Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network
•A constant life diagram modelling based on artificial neural network is proposed.•An artificial neural network based on a MLP network trained with the BPM is trained.•A hybrid ANN-Stüssi model to determine the values is proposed.•The probabilistic Stüssi fatigue model based on Weibull distribution...
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| Vydané v: | International journal of fatigue Ročník 135; s. 105527 - 11 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Kidlington
Elsevier Ltd
01.06.2020
Elsevier BV |
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| ISSN: | 0142-1123, 1879-3452 |
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| Abstract | •A constant life diagram modelling based on artificial neural network is proposed.•An artificial neural network based on a MLP network trained with the BPM is trained.•A hybrid ANN-Stüssi model to determine the values is proposed.•The probabilistic Stüssi fatigue model based on Weibull distribution is applied.•The experimental fatigue data for P355NL1 steel and notched details are used.
The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes high-cycle fatigue behaviour, directly decreasing the fatigue limit with the increase of the mean stress. Fatigue design of structural details and mechanical components must account for mean stress effects in order to guarantee the performance and safety criteria during their foreseen operational life. The purpose of this research work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stüssi fatigue S-N fields. This proposed methodology can estimate the safety region for high-cycle fatigue regimes as a function of the mean stress and stress amplitude in regions where tensile loading is predominance, using fatigue S-N curves only for two stress R-ratios. In this approach, the experimental fatigue data of the P355NL1 pressure vessel steel available for three stress R-ratios (−1, −0.5, 0), are used. A multilayer perceptron network has been trained with the back-propagation algorithm; its architecture consists of two input neurons (σm,N) and one output neuron (σa). The suggested CLD based on trained artificial neural network algorithm and probabilistic Stüssi fatigue fields applied to dog-bone shaped specimens made of P355NL1 steel showed a good agreement with the high-cycle fatigue experimental data, only using the stress R-ratios equal to 0 and −0.5. Furthermore, a procedure for estimating the fatigue resistance reduction factor, Kf, for the fatigue life prediction of structural details (stress R-ratios equal to 0, 0.15 and 0.3) in extrapolation regions is suggested and used to generate the Kf results for stress R-ratios from −1 to 0.3, based on machine learning artificial neural network algorithm. |
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| AbstractList | •A constant life diagram modelling based on artificial neural network is proposed.•An artificial neural network based on a MLP network trained with the BPM is trained.•A hybrid ANN-Stüssi model to determine the values is proposed.•The probabilistic Stüssi fatigue model based on Weibull distribution is applied.•The experimental fatigue data for P355NL1 steel and notched details are used.
The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes high-cycle fatigue behaviour, directly decreasing the fatigue limit with the increase of the mean stress. Fatigue design of structural details and mechanical components must account for mean stress effects in order to guarantee the performance and safety criteria during their foreseen operational life. The purpose of this research work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stüssi fatigue S-N fields. This proposed methodology can estimate the safety region for high-cycle fatigue regimes as a function of the mean stress and stress amplitude in regions where tensile loading is predominance, using fatigue S-N curves only for two stress R-ratios. In this approach, the experimental fatigue data of the P355NL1 pressure vessel steel available for three stress R-ratios (−1, −0.5, 0), are used. A multilayer perceptron network has been trained with the back-propagation algorithm; its architecture consists of two input neurons (σm,N) and one output neuron (σa). The suggested CLD based on trained artificial neural network algorithm and probabilistic Stüssi fatigue fields applied to dog-bone shaped specimens made of P355NL1 steel showed a good agreement with the high-cycle fatigue experimental data, only using the stress R-ratios equal to 0 and −0.5. Furthermore, a procedure for estimating the fatigue resistance reduction factor, Kf, for the fatigue life prediction of structural details (stress R-ratios equal to 0, 0.15 and 0.3) in extrapolation regions is suggested and used to generate the Kf results for stress R-ratios from −1 to 0.3, based on machine learning artificial neural network algorithm. The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes high-cycle fatigue behaviour, directly decreasing the fatigue limit with the increase of the mean stress. Fatigue design of structural details and mechanical components must account for mean stress effects in order to guarantee the performance and safety criteria during their foreseen operational life. The purpose of this research work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stüssi fatigue S-N fields. This proposed methodology can estimate the safety region for high-cycle fatigue regimes as a function of the mean stress and stress amplitude in regions where tensile loading is predominance, using fatigue S-N curves only for two stress R-ratios. In this approach, the experimental fatigue data of the P355NL1 pressure vessel steel available for three stress R-ratios (−1, −0.5, 0), are used. A multilayer perceptron network has been trained with the back-propagation algorithm; its architecture consists of two input neurons […] and one output neuron […]. The suggested CLD based on trained artificial neural network algorithm and probabilistic Stüssi fatigue fields applied to dog-bone shaped specimens made of P355NL1 steel showed a good agreement with the high-cycle fatigue experimental data, only using the stress R-ratios equal to 0 and −0.5. Furthermore, a procedure for estimating the fatigue resistance reduction factor, […], for the fatigue life prediction of structural details (stress R-ratios equal to 0, 0.15 and 0.3) in extrapolation regions is suggested and used to generate the […] results for stress R-ratios from −1 to 0.3, based on machine learning artificial neural network algorithm. ([...] denotes ProQuest formulae omitted) |
| ArticleNumber | 105527 |
| Author | Barbosa, Joelton Fonseca Jesus, Abílio M.P. De Júnior, R.C.S. Freire Correia, José A.F.O. |
| Author_xml | – sequence: 1 givenname: Joelton Fonseca surname: Barbosa fullname: Barbosa, Joelton Fonseca email: joelton.fonseca@ufersa.edu.br organization: Federal University of Rio Grande do Norte, Natal, Brazil – sequence: 2 givenname: José A.F.O. surname: Correia fullname: Correia, José A.F.O. email: jacorreia@fe.up.pt organization: CONSTRUCT, Faculty of Engineering, University of Porto, Portugal – sequence: 3 givenname: R.C.S. Freire surname: Júnior fullname: Júnior, R.C.S. Freire email: freirej@ufrnet.br organization: Federal University of Rio Grande do Norte, Natal, Brazil – sequence: 4 givenname: Abílio M.P. De surname: Jesus fullname: Jesus, Abílio M.P. De email: ajesus@fe.up.pt organization: INEGI, Faculty of Engineering, University of Porto, Porto, Portugal |
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| Keywords | Stüssi model Constant life diagram Fatigue Artificial neural network Back-propagation algorithm |
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| Snippet | •A constant life diagram modelling based on artificial neural network is proposed.•An artificial neural network based on a MLP network trained with the BPM is... The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes high-cycle fatigue behaviour, directly... |
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| SubjectTerms | Algorithms Artificial neural network Artificial neural networks Back propagation networks Back-propagation algorithm Constant life diagram Fatigue Fatigue life Fatigue limit Fatigue strength High cycle fatigue Life prediction Machine learning Materials fatigue Mechanical components Multilayer perceptrons Neural networks Pressure vessels Resistance factors S N diagrams Safety Stüssi model |
| Title | Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network |
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