NDLI: Back-propagation neural network-based approximate analysis of true stress-strain behaviors of high-strength metallic material

Content Provider	Springer Nature Link
Author	Doh, Jaehyeok Lee, Seung Uk Lee, Jongsoo
Copyright Year	2016
Abstract	In this study, a Back-propagation neural network (BPN) is employed to conduct an approximation of a true stress-strain curve using the load-displacement experimental data of DP590, a high-strength material used in automobile bodies and chassis. The optimized interconnection weights are obtained with hidden layers and output layers of the BPN through intelligent learning and training of the experimental data; by using these weights, a mathematical model of the material’s behavior is suggested through this feed-forward neural network. Generally, the material properties from the tensile test cannot be acquired until the fracture regions, since it is difficult to measure the cross-section area of a specimen after diffusion necking. For this reason, the plastic properties of the true stress-strain are extrapolated using the weighted-average method after diffusion necking. The accuracies of BPN-based meta-models for predicting material properties are validated in terms of the Root mean square error (RMSE). By applying the approximate material properties, the reliable finite element solution can be obtained to realize the different shapes of the finite element models. Furthermore, the sensitivity analysis of the approximate meta-model is performed using the first-order approximate derivatives of the BPN and is compared with the results of the finite difference method. In addition, we predict the tension velocity’s effect on the material property through a first-order sensitivity analysis.
Starting Page	1233
Ending Page	1241
Page Count	9
File Format	PDF
ISSN	1738494X
Journal	Journal of Mechanical Science and Technology
Volume Number	30
Issue Number	3
e-ISSN	19763824
Language	English
Publisher	Korean Society of Mechanical Engineers
Publisher Date	2016-03-12
Publisher Place	Seoul
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Back-propagation neural network Weighted-average method Diffusion necking Meta-model Sensitivity analysis DP590 Mechanical Engineering Vibration, Dynamical Systems, Control Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Mechanical Engineering

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