 publications

Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

Deformation Intelligent Prediction of Titanium Alloy Plate Forming Based on BP Neural Network and Sparrow Search Algorithm

Using finite element modeling to predict stress concentration factors in tubular T, Y and K joints

Formation cause and oxidation behaviour of surface crack on the hot-rolled thick plate of high strength low alloy steel

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