2021
DOI: 10.1007/s11340-021-00781-y
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Accurate Strain Field Measurement During Strip Rolling by Exploiting Recurring Material Motion with Time-Integrated Digital Image Correlation

Abstract: Background 95% Of all metals and alloys are processed using strip rolling, explaining the great number of existing strip rolling optimization models. Yet, an accurate in-situ full-field experimental measurement method of the deformation, velocity and strain fields of the strip in the deformation zone is lacking. Objective Here, a novel time-Integrated Digital Image Correlation (t-IDIC) framework is proposed and validated that fully exploits the notion of c… Show more

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Cited by 8 publications
(7 citation statements)
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“…To cope with these contrast and brightness changes that make the correlation unstable, GDIC enables adding extra degrees of freedom to the correlation to match the grey value spectrum of the images, which have been found to be essential for finding convergence. In all correlations, a polynomial shape function is used to describe the displacement field of which the optimal kinematic regularisation is iteratively found, similar to earlier studies [45,46] . This optimisation resulted in a 12 th ‐ and 2 nd ‐order polynomial in, respectively, longitudinal and transverse directions for the single fibres, a 13 th ‐ and 2 nd ‐order polynomial in, respectively, longitudinal and transverse directions for both fibres of the interfibre bonds and a 5 th ‐order polynomial in horizontal and vertical directions for the interfibre bond CS.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…To cope with these contrast and brightness changes that make the correlation unstable, GDIC enables adding extra degrees of freedom to the correlation to match the grey value spectrum of the images, which have been found to be essential for finding convergence. In all correlations, a polynomial shape function is used to describe the displacement field of which the optimal kinematic regularisation is iteratively found, similar to earlier studies [45,46] . This optimisation resulted in a 12 th ‐ and 2 nd ‐order polynomial in, respectively, longitudinal and transverse directions for the single fibres, a 13 th ‐ and 2 nd ‐order polynomial in, respectively, longitudinal and transverse directions for both fibres of the interfibre bonds and a 5 th ‐order polynomial in horizontal and vertical directions for the interfibre bond CS.…”
Section: Methodsmentioning
confidence: 99%
“…In all correlations, a polynomial shape function is used to describe the displacement field of which the optimal kinematic regularisation is iteratively found, similar to earlier studies. [45,46] This optimisation resulted in a 12 th -and 2 nd -order polynomial in, respectively, longitudinal and transverse directions for the single fibres, a 13 th -and 2 nd -order polynomial in, respectively, longitudinal and transverse directions for both fibres of the interfibre bonds and a 5 th -order polynomial in horizontal and vertical directions for the interfibre bond CS. The obtained displacement fields are converted to strain fields using a Green-Lagrange strain definition.…”
Section: Dedicated Global Digital Image Correlation (Gdic) Algorithmmentioning
confidence: 99%
“…This type of regularization applied to DIC is often denoted as 'integrated'. [35,41,43,46,[49][50][51] Any prior information regarding the sought quantity can be used to improve the convergence of the algorithm and find a solution. In this particular case, the displacement of interest must satisfy some basic properties guaranteed by the mechanical principles.…”
Section: Mechanical Regularizationmentioning
confidence: 99%
“…The major advantage of the global approach to DIC, used hereafter, is that one can precisely inject in the formulation the expected regularity of the displacement field from the beginning (e.g., a more or less strong constraint on the search space), [34][35][36][37][38][39][40][41][42][43][44] rather than projecting it a posteriori on a reduced basis (e.g., to obtain smooth derivatives). This kind of approach allows us to limit the influence of image noise on the displacement measurement or even the identification of constituent parameters.…”
Section: Introductionmentioning
confidence: 99%
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