20.500.12556/RUL-108215 An improved model for predicting the scattered S-N curves Izboljšan model za napoved S-N krivulj in njihovih raztrosov In this article an improved neural network model is presented that allows us to predict the scattered S-N curves. The model is capable of predicting the S-N curve in its high-cycle and very-high-cycle fatigue domains by considering also the increased scatter of the fatigue-life data below the knee point of the S-N curve. The scatter of the fatigue-life data for an arbitrary amplitude-stress level is modelled with a two-parametric Weibull%s probability density function, the parameters of which are varied as a function of the amplitude-stress level. The parameters of the S-N curve trend and its scatter distribution are not fixed, but depend on the parameters of the production process via a serial-hybrid neural network. The article presents the theoretical background and the application in the case of real experimental fatigue data for 51CrV4 spring steel manufactured with two different manufacturing technologies and two different heat treatments. 51CrV4 steel conventional manufacturing technology electro-slag remelting S-N curve neural network jeklo 51CrV4 konvencionalne izdelovalne tehnologije elektro-pretaljevanje S-N krivulja nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2019-06-21 09:09:55 2019-06-21 09:09:56 2022-08-20 03:45:09 0000-00-00 00:00:00 2019 0 0 Str. 265-275 no. 5 Vol. 65 May 2019 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.7:681.5(045) 0039-2480 10.5545/sv-jme.2018.5918 16644891 762116 RAZ_Klemenc_Jernej_i2019.pdf RAZ_Klemenc_Jernej_i2019.pdf 1 C208FE072DB0815465C35A4DE5774272 45a557c873a58ad03ed09ca0f661e6ee5b00f91ddfb69c4a65b6c67237728f20 994473e6-a1b6-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=119161 https://www.sv-jme.eu/sl/article/an-improved-model-for-predicting-the-scattered-s-n-curves/ 1 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=119160 Fakulteta za strojništvo Strojniški vestnik 0 0 0