Large strain flow curve characterization considering strain rate and thermal effect for 5182-O aluminum alloy

Shang, Hongchun; Zhang, Chong; Wang, Songchen; Lou, Yanshan

doi:10.1007/s12289-022-01721-4

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…Inverse engineering method is combined with neural network-based plasticity models to characterize coupling effects [15]. The FCNN model can predict the plastic behavior under large strains based on the stress-strain curves calibration obtained from the inverse engineering method [16]. In order to reveal how electric pulse improve ductility at different temperatures, the non-monotonic effect of fracture loading paths was analyzed experimentally and numerically.…”

Section: Methodsmentioning

confidence: 99%

Study on electric pulse-assisted plastic deformation behavior of 5182-O aluminum alloy

Shang,

Wang,

Lou

2024

IOP Conf. Ser.: Mater. Sci. Eng.

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Plastic model-based neural networks are emerging phenomenological models with excellent calibration accuracy and interpretability of parameters, and they do not significantly increase the finite element calculation time of neural network models with a simple structure optimized by the algorithm. Because aluminum and magnesium have low ductility at room temperature, complex components made from these alloys need to be forged at high temperatures. In comparison with traditional thermoforming, advanced current-assisted processing provides energy savings, efficiency and green production. Experimental and constitutive modeling are carried out in this study to investigate the coupling effects of electrical pulse, temperature, strain rate, and strain on flow behavior and ductile fracture. It has been shown that electric pulses induce Joule heating effects and electroplastic effects, which reduce deformation resistance and improve formability. Electrical pulses suppress negative strain rate effects caused by dynamic strain aging, which is the main reason for the non-monotonic relationship between temperature and strain rate. The behavior of plasticity and fracture initiation can be described by simulations based on a neural network-based evolving plasticity model that incorporates stress states, current density, temperature, and strain rates.

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Section: Methodsmentioning

confidence: 99%

Study on electric pulse-assisted plastic deformation behavior of 5182-O aluminum alloy

Shang,

Wang,

Lou

2024

IOP Conf. Ser.: Mater. Sci. Eng.

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“…An example of such approach is the p-model proposed by Coppieters and Kuwabara [10]. The p-model essentially combines Swift's and Voce's hardening and was recently successfully adopted to capture the large strain flow curve of 5182-O aluminium alloy [11].…”

Section: Numericalmentioning

confidence: 99%

Identification of the large strain flow curve of high strength steel via the torsion test and FEMU

VANCRAEYNEST

2023

Materials Research Proceedings

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Abstract. A torsion specimen is removed from the as-received thick high strength steel sheet (S700MC with a nominal thickness of 12 mm). The test material has a maximum uniform tensile strain of about 12 %. The torsion test is conducted up to fracture. The experimentally acquired torque-angle curve is then used to inversely identify the large strain flow curve up to an equivalent plastic strain of approximately 1. The identification strategy is based on the Finite Element Model Updating (FEMU) approach.

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Analysis of electric pulse-assisted forming based on neural network plastic evolution model

Shang,

Wang,

Zhou

et al. 2024

CIRP Journal of Manufacturing Science and Technology

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Large strain flow curve characterization considering strain rate and thermal effect for 5182-O aluminum alloy

Cited by 7 publications

References 38 publications

Study on electric pulse-assisted plastic deformation behavior of 5182-O aluminum alloy

Study on electric pulse-assisted plastic deformation behavior of 5182-O aluminum alloy

Identification of the large strain flow curve of high strength steel via the torsion test and FEMU

Analysis of electric pulse-assisted forming based on neural network plastic evolution model

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