# Influence of DIC settings on temperature fields in combined thermal-kinematic measurements for heterogeneous tests

Alessandro Lambrughi, Sandrine Thuillier, Sam Coppieters

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Alessandro Lambrughi, Sandrine Thuillier, Sam Coppieters. Influence of DIC settings on temperature fields in combined thermal-kinematic measurements for heterogeneous tests. ESAFORM 2025, May 2025, Paestum, Italy. pp.1510-1519, 10.21741/9781644903599-163. hal-05062353

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# Influence of DIC settings on temperature fields in combined thermal-kinematic measurements for heterogeneous tests

Alessandro Lambrughi¹,ᵃ*, Sandrine Thuillier²,ᵇ and Sam Coppieters¹,ᶜ

¹KU Leuven, Department of Materials Engineering, Ghent Campus, 9000 Ghent, Belgium

²Univ. Bretagne Sud, UMR CNRS 6027, IRDL, Lorient F-56100, France

ᵃalessandro.lambrughi@kuleuven.be, ᵇsandrine.thuillier@univ-ubs.fr,

ᶜsam.coppieters@kuleuven.be

**Keywords:** Digital Image Correlation, Infrared Thermography, Digital Virtual Twin, Temperature Error Quantification

**Abstract.** The integration of Digital Image Correlation with Infrared Thermography (DIC-IRT) in heterogeneous tests provides information rich kinematic and thermal field data. Thanks to the commercial availability of optical devices, this technique is gaining attraction to characterize the thermomechanical behavior of metals. However, achieving accurate full-field results requires a deep understanding of all the uncertainties arising from the complex measurement and post-processing chains. To this end, this paper studies the previously unexplored effect of user-dependent DIC settings on temperature fields. The solution approach relies on a Digital Virtual Twin (DVT) simulating a post-necking tensile test on a thick S700MC steel assisted with DIC-IRT. The novelty here is that the DVT combines the state-of-the-art virtual image deformation with the metrological aspect of thermal measurement. The results show that, under heterogeneous temperature and strain fields, DIC settings introduce a systematic temperature error with local biases comparable to the measurement error. According to this finding, a spatially converged displacement field is required to measure an accurate thermal field in heterogeneous tests.

**Introduction**

Subset-based Digital Image Correlation (DIC) is widely used to measure deformation fields in materials subjected to mechanical loads. On the other hand, Infrared Thermography (IRT) is a well-established method to measure the surface temperature of the specimen. In the material testing community, these full-field measurement techniques are principally used independently of one another. However, for metals, strain and temperature are often coupled giving rise to a complex thermomechanical behavior. To characterize such behavior, there is a growing interest in combining these techniques to achieve simultaneous thermal and kinematic fields measurements (DIC-IRT). In the early years of the 2000s, DIC-IRT has been initially applied to quantify the energy stored in the microstructure of materials during plastic deformation. An important quantity enabling to verify that constitutive laws for plasticity and damage satisfy the physical energy balance. For various steel grades, this was shown by Bodelot [1] by performing standard tensile tests enriched with DIC-IRT. The experiments were conducted at room temperature and quasi-static loads, where the temperature only increased up to a few degrees. Moreover, recently, DIC-IRT has been extended for tests at a higher temperature to calibrate thermomechanical constitutive models through inverse methods. For example, Martins et al. [2] performed three tensile tests at different quasi-static strain rates in which heterogeneous strain and temperature fields were generated between 200 and 500 °C. From these nonconventional tests assisted with DIC-IRT, a modified version of the Johnson-Cook model was calibrated for a dual phase steel (DP980). This showed that three heterogeneous thermomechanical experiments could replace dozens of isothermal tests required with the standard method.

<figure><img src="image_2.png" /></figure>
However, despite these benefits, full-field measurements are faced with an important challenge related to uncertainty quantification. Particularly for heterogeneous tests, user-dependent DIC settings play a key role for the accuracy of full-field results. In the framework of this paper, DIC settings only refer to the parameters involved in the computation of the displacement field. Since the introduction of the Digital Virtual Twin (DVT) based on synthetic image deformation [3], extensive studies have examined their influence on heterogeneous kinematic fields [3,4]. Nevertheless, when DIC is combined with IRT, their effect on the temperature is unexplored. Accordingly, the aim of this paper is to assess the influence of DIC settings, specifically step and subset sizes, on thermal fields measured during heterogeneous tests. To this end, a DVT reproducing thermal and kinematic fields measurement is developed to simulate the tensile test depicted in Fig. 1. The test involves a thick S700MC steel sample loaded at room temperature in the post-necking regime. Simultaneously, DIC-IRT measures strain and the temperature increase due to the dissipated heat. Denys [5] showed that a thick S690QL loaded under the same conditions generates highly localized temperatures reaching 130 °C, making this test an ideal case study to underscore the importance of DIC settings also on heterogeneous thermal fields.

<figure><img src="image_3.png" /><figcaption>Figure 1 – Tensile test simulated with the Digital Virtual Twin, dimensions are in mm. In this paper the DVT of the experiment is used to study the impact of step and subset size on the temperature field measured by DIC-IRT.</figcaption></figure>

The remaining of the paper is organized into three sections. The first section explains why DIC settings affect thermal fields extracted from DIC-IRT. The second section describes the DVT developed in this work. To conclude, the third section outlines the method defined to evaluate the influence of step and subset sizes on the temperature, followed by a discussion of the corresponding results.

**Background: Lagrangian thermography**

The relation between DIC settings and thermal fields arises from the post-processing operations performed to spatially synchronize temperature with displacement data after the acquisition of optical and infrared images. This alignment is required for three reasons. First, optical and infrared cameras provide data expressed in the local reference frames of the corresponding devices. Second, their sensors have different resolutions. Third, the kinematic field extracted from DIC is expressed in the reference configuration, while infrared images measure a thermal field in the current (Eulerian) configuration.
To address these problems, the procedure illustrated in Fig. 2 enables the thermal field to be expressed in the reference configuration and aligned with the same points as the kinematic field. Initially, DIC computes coordinates and displacement field inside a specific region of interest (ROI). Considering stereo DIC, this step requires the calibration coefficients linking the coordinate frames of the optical cameras and specifying the DIC settings. Next, the kinematic field is geometrically projected onto the sensor frame of the thermal camera using the calibration parameters relating infrared and optical cameras frames. Finally, a spatial interpolation assigns a temperature value to each DIC point. As a result, the temperature history can be tracked in the reference configuration using the DIC displacement field. Given that the thermal field is now available in the reference configuration, in literature this approach is defined as Lagrangian thermography. More details on this topic are provided by Bodelot [1] and Toussaint et al. [6].

<figure><img src="image_4.png" /><figcaption>Figure 2 – Operations performed to spatially synchronize thermal with kinematic fields and express the thermal field in the reference configuration. The flowchart is illustrated considering stereo DIC-IRT measurements.</figcaption></figure>

Accordingly, DIC settings influence the Lagrangian temperature field for two reasons. Firstly, they determine the spatial resolution of the data points used to express the temperature field. For heterogeneous fields, this spatial resolution must be sufficient to capture the temperature gradients. Secondly, for a specific data point, they define the accuracy of its displacement history and, consequently, the accuracy of the corresponding temperature history. This is relevant for tests where the material is subjected to large strain and temperature localizations.

## Digital Virtual Twin of the DIC-IRT experiment

To generate the virtual experiment, two elements are required. Firstly, a numerical model simulating the test introduced in Fig. 1 according to a reference material behavior. Secondly, an experimental configuration reproducing the metrological aspects of the real test.

Finite Element (FE) model. The FE model, built in Abaqus/Standard [7] with a coupled thermal-stress analysis, is based on three assumptions listed in Table 1. The first one, validated numerically, neglects heat exchanges with the surroundings due to the high strain rates reached during post-necking. The second and the third assumption concern the material behavior, compensating for the lack of experimental data required to model the described phenomena. It must be noted that these two assumptions may oversimplify the behavior of the S700MC steel, especially under post-necking conditions. However, they do not significantly affect how the DVT simulates the influence of spatial resolution biases and displacement errors associated with DIC settings on thermal fields.

<table><caption>Table 1 – Assumptions defined to generate the reference FE model.</caption><tr><td>1</td><td>Deformation considered as adiabatic process</td></tr><tr><td>2</td><td>Mechanical material behavior: isotropic plasticity, no rate-sensitivity and no damage</td></tr><tr><td>3</td><td>Thermal material behavior: thermal properties are temperature-independent</td></tr></table>

Consequently, these simplifications will not bias the main outcomes of the study, and considering more advanced models that include rate-sensitivity or temperature-dependent thermal properties would not provide better or additional insights. Based on these assumptions, the
thermomechanical behavior of the S700MC steel is defined with the parameters given in Table 2. For the mechanical properties, the Young's modulus is calibrated following EN ISO 6892-1 [8], while the thermal dilatation coefficient is taken from Eurocode 3 [9]. As indicated above, the expected maximal temperature is around 130 °C. Therefore, both parameters are considered temperature-independent, as reported by Shakil et al. [10] and by Eurocode 3 for elastic modulus and dilatation coefficient, respectively. Concerning the thermal properties, conductivity and specific heat are taken from [11], while the inelastic heat fraction follows the value typically used for steels.

<table><caption>Table 2 – Constitutive parameters defining the thermomechanical behavior of the S700MC steel in the FE model.</caption><thead><tr><th>Type</th><th>Property</th><th>Value</th><th>Unit</th></tr></thead><tbody><tr><td rowspan="3">Mechanical</td><td>Young's modulus</td><td>190.5</td><td>GPa</td></tr><tr><td>Poisson's ratio</td><td>0.28</td><td>-</td></tr><tr><td>Dilatation coefficient</td><td>13.0·10<sup>-6</sup></td><td>K<sup>-1</sup></td></tr><tr><td rowspan="3">Thermal</td><td>Conductivity</td><td>45</td><td>W/mK</td></tr><tr><td>Specific heat</td><td>520</td><td>J/kgK</td></tr><tr><td>Inelastic heat fraction</td><td>0.90</td><td>-</td></tr></tbody></table>

The last input required to define the material behavior is the flow curve. To simulate the post-necking strain hardening behavior, the stress-strain relation must be defined beyond necking. In addition, Shakil et al. [10] and Keränen et al. [12] report thermal softening of S700MC steel yield stress directly above room temperature. Accordingly, the following method is used to characterize the temperature-dependent strain hardening behavior. Initially, Eq. 1 is used to describe the equivalent stress ($\sigma_{eq}$) - equivalent plastic strain ($\varepsilon_{eq}^{pl}$) relation at room temperature. As indicated, the pre-necking strain hardening is described by experimental data obtained from standard tensile tests. After the maximum uniform elongation ($\varepsilon_{max}$), Swift's law is used to extrapolate the post-necking strain hardening based on a least-squares fit on pre-necking data. Next, the temperature effect is considered using the reduction factor proposed by Shakil et al. [10], establishing an empirical yield stress-temperature relation between 20 and 200 °C. The presented approach results in the flow curve illustrated in Fig. 3.

$$ \begin{cases} \text{if } \varepsilon_{eq}^{pl} \le \varepsilon_{max}: \sigma_{eq,20} = \text{experimental data} \\ \text{if } \varepsilon_{eq}^{pl} > \varepsilon_{max}: \sigma_{eq,20} = 1149(0.0064 + \varepsilon_{eq}^{pl})^{0.099}. \end{cases} \quad (1) $$

<figure><img src="image_5.png" /><figcaption>Figure 3 – Strain hardening behavior with temperature dependency considered for the FE model.</figcaption></figure>

To conclude, Fig. 4 a) describes the boundary conditions and the mesh generated in the gauge length of the sample using quadratic coupled temperature-displacement elements (C3D20RT). It
is worth noting that this mesh is the result of a convergence study conducted on both thermal and kinematic fields. Furthermore, Fig. 4 b) presents the model results expressed through the axial true strain and the surface temperature in the region of interest. Both fields are evaluated at a crosshead displacement of 21 mm. As highlighted, during diffuse necking, the strain reaches local values about 1 while, at the same position, the surface temperature increases up to 137 °C.

<figure><img src="image_6.png" /><figcaption>Figure 4 – Information FE model: a) Boundary conditions and local seed edges defined to mesh the gauge length of the sample, unit mm, b) Numerical results in the ROI evaluated at the last frame of the simulation.</figcaption></figure>

Experimental configuration. To generate virtual DIC-IRT data that reproduce the physical experiment, the setup illustrated in Fig. 5 is designed. This setup emulates the optical configuration as if cameras were mounted in a 250 kN tensile machine for the real test. The kinematic field is measured with two optical cameras equipped with complementary metal-oxide-semiconductor sensors (CMOS), while the thermal field is captured with a long-wave infrared camera (LW-IRT). Both optical and infrared cameras observe the same face of the sample. Therefore, to measure temperature and kinematic fields on the same surface, the region of interest is coated with a uniform black coating enriched with white speckles. Compared to the standard DIC coating (white background with black speckles), the chosen coating maximizes the background emissivity while minimizing the effect of spatial emissivity variations in infrared images [13]. This configuration is chosen because, for this test, thermal and kinematic fields cannot be measured on opposite surfaces. Indeed, due to the sample thickness, the spatial synchronization introduced earlier cannot assume that these fields are symmetric with respect to the mid-thickness plane.

<figure><img src="image_7.png" /><figcaption>Figure 5 – Optical configuration for thermal and optical cameras considered to create the DVT of the experiment.</figcaption></figure>

The setup introduced in Fig. 5 is described by the specifications detailed in Table 3. For each field measurement, three types of information are provided: relevant cameras characteristics, optical configuration, and the required coating properties. To measure the thermal field, a built-in
detector calibration enabling to measure between -40 and 150 °C is used. In addition, the coating emissivity is calibrated using the contact method. In the visible range for DIC measurements, the coating is characterized by a speckle size of 4.4 pixels. Concerning the optical configuration, the reported images resolutions result from the same field of view imposed for optical and infrared cameras, while the noise in optical images is quantified from a series of stationary images.

<table><caption>Table 3 – Specifications measurement conditions to measure thermal and kinematic fields.</caption><thead><tr><th>Field measurement</th><th>Specifications</th><th>Value</th><th>Unit</th></tr></thead><tbody><tr><td rowspan="4">Thermal</td><td>Camera</td><td>Detector calibration range<br>640x480</td><td>°C<br>pixels<sup>2</sup></td></tr><tr><td>Optical configuration</td><td>Image resolution</td><td>0.312<br>mm/pixel</td></tr><tr><td>Coating</td><td>Surface emissivity</td><td>0.95 ± 0.01<br>-</td></tr><tr><td>Camera</td><td>Detector resolution</td><td>2448x2048<br>pixels<sup>2</sup></td></tr><tr><td rowspan="3">Kinematic</td><td>Optical configuration</td><td>Image resolution<br>Image noise</td><td>0.071<br>1.79<br>mm/pixel<br>grey levels</td></tr><tr><td>Coating</td><td>Speckle size</td><td>4.4<br>pixels</td></tr></tbody></table>

Finally, as this work focuses on temperature error quantification, Table 4 summarizes the main uncertainties associated with thermal field measurement. This information is provided to assess the relevance of the post-processing error linked to DIC settings, which is studied in this paper, compared to errors introduced by the measurement process. To this end, for each source of uncertainty, the maximum error is reported for the highest surface temperature predicted by the simulation (137 °C). Except for the uncertainty related to the detector calibration, which is available in the camera datasheet, the errors related to noise and coating emissivity are experimentally quantified. Therefore, they reflect cameras configuration and measurement conditions defined to create the virtual experiment. As indicated, the table reports two systematic errors related to coating emissivity. The first takes into account local temperature variations associated with the heterogeneous coating. Moreover, the second considers the global temperature error arising from emissivity uncertainty (±0.01). Based on this information, the total measurement error given in the last row is estimated as the sum of the four uncertainties.

<table><caption>Table 4 – Temperature measurement uncertainties evaluated for the highest surface temperature (137 °C). They are determined according to the experimental conditions defined to create the virtual experiment.</caption><thead><tr><th>Type</th><th>Error source</th><th>Maximum absolute error [°C]<br>(corresponding relative error in %)</th></tr></thead><tbody><tr><td>Random</td><td>Detector noise</td><td>±0.14 (0.1)</td></tr><tr><td rowspan="3">Systematic</td><td>Detector calibration uncertainty</td><td>±2.74 (2.0)</td></tr><tr><td>Coating: spatial emissivity changes<br>due to white speckles</td><td>+0.79 (0.6)</td></tr><tr><td>Coating: emissivity uncertainty</td><td>±0.91 (0.7)</td></tr><tr><td colspan="2">Total temperature measurement error</td><td>+4.6 (3.3) / -3.8 (2.8)</td></tr></tbody></table>

Virtual experiment. After defining FE model and experimental configuration, the DVT is generated as shown in Fig. 6. From the FE model, temperature and displacement fields are extracted within the region of interest at each simulation frame. Using the MatchID software [14], the displacement field is used to simulate the acquisition of optical images through virtual image deformation. To this end, noise, calibration parameters, and a reference image are provided based
on the setup illustrated in Fig. 5. Next, DIC calculates the displacement field. Finally, the temperature is spatially interpolated onto the DIC points using the *griddata* method available in Python, without introducing any measurement uncertainty such as noise or emissivity biases. In this way, the bias arising from DIC settings can be disentangled from the error introduced in Table 4. It must be noted that the DVT neglects two aspects of IRT measurements. First, the resolution of real infrared images is not considered, leading to an underestimation of the error calculated in Table 4. In fact, the temperature bias associated with the limited resolution of infrared images is not quantified. Second, the spatial synchronization required to convert the thermal field in the Lagrangian configuration is not mimicked, which introduces a slight smoothing of the thermal field, as reported by Toussaint et al. [6]. These aspects will be included in future works by creating virtual infrared images that emulate the physical ones. However, for this study, both assumptions can be accepted, as this paper only focuses on the influence of DIC settings, which bias is mainly reproduced through the DIC process. Finally, it is worth emphasizing that a DVT reproducing thermal and kinematic fields measurement does not yet exist. To the best authors' knowledge, this work therefore represents the first contribution towards a DVT for DIC-IRT measurements.

<figure><img src="image_8.png" /><figcaption>Figure 6 – Flowchart of the virtual experiment developed to simulate the tensile test enriched with DIC-IRT.</figcaption></figure>

## Impact of DIC settings on thermal fields

The virtual experiment developed in the previous section is finally used to assess the influence of step and subset sizes on thermal fields measured by DIC-IRT.

Method. To this end, initially, the Performance Analysis module (PA) available in MatchID is used to optimize the DIC settings for the heterogeneous displacement field that develops during post-necking. This optimization yields the reference settings reported in Table 5. It must be noted that incremental correlation is used to ensure correlation during the diffuse neck, where the DIC pattern is subjected to large, localized deformations. Additionally, it is worth noting that additional parameters are optimized. However, as this paper only focuses on step and subset sizes, other settings are not reported for the sake of clarity. Subsequently, the DVT is used to generate a virtual temperature field for each synthetic image based on spatially converged DIC data. The resulting history of thermal fields serves as the reference. Next, virtual temperature fields are generated by correlating synthetic images using coarser step and subset sizes, as specified in Table 6. When one parameter is varied, the other one is kept fixed as its optimized value. As a result, this yields to eight biased thermal fields histories. Notably, modifying DIC settings changes the spatial resolution of the temperature data. Therefore, to estimate the temperature error, the biased fields are linearly interpolated onto the grid defined with the optimized settings. At this stage, the error is quantified by computing the absolute difference between reference and biased temperature fields, resulting in a history of error fields associated with each setting given in Table 6. From these error fields, two error indicators are finally calculated for each synthetic image: the mean temperature error $|\Delta \bar{T}|$ and the maximum temperature error $|\Delta T^{max}|$.
<table><caption>Table 5 – Reference step and subset sizes optimized with PA to obtain a spatially converged displacement field.</caption><tr><td>Subset size, SS [pixels]</td><td>31</td></tr><tr><td>Step size, ST [pixels]</td><td>2</td></tr></table>

<table><caption>Table 6 – Step and subset sizes considered to evaluate the temperature error linked to DIC settings.</caption><tr><td>Step size, ST [pixels]</td><td>4, 8, 12, 16</td></tr><tr><td>Subset size, SS [pixels]</td><td>35, 39, 43, 47</td></tr></table>

Results. The evolution of the temperature error during the test is analysed in Fig. 7 by plotting the normalized error field at four time instants of the test. The data are illustrated only for a step size of 12. To represent the test history, the average longitudinal strain reported in the top row is used. This strain is obtained from the elongation measured with an extensometer placed on synthetic images over an initial gauge length of 60 mm. Hence, until the maximum uniform elongation ($ε_{max}$), the strain values mentioned in Fig. 7 correspond to the actual true strain. Beyond this point, the strain represents a global averaged deformation. The first plot shows that before $ε_{max}$, when thermal and kinematic fields are homogeneous, the temperature error is close to zero over the entire region of interest. When the strain reaches $ε_{max}$, the second plot highlights that a systematic error starts developing close to the grips, where both temperature and strain gradients are more pronounced. On the other hand, when necking occurs and both fields localize in the center of the sample, the largest temperature error is also located in the necking region. Although not illustrated here, the same observations can be generalized regardless of the step or the subset size which is considered. From these results, two conclusions can be outlined. First, in experiments enriched with DIC-IRT, DIC settings influence the thermal field only when the material undergoes heterogeneous temperatures and deformations. Second, the location of the largest temperature error coincides with the area where the kinematic field localizes.

<figure><img src="image_9.png" /><figcaption>Figure 7 – Evolution of the normalized temperature error field during the test. The normalization is performed by dividing, at each data point, the absolute temperature error with the maximum error $ΔT^{max}$.</figcaption></figure>

However, from Fig. 7, the error cannot be quantified. Specifically, it is not possible to determine whether the bias associated with DIC settings significantly impacts the temperature accuracy. To assess this, Fig. 8 plots for each step and subset size both error indicators as a function of the average longitudinal strain. The results show that, beyond $ε_{max}$, $ΔT^{max}$ (right hand side graphs) increases as DIC settings deteriorate the spatial resolution. In contrast, $ΔT$ (left hand side graphs) does not follow this trend. In fact, the mean error calculated with a step size of 12 is higher than the one obtained with a step size of 16 (top-left graph). This is attributed to the spatial distribution of the temperature error. Specifically, with a step size of 16, the bias localizes on a few data points,
whereas with a step size of 12 the error is spread over a larger region within the diffuse neck. As a result, the mean error is higher for a step size of 12, but the maximum error remains lower. Furthermore, comparing the error indicators obtained by varying step and subset sizes highlights that the step size has a dominant effect on the temperature accuracy. Indeed, increasing the step size results in maximal errors around 4 °C (top-right graph), while increasing the subset size yields errors one order of magnitude lower (bottom-right graph). This difference can be explained by analysing the effect of both settings on the displacement field. Although not shown here, this study reveals that the step size is also more crucial for the displacement accuracy. Given that the accuracy of the Lagrangian temperature directly depends on the accuracy of the displacement, it follows that, for this test, the temperature is more sensitive to the step size. Finally, the bias linked to DIC settings is compared with the measurement error. At the end of the test, the measurement error is estimated at +4.6 / -3.8 °C (Table 4). At the same moment, the bias introduced by the step size can reach 4.1 °C (Fig. 8 top-right graph). Therefore, the error arising from a non-converged displacement field can be of the same order of magnitude as the error associated with the test setup. This shows the importance of optimizing DIC settings to accurately measure thermal fields in heterogeneous tests. However, it must be noted that this comparison cannot be generalized, as the magnitude of the temperature bias introduced by non-converged DIC settings strongly depends on the heterogeneity of displacement and temperature fields. Therefore, its significance varies with each test configuration, and this study alone is not sufficient to draw a general conclusion.

<figure><img src="image_10.png" /><figcaption>Figure 8 – Impact of step and subset size on mean and maximum temperature error as a function of the average longitudinal strain. The black vertical lines indicate the maximum uniform elongation defining when necking starts.</figcaption></figure>

Conclusion

This paper studies the influence of two DIC settings, step and subset sizes, on thermal fields
measured in heterogeneous tests by combining Digital Image Correlation with Infrared
Thermography (IRT). A Digital Virtual Twin (DVT) reproducing both fields measurement is
developed to simulate a post-necking tensile test on a thick S700MC steel. To incorporate
metrological aspects into the DVT, a DIC-IRT setup is designed to mimic the measurement
conducted in the physical test. The main outcomes of the virtual experiment are the following:

* Under uniform thermal and kinematic fields, DIC settings do not affect the temperature. However, when both fields localize, they introduce an additional temperature uncertainty.

* The most important DIC setting for achieving accurate temperatures corresponds to the one that has the greatest impact on the displacement field.

* The location of the largest temperature error coincides with the localization of the kinematic field. In this area, the bias linked with DIC settings can reach the same order of magnitude as the measurement error. As such, DIC settings must be spatially converged to measure accurate thermal fields in heterogeneous tests.
## Acknowledgement

The authors gratefully acknowledge the support from the Research Fund for Coal and Steel under grant agreement No 888153 (EU-RFCS 2019 project No. 888153 | vForm-xSteels).

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