Issue 77

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

Integrated DIC Method / Technology

Main Findings & Comparative Insights Correlated surface strain hotspots with AE frequency bands (<50kHz for matrix, >150kHz for fiber). Resolved internal 3D strain tensors; identified strain development along internal raster angles. OCT offers lower cost and in-line suitability for translucent polymers; µ-CT remains the gold standard for depth. Established that internal displacement fields are inherently coupled to toolpath architecture and infill density. Achieved near real-time crack tracking with >95% accuracy; transformed raw maps into diagnostic data. Accurately measured independent displacement of multi-layered plates using synthetic reference images. Positioned DIC as a passive observer of surface strain

Key Insights for AM Certification

Focus Area

Ref

Essential for verifying multi stage failure in safety-critical AM components. Moves characterization from surface-level estimation to true 3D «as-built» validation. Standardized protocols must define which technique is required based on material opacity and depth. Validates the need for toolpath aware numerical models in high performance polymers. Facilitates high-throughput industrial quality control for automated AM production lines. Opens new possibilities for testing AM parts within high temperature build chambers or closed housings. Integration of DIC with digital twins is the prerequisite for «zero-defect» manufacturing environments.

Damage Categorization

AE-DIC-Micro CT Fusion

[37,65]

Volumetric 3D Strain

FIDVC (DVC)

[41,103]

Metrological Performance

OCT vs. µ-CT Comparison

[52]

Internal Patterning

DVC on FFF Structures

[16,41]

DL-DIC Integration (YOLOv7)

Intelligent Diagnostics

[111]

Obstructed Environments

Path-Integrated X ray DIC

[30]

Optical Radiographic NDE 4.0

Hybrid Structure NDE

[1]

hotspots caused by subsurface damage.

Table 7: Multimodal NDE Integration and Comparative DIC Analysis.

T ECHNICAL EVOLUTION OF DIC: A DVANCED ALGORITHMS , METROLOGICAL STANDARDS , AND ALGORITHMIC INTELLIGENCE ust as additive manufacturing (AM) aims to converge on production quality parameters aimed at high-performance components, the technical metrology core of Digital Image Correlation (DIC) has at last fully pivoted away from underlying traditional robustness in iterative matching towards a metrologically-aware intelligent and self-adaptive solver amenities. Particularly to address the interpolation associated with 3D-printed polymers, random behaviour fragments and complex surface topologies [63,79]. Metrological efficiency and boundary optimization Arguments on DIC accuracy should be in context subset dimensions, where the spatial resolution increases with the size of the subset [91]. To remove the bias of manual parameters, Li et al. [59] proposed feature-based adaptive subset configurations, where geometry is optimized at each point of interest dynamically, allowing inhomogeneous fields of deformation to be accurately captured. To augment this, Su and Lao [98] developed mathematical frameworks of one dimensional boundary subsets, which explicitly define the effects of invalid pixels in the specimen edges or crack paths on the accuracy of measurements, and thus improve the fidelity of fractured AM components. J

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