Parameters identification in strain-rate and thermal sensitive visco-plastic material model for an alumina dispersion strengthened copper

Abstract

a b s t r a c tThe main objective of this paper is getting strain-hardening, thermal and strain-rate parameters for a material model in order to correctly reproduce the deformation process that occurs in high strain-rate scenario, in which the material reaches also high levels of plastic deformation and temperature. In particular, in this work the numerical inverse method is applied to extract material strength parameters from experimental data obtained via mechanical tests at different strain-rates (from quasi-static loading to high strain-rate) and temperatures (between 20 C and 1000 C) for an alumina dispersion strengthened copper material, which commercial name is GLIDCOP Ò . Thanks to its properties GLIDCOP Ò finds several applications in particle accelerator technologies, where problems of thermal management, combined with structural requirements, play a key role. Currently, it is used for the construction of structural and functional parts of the particle beam collimation system. Since the extreme condition in which the material could operate, it is fundamental to characterize it in a wide range both in strain-rate and temperature.The numerical inverse method used in this work is particularly useful to reproduce experimental results when the stressestrain fields in the specimen cannot be correctly described via analytical models. Furthermore this procedure is useful to take into account thermal phenomena generally affecting high strain-rate tests in which the heat conversion of plastic work produces an adiabatic overheating. So, the applicability of this method is particularly indicated in special fields, such as aerospace engineering, ballistic, crashworthiness studies or particle accelerator technologies.The attention is focused on evaluating the most suitable strategy of material model parameters optimization to obtain the best fit between experimental data and numerical results. In this regards, it is important to determine which material model coefficients can be considered as optimization variables and for each of them the most suitable range of variation.

Mechanical characterization and modeling of the heavy tungsten alloy IT180

“…For what concerns the tensile results, the last point of each curve corresponds to the maximum of the engineering stress: this justifies the analytical approach (neglecting the post-instability phase). The stress-strain analysis of the material response could also be done with a numerical inverse approach [28]: this technique allows treating the experimental results with a high level of accuracy, taking into account the effect of the geometry changes during the necking phase. For IT180, the necking phase is very limited (in many test conditions it is completely absent), hence a simplified analytical approach is sufficient and justified.…”

High-Speed Interaction Rod and Segmented Projectiles into Massive Obstacles

“…The introduction of such algorithm is equivalent to the procedure of changing boundary conditions on the contact surface with a perfect slip condition at the rigid couplings [20][21]. Note that the use of such algorithm is possible only under the condition that all segments are made of the same material [22][23][24][25].…”

Mechanical properties at high strain-rate of lead core and brass jacket of a NATO 7.62 mm ball bullet

“…The use of an optimization tool could allow the implementation of a multi-objective procedure, in which all the parameters are obtained on the basis of both static and dynamic curves. The problem encountered with the J-C model is that the variation of some parameters produces the same effect on the flow stress of the variation of other parameters [11]. For this reason, the authors choose to implement a stepby-step procedure in order to control the optimized values and check their verisimilitude.…”

“…LS-DYNA, Abaqus) this choice would mean that the strain-rate influence is neglected for all the experimental set data with strain-rate less than this value. If alsoε 0 is used as optimization variable, an improvement in the experimental data fit could be obtained [11]. The set of strain-hardening coefficients is normalized with respect to the static mean value (σ/σ st ) and reported in function Strain-rate sensitivity parameters identification.…”