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.read more
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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Investigation of the effective parameters in tube hydroforming process by using experimental and finite element method for manufacturing of tee joint products
Hadi Badri Ahmadi,Mehdi Zohoor +1 more
TL;DR: In this paper, the effect of loading path, friction coefficient, strain-hardening exponent, and fillet radius of the die on height of protrusion in expansion zone and thickness distribution in three different cross-sections is perused.
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Localised tuneable composition single crystal silicon-germanium-on-insulator for low cost devices
Callum G. Littlejohns,Thalia Dominguez Bucio,Milos Nedeljkovic,Goran Z. Mashanovich,Graham T. Reed,Frederic Y. Gardes +5 more
TL;DR: In this paper, the authors present a method of forming single crystal, defect free SGOI using a rapid melt growth technique, which is suitable for state-of-the-art device fabrication.
Journal ArticleDOI
Parameter identification and shape optimization
TL;DR: In this paper, an integrated optimization approach, using a finite element code together with a numerical optimization program, was employed to solve the problem of parameter identification and shape optimization in metal forming.
Journal ArticleDOI
A new hybrid identification method for determining the material parameters of thin-walled tube under compressive stress state
TL;DR: In this article, a hybrid inverse identification method is proposed based on tube lateral compression test with combining finite element simulation, regression analysis and genetic algorithm to identify the Swift law hardening parameters of thin-walled tubes with different materials and specifications under compressive stress state.
Journal ArticleDOI
Evaluation of models for tube material characterization with the tube bulging test in an industrial setting
TL;DR: In this paper, three models for post-processing measures obtained from the tube bulging test are compared based on the same experimental procedure, and the results obtained for the local (stress and strain) and global components (the thickness distribution along the tube and the deformed tube profile) are very close, whatever the models.