A machine-learning fatigue life prediction approach of additively manufactured metals

Bao, Hongyixi; Wu, Shengchuan; Wu, Zhengkai; Kang, Guozheng; Peng, Xin; Withers, Philip J.

doi:10.1016/j.engfracmech.2020.107508

Cited by 180 publications

(57 citation statements)

References 38 publications

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“…More efforts should be made to develop the fatigue life evaluation model suitable for large‐scale parts. Recently, machine learning, as a data‐driven method, has shown great potential in the complex engineering problem, such as in the field of crack defect prediction, fracture toughness calculation, and fatigue life prediction 13,19,33 …”

Section: Discussionmentioning

confidence: 99%

“…These manufacturing defects are the potential crack nucleation sites due to high stress concentration. The sample‐to‐sample variation in crack initiation life is collaboratively controlled by the geometric parameters (location, size, and shape) of the defects, and the severity of the effect can be ranked in terms of the decreasing significance as location > size > morphology 16–19 . The large‐sized defects having an irregular shape close to the material surface pose the most threat to the fatigue fracture of the material.…”

Section: Introductionmentioning

confidence: 99%

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Fatigue life evaluation of Ti–6Al–4V welded joints manufactured by electron beam melting

Xie

et al. 2021

Fatigue Fract Eng Mat Struct

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Electron beam melting (EBM) is a promising three‐dimensional printing technology for the fabrication of components with high complexity and freedom of structural design. However, the major limit for its wider industrial applications is to preclude manufacturing the large‐scale part in one piece. It is because the part size is largely limited by the enclosed vacuum chamber. An alternative option is the use of welding to join EBM‐processed subparts together. In this study, fatigue crack growth (FCG) rate and high‐cycle fatigue (HCF) performance were measured for both EBM‐processed Ti–6Al–4V alloy and its welded joints. An X‐parameter model was then proposed to correlate stress amplitude and geometric parameters (location, size, and shape) of the critical defects at the crack origins with fatigue life. Research framework and result from this work are expected to serve a reference for the defect‐based fatigue life evaluation of welded parts individually manufactured using additive manufacturing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fatigue life evaluation of Ti–6Al–4V welded joints manufactured by electron beam melting

Xie

et al. 2021

Fatigue Fract Eng Mat Struct

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“…In addition, Zhou et al 21 proposed a method of multi‐neural network integration to improve the accuracy of contact fatigue life prediction of AT40 ceramic coatings. Bao et al 11 used SVM method to explore the effect of defect location, size, and morphology on the fatigue life of selective laser melted Ti‐6Al‐4V alloy. To optimize traditional life prediction models for higher life prediction accuracies. The ANN was used for establishing the S–N method of multidirectional composite laminates by Vassilopoulos et al 22 They then applied adaptive fuzzy neural system to determine the allowable value of reliable fatigue design of multidirectional composite laminates 23 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based genetic feature identification and fatigue life prediction

Zhou

Sun

Shi

et al. 2021

Fatigue Fract Eng Mat Struct

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Considering the nonlinear relationship between variables and fatigue life and the computational burden, a machine learning method integrating the artificial neural network (ANN) and partial least squares (PLS) algorithm was proposed as a framework to identify the genetic features through optimizing fatigue life prediction. Twenty‐seven specimens of 316LN stainless steel under uniaxial and multiaxial loadings were used as examples. As results, early fatigue data were proved to be informative for fatigue life prediction. Moreover, five genetic features were identified out of them, and a predicting model was developed. The predicted fatigue life of these samples using only these five genetic features were all located within the 1.5‐factor band. This framework can be easily extended to identify genetic features and to predict fatigue life of other materials under different loadings. Therefore, it provides an efficient option in this field to greatly reduce experimental time and cost.

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“…They adopted six different machine learning algorithms to predict the fatigue life and the deep neural network exhibits the best, the average error is 14.3%. Hongyixi Bao et al [32] use a support vector machine algorithm to predict the influence of defect parameters on the fatigue life of Ti-6Al-4V alloy. In the training process, they use the cross-validation method to utilize the data sufficiently and quickly.…”

Section: Introductionmentioning

confidence: 99%

Creep–Fatigue Experiment and Life Prediction Study of Piston 2A80 Aluminum Alloy

Dong¹,

Liu²,

Liu³

et al. 2021

Materials

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In order to improve the reliability and service life of vehicle and diesel engine, the fatigue life prediction of the piston in a heavy diesel engine was studied by finite element analysis of piston, experiment data of aluminum alloy, fatigue life model based on energy dissipation criteria, and machine learning algorithm. First, the finite element method was used to calculate and analyze the temperature field, thermal stress field, and thermal–mechanical coupling stress field of the piston, and determine the area of heavy thermal and mechanical load that will affect the fatigue life of the piston. Second, based on the results of finite element calculation, the creep–fatigue experiment of 2A80 aluminum alloy was carried out, and the cyclic response characteristics of the material under different loading conditions were obtained. Third, the fatigue life prediction models based on energy dissipation criterion and twin support vector regression are proposed. Then, the accuracy of the two models was verified using experiment data. The results show that the model based on the twin support vector regression is more accurate for predicting the material properties of aluminum alloy. Based on the established life prediction model, the fatigue life of pistons under actual service conditions is predicted. The calculation results show that the minimum fatigue life of the piston under plain condition is 2113.60 h, and the fatigue life under 5000 m altitude condition is 1425.70 h.

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A machine-learning fatigue life prediction approach of additively manufactured metals

Cited by 180 publications

References 38 publications

Fatigue life evaluation of Ti–6Al–4V welded joints manufactured by electron beam melting

Fatigue life evaluation of Ti–6Al–4V welded joints manufactured by electron beam melting

Machine learning‐based genetic feature identification and fatigue life prediction

Creep–Fatigue Experiment and Life Prediction Study of Piston 2A80 Aluminum Alloy

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