Issue 77

T. Hachimi et alii, Fracture and Structural Integrity, 77 (2026) 173-206; DOI: 10.3221/IGF-ESIS.77.11

it never was: a core application for (safety-relevant) industrial applications. This paper attempts to pull these gains negotiated by other researchers as a clearer whole into a larger logical/conceptual frame with DIC standing/pivotal to tying together what-is-designed and what-is-actually-installed/certified as the respective linkages, to ‘map’ who should get onto putting in place so that AM polymers/plastics etc can be used safely for safety-deterministic engineering applications.

T HEORETICAL FOUNDATIONS AND METROLOGICAL PRINCIPLES OF DIC IN A DDITIVE M ANUFACTURING

T

he advantage of developing a thorough understanding of the mathematical, optical, and metrological principles underlying DIC becomes clear in the context of focusing on the applications of DIC to additively manufactured polymers. Since the printed materials are often heterogeneous and 3D anisotropic (due to factors such as material raster 3D printing paths and interfacing between subsequent layers) [6,24,44]. The performance of the DIC measurement system cannot be expressed in standard instrument specifications but is actually a function of material mesostructure as well [2,91,120]. Historical evolution and core principles DIC itself has developed through two major phases since Peters and Ranson [86] first considered how speckle could be tracked to yield a 2D-DIC technique, as Sutton et al. [99] implemented random speckle tracking using a Newton-Raphson optimization routine, and now into stereo-DIC and Digital Volume Correlation, which covers engineering scales, allowing full-field (2.5D) mapping of strain over an entire surface [3,41,80,103]. DIC essentially statistically tracks speckles (high contrast stochastic speckle patterns applied to the specimen surface to optimize correlation parameters in the correlation process, usually the DIC tables make the user choose parameters to be stochastically masked) by matching square subsets between the reference speckled surface and its altered post-deformation state with a mapping function describing the statistical data [59,81]. The technique is non-contact, capable of tracking high-resolution fields of displacement and strain, and requiring no sensors due to allowing just-in-time finite element mesh generation and quantifying localised gradients (Figure 2)[1,84].

Figure 2: Principle of the subset-based Digital Image Correlation (DIC) method, illustrating reference/deformed image matching and displacement vector calculation. Mathematical framework and correlation criteria The general calculation of the displacement occurring at the centre of a subset uses a shape function to model local deformation. For most AM applications, a first-order shape function based on translation, rotation, and normal strains as defined by Equations 1 and 2 above is all that is usually used [2,26].



 

u x u x

u

  

Δ Δ x y 

' x x u

(1)



y



 

u

  

Δ Δ x y 

' y y v

(2)



y

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