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Journal ArticleDOI

Identification of constitutive parameters for thin-walled aluminium tubes using a hybrid strategy

TLDR
In this paper, a hybrid strategy to determine constitutive parameters for thin-walled tubes based on experimental responses from hydraulic bulge tests is presented, where initial guesses of material parameters are generated quickly by a theoretical method, then they are input to an inverse framework integrating Gauss-Newton algorithm and finite element method.
Abstract
The paper presents a hybrid strategy to determine constitutive parameters for thin-walled tubes based on experimental responses from hydraulic bulge tests. This developed procedure integrates the analytical model, finite element analysis and gradient-based optimization algorithm, where initial guesses of material parameters are generated quickly by a theoretical method, then they are input to an inverse framework integrating Gauss-Newton algorithm and finite element method. The solving for this inverse problem leads to a more accurate identification of material parameters by reducing the discrepancies between simulated results and experimental data. To evaluate its feasibility and performance, hydraulic bulge tests with different end-conditions for annealed 6060 and 5049 aluminium tubes are carried out. The strength coefficient and hardening exponent are determined using the hybrid strategy based on the collected measurements in the experiment. These material parameters are used to compare with those obtained by a single analytical model and inverse model. The comparison validates that the proposed hybrid strategy is not sensitive to starting points and can improve the calculation efficiency and determine more accurate constitutive parameters.

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Citations
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Journal ArticleDOI

Machine learning-assisted parameter identification for constitutive models based on concatenated loading path sequences

TL;DR: In this article , a hybrid strategy for the identification of material parameters of constitutive models is presented, which employs an artificial neural network that is trained with data obtained from the material model, more precisely speaking, from different solutions of the direct problem for homogeneous states of deformation.

An optimisation strategy for industrial metal forming processes (CD-ROM)

TL;DR: In this paper, the authors proposed a general applicable optimisation strategy that makes use of FEM simulations of metal forming processes, which can be applied to a hydroforming process in general and more specific to a specific metal forming problem.

Identification of the Constitutive Parameters of Viscosity and the Prediction of the Cutting Force of S32760 Duplex Stainless Steel under a High Strain Rate

TL;DR: In this article , an inverse identification method of the constitutive parameters of S32760 duplex stainless steel were reversely modified using an equal shear zone model and an orthogonal cutting experiment.
Journal ArticleDOI

A multi-objective framework for identification of material parameters based on multiple mechanical tests

TL;DR: In this article , a multi-objective optimization approach is proposed to identify hardening and damage parameters based on tensile and compression tests featuring the effects of tensiledominant and compressive-dominant stress states.
Journal ArticleDOI

Identification of the Constitutive Parameters of Viscosity and the Prediction of the Cutting Force of S32760 Duplex Stainless Steel under a High Strain Rate

TL;DR: In this paper , an inverse identification method of the constitutive parameters of S32760 duplex stainless steel were reversely modified using an equal shear zone model and an orthogonal cutting experiment.
References
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Journal ArticleDOI

New Mandrel Design for Ring Hoop Tensile Testing

TL;DR: In this paper, a new mechanical D-shaped block mandrel was designed, manufactured and used to carry out experimental ring-hoop tensile tests, which is able to reduce the friction issue between the ring sample and the fixture mandrel without resorting to any lubricants.
Journal ArticleDOI

Identification of friction coefficients and hardening parameters using optimization methods coupled with a 3D finite element code

TL;DR: In this article, a method for analytical sensitivity analysis is presented and a non-linear least square technique is used to solve the inverse problem, i.e., minimize the least-square error between a experimental and a model data set.
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