Modelling the Flow Stress of Alloy 316L using a Multi-Layered Feed Forward Neural Network with Bayesian Regularization

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;    Deformation conditions;    High strain rates;    High temperature plastic deformation;    Model prediction;    Multi-layered feed-forward neural networks;    Multilayer feedforward neural networks;    Prediction quality;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/225/1/012052/pdf
DOI  :  10.1088/1757-899X/225/1/012052

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In this paper, a multilayer feedforward neural network with Bayesian regularization constitutive model is developed for alloy 316L during high strain rate and high temperature plastic deformation. The input variables are strain rate, temperature and strain while the output value is the flow stress of the material. The results show that the use of Bayesian regularized technique reduces the potential of overfitting and overtraining. The prediction quality of the model is thereby improved. The model predictions are in good agreement with experimental measurements. The measurement data used for the network training and model comparison were taken from relevant literature. The developed model is robust as it can be generalized to deformation conditions slightly below or above the training dataset.