On the direct estimation of creep and relaxation functions

Sorvari, Joonas; Malinen, Matti

doi:10.1007/s11043-007-9038-1

Cited by 46 publications

(24 citation statements)

References 23 publications

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“…with a numerical approach [12]. It is important to curve-fit the experimental data with a proper equation to reduce the error due to the fluctuations of data.…”

Section: Stress-relaxation Behaviormentioning

confidence: 99%

“…Creep of non-metallic materials were predicted successfully with a graphical conversion process [14,15]. However, the existence of delayed elasticity in metals interfered the proper estimation [10,12]; although, the contribution could be measured by a specific test of strain relaxation and recovery [10]. In the case of an analytic conversion, the governing equations are still developing [11,12].…”

Section: Tablementioning

confidence: 99%

“…The generalized equations could predict the stress-relaxation from creep data and vice versa. Until recently, lots of researches are being investigated to generate the proper form of equations [10][11][12]. On the other hand, Woodford [13] estimated stress-relaxation from creep data by applying a graphical conversion method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Behavior of stress–relaxation and the estimation of creep in Zr–1.1Nb–0.05Cu alloy

et al. 2012

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“…with a numerical approach [12]. It is important to curve-fit the experimental data with a proper equation to reduce the error due to the fluctuations of data.…”

Section: Stress-relaxation Behaviormentioning

confidence: 99%

Section: Tablementioning

confidence: 99%

See 1 more Smart Citation

Behavior of stress–relaxation and the estimation of creep in Zr–1.1Nb–0.05Cu alloy

et al. 2012

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“…These parameters can be estimated by several techniques that utilize the response of the material to a properly selected external excitation. It is commonplace that estimates for the parameters are 1 found by a least squares fit to measurement data, see, for example, (Vuoristo et al, 2000;Sorvari and Malinen, 2007;Weick, 2009;Acton and Weick, 2011). A particular feature of mechanical analog models for viscoelastic materials, consisting of arrangements of springs and dashpots, is that the number of material parameters, that is, the number of springs and dashpots, is not fixed beforehands.…”

Section: Introductionmentioning

confidence: 99%

Identification of the viscoelastic parameters of a polymer model by the aid of a MCMC method

Haario

Hertzen

Karttunen

et al. 2014

Mechanics Research Communications

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A procedure to identify the viscoelastic material parameters of a solid amorphous polymer and to estimate their values is presented. Stress-strain material data is obtained for the polymer by a compression experiment. The material behavior of the polymer is modeled according to the generalized Maxwell model, which is fitted to the experimental data by the method of least squares to obtain a first approximation for the model parameters. The identification of the model parameters is completed by a Markov chain Monte Carlo (MCMC) method, which generates the probability distributions of the relevant parameters of the material. The utilized MCMC method enables us to determine a suitable complexity (i.e., the number of Maxwell elements) for the generalized Maxwell model, so that the model best fits the data and, simultaneously, leads to an identifiable set of parameters. The numerical results imply that the uniqueness of the solution is lost when the number of model parameters becomes redundant.

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“…Experimental data -for the first twenty seconds of load -were adapted to a logarithmic equation that represents the fractionate increase of depth in penetration during creep and, by adjusting creep data, it was possible to predict the extension and creep ratio for load ratio and maximum load. Two alternative approaches for estimate viscoelastic material functions under random excitation were proposed and analyzed by Sorvari & Malinen (2007). In the first one, Boltzmann's superposition principle and Tikhonov's regularization were used in a linear equation system.…”

mentioning

confidence: 99%

Viscoelastic Relaxation Modulus Characterization Using Prony Series

Pereira

Bavastri

Pacheco

2015

Lat. Am. j. solids struct.

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The mechanical behavior of viscoelastic materials is influenced, among other factors, by parameters like time and temperature. The present paper proposes a methodology for a thermorheologically and piezorheologically simple characterization of viscoelastic materials in the time domain based on experimental data using Prony Series and a mixed optimization technique based on Genetic Algorithms and Nonlinear Programming. The text discusses the influence of pressure and temperature on the mechanical behavior of those materials. The results are compared to experimental data in order to validate the methodology. The final results are very promising and the methodology proves to be effective in the identification of viscoelastic materials. KeywordsViscoelasticity, material characterization, Prony Series, Wiechert model, optimization. Viscoelastic Relaxation Modulus Characterization Using Prony Series 1 INTRODUCTIONPolymers are materials that have increasingly been used in engineering projects mainly due to their versatility as well as their mechanical resistance. However, the study of their behavior, when submitted to mechanical loads, is still being developed, due to its complex molecular structure, which molds mechanical properties that change according to time and temperature.In order to predict the mechanical behavior of such material, some methods have been developed -starting from a few characteristic material parameters -aiming at determining the results of different loading application throughout time and under temperature change effects, inherent to the use of structural components using viscoelastic materials (VEMs).The mechanical behavior model of VEMs could be represented by springs and dampers in parallel or in series, as seen in the models by Maxwell and Kelvin/Voigt, respectively, also known as

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On the direct estimation of creep and relaxation functions

Cited by 46 publications

References 23 publications

Behavior of stress–relaxation and the estimation of creep in Zr–1.1Nb–0.05Cu alloy

Behavior of stress–relaxation and the estimation of creep in Zr–1.1Nb–0.05Cu alloy

Identification of the viscoelastic parameters of a polymer model by the aid of a MCMC method

Viscoelastic Relaxation Modulus Characterization Using Prony Series

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