## 1 Introduction  
Welding can be classified as an application of additive manufacturing (albeit partial and crude), as even large and geometrically complex assemblies can be produced by decreasing the number of scraps to reasonable levels. For this reason, welding continues to have a massive application in the manufacturing of engineering structures that are critical in terms of safety and reliability. This is made possible by the continuous development of welding technologies and process control systems. However, some critical issues remain, mainly related to the high and localized thermal input inherent to the process itself, which can lead to significant shape variations in the finished part [1]. In particular, the plastic thermomechanical strain, induced by localized heating, melting, and cooling in the vicinity of the weld bead, causes permanent distortions that affect the entire welded structure. Such distortions can be detrimental to the appearance, dimensional accuracy, and strength of the welded structure [1]. The deviation from the nominal geometry depends on the size and geometrical complexity of the assembly and can be large enough to require additional machining (e.g., to solve fit-up problems when assembling welded components to other structures). The non-linear, irreversible nature of the welding process and the presence of residual stresses make it impractical to prevent or completely correct welding distortion. However,distortion needs to be minimized to an acceptable level in order to remain within the dimensional tolerances of the assembly. Currently, in many industrial contexts where the use of manual welding is still relevant, conventional design methodologies either ignore or use empirical techniques to evaluate the effect of welding on the structural integrity and dimensional accuracy of the welded assembly. In order to implement effective mitigation techniques to control or counteract welding distortion, robust prediction methodologies were developed [2; 3] based on both theoretical and numerical analyses. Most of the published work in the relevant literature focuses on finite element (FE) simulations [2; 3; 4; 5]. Although FE can model transient non-linear coupled thermo-structural problems, the complexity of the welding process and the large uncertainty regarding the parameters involved (e.g., thermomechanical temperature-dependent material properties, thermal and mechanical boundary conditions, thermal loads, etc.) demand careful validation [3; 6; 7; 8; 9]. Over the years, many contact and non-contact experimental methods have been proposed to measure weld distortion. The less recent experimental data come from contact techniques (dial-gauges, calipers, coordinate measuring machines -CMM- and linear variable-differential transformers-LVDT-) [1; 10; 11; 6; 12]. With these techniques, experimental data is usually obtained by comparing the _shape_ of the assembly at a given set of points, before welding and in the post-veld cooled state, relative to a given reference system. The main disadvantage of contact methods is that the measurement is carried out sequentially on a limited and sparse set of points or along a few lines. In addition, the time required for the measurement precludes time-resolved measurements (e.g., shape change during cooling). Finally, the results are highly dependent on the reference system adopted (usually a flat plane) in which the two geometries -before and after welding-are measured and compared. Most of these limitations have recently been overcome with the introduction of full-field non-contact measurement techniques using lasers or camera systems for both in situ and off-line shape and deformation measurements [13; 14; 15; 16; 17]. Among the non-contact measurement techniques, Digital Image Correlation (DIC) [18; 19] has become very popular due to its relative ease of use, its adaptability to non-laboratory contexts, and the wide availability of both turn-key commercial systems and open-source codes for camera calibration and data analysis [20].