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دسته بندی:
شبکه های نورونی - neuron-networks
سال انتشار:
2020
عنوان انگلیسی مقاله:
Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network
ترجمه فارسی عنوان مقاله:
پیش بینی طول عمر خستگی مواد فلزی با توجه به میانگین اثرات استرس با استفاده از شبکه عصبی مصنوعی
منبع:
Sciencedirect - Elsevier - International Journal of Fatigue, 135 (2020) 105527. doi:10.1016/j.ijfatigue.2020.105527
نویسنده:
Joelton Fonseca Barbosaa,b,c,⁎, José A.F.O. Correiab,d,⁎, R.C.S. Freire Júniora,⁎, Abílio M.P. De Jesusd
چکیده انگلیسی:
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.
Keywords: Fatigue | Artificial neural network | Back-propagation algorithm | Stüssi model | Constant life diagram
قیمت: رایگان
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