Data‐Driven Stress Prediction for Thermoplastic Materials

نویسندگان

چکیده

The present study applies two different machine learning (ML) algorithms to predict the stress-strain mapping for non-linear behaviour of thermoplastic materials: a Long Short-Term Memory (LSTM) algorithm and Feed-Forward Neural Network (FFNN). approach this work requires generation curve specific material parameters. training data are obtained from von Mises law Ramberg-Osgood equation. four combinations ML with constitutive laws evaluated show good agreement numerical data.

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ژورنال

عنوان ژورنال: Proceedings in applied mathematics & mechanics

سال: 2021

ISSN: ['1617-7061']

DOI: https://doi.org/10.1002/pamm.202100225