Search Results (82)

Owing to their exceptional thermal insulation and protective capabilities, thermal barrier coatings (TBCs) are widely applied to critical hot-section components of aero-engines. However, under increasingly harsh service environments, internal defects such as delamination tend to form within the coatings, posing a severe threat to engine operational safety and service life. To effectively evaluate delamination defects in TBCs, this study employs the immersion ultrasonic pulse-echo technique to inspect specimens subjected to various thermal cycling treatments. Four specimens, subjected respectively to 21, 32, 43, and 54 thermal cycles at 1200 °C, were tested. Ultrasonic response data were systematically acquired via normal incidence scanning from both the superalloy substrate side and the ceramic top coat side. Combining Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT) based on the generalized Morse wavelet, Wavelet Packet Energy Entropy (WPEE), and peak-to-peak amplitude variations of the second echo, multi-dimensional features were extracted from ultrasonic signals across the frequency domain, joint time-frequency domain, and energy distribution profiles. Through comparative analysis, ultrasonic waveform and time-frequency characteristics representing defect evolution were obtained. A significant monotonically decreasing trend of WPEE with the aggravation of interfacial delamination was established, characterizing the acoustic energy confinement process induced by interfacial damage. Furthermore, a multilayer finite element (FE) model reasonably reproduced dynamic acoustic wave propagation; numerical results are in agreement with experimental data, validating the feasibility of the proposed detection method. The detection and evaluation framework established in this study provides a reference for safety monitoring and lifespan prediction of aero-engine TBCs. Full article

This study extended a previously established intraoral scanning (IOS) and optical coherence tomography (OCT) dual-modality monitoring workflow for computer-aided design/computer-aided manufacturing (CAD/CAM) restorations to three additional crown material classes alongside a resin composite (RECO) reference. Four material classes were investigated ($n=8$ each): RECO, polymer-infiltrated ceramic network (PICN), lithium disilicate ceramic (LDSC), and zirconia-reinforced lithium silicate ceramic (ZLSC). Monolithic crowns were adhesively luted to standardized human molar abutment teeth and aged by cyclic loading (50500$\mathrm{N}$, 2$\mathrm{Hz}$, 37 2${}^{\circ }\mathrm{C}$, up to 1250000 cycles) in a mouth-motion simulator. IOS and handheld OCT were performed at baseline and after every 250000 cycles under phantom-head conditions; correspondence was assessed using Spearman’s rank correlation coefficient (exploratory, uncorrected for multiple comparisons). OCT consistently showed higher defect extents than IOS across all material classes and timepoints. While no significant IOS-OCT associations were found for RECO and the PICN, OCT detected full-thickness vertical subsurface damage propagation from the earliest timepoint in LDSC and ZLSC, with IOS-derived surface wear remaining markedly lower. Surface-based monitoring alone did not reliably reflect subsurface damage propagation, a dissociation most pronounced in the vertical dimension and silicate-based materials. Intraoral OCT may provide complementary, non-invasive subsurface information to support individualized recall scheduling and minimally invasive repair decisions. Full article

The rapid adoption of Wide Band Gap (WBG) semiconductor technologies, particularly Silicon Carbide (SiC) and Gallium Nitride (GaN), together with emerging Ultra-Wide Band Gap (UWBG) materials such as AlGaN, Aluminum Nitride (AlN), Diamond, β-gallium oxide (β-Ga₂O₃), and Hexagonal Boron Nitride (h-BN), is reshaping wafer-level electrical testing beyond the capabilities of conventional silicon-based probing infrastructures. The increasingly demanding electrical, thermal, and mechanical operating conditions of these devices require probe cards to evolve from passive interconnects into integrated multiphysics systems capable of supporting high voltages, high current densities, and fast switching transients. This review analyzes the fundamental design constraints governing advanced probe card technologies, including probe-to-wafer contact physics, electrothermal behavior, insulation requirements, parasitic effects, and high-frequency performance. Particular attention is devoted to Vertical MEMS probe card architectures, which enable high contact density, low parasitic inductance, and improved current-carrying capability, making them particularly suitable for modern WBG applications. Emerging solutions, including ceramic insulation structures, controlled-atmosphere testing environments, integrated sensing, and advanced thermal management techniques, are also discussed. Furthermore, the paper examines the evolution of wafer-level testing strategies, from conventional parametric screening to reliability-oriented methodologies inspired by burn-in procedures, highlighting the growing importance of body-diode characterization for early defect detection in SiC devices. Beyond reviewing the current state of the art, this work proposes a structured taxonomy of probe card technologies and outlines a technology roadmap linking future WBG and UWBG device requirements with the evolution of wafer-level testing infrastructures. Full article

The ceramic and refractory industries are undergoing a progressive transition toward more sustainable and resource-efficient manufacturing systems driven by increasing environmental regulations, rising energy demands, and the need to reduce dependence on virgin raw materials. In this context, artificial intelligence (AI) has emerged as a promising tool for improving material design, process optimization, predictive maintenance, and circular manufacturing strategies. This review provides a comprehensive analysis of recent advances in AI applications within ceramic and refractory systems, with particular emphasis on their role in enabling circular economy approaches and intelligent manufacturing environments. The study examines the integration of machine learning, deep learning, computer vision, digital twins, and Industry 4.0 technologies across multiple domains, including materials discovery, defect detection, waste classification, process control, and sustainability assessment. In addition, the review discusses the incorporation of secondary raw materials such as fly ash, slag, waste glass, ceramic sludge, and spent refractories into circular ceramic production systems. The analysis highlights the potential of AI-driven methodologies to improve resource efficiency, reduce environmental impact, and enhance process adaptability under complex industrial conditions. Furthermore, current limitations associated with data availability, model interpretability, industrial scalability, and integration with life cycle assessment frameworks are critically discussed. Finally, future research directions are identified, emphasizing the development of standardized datasets, hybrid experimental–AI methodologies, digital manufacturing ecosystems, and intelligent decision-making systems for next-generation sustainable ceramic and refractory technologies. Full article

Ceramic Matrix Composite (CMC) are widely used in aerospace due to the advantages such as high-temperature resistance and lightweight properties. Detecting defects within these materials is crucial for ensuring the safety of corresponding structures. In this paper, a finite element model of CMC model for layered structures is established for the ultrasonic non-destructive testing. Based on the computed tomography (CT) scan images and porosity of the material, a randomly distributed pore model is constructed to investigate the effect of pores on the ultrasonic signals. Random pores are also introduced in the simulation to ensure that the model corresponds as closely as possible to reality. Moreover, the feasibility of utilizing air-coupled ultrasonic excitation to generate specific frequency Lamb waves is verified. The effect of pore presence on the signal propagation is analyzed, and the effects of layered structures at different positions and lengths on the signal propagation are investigated. The results demonstrate that the Lamb waves with a specified frequency can be excited using the method described in this paper, and the presence of pores and delamination defects can affect the propagation of the Lamb wave in CMC, in which the signal attenuation can reach up to 7.6 dB. Full article

In ceramic cup manufacturing, manual inspection is prone to missed detections and false positives, particularly for small surface defects. To address these challenges, this study presents an effective and efficient YOLOv11m-based detection framework, termed CEL-YOLOv11m, for precise identification of small-scale defects on ceramic surfaces. Specifically, a multi-scale convolution module (EMSC) is introduced to enhance the backbone feature extraction structure. By integrating convolution kernels of varying sizes, the module improves multi-scale feature representation, while grouped convolution is employed to reduce computational overhead. In the feature aggregation stage, a CRGseg-based structure is incorporated, and a refinement component (RCM) is designed to strengthen fine-grained information for small targets. Additionally, a cross-scale feature fusion strategy is applied to improve contextual representation across different resolutions. For the detection stage, a Layer-shared Detail-Enhanced Convolutional Detection Head (LSDECD) is adopted to improve fine-grained localization while improving computational efficiency through parameter sharing. Experiments conducted on a self-constructed ceramic defect dataset and the VisDrone2019 benchmark show that the proposed framework achieves competitive performance compared with representative methods. The model attains an mAP@50(%) of 54.8% with an inference speed of 89.9 FPS, providing a favorable trade-off between detection accuracy and computational efficiency while maintaining strong precision in small defect detection. Full article

As the cornerstone of contemporary electronics, the quality of electronic substrates—including Printed Circuit Boards (PCBs) and Ceramic Package Substrates (CPSs)—is intrinsic to product reliability. However, automated inspection is currently impeded by two persistent obstacles: the drastic multi-scale variation in defects and the acute class imbalance within defect datasets. Conventional deep learning approaches often fail to reconcile these challenges simultaneously, leading to suboptimal recognition of rare defect categories. To bridge this gap, we propose Multi-scale Partial Vision Transformer—Simple, Parameter-free Attention Module (MulPViT-SimAM), a robust framework designed for class-imbalanced electronic substrate defect detection. Our method features a novel multi-scale backbone (MulPViT) that synergizes partial convolutions with hierarchical attention mechanisms, facilitating the efficient extraction of both fine-grained local textures and global contextual dependencies. Additionally, we embed the Simple, Parameter-free Attention Module (SimAM) into the feature fusion stage to adaptively highlight defect-specific features while dampening background noise. To further mitigate data imbalance, we utilize the Equalized Focal Loss (EFL) function, which employs a category-specific modulating factor to dynamically equilibrate the learning focus across different classes. Comprehensive benchmarking reveals state-of-the-art performance, achieving mAP@0.5 scores of 95.7% on the standard PKU-MARKET-PCB dataset and 54.2% on the highly challenging CPS2D-AD dataset. Significantly, our approach effectively mitigates class imbalance, narrowing the performance deviation of rare categories to just 4.3% on the PKU-Market-PCB dataset and 1.4% on the CPS2D-AD dataset, compared to 11.8% and 7.5% in baseline models. These findings position MulPViT-SimAM as a viable and efficient solution for industrial quality control. Full article

The effect of Mn addition on the structural, dielectric, ferroelectric, and mechanical properties of Bi_0.5Na_0.5TiO₃ (BNT) and 0.94(Bi_0.5Na_0.5TiO₃)–0.06(BaTiO₃) (BNT–BT) ceramics was systematically investigated under identical processing conditions. Powders were calcined at 750 °C for 2 h and 900 °C for 2 h, followed by sintering at 1060 °C for 5 h. Mn contents of 0.5 and 5 mol% were selected to represent low-level substitution and near-saturation regimes. XRD confirmed single-phase perovskite formation within laboratory detection limits, while Raman spectroscopy revealed Mn-induced lattice distortions. Low Mn addition (0.5 mol%) enhanced densification and improved remanent polarization in BNT–BT (Pr = 33.5 μC/cm²). In contrast, 5 mol% Mn promoted grain coarsening, increased porosity, and reduced functional performance. Mechanical properties evaluated using two-parameter Weibull statistics showed composition-dependent variations in characteristic hardness and elastic modulus. The results demonstrate that Mn-doping effects depend strongly on both dopant concentration and host-lattice structural state, distinguishing beneficial substitution from defect-saturation behavior in lead-free BNT-based ceramics. Full article

Ceramic Matrix Composites (CMC) are widely used in critical applications such as leading edges of aircraft wings and thermal insulation layers of thermal protection systems due to their advantages of being lightweight, high-temperature resistant, and impact-resistant. However, influenced by manufacturing processes and service environments, internal defects such as pores and delamination are prone to occur, significantly compromising the mechanical properties and service reliability of the material. This paper primarily evaluates the feasibility and applicability of using Terahertz Frequency Modulated Continuous Wave (FMCW) technology for the non-contact detection of CMC. First, the measurement principle of FMCW is introduced, and the structure of the detection system, including a two-dimensional mechanical scanning platform, optical lenses, a control platform, and a data acquisition unit, is outlined. Subsequently, scanning imaging was performed on CMC specimens and their bonded thermal protection structure (TPS) specimens, demonstrating the feasibility of Terahertz FMCW technology as an advanced non-destructive testing tool for CMC inspection. The issues of diffraction and the Rayleigh limit inherent in real-aperture terahertz imaging were analyzed and discussed. A multi-scale fusion defect detection method incorporating background estimation is proposed to enable precise delineation of defect regions. Experimental results show that, after processing with the proposed algorithm, the minimum detectable pore diameter at the focal plane is 1 mm, with a regional error of approximately 3%. The detection error for pores and debonding areas in CMC is maintained within 6.44%. Analysis indicates that combining terahertz imaging technology with image processing algorithms enables the quantitative analysis of internal defects in composite materials, offering a new technical approach for defect detection in composite materials. Full article

This work addresses the low pyroelectric coefficient that limits the practical application of lithium niobate (LN) crystals. A defect modulation process based on reduction annealing treatment is proposed. This reduction annealing treatment increased the pyroelectric coefficient of LN crystals maximally to 3.362 × 10⁻⁴ C/m²K. At room temperature, the voltage responsivity figure of merit (F_V) and detectivity figure of merit (F_D) were both improved more than three-fold. All material properties exceeded those of commercial lead zirconate titanate (PZT) ceramic. This process achieves the simultaneous modulation of high pyroelectric coefficients and low impedance in LN crystals. Based on the LN crystals with optimized properties, pyroelectric infrared detectors (center wavelength 9.4 μm) without external matching resistors were prepared. The response voltage of the detector reached 2.8 times that of commercial PZT detectors while exhibiting lower noise, and has achieved practical applicability. This work provides a simple and efficient method for developing environmentally friendly, low-cost, high-sensitivity pyroelectric infrared detectors. It also establishes the foundations for the application of LN crystals in emerging pyroelectric detection fields. Full article

Digital Light Processing (DLP), which operates through a layer-by-layer deposition, has proven to be a promising technique for obtaining complex and customized architectures. However, there are still numerous unresolved challenges in ceramics additive manufacturing, among which is delamination due to suboptimal adhesion between the layers, which threatens the structural integrity and properties of samples. According to recent findings, excess surface hydroxyl groups were identified as being responsible for this defect; a suitable calcination pre-treatment of the ceramic powder could be effective in significantly mitigating delamination flaws in mullite DLP printed bodies. Therefore, in addition to optimizing the printable slurry formulation and printing parameters (mainly in terms of curing energy and layer resolution), this work aimed at investigating the influence of the calcination of a commercial mullite powder (added with magnesium nitrate hexahydrate, as a precursor of the sintering aid MgO) as a simple and effective treatment to additively shape ceramic bodies with limited flaws and enhanced density. The surface characteristics evolution of the mullite powder was investigated, specifically comparing samples after magnesium nitrate hexahydrate addition and ball-milling in water (labeled as BM), and after an additional calcination (BMC). In particular, the effect of the superficial -OH groups detected by FTIR analysis in the BM powder, but not in the BMC sample, was studied and correlated to the properties of the respective ceramic slurry in terms of rheological behavior and curing depth. The hydrophilicity of BM powders, due to superficial hydroxyls groups, affects ceramic powder dispersion and wettability by the resin, causing a weak interface. At the same time, it promotes photopolymerization of the light-sensitive resin, thus inducing the as-printed matrix embrittlement. Anyhow, its photopolymerization degree, equal to 67% and 55% for BM and BMC, respectively, was enough to guarantee the printability of both slurries. However, the use of BMC significantly reduced flaw occurrence in the as-printed bodies and the final density of the samples sintered at 1450 °C (without an isothermal step) was increased (approx. 60% and 50% of the theoretical value for BMC and BM, respectively). Thus, the target porosity of the ceramic bodies was guaranteed, and their structural integrity achieved without any increase in sintering temperature but with a simple powder treatment. Full article

We report the synthesis and multifunctional characterization of copper-reinforced Ba_0.85Sr_0.15TiO₃ (BST) ceramic composites with Cu contents ranging from 0 to 40 wt%, prepared by a sol–gel route and densified using spark plasma sintering (SPS). X-ray diffraction and FT-IR analyses confirm the coexistence of cubic and tetragonal BST phases, while Cu remains as a chemically separate metallic phase without detectable interfacial reaction products. Microstructural observations reveal abnormal grain growth induced by localized liquid-phase-assisted sintering and progressive Cu agglomeration at higher loadings. Scanning electron microscopy reveals abnormal grain growth, with the average BST grain size increasing from approximately 3.1 µm in pure BST to about 5.2 µm in BST–Cu40% composites. Optical measurements show a continuous reduction in the effective optical bandgap (apparent absorption edge) from 3.10 eV for pure BST to 2.01 eV for BST–Cu40%, attributed to interfacial electronic states, defect-related absorption, and enhanced scattering rather than Cu lattice substitution. Electrical characterization reveals a percolation threshold at approximately 30 wt% Cu, where AC conductivity and dielectric permittivity reach their maximum values. Impedance spectroscopy and equivalent-circuit analysis demonstrate strong Maxwell–Wagner interfacial polarization, yielding a maximum permittivity of ~1.2 × 10⁵ at 1 kHz for BST–Cu30%. At higher Cu contents, conductivity and permittivity decrease due to disrupted Cu connectivity and increased porosity. These findings establish BST–Cu composites as tunable ceramic–metal systems with enhanced dielectric and optical responses, demonstrating potential for specialized high-capacitance decoupling applications where giant permittivity is prioritized over low dielectric loss. Full article

The ceramic defect detection technology based on deep learning suffers from the problems of scarce rare defect samples and class imbalance. However, the current deep generative image augmentation techniques are limited when applied to the task of augmenting rare ceramic defects due to issues such as uneven image brightness and insufficient features of small-sized defects, resulting in poor image quality and limited improvement in detection results. This paper proposes a ceramic rare defect image augmentation method based on TC-CycleGAN. TC-CycleGAN is based on the CycleGAN framework and optimizes the generator and discriminator structures to make them more suitable for ceramic defect features, thereby improving the quality of generated images. The generator is TC-UNet, which introduces the scSE and DehazeFormer modules on the basis of UNet, effectively enhancing the model’s ability to learn the subtle defect features on the ceramic surface; the discriminator is the TC-PatchGAN architecture, which replaces the original BatchNorm module with the ContraNorm module, effectively increasing the discriminator’s sensitivity to the representation of tiny ceramic defect features and enhancing the diversity of generated images. The image quality assessment experiments show that the method proposed in this paper significantly improves the quality of generated defective images. For the concave type images, the FID and KID values have decreased by 49% and 73%, respectively, while for the smoke stains type images, the FID and KID values have decreased by 57% and 63% respectively. The further defect detection experiments results show that when using the data set expanded by the method in this paper for training, the recognition accuracy of the detection model for rare defects has significantly improved. The detection accuracy of the concave and smoke stains types of defects has increased by 1.2% and 3.9% respectively. Full article

This work employs moiré interferometry to investigate the influence of sintering temperature and sandblasting on the optical and mechanical properties of magnesium aluminate spinel (MgAl₂O₄). S25CRX14 Spinel pellets were fabricated via Spark Plasma Sintering (SPS) at 1300 °C, 1350 °C, and 1400 °C. The sintered samples were subsequently analyzed before and after sandblasting. Moiré interferometry, a non-destructive and contactless technique based on the superposition of tow linear transmission gratings, has proven particularly suitable for detecting micro-defects in transparent materials. The analysis of moiré fringes provided essential insights into the presence and size of defects, enabling accurate quality assessment without altering the samples. Its high spatial resolution, allowed the detection of even low-contrast defects. The results confirmed that the sintering temperature and sandblasting significantly influenced the mechanical and optical properties of the S25CRX14 spinel samples. The specimens sintered at 1350 °C exhibited the highest light transmission and the superior hardness. In contrast, the samples sintered at 1400 °C showed a notable degradation in their optical and mechanical properties. In conclusion, the pellets sintered at 1350 °C demonstrated the most favorable overall performance. This study confirms that moiré interferometry is a straightforward, accurate, and highly effective method for evaluating transparent ceramics, with very low implementation costs. Full article

Infrared thermography non-destructive testing technology has been widely used in the defect detection of composite structures due to its advantages, including non-contact operation, rapidity, low cost, and high precision. In this study, a laser-line scanning system combined with an infrared thermography was developed, along with a corresponding dynamic sequence image reconstruction method, enabling rapid localization of surface damages. Then, high-precision quantitative characterization of defect morphology in reconstructed images was achieved by integrating an edge gradient detection algorithm. The reconstruction method was validated through finite element simulations and experimental studies. The results demonstrated that the laser-line scanning thermography effectively enables both rapid localization of surface damages and precise quantitative characterization of their morphology. Experimental measurements of ceramic materials indicate that the relative error in detecting crack width is about 6% when the crack is perpendicular to the scanning direction, and the relative error gradually increases when the angle between the crack and the scanning direction decreases. Additionally, an alumina ceramic plate with micrometer-width cracks is inspected by the continuous laser-line scanning thermography. The morphology detection results are completely consistent with the actual morphology. However, limited by the spatial resolution of the thermal imager in the experiment, the quantitative identification of the crack width cannot be carried out. Finally, the proposed method is also effective for detecting surface damage of wrinkles in ceramic matrix composites. It can localize damage and quantify its geometric features with an average relative error of less than 3%, providing a new approach for health monitoring of large-scale ceramic matrix composite structures. Full article