Fatigue & Fracture of Engineering Materials & Structures · 2018 · 26 citations · 17 references
EngineeringMechanical EngineeringMean StressStructural EngineeringFatigue ManagementStrength PropertyStressstrain AnalysisFatigue ResistanceService Life PredictionElectrical EngineeringDurability PerformanceAluminum Conductor SteelStructural Health MonitoringLow-cycle FatigueArtificial Neural NetworksCivil EngineeringStructural MechanicsOverhead ConductorsArtificial Neural NetworkMechanics Of Materials
Abstract The aim of this work was to use artificial neural networks (ANNs) to model the effect of mean tensile stresses on the fatigue resistance of an aluminum conductor steel reinforced. To train the ANN, fatigue data available for this type of conductor subjected to 2 different levels of mean stresses in the aluminium wires (49 and 74 MPa) were used. It is shown that the use of ANN enabled the construction of constant life diagrams (10 5 , 10 6 , 10 7 , and 10 8 fatigue loading cycles) for the conductor. These results confirmed that the ANN is able to accurately estimate the effect of mean tensile stresses on conductor durability even considering just a limited number of data for its training.
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A Stress-Strain Function for the Fatigue of Metals
KN Smith · Journal: Materials · 1970 · 1.7K citations