Constitutive Modelling of INCONEL 718 using Artificial Neural Network

International Conference on Materials, Alloys and Experimental Mechanics 2017

材料科学;金属学;机械制造

Abiri, Olufunminiyi^1 ; Twala, Bhekisipho^1

Institute of Intelligent Systems, University of Johannesburg, Auckland Park

2006, South Africa^1

关键词: Bayesian regularization;    Early stopping;    Input variables;    Measurement data;    Model prediction;    Neural network model;    Output variables;    Precipitate hardening;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/225/1/012054/pdf
DOI  :  10.1088/1757-899X/225/1/012054

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Artificial neural network is used to model INCONEL 718 in this paper. The model accounts for precipitate hardening in the alloy. The input variables for the neural network model are strain, strain rate, temperature and microstructure state. The output variable is the flow stress. The early stopping technique is combined with Bayesian regularization process in training the network. Sample and non-sample measurement data were taken from the literature. The model predictions of flow stress of the alloy are in good agreement with experimental measurements.