Fatigue Strength of CP Grade 2 Titanium Fillet Welded Joint for Ship Structure

Iwata, Toshiaki; Matsuoka, Kazumi

doi:10.1007/bf03266442

Cited by 21 publications

(27 citation statements)

References 1 publication

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“…In this work, a neighborhood decision system (Table 1) of titanium alloy welded joints was established based on the well-documented fatigue data. Forty three (43) sets of titanium alloy fatigue data [25] were cited. The test specimens were manufactured using 2 and 10 mm thick commercial pure titanium mill products (JISH4600 TP340C/H).…”

Section: Fatigue Characteristic Domain Determinationmentioning

confidence: 99%

An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

Zou

Sun

Yang

2019

Entropy

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In order to obtain comprehensive assessment of the factors influencing fatigue life and to further improve the accuracy of fatigue life prediction of welded joints, soft computing methods, including entropy-based neighborhood rough set reduction algorithm, the particle swarm optimization (PSO) algorithm and support vector regression machine (SVRM) are combined to construct a fatigue life prediction model of titanium alloy welded joints. By using an entropy-based neighborhood rough set reduction algorithm, the influencing factors of the fatigue life of titanium alloy welded joints such as joint type, plate thickness, etc. are analyzed and the reduction results are obtained. Fatigue characteristic domains are proposed and determined subsequently according to the reduction results. The PSO-SVRM model for fatigue life prediction of titanium alloy welded joints is established in the suggested fatigue characteristic domains. Experimental results show that by taking into account the impact of joint type, the PSO-SVRM model could better predict the fatigue life of titanium alloy welded joints. The PSO-SVRM model indicates the relationship between fatigue life and fatigue life influencing factors in multidimensional space compared with the conventional least-square S-N curve fitting method, it could predict the fatigue life of the titanium alloy welded joints more accurately thus helps to the reliability design of the structure. joint thickness and load model, the S-N sample data is compressed into a narrow band. In the present study, comprehensive and quantitative analysis of the influencing factors which affect the fatigue life of welded joints is lacking and the problem of low precision of fatigue life prediction of welded joints still exists.Thermodynamics entropy has been used for fatigue life prediction for a long time. For example, a damage metric based on the second law of thermodynamics and statistical mechanics was presented [8]. The proposed thermodynamic framework treated a solid body as a thermodynamic system and required that the entropy production be non-negative. A thermodynamic framework was presented for damage mechanics of solid materials [9]. It was believed that there was no need for physically meaningless empirical parameters to define a phenomenological damage potential surface or a Weibull function to trace damage evolution in solid continuum. A generalized theory of evolution based on the concept of tribo-fatigue entropy was proposed where tribo-fatigue entropy was determined by the processes of damage ability conditioned by thermodynamic and mechanical effects causing to the change of states of any systems [10]. The Shannon's differential entropies of both distributions of the strain in the loading direction and the first principal strain were employed at the tension peak of the cycles as measuring parameters of strain inhomogeneity [11]. A thermo-mechanical fatigue life prediction model based on the theory of damage mechanics was presented in [12]. The damage evolution, corresponding to the mate...

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Section: Fatigue Characteristic Domain Determinationmentioning

confidence: 99%

An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

Zou

Sun

Yang

2019

Entropy

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“…In this report, the result of HFMI and Shot peening are evaluated with the result of the as-welded condition and conventional Burr grinding by nominal stress approach. In addition, the results were assessed using the equivalent stress approach by the modified version [3][4][5] of the approach MIL-HDBK-5 where both the stress concentration of weld toe and the weld residual stress are separated from nominal stress. Original MIL-HDBK-5 is contained in the US Military Standard usually applied to aircraft.…”

Section: Object Of This Studymentioning

confidence: 99%

Thickness Effect on Fatigue Strength of Welded Joint Improved by HFMI

Iwata

Niwa

Tanaka

et al. 2016

QUARTERLY JOURNAL OF THE JAPAN WELDING SOCIETY

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“…Firstly, rough set models were established based on well-documented fatigue data. Titanium alloy low cycle fatigue data for TIG [13], Manual TIG [14], laser welding [15], and various fillet welded joints [16]with different bending versus tension [17] in the as-welded conditions without stress-relief were cited. 106 sets of welding fatigue data were arranged by welding factors including joint type (J), plate thickness (t), load ratio (R), stress ratio (r), welding process (W) and material type (M) and entered into the rough set database as condition attributes.…”

Section: Establishment Of Rough Sets Analysis Modelmentioning

confidence: 99%

“…106 sets of welding fatigue data were arranged by welding factors including joint type (J), plate thickness (t), load ratio (R), stress ratio (r), welding process (W) and material type (M) and entered into the rough set database as condition attributes. The joint types marked with two capital letters were cited from reference [16]. The first letters refer to the directions along (L) and transverse (C) to the plate rolling.…”

Section: Establishment Of Rough Sets Analysis Modelmentioning

confidence: 99%

Study on the Correction of S-N Distribution in the Welding Fatigue Analysis Method Based on the Battelle Equivalent Structural Stress by Rough Set Theory

Sun¹,

Yang²

2014

SV-JME

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In welding techniques, failure is a key problem that is related to the stability and safety of the welded structure. In the commonly used welding fatigue analysis methods, the Master S-N curve method based on Battelle structural stress solves the problem of inconsistencies in stress calculation and S-N curve selection better than the nominal stress method and hot spot stress method. In this paper, rough set theory was employed to study the S-N distribution based on the Battelle equivalent structural stress. Firstly, rough set analyses of the S-N distribution based on the nominal stress, structure stress and Battelle equivalent structure stress were carried out. Then the S-N distributions based on the three stresses were studied. The results indicated that structural stress rearranges the S-N point making it much more concentrated in the region near the mean S-N curve and that equivalent structural stress places the S-N point more uniformly on both sides of the curve. Subsequently, the corrections from the plate thickness and stress ratio in the Master S-N curve method were studied. It was concluded that with two steps of corrections the decision-making degrees of welding factors are weakened and harmonized in the Battelle equivalent structural stress. This made the S-N distribution much more concentrated and uniform, which allows more accurate welding fatigue prediction by the Master S-N curve.

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Fatigue Strength of CP Grade 2 Titanium Fillet Welded Joint for Ship Structure

Cited by 21 publications

References 1 publication

An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

Thickness Effect on Fatigue Strength of Welded Joint Improved by HFMI

Study on the Correction of S-N Distribution in the Welding Fatigue Analysis Method Based on the Battelle Equivalent Structural Stress by Rough Set Theory

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