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Quality control in Additive Manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical, and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the Computer-Aided Design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice score, we introduce a Binary Difference Map (BDM) that quantifies voxel-wise mismatches between binarized CAD and XCT volumes to evaluate the accuracy of the registration. Our approach shows a 9.2% improvement in the Dice score and a 9.9% improvement in the voxel match rate compared to classic DVC methods, while reducing the interaction time from days to minutes. This work sets the foundation for deep learning-based DVC methods to generate compensation meshes that can then be used in closed-loop correlations during the AM production process. Such a system would be of great interest to industry, as it would make the manufacturing process more reliable and efficient, saving time and material.
The microstructure of refractory materials is complex, featuring a variety of mineral phases, agglomerates, defects, and controlled porosity. The behavior of refractories at high temperatures adds another layer of complexity, as phase transitions and particle rearrangements can strongly affect their properties. To analyze such intricate microstructure, advanced imaging techniques such as Synchrotron X-ray Computed Tomography (SXCT) allow detailed 3D visualization and quantification of features up to 1 μm. However, the intricacy of these microstructures makes phase identification (known as image segmentation) in digital images a challenging process. X-ray images often contain noise and image artifacts, making the analysis more difficult. Therefore, this work describes image segmentation and artifact reduction methods to characterize refractories using X- ray imaging. We studied refractory ceramics used in the aerospace industry, primarily composed of fused silica. For image segmentation, the traditional approach of greyscale thresholding was compared with machine learning. Greyscale thresholding relies on predefined algorithms to assign phases based on intensity values. In contrast, machine learning extracts patterns from large datasets, enabling more adaptive and accurate segmentation. By combining high-resolution SXCT and machine learning analysis algorithms, we successfully segmented previously uncharacterized 3D microstructural key features of refractories, including agglomerates, grain boundaries, pore size distribution and interconnectivity. Compared to traditional methods, the machine learning-enhanced segmentation presented a more accurate quantification of porosity and defects. The integration of advanced imaging techniques with machine learning segmentation significantly improves the characterization of refractory materials, providing a more precise understanding of the relationship between microstructure and material performance, supporting the development of innovative industrial solutions.
It is classically considered that the creep mechanisms for type M (e.g., pure Al) and type A alloys (e.g., Al–Mg alloys) are different. In previous studies, it is predicated that fractal dislocation structures build up during creep can unify the creep behavior of pure Al and Al–Mg alloys. So far, good agreement between model and experimental data for pure Al is obtained. In this work, an Al‐3.85%Mg material crept at different strain levels (6%, 12%, 24%, and 35%) is analyzed by means of electron channelling contrast imaging. The formation of subgrains is observed at very large deformations (35%). Further examinations at 35% deformation, using electron backscatter diffraction, indicate that the subgrains and cavities tend to localize at regions where high intergranular stress mismatch is expected to occur. Laboratory X‐ray computed tomography is used to analyze the evolution of cavities between 12% and 24% stages, indicating that the fractal dimension of the cavities smaller than 100 μm varies with creep strain, as a reflection of the evolution of dislocation distribution. It is explained how the present data feed the model of the creep behavior of Al‐3.85%Mg.
Aluminum Titanate (AT) refractory ceramics (as some other ceramic composites) are prone to microcracking, due to the thermal expansion anisotropy of AT and to the mismatch with the thermal expansion of the constituents. Such microcracks cause the room temperature Young's modulus to be only a fraction of that of the non-microcracked material. As a function of temperature, the Young's modulus increases non-linearly. Such increase suggests that microcracks close or even heal at high temperatures. Upon cooling, thermal stress accumulates again, and microcracks re-open. This cycle is fully reversible. While confirming the hysteretic behavior of the Young's modulus, we observe that the amount of microcracks (as determined by in-situ Synchrotron X-ray refraction radiography) decreases linearly upon heating. The apparent mismatch between the Young's modulus and the microcrack content dependence on temperature is explained by a simple FEM model. Such model employs cohesive elements upon cooling, in order to estimate the amount of initial microcracks. On purpose, the model does not include healing upon heating and only allows crack closure. It predicts that crack closure continuously occurs upon heating, thereby qualitatively reproducing the nearly linear dependence of the X-ray refraction signal. It is therefore concluded that the sudden and non-linear increase of Young's modulus with temperature is mainly caused by crack healing. Such finding agrees with previous work and paves the road to a more systematic separation of crack closure and healing in flexible ceramics.
The ever-growing volume of data in imaging sciences stemming from advancements in imaging technologies, necessitates efficient and reliable storage solutions for such large datasets. This study investigates the compression of industrial X-ray computed tomography (XCT) data using deep learning autoencoders and examines how these compression algorithms affect the quality of the recovered data. Two network architectures with different compression rates were used, a deep convolution neural network (D-CNN) and a vector quantized variational autoencoder (VQ-VAE). The XCT data used was from a sandstone sample with a complex internal pore network as a good test case for the importance of feature preservation. The quality of the decoded images obtained from the two different deep learning architectures with different compression rates were quantified and compared to the original input data. In addition, to improve image decoding quality metrics, we introduced a metric sensitive to edge preservation, which is crucial for three-dimensional data analysis. We showed that different architectures and compression rates are required depending on the specific characteristics needed to be preserved for later analysis. The findings presented here can aid scientists in determining the requirements and strategies needed for appropriate data storage and analysis.
Hydrogen storage is a crucial part of the hydrogen value chain, particularly in terms of safety and social acceptance of hydrogen technologies. For on-board and transportation applications, hydrogen is commonly stored as a compressed gas in pressure vessels. Full composite wrapped Type 4 pressure vessels are especially beneficial due to their high weight-saving potential and their capability to withstand high working pressures. To maintain an appropriate safety level, the mechanical behavior of the composite structure has been the subject of investigation in numerous studies. In this study, the failure mechanisms of two designs of Type 4 cylinders are investigated. The two designs differ solely in the manufacturing process parameters, particularly the internal pressure applied during the filament winding process. Variations in internal pressure result in different residual stress states and changes in the quality of the composite structure. The stacking sequence, materials used, and other manufacturing parameters remain unchanged. The cylinders show different failure mechanisms in slow burst tests: one design fails in hoop windings, while the other one fails in helical windings, despite no stress exaggeration in the fiber direction being observed with embedded optical fibers or in numerical simulations. To explain the failure mechanisms, the quality of the cylinders is investigated. The results of X-ray computed tomography and pulse-echo investigation, highlight the complexity of the interactions between manufacturing process parameters, residual stresses, manufacturing quality, and the mechanical behavior of composite pressure vessels. Resin-rich areas in the composite material are identified as a possible cause of the differing failure mechanisms.
Hydrogen storage is a crucial part of the hydrogen value chain, particularly in terms of safety and social acceptance of hydrogen technologies. For on-board and transportation applications, hydrogen is commonly stored as a compressed gas in pressure vessels. Full composite wrapped Type 4 pressure vessels are especially beneficial due to their high weight-saving potential and their capability to withstand high working pressures. To maintain an appropriate safety level, the mechanical behavior of the composite structure has been the subject of investigation in numerous studies. In this study, the failure mechanisms of two designs of Type 4 cylinders are investigated. The two designs differ solely in the manufacturing process parameters, particularly the internal pressure applied during the filament winding process. Variations in internal pressure result in different residual stress states and changes in the quality of the composite structure. The stacking sequence, materials used, and other manufacturing parameters remain unchanged. The cylinders show different failure mechanisms in slow burst tests: one design fails in hoop windings, while the other one fails in helical windings, despite no stress exaggeration in the fiber direction being observed with embedded optical fibers or in numerical simulations. To explain the failure mechanisms, the quality of the cylinders is investigated. The results of X-ray computed tomography and pulse-echo investigation, highlight the complexity of the interactions between manufacturing process parameters, residual stresses, manufacturing quality, and the mechanical behavior of composite pressure vessels. Resin-rich areas in the composite material are identified as a possible cause of the differing failure mechanisms.
Advanced statistical image analysis workflows were developed to segment and quantitatively evaluate 2D electron-backscatter diffraction (EBSD) maps and 3D synchrotron X-ray computed tomography (SXCT) volumes of a polycrystalline Al2TiO5 refractory composite that contains microcracks and pores. Several size, shape, and further geometric descriptors were determined for both the solid phase (Al2TiO5 grains) and the pore space. The resulting pore-size distribution is distinctly bimodal: coarse pores (tens to hundreds of micrometers), traced to incomplete powder compaction, coexist with fine pores generated during sintering. The two pore populations appear to be correlated with grain growth and crystallographic orientation in different ways. Finally, the descriptors obtained from the 2D EBSD and 3D SXCT data sets are internally consistent but complementary, highlighting the value of characterizations based on EBSD and SXCT in the microstructural study of refractory ceramics.
Epoxy (EP) glass fiber reinforced composites (GFRCs) are extensively used in structural applications due to their excellent thermal and mechanical properties, but their inherent flammability limits fire-safe deployment. While numerous studies examine the flame retardancy of resins or individual composite systems, comprehensive studies evaluating simultaneous improvements in flame retardancy and post-fire mechanical integrity, specifically through prepreg processing, remain challenging. This study investigates the effectiveness and transferability of phosphorus-based flame retardant (FR) systems to diglycidyl ether of bisphenol A (DGEBA) and EP novolac resin matrices and their corresponding bidirectional glass fiber composites via prepregs. The FRs are chosen based on varying modes of action: ammonium polyphosphate with inorganic silicate (APP/InSi) primarily acts in the condensed phase (CP), and aluminum diethyl phosphinate with zinc hydroxystannate (AldietPO2/ZHS) is known to demonstrate both gas-phase (GP) and CP activity. Fire residues are tailored to compensate for structural defects from fire exposure. EP novolac, with higher aromaticity and cross-linking, possesses better inherent flame resistance compared to DGEBA. The novolac composites containing AldietPO2/ZHS simultaneously showed the highest retention of flexural properties after fire exposure and the best fire safety index. The experimental values of the post-fire flexural properties in the composites calibrated damage parameters in two theoretical models.
Low-Cycle Fatigue Behavior of Laser Powder Bed Fused Inconel 718 at Room and High Temperature
(2025)
The nickel-base superalloy Inconel 718 (IN718) is one of the most commonly used Ni-based superalloys for high temperature structural applications for its remarkable strength, as well as creep, fatigue, and corrosion resistance up to 650 °C. While IN718 has traditionally been employed as cast or wrought material, it is difficult to machine because of its high strength and toughness. The additive manufacturing of IN718 components made by metal AM has thus gained extensive attention to produce expensive near-net shaped components of high-temperature alloys such as IN718, for it saves material and costs in processing and machining steps. Among all metal additive manufacturing (AM) technologies, laser powder bed fusion (PBF-LB/M) is the most widespread, IN718 being one of the most common alloys produced with it. However, high cooling rates associated to the PBF-LB/M process, hinders the primary strengthening phases γ’’ and γ’ to form, as these cooling rates induce a dislocation cellular substructure, at which walls primary Laves phases bind segregating Nb, Ti and Mo. Many of the therefore needed heat-treatment strategies can then promote Laves-phase transformation into the stable δ phase along the cell and grain boundaries. Laves and δ phases, as well as grain-boundary primary carbides may have adverse effects on mechanical properties. The mostly needle-shaped δ phase was namely found to have a detrimental effect on creep rupture life while no direct effect on LCF fatigue life was evident. In this work room- and high-temperature (650 °C) low-cycle fatigue behavior of PBF-LB/M IN718 is investigated in the four-step heat-treated state and compared to wrought IN718. The microstructure of both materials is characterized across length scales via microscopy methods. The fatigue life at room temperature of the PBF-LB/M IN718 material is slightly lower than that for the wrought material, which is reversed at 650 °C. The cyclic stress response for both materials is marked by cyclic softening that is more pronounced at higher test temperatures. Multiple secondary cracks form at high strain amplitudes, at both room and high temperatures. High testing temperatures enhance specially crack formation at the transitions of regions between elongated grains and columns of stacked grains with ripple patterns in the PBF-LB/M material. Additional to this behavior, pronounced crack branching and deflection indicate that the cracks are controlled by sharp micromechanical gradients.