Search Results (8)

Eddy-current inspection of anisotropic composites, such as aeronautical CFRP, demands models that ensure mathematical rigor, tensorial consistency, and clear energetic interpretation. This work presents a novel unified variational framework with a multiplicative tensor perturbation for the time-harmonic eddy-current problem in anisotropic media with defective regions. The formulation is posed in the natural spaces $H(\mathrm{curl};\Omega )\times H^1\left({\Omega }_c\right)$, and the well-posedness is established via the Lax–Milgram theorem under physically consistent assumptions on permeability and conductivity. The sesquilinear form admits a Hermitian decomposition that separates dissipative and reactive contributions, revealing the energetic structure of the weak formulation. Defects are modeled through multiplicative modifications of the baseline anisotropic conductivity tensor. This congruence-based approach preserves symmetry and positive definiteness, ensuring non-negative Joule losses and structural stability, allowing a modular representation of subsurface delamination, fiber breakage, conductive inclusions, and distributed porosity within a single tensorial framework. A central result of the present formulation is the reconstruction of the complex power functional from the evaluation of the weak form at the solution, showing that the active dissipated power and the magnetic reactive power arise directly from the same integral terms. Through the complex Poynting theorem, the quadratic form is linked to the internal complex power, establishing a direct connection between the variational formulation and measurable quantities such as probe impedance variations. Simulations of realistic layered CFRP configurations, including single- and multi-defect scenarios, confirm that, compared with additive perturbations, the multiplicative model provides enhanced energetic contrast, particularly in strongly anisotropic and interacting defect conditions. Agreement with experimental measurements, supported by a quantitative comparison of dissipated power variations obtained from controlled EC experiments, corroborates the physical relevance and robustness of the proposed complex power functional. Full article

To replicate delaminations at the coupon and substructural scales, simulated defects are often introduced into test specimens; therefore, understanding their behaviour within the laminate is essential. Full-field imaging is employed to investigate the effects of artificial defects in Carbon Fibre-Reinforced Polymer (CFRP) composites. Centre Crack Ply (CCP) specimens are used to evaluate the Mode II fracture toughness of laminated composites from a simple tensile test. Two batches of specimens are manufactured using IM7/8552. Artificial defects are introduced using a steel film insert of 5 µm thickness. For the first type of samples, the inserts were coated with Frekote release agent, while for the second type, the steel inserts were incorporated into the laminate without coating. Additionally, a third batch of specimens with a [0₄, 90]s layup is manufactured. Thermoelastic Stress Analysis (TSA) and Digital Image Correlation (DIC) are employed to obtain full-field temperature and displacement data from the tested samples. The inclusion of 90-degree plies enhances thermal contrast exploiting, their anisotropic mechanical and thermal properties. First, the specimens are tested under monotonic loading to failure, with DIC used to capture strain distributions at damage initiation and failure. In addition, acoustic emission is employed to evaluate damage initiation. Load drops provide an indirect evaluation of fracture toughness. Results show that full-field imaging is capable of establishing how the release agent and the layup configuration influence damage initiation and propagation. The non-adiabatic thermoelastic response is shown to be effective in observing subsurface damage. Finally, a novel approach to evaluate fracture toughness from the temperature increase at the failure event is proposed. Full article

Ground penetrating radar (GPR) is a non-destructive testing (NDT) method increasingly used for evaluating concrete structures by identifying internal flaws and embedded objects. This study presents a structured image-based methodology for interpreting GPR B-scan data using a practical flowchart designed to aid in distinguishing common subsurface anomalies. The methodology was validated through a laboratory experiment involving four concrete slabs embedded with simulated defects, including corroded rebar, hollow pipes, polystyrene sheets (to represent delamination), and hollow containers (to represent voids). Scans were performed using a commercially available device, and the resulting radargrams were analyzed based on signal reflection patterns. The proposed approach successfully identified rebar positions, spacing, and depths, as well as low-dielectric anomalies such as voids and polystyrene inclusions. Some limitations were noted in detecting non-metallic materials with weak dielectric contrast, such as hollow pipes. Overall, the findings demonstrate the reliability and adaptability of the proposed method in improving the interpretation of GPR data for structural diagnostics. The proposed methodology achieved a detection accuracy of approximately 90% across all embedded features, which demonstrates improved interpretability compared to traditional manual GPR assessments, typically ranging between 70 and 80% in similar laboratory conditions. Full article

As one of the widely used materials for hydro turbine runners, 13Cr4Ni martensitic stainless steels (13/4 MSS) manufactured by forging and wire arc additive manufacturing (WAAM), respectively, were selected for high-cycle fatigue tests, and the effects of microstructures and defect characteristics on fatigue mechanism were investigated. The results indicate that compared to the forged 13/4 MSS, the microstructure of the WAAM test piece is very fine, and the martensite units, consequently, are smaller in size. The yield strength and ultimate tensile strength are 685 MPa and 823 MPa for the forged specimen and 850 MPa and 927 MPa for the WAAM specimens, respectively. The fatigue strength of 10⁷ cycles at room temperature is 370 MPa for forged specimens and 468 MPa for WAAM specimens. The predominant defect of the forged 13/4 MSS specimen is inclusion, and the fatigue initiates mainly at the surface and subsurface. While for the WAAM specimen, the most commonly found defects are pores, and the fatigue initiation is internal and at the subsurface. In addition, the fine microstructure, as well as the high strength and hardness, enable the WAAM material to have higher fatigue strength. In order to assess the effect of defects on fatigue performance, the stress intensity factor and El-Haddad model were adopted in the present study. It was found that the forged specimens with fish-eye (FIE) zones and the WAAM specimens with granular bright facet (GBF) zones have longer fatigue life. The fatigue strengths of the forged 13/4 MSS were therefore predicted by defect size. In contrast, the fatigue strengths of the WAAM 13/4 MSS were predicted by both defect and GBF sizes. Full article

Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of the hot charge rolling process and may even cause breakout accidents. The defects are, however, hard to detect online by traditional mechanism-model-based and physics-based methods. In the present paper, a comparative study is carried out based on data-driven methods, which are only sporadically discussed in the literature. As a further contribution, a scatter-regularized kernel discriminative least squares (SR-KDLS) model and a stacked defect-related autoencoder back propagation neural network (SDAE-BPNN) model are developed to improve the forecasting performance. The scatter-regularized kernel discriminative least squares is designed as a coherent framework to directly provide forecasting information instead of low-dimensional embeddings. The stacked defect-related autoencoder back propagation neural network extracts deep defect-related features layer by layer for a higher feasibility and accuracy. The feasibility and efficiency of the data-driven methods are demonstrated through case studies based on a real-life continuous casting process, where the imbalance degree drastically vary in different categories, showing that the defects are timely (within 0.01 ms) and accurately forecasted. Moreover, experiments illustrate the merits of the developed scatter-regularized kernel discriminative least squares and stacked defect-related autoencoder back propagation neural network methods regarding the computational burden; the F1 scores of the developed methods are clearly higher than common methods. Full article

In this paper, the axial loading fatigue tests are at first conducted on specimens ofG20Mn5QT steel from axle box bodies in high-speed trains. Then, the size and shape effects on fatigue behavior are investigated. It is shown that the specimen size and shape have an influence on the fatigue performance of G20Mn5QT steel. The fatigue strength of the hourglass specimen is higher than that of the dogbone specimen due to its relatively smaller highly stressed region. Scanning electron microscope observation of the fracture surface and energy dispersive X-ray spectroscopy indicate that the specimen size and shape have no influence on the fatigue crack initiation mechanism. Fatigue cracks initiate from the surface or subsurface of the specimen, and some fracture surfaces present the characteristic of multi-site crack initiation. Most of the fatigue cracks initiate from the pore defects and alumina inclusions in the casting process, in which the pore defects are the main crack origins. The results also indicate that the probabilistic control volume method could be used for correlating the effects of specimen size and shape o the fatigue performance of G20Mn5QT steel for axle box bodies in high-speed trains. Full article

The sugar mill roller shaft is one of the critical parts of the sugar industry. It requires careful manufacturing and testing in order to meet the stringent specification when it is used for applications under continuous fatigue and wear environments. For heavy industry, the manufacturing of such heavy parts (>600 mm diameter) is a challenge, owing to ease of occurrence of surface/subsurface cracks and inclusions that lead to the rejection of the final product. Therefore, the identification and continuous reduction of defects are inevitable tasks. If the defect activity is controlled, this offers the possibility to extend the component (sugar mill roller) life cycle and resistance to failure. The current study aims to explore the benefits of using ultrasonic testing (UT) to avoid the rejection of the shaft in heavy industry. This study performed a rigorous evaluation of defects through destructive and nondestructive quality checks in order to detect the causes and effects of rejection. The results gathered in this study depict macro-surface cracks and sub-surface microcracks. The results also found alumina and oxide type non-metallic inclusions, which led to surface/subsurface cracks and ultimately the rejection of the mill roller shaft. A root cause analysis (RCA) approach highlighted the refractory lining, the hot-top of the furnace and the ladle as significant causes of inclusions. The low-quality flux and refractory lining material of the furnace and the hot-top, which were possible causes of rejection, were replaced by standard materials with better quality, applied by their standardized procedure, to prevent this problem in future production. The feedback statistics, evaluated over more than one year, indicated that the rejection rate was reduced for defective production by up to 7.6%. Full article

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required. Full article