Estimation of Fatigue Limits from Temperature Data Measured by IR Thermography

Hayabusa, Keisuke; Inaba, Kazuaki; Ikeda, Hayato; Kishimoto, Kikuo

doi:10.1007/s11340-016-0221-7

Cited by 22 publications

(9 citation statements)

References 11 publications

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“…Then, the shooting time was selected as moderate value. Hayabusa et al concluded that fatigue limit was not changed by shooting time up to 300 s. 12) It is also reported a shorter time of 10 s is applied for the thermography, showing good agreement with a fatigue limit obtained by conventional fatigue tests. 13) However, these tests were conducted at R = ¹1 where relatively large dissipated energy is observed.…”

Section: Methodsmentioning

confidence: 71%

Lock-in Infrared Thermography for Fatigue Limit Estimation in Ti–6Al–4V Alloy

2021

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Lock-in infrared thermography was used to estimate the fatigue limit for Ti6Al4V alloy at room temperature. The method detected infrared emitted from specimen during cyclic loading, i.e., temperature change related to frequency ( f ) of the loading. The temperature change contained reversible component and irreversible component, which related to thermos-elastic effect and plastic deformation, respectively. The latter component was divided from the former one by lock-in analysis to estimate fatigue limit. Estimated fatigue limits corresponded to those obtained from conventional fatigue tests at several stress ratios. A new assessment line was identified as, where • a represents the stress amplitude, • w signifies the fatigue limit, • m denotes mean stress, • B expresses tensile strength, and the n exponent for the alloy is about 2.3. The non-failure region of the new diagram was smaller than that of the modified Goodman diagram because of a reduced fatigue limit at near-zero stress ratios in the titanium alloy.

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Section: Methodsmentioning

confidence: 71%

Lock-in Infrared Thermography for Fatigue Limit Estimation in Ti–6Al–4V Alloy

2021

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“…In engineering applications, data obtained from experiments are unavoidably affected by noise, and in some cases, the collection of a single data point can be expensive and time consuming; this is precisely the case of experiments performed for fatigue life prediction. 23,24 In disciplines such as materials science, it is common to work with relatively small datasets 25 ; therefore, domain knowledge is especially important to select the most relevant features and to ensure that a given model is not prone to overfitting, that is, that it will generalize well when used on new data. Such feature engineering process may result in the detection of non-trivial relationships between inputs, thus allowing a reduction of the parameter space and of a model's complexity.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints

Silva

Beber

Pitz

2021

Fatigue Fract Eng Mat Struct

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Since fatigue investigations are expensive and time consuming, models capable of predicting lifetime by leveraging existing experimental data are desirable.Here, this task is accomplished by combining machine learning (ML) and finite element analysis (FEA). The dataset contains 365 points comprising four adhesives with four different joint types. The model is fed with four input parameters: stress ratio and stress amplitude (functions of the applied load), and stress concentration factor and multiaxiality, which are obtained from FEA. An extremely randomized trees (ERT) algorithm, capable of dealing with small and noisy datasets, is used to design the model. After calibration, the model's performance was assessed on unseen data and compared with a linear regression model. The ERT predictions yield a significantly smaller error factor (ER) of 2.13 than that of the linear model (ER = 5.89). A relevance analysis shows that at least one FEA-based parameter must be fed into the model.

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“…Full‐field measurement techniques have been applied in literature for the experimental investigation of metallic and composite materials subjected to mechanical and thermal loading …”

Section: Introductionmentioning

confidence: 99%

“…11 Full-field measurement techniques have been applied in literature for the experimental investigation of metallic and composite materials subjected to mechanical and thermal loading. [12][13][14][15][16] The IR thermography was applied for the analysis of different materials subjected to different loading conditions: notched steel specimens under tensile static tests, 2,17 short glass fiber-reinforced polyamide composites under static and fatigue loading, 15 high-cycle, 1,8,[18][19][20][21] and very-high cycle 22,23 fatigue regimes. The determination of the Energy Parameter by means of IR thermography has been also used to evaluate the causes of failure of in-service mechanical components.…”

Section: Introductionmentioning

confidence: 99%

Fatigue assessment of a marine structural steel and comparison with Thermographic Method and Static Thermographic Method

Corigliano

Cucinotta

Guglielmino

et al. 2019

Fatigue Fract Eng Mat Struct

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Fatigue properties are of fundamental importance and extremely time consuming to be assessed. The aim of this research activity is to apply the Thermographic Method (TM) and the Static Thermographic Method (STM) during fatigue and tensile tests to correlate the temperature trend to the fatigue properties of an S355 steel. The material was retrieved from an in‐service port crane. Traditional fatigue tests were performed in order to evaluate the S‐N curve with a scatter band. Step load tests were carried out deriving the fatigue limit and the Energy Parameter of the material. Static tensile tests were performed to obtain the stress at which the temperature trend deviates from the thermoelastic behavior. The fatigue properties obtained by means of the energetic methods were compared to the traditional ones showing a good agreement.

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Estimation of Fatigue Limits from Temperature Data Measured by IR Thermography

Cited by 22 publications

References 11 publications

Lock-in Infrared Thermography for Fatigue Limit Estimation in Ti–6Al–4V Alloy

Lock-in Infrared Thermography for Fatigue Limit Estimation in Ti–6Al–4V Alloy

Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints

Fatigue assessment of a marine structural steel and comparison with Thermographic Method and Static Thermographic Method

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