Development and validation of a probabilistic model for notch fatigue strength prediction of tool steels based on surface defects

Abstract A new model for the high cycle notch fatigue strength prediction of tool steels subjected to axial loading is proposed, based on previous literatures studies and experimental tests carried out six different steels, including rotating bending notched specimens, fractographic analyses, hardness, residual stress, roughness measurements. The novelty assumption that surface defects are main cause failures such steels. probabilistic approach was implemented by modeling size distributions defects, resulting in normal strength. Like other models, effect steel severity, specimen also taken into account. Model calibration validation were performed using data collected activity. behavior investigated performing a sensitivity analysis, aiming verify response variations considered input variables. Prediction errors only 1.3% (on average) 3.1% (maximum) resulted from comparison between model‐predicted