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DIC Course 2022

Ghent (Belgium), 4th-8th July 2022

MatchID now proudly announces the 9th upcoming intensive 5-day course on Digital Image Correlation(DIC).


Professor Fabrice PIERRON joins MatchID as R&D Director

A word from the new R&D Director
Professor Fabrice PIERRON

I am delighted to announce that I have now joined MatchID as R&D Director and
look forward to my new role. I would like to take this opportunity to share some
thoughts with you about the future of Digital Image Correlation (DIC) and
associated tools.

In a way, one could say that DIC is now a mature technology. It is rather widespread
in testing labs and the recent Guide of Good Practice from the International DIC
Society [1] provides a strong basis for an informed and robust use of DIC. However, the engineering
practices that would take full advantage of this powerful tool are still largely to be established.
For instance, material testing is still mostly performed through standards developed for
extensometers or strain gauges, using simple geometries with statically determinate stress states.
These tests provide a limited amount of information per test and lead to the need for a large number
of tests to calibrate a given material model. There is no doubt that the next generation of mechanical
tests of materials will rely on DIC. But to get there, new test configurations will be needed where
shape and loading are not constrained by the need for an a priori stress distribution anymore. This
was recently coined “Materials Testing 2.0” (illustrated in Figure 1, left) as it was felt that a new
expression was needed to materialize the concept, though maybe better names could be found [2].
There is a lot of work ahead of our community to develop these new tests and eventually bring them
to standardization, and MatchID has the ambition to play an active part in this process. One route
to standardization could be through VAMAS1 where an MT2.0 Technical Working Area (TWA) group
is currently being explored.

Another important area where DIC is bound to bring a revolution is the validation of engineering
designs through structural testing. DIC provides spatially-dense data that are a match for
simulations but the tools to integrate both in a single streamlined procedure are still missing. There
are many challenges to be overcome as test and simulations ‘live’ in different worlds. This
integration, which I have christened “Tessimulation” for want of a better word, is illustrated in
Figure 1, right. MatchID has developed dedicated tools to ‘push’ the simulation data through the
same filter as the DIC one to allow for a fair comparison of the data independently from the choice
of the many DIC parameters like subset size, shape function and strain window [3]. In the future,
DIC will certainly be integrated with both CAD (to design the experiment itself) and Finite Element
packages to ensure a seamless flow of data between the different facets of the design. Again,
MatchID is actively working towards this goal.

In the coming years, we are hoping to develop a strong R&D department in close collaboration with
our customers and partners to advance the use of camera-based deformation measurements in
engineering, among the lines of the vision statement above. Our ambition is not only to be a leading
software provider for the next generation of DIC-based engineering, but also a valuable R&D partner
to help our customers realize the full potential of the wealth of data and opportunities provided by
our tools. Our unique blend of expertise ranging from DIC algorithms and hardware integration to
innovative use of DIC measurements in engineering makes us an ideal partner to work with industry
and academia to bring this vision to reality. So do not hesitate to contact us if you have a project in

Dr Fabrice PIERRON

30 September, 2021
MatchID NV – Leiekaai 25A, Gent, Belgium +3


1. Jones, E.M.C. and M.A. Iadicola, A Good Practices Guide for Digital Image Correlation. 2018,
International Digital Image Correlation Society.
2. Pierron, F. and M. Grédiac, Towards Material Testing 2.0. A review of test design for identification of
constitutive parameters from full-field measurements. Strain, 2021. 57(1): p. e12370.
3. Lava, P., et al., Validation of finite-element models using full-field experimental data: Levelling finiteelement analysis data through a digital image correlation engine. Strain, 2020. 56(4): p. e12350.
4. Toniuc, H. and F. Pierron, Infrared deflectometry for surface slope deformation measurements.
Experimental Mechanics, 2019. 59(8): p. 1187-1202

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