An effective inverse analysis tool for parameter identification of anisotropic material models

Bolzon, Gabriella; Talassi, Marco

doi:10.1016/j.ijmecsci.2013.09.009

Cited by 28 publications

(23 citation statements)

References 45 publications

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“…The identifiability of the different parameters contained in the hygro-thermo-chemical model has been investigated following a numerical procedure well established in literature (see [24], [25] and [26]) that involves the setting up and implementation of different inverse analyses that start from the so-called pseudo-experimental results, i.e. results numerically generated starting from a given set of model parameters and supplied in input to the inverse problem.…”

Section: Inverse Analysismentioning

confidence: 99%

An inverse analysis approach for the identification of the hygro-thermo-chemical model parameters of concrete

Bocciarelli

Ranzi

2018

International Journal of Mechanical Sciences

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Hygro-thermo-chemical models provide useful representations of the mechanisms of moisture transport and temperature variations that take place in concrete structures and that can influence their durability and service behaviour. Several material parameters need to be specified when performing a hygro-thermo-chemical simulation. While some of these parameters can be evaluated based on the concrete mix specifications or from data reported in the literature, some other parameters are not readily available from the literature, partly validated against a set of pseudo-experimental results affected by different levels of noise, highlighting the robustness of the proposed methodology when applied with the arrangements suggested in terms of discrete relative humidity and temperature measurements and monitoring periods.

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Section: Inverse Analysismentioning

confidence: 99%

An inverse analysis approach for the identification of the hygro-thermo-chemical model parameters of concrete

Bocciarelli

Ranzi

2018

International Journal of Mechanical Sciences

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“…We represent the application of our proposed computational framework to rank not only the different responses of an indentation test, but also the different response spaces. This contrasts with the current state of practice where study of the indentation test responses is done deterministically and limited to ranking the known material responses at a limited number of observations [17][18][19]. Nevertheless, we note here that the choice of the spherical indentation test in this work is only for the sake of illustration and the approaches and tools that comprise the proposed framework are applicable to other material characterization tests.…”

Section: Introductionmentioning

confidence: 95%

“…Several studies in material model identification literature have studied the value of the material response candidates in a deterministic way [17][18][19]. For instance, in the study conducted in [19], the weighted sum of residual imprint responses obtained in an indentation test was employed to form the material response to identify the hardening parameters.…”

Section: Introductionmentioning

confidence: 99%

On the value of test data for reducing uncertainty in material models: Computational framework and application to spherical indentation

Asaadi

Heyns

Haftka

et al. 2019

Computer Methods in Applied Mechanics and Engineering

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“…However, the latter needs a priori assumption of the constitutive hardening model (e.g. see (Aguir et al, 2011, Prates et al, 2014, Bolzon and Talassi, 2013, Prates et al, 2015, Zhang et al, 2014).…”

Section: Fundamental Concepts Of the Progressive Inverse Stress-straimentioning

confidence: 99%

Flow stress identification of tubular materials using the progressive inverse identification method

Asaadi

Heyns

2016

Engineering Computations

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Purpose-Propose a progressive inverse identification algorithm to characterize flow stress of tubular materials from the material response, independent of choosing an a priori hardening constitutive model. Design/methodology/approach-In contrast to the conventional forward flow stress identification methods, the flow stress is characterized by a multi-linear curve rather than a limited number of hardening model parameters. The proposed algorithm optimizes the slopes and lengths of the curve increments simultaneously. The objective of the optimization is that the finite element simulation response of the test estimates the material response within a predefined accuracy. Findings-We employ the algorithm to identify flow stress of a 304 stainless steel tube in a tube bulge test as an example to illustrate application of the algorithm. Comparing response of the finite element simulation using the obtained flow stress with the material response shows that the method can accurately determine the flow stress of the tube. Practical implications-The obtained flow stress can be employed for more accurate finite element simulation of the metal forming processes as the material behaviour can be characterized in a similar state of stress as the target metal forming process. Moreover, since there is no need for a priori choosing the hardening model, there is no risk for choosing an improper hardening model, which in turn facilitates solving the inverse problem. Originality/value-The proposed algorithm is more efficient than the conventional inverse flow stress identification methods. In the latter, each attempt to select a more accurate hardening model, if it is available, result in constructing an entirely new inverse problem. However, this problem is avoided in the proposed algorithm.

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An effective inverse analysis tool for parameter identification of anisotropic material models

Cited by 28 publications

References 45 publications

An inverse analysis approach for the identification of the hygro-thermo-chemical model parameters of concrete

An inverse analysis approach for the identification of the hygro-thermo-chemical model parameters of concrete

On the value of test data for reducing uncertainty in material models: Computational framework and application to spherical indentation

Flow stress identification of tubular materials using the progressive inverse identification method

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