Modelling the relationship between process parameters and mechanical properties using Bayesian neural networks for powder metal parts
暂无分享,去创建一个
[1] R. Grylls,et al. Mechanical properties of a high-strength cupronickel alloy-Bayesian neural network analysis , 1997 .
[2] Walter Bogaerts,et al. Neural networks for materials data analysis: Development guidelines , 1995 .
[3] Hans Henrik Thodberg,et al. A review of Bayesian neural networks with an application to near infrared spectroscopy , 1996, IEEE Trans. Neural Networks.
[4] Richard Webb,et al. Advances in Powder Metallurgy & Particulate Materials 1997 : proceedings of the 1997 International Conference & Exhibition on Powder Metallurgy & Particulate Materials sponsored by the Metal Powder Industries Federation and APMI International, June 29 - July 2, Chicago, Illinois , 1997 .
[5] Hidetoshi Fujii,et al. Application of Bayesian neural network to materials diagnosis and life assessment , 1997 .
[6] J. R. Moon. Elastic Moduli of Powder Metallurgy Steels , 1989 .
[7] Warren S. Sarle,et al. Stopped Training and Other Remedies for Overfitting , 1995 .
[8] Bjarne Bergquist. Property variation in sintered steel : Design of experiments , 1997 .
[9] Jianhua Chen,et al. Application of expert network for material selection in engineering design , 1996 .
[10] T. J. Griffiths,et al. Analytical Study of Effects of Pore Geometry on Tensile Strength of Porous Materials , 1979 .
[11] D. Mackay,et al. Bayesian Neural Network Analysis of Fatigue Crack Growth Rate in Nickel Base Superalloys , 1996 .
[12] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[13] H. K. D. H. Bhadeshia,et al. The yield and ultimate tensile strength of steel welds , 1997 .