Abstract. This work investigates the cyclic response and low-cycle fatigue behaviour of a CuAg alloy used in crystallizer for continuous casting lines. Therefore isothermal strain-based fatigue tests are first performed on CuAg specimens at different temperature levels (20 o C, 250 °C, 300 °C). The evolution of stress-strain loops recorded during the cyclic tests is used for the parameter identification of several nonlinear hardening models (nonlinear kinematic, nonlinear isotropic). Cyclic stress-strain data from experiments are compared with results from numerical simulations with the identified material parameters, showing a satisfying agreement. Critical examination of numerical results from different models is also performed. Finally, the strainlife fatigue curves estimated from experimental data are compared with approximate strain-life equations (Universal Slopes Equation, 10% Rule) which are obtained from simple tensile tests. The material parameters determined in this work can conveniently be used as inputs in a elastoplastic finite element simulations of a crystallizer.
AISI 316L stainless steels are widely employed in applications where durability is crucial. For this reason, an accurate prediction of its behaviour is of paramount importance. In this work, the spotlight is on the cyclic response and low-cycle fatigue performance of this material, at room temperature. Particularly, the first aim of this work is to experimentally test this material and use the results as input to calibrate the parameters involved in a kinematic and isotropic nonlinear plasticity model (Chaboche and Voce). This procedure is conducted through a newly developed calibration procedure to minimise the parameter estimates errors. Experimental data are eventually used also to estimate the strain–life curve, namely the Manson–Coffin curve representing the 50% failure probability and, afterwards, the design strain–life curves (at 5% failure probability) obtained by four statistical methods (i.e., deterministic, “Equivalent Prediction Interval”, univariate tolerance interval, Owen’s tolerance interval for regression). Besides the characterisation of the AISI 316L stainless steel, the statistical methodology presented in this work appears to be an efficient tool for engineers dealing with durability problems as it allows one to select fatigue strength curves at various failure probabilities depending on the sought safety level.
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