Direct and Inverse Characterization of the Asymmetric Hardening Behavior of Bulk Ti64 Alloy

Tuninetti, Víctor; Gilles, Gaëtan; Sepúlveda, Héctor; Pincheira, Gonzalo; Flores, Paulo; Duchêne, Laurent; Habraken, Anne Marie

doi:10.3390/psf2022004002

Cited by 1 publication

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References 11 publications

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“…The testing of four different feed-forward architectures, primarily trained with constitutive flow equations for a wide parameter spectrum, are consequently assessed with an experimental dataset of flow curves. The application of new techniques for calibration or identification of mathematical models of the mechanical behavior of materials remains a relevant subject in mechanical and civil engineering research, as has been demonstrated in previous research [16,17,20,[28][29][30][31][32], with particular focus on the simplicity, accuracy, and robustness.…”

Section: Dynamic and Static Flow Stress With Thermal Softening Of Ti64mentioning

confidence: 99%

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

Tuninetti,

Forcael,

Valenzuela

et al. 2024

Materials

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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.

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Section: Dynamic and Static Flow Stress With Thermal Softening Of Ti64mentioning

confidence: 99%

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

Tuninetti,

Forcael,

Valenzuela

et al. 2024

Materials

Self Cite

View full text Add to dashboard Cite

show abstract

Direct and Inverse Characterization of the Asymmetric Hardening Behavior of Bulk Ti64 Alloy

Cited by 1 publication

References 11 publications

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

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

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