KLEMENC, Jernej ;PODGORNIK, Bojan . An Improved Model for Predicting the Scattered S-N Curves. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 65, n.5, p. 265-275, june 2019. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/an-improved-model-for-predicting-the-scattered-s-n-curves/>. Date accessed: 10 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2018.5918.
Klemenc, J., & Podgornik, B. (2019). An Improved Model for Predicting the Scattered S-N Curves. Strojniški vestnik - Journal of Mechanical Engineering, 65(5), 265-275. doi:http://dx.doi.org/10.5545/sv-jme.2018.5918
@article{sv-jmesv-jme.2018.5918, author = {Jernej Klemenc and Bojan Podgornik}, title = {An Improved Model for Predicting the Scattered S-N Curves}, journal = {Strojniški vestnik - Journal of Mechanical Engineering}, volume = {65}, number = {5}, year = {2019}, keywords = {51CrV4 steel, conventional manufacturing technology, electro-slag remelting, S-N curve, serial hybrid neural network}, abstract = {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.}, issn = {0039-2480}, pages = {265-275}, doi = {10.5545/sv-jme.2018.5918}, url = {https://www.sv-jme.eu/sl/article/an-improved-model-for-predicting-the-scattered-s-n-curves/} }
Klemenc, J.,Podgornik, B. 2019 June 65. An Improved Model for Predicting the Scattered S-N Curves. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 65:5
%A Klemenc, Jernej %A Podgornik, Bojan %D 2019 %T An Improved Model for Predicting the Scattered S-N Curves %B 2019 %9 51CrV4 steel, conventional manufacturing technology, electro-slag remelting, S-N curve, serial hybrid neural network %! An Improved Model for Predicting the Scattered S-N Curves %K 51CrV4 steel, conventional manufacturing technology, electro-slag remelting, S-N curve, serial hybrid neural network %X 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. %U https://www.sv-jme.eu/sl/article/an-improved-model-for-predicting-the-scattered-s-n-curves/ %0 Journal Article %R 10.5545/sv-jme.2018.5918 %& 265 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 65 %N 5 %@ 0039-2480 %8 2019-06-18 %7 2019-06-18
Klemenc, Jernej, & Bojan Podgornik. "An Improved Model for Predicting the Scattered S-N Curves." Strojniški vestnik - Journal of Mechanical Engineering [Online], 65.5 (2019): 265-275. Web. 10 Dec. 2024
TY - JOUR AU - Klemenc, Jernej AU - Podgornik, Bojan PY - 2019 TI - An Improved Model for Predicting the Scattered S-N Curves JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5918 KW - 51CrV4 steel, conventional manufacturing technology, electro-slag remelting, S-N curve, serial hybrid neural network N2 - 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. UR - https://www.sv-jme.eu/sl/article/an-improved-model-for-predicting-the-scattered-s-n-curves/
@article{{sv-jme}{sv-jme.2018.5918}, author = {Klemenc, J., Podgornik, B.}, title = {An Improved Model for Predicting the Scattered S-N Curves}, journal = {Strojniški vestnik - Journal of Mechanical Engineering}, volume = {65}, number = {5}, year = {2019}, doi = {10.5545/sv-jme.2018.5918}, url = {https://www.sv-jme.eu/sl/article/an-improved-model-for-predicting-the-scattered-s-n-curves/} }
TY - JOUR AU - Klemenc, Jernej AU - Podgornik, Bojan PY - 2019/06/18 TI - An Improved Model for Predicting the Scattered S-N Curves JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 65, No 5 (2019): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5918 KW - 51CrV4 steel, conventional manufacturing technology, electro-slag remelting, S-N curve, serial hybrid neural network N2 - 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. UR - https://www.sv-jme.eu/sl/article/an-improved-model-for-predicting-the-scattered-s-n-curves/
Klemenc, Jernej, AND Podgornik, Bojan. "An Improved Model for Predicting the Scattered S-N Curves" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 65 Number 5 (18 June 2019)
Strojniški vestnik - Journal of Mechanical Engineering 65(2019)5, 265-275
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.