Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of haz...

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Bibliographic Details
Published in:	Materials Vol. 17; no. 2; p. 317
Main Authors:	Tuninetti, Víctor, Forcael, Diego, Valenzuela, Marian, Martínez, Alex, Ávila, Andrés, Medina, Carlos, Pincheira, Gonzalo, Salas, Alexis, Oñate, Angelo, Duchêne, Laurent
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01-01-2024
Subjects:	Accuracy Alloys artificial neural network Artificial neural networks Back propagation Back propagation networks Calibration Cement Computational mechanics Constitutive models Deformation Design optimization Explosive impact tests Heat treating Impact loads Manufacturing Material properties Mathematical models mechanical behavior modeling Neural networks Neurons Parameter identification plastic flow Product design Software Strain hardening strain rate Strain rate sensitivity Temperature Titanium alloys Titanium base alloys Yield strength Chile plastic flow artificial neural network strain rate modeling mechanical behavior
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Summary:	The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson-Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model's predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress-strain behavior of Ti64 alloy and three virtual materials.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1996-1944 1996-1944
DOI:	10.3390/ma17020317