Nondestructive Detection Research Articles

Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications.

Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach.

As one of the world's most important vegetable crops, eggplant production is often severely affected by verticillium wilt, leading to significant declines in yield and quality. Traditional multispectral disease-imaging equipment is expensive and complicated to operate. Low-cost multispectral devices cannot capture images and cover less information. The traditional approach to early disease diagnosis involves using multispectral disease-imaging equipment in conjunction with machine learning technology. However, this approach has significant limitations in early disease diagnosis, including challenges such as high costs, complex operation, and low model performance. The aim of this study was to combine low-cost multispectral cameras with deep learning technology to detect early stage Verticillium wilt in eggplant effectively. Using the Manual FS-3200T-10GE-NNC multispectral camera to perform multispectral imaging of the leaves of eggplant seedlings at the early infection stage, information fusion was performed on the collected multispectral images, and a five-channel image information fusion model was established. Image information fusion technology was combined with deep learning technology, among which the VGG16-triplet attention model performed the best, achieving a precision of 86.73% on the test set. Model validation on 48- and 72-hour data reached a precision of 75% and 82%, respectively, achieving an early diagnosis of Verticillium wilt. This highlighted the potential of multispectral cameras for early disease detection. In this study, we successfully developed a method for the non-destructive detection of the early stages of eggplant wilt disease by combining multispectral imaging technology with deep learning algorithms. While ensuring high accuracy, this method significantly reduces the cost of experimental equipment. The application of this method can reduce the cost of agricultural equipment and provide a scientific basis for agricultural production practices, helping to reduce losses caused by diseases.

Nondestructive perch target detection and size measurement from RGB-D images in recirculating aquaculture system

Nondestructive perch target detection and size measurement from RGB-D images in recirculating aquaculture system

Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology

Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the Rp2 values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. Then, these quantitative detection models were extended to each pixel in hyperspectral images, achieving point-by-point prediction of pigment components, so the distribution of pigments of stacked tea leaves during processing procedures was successfully visualized on the processing line in situ. By integrating a hyperspectral imaging system into the real-world environment, operators can monitor pigment levels in real time and thus dynamically adjust processing parameters based on real-time data. This study enhances pigment detection efficiency in tea processing, supports process optimization, and aids in quality control.

Antithermal-Quenching Near-Infrared Emitting Lu2SrAl4SiO12:Cr3+ Garnet-Type Phosphor for High-Resolution Nonvisual Imaging.

Near-infrared (NIR) light allows fast and nondestructive detection with deep penetration into biological tissues and is widely used in food inspection, biomedical imaging, night vision security, and other fields. Cr3+-doped NIR first region (NIR-I) phosphors have many interesting features that have attracted a lot of attention recently. However, practical issues, such as low photoluminescence quantum efficiency and poor thermal stability, need to be addressed. We synthesized Cr3+-doped Lu2SrAl4SiO12 garnet-type phosphors using a high-temperature solid-state reaction method. Under blue-light (@ 430 nm) excitation, the phosphors exhibited broadband NIR-I emission in the range of 600-1000 nm, with an emission peak at 710 nm. This system is unique, as the emission had multipeak sharp-line superposition and the Cr3+ ions were situated in a relatively strong crystal field environment, as opposed to a weak crystal field environment for other matrix. The optimal doping content of Cr3+ ions was 2 mol %, and its internal quantum efficiency was ∼76.8%. Surprisingly, this NIR phosphor showed an antithermal quenching effect, and the integrated luminescence intensity of NIR-I emission measured at 573 K was 206.3% of that measured at 303 K. We found that the antithermal quenching of NIR-I luminescence was caused by the extremely low thermal expansion coefficient and rigid structure of the Lu2SrAl4SiO12 matrix in the temperature range, as well as the weakening of electron-phonon coupling with increasing temperature. The optimized phosphor was packaged with a blue chip into a NIR phosphor-converted light-emitting diode device. The light source device showed an output power of 119.02 mW and an electro-optical conversion efficiency of 11.02% under a driving current of 300 mA. The application potential of this NIR phosphor was demonstrated in the field of high-resolution nondestructive imaging.

Non-destructive characterization of individual particle bunches by a resistive-cooling measurement in a Penning trap

We have developed and operated an electronic system for the non-destructive detection and cooling of charged-particle bunches that are captured and confined in a Penning trap, together with methods for the evaluation of corresponding measurements that allow for a detailed characterization of each individual particle bunch. Once calibrated, from a single measurement of the particles’ induced electronic signal as a function of time directly upon capture, the setup and method allow for a fast determination of the initial and final absolute particle energies, of the cooling rate, and of the absolute number of particles in the bunch. We demonstrate this with highly charged ions (Ne8+) that are injected into the Penning trap of the HILITE experiment.

Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data

Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in real-world scenarios with diverse label distributions. This results in suboptimal performance when less frequent classes are overshadowed by the majority class during training. Thus, the critical research challenge emerges of how to develop an effective classifier on a small-scale imbalanced dataset without significant bias from the dominant class. In this paper, we propose a novel nondestructive detection approach, which we call the Dice Loss Improved Self-Supervised Learning-Based Prototypical Network (Proto-DS), designed to address this imbalanced learning challenge. The proposed amalgamation mitigates the label bias on the most frequent class, further improving robustness. We validate our proposed method on three collected hyperspectral food image datasets with varying degrees of data imbalance: Citri Reticulatae Pericarpium (Chenpi), Chinese herbs, and coffee beans. Comparisons with state-of-the-art imbalanced learning techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and class-importance reweighting, reveal our method’s superiority. Notably, our experiments demonstrate that Proto-DS consistently outperforms conventional approaches, achieving the best average balanced accuracy of 88.18% across various training sample sizes, whereas the Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) approaches attain only 59.42%, 60.38%, and 66.34%, respectively. Overall, self-supervised learning is key to improving imbalanced learning performance and outperforms related approaches, while both prototypical networks and the Dice loss can further enhance classification performance. Intriguingly, self-supervised learning can provide complementary information to existing imbalanced learning approaches. Combining these approaches may serve as a potential solution for building effective models with limited training data.

Enhancement strategy of thermal stability and photoluminescent intensity of Cr3+ doped Li3+xZn2–2xGaxSbO6 phosphor in a strong crystal field

Near-infrared phosphor-converted light-emitting diodes (pc-LEDs) have shown enormous influence in various applications such as night vision, non-destructive detection，especially in the realm of plant cultivation. However, the role of phosphors presently employed in plant cultivation is typically limited, serving primarily for the purpose of illumination. In this work, a series of near-infrared phosphors Li3+xZn2–2xGaxSbO6: 0.02Cr3+(LZGS: 0.02Cr3+) under 450 nm excitation was successfully synthesized by high-temperature solid-state method. Through precise modulation of Ga3+ ions, a blue shift in the emission spectrum was observed, demonstrating excellent overlap with the absorption spectrum of Phytochrome. To explain two phenomena of spectral blue shift and the enhancement in luminescence intensity, density functional theory calculations have been performed. Additionally, a crystal field strength Dq/B of 3.48 was achieved, which represents the highest value currently reported for Cr3+-doped phosphors. The results offer potential applications of LZGS: 0.02Cr3+ in temperature sensing, enabling precise detection of the environmental temperature for plant growth. Finally, pc-LED devices were made by combining LZGS: 0.02Cr3+ with 450 nm blue light chips and applied to plant lighting, temperature measurement, and anti-counterfeiting, demonstrating the potential of LZGS: 0.02Cr3+ in multifaceted applications.

Non-Destructive Pest Detection: Innovations and Challenges in Sensing Airborne Semiochemicals.

Pests, especially invasive ones, pose significant threats to the global ecosystem, crop security, and agriculture economy. Sensing airborne semiochemicals as a nondestructive detection method has been recognized as a promising strategy to detect the presence of these living pests on site. However, sensing airborne semiochemicals in fields is challenging, as they are transmitted in concentrations as low as several nanograms per cubic meter in chemically diverse environments. This low vapor pressure together with similarity in functional groups of pheromones among different species have curtailed the practical deployment of corresponding sensors for real world applications. This review describes the advances in semiochemical detection methods and technologies including traditional analytical instruments, trained animals, and electroantennography with a focus on electronic noses (e-noses). Several key types of volatile organic compound (VOC) sensors used in e-noses are summarized, including their transduction methods, sensing materials, and sensing performance for semiochemical and simulants detection. Notably, it was found that many commercial VOC sensors failed to respond to airborne semiochemicals effectively, leading to a reduced efficiency of e-noses. Future work may focus on developing stable and robust sensing materials with higher sensitivity and selectivity to pheromones and understanding the feasibility of the deployment of the sensors under field conditions.

A Nondestructive Detection Method for the Muti-Quality Attributes of Oats Using Near-Infrared Spectroscopy

This study aimed to achieve a precise and non-destructive quantification of the amounts of total starch, protein, β-glucan, and fat in oats using near-infrared technology in conjunction with chemometrics methods. Eight preprocessing methods (SNV, MSC, Nor, DE, FD, SD, BC, SS) were employed to process the original spectra. Subsequently, the optimal PLS model was obtained by integrating feature wavelength selection algorithms (CARS, SPA, UVE, LAR). After SD-SPA, total starch reached its optimal state (Rp2 = 0.768, RMSEP = 2.057). Protein achieved the best result after MSC-CARS (Rp2 = 0.853, RMSEP = 1.142). β-glucan reached the optimal value after BC-SPA (Rp2 = 0.759, RMSEP = 0.315). Fat achieved the optimal state after SS-SPA (Rp2 = 0.903, RMSEP = 0.692). The research has shown the performance of the portable FT-NIR for a rapid and non-destructive quantification of nutritional components in oats, holding significant importance for quality control and quality assessment within the oat industry.

Multiple Defect-Induced High-Resolution Near-Infrared Mechanoluminescent Materials for Non-Destructive Detection of Blood Glucose and Lipids.

Mechanoluminescence (ML) materials, known for their ability to convert mechanical energy into light, are increasingly recognized for their potential applications, such as in intelligent stress sensing, in vivo bioimaging, and stress non-destructive monitoring. However, the low signal-to-noise ratio (SNR) and narrow-band emission of single-defect-induced ML materials usually limit their biological-related practical applications. Here, these limitations will be addressed by modulating the microstructure evolution in Y3Ga3MgSiO12:Cr3+ through the [Si4++Mg2+] → [Ga3++Ga3+] chemical substitution strategy. Density functional theory (DFT) calculation reveals the defect types and dynamic charge migration processes. In addition, Y3Ga3MgSiO12: Cr3+ with continuously distributed "shallow-deep" defects (0.68-1.61eV) can avoid persistent luminescence (PersL) and bright-field environment interference. Herein, such high SNR near-infrared broadband ML emission may provide a reliable way for high-quality biological non-destructive sensing and detection. Finally, benefiting from the different absorption of ML signals in glucose/lipid, one may find a novel non-invasive blood glucose/lipid testing technology in patients.

Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

Abstract The quantitative determination of wool and cashmere mixed fiber is an indispensable quality control link in the textile industry, crucial for improving international trade status, ensuring product quality, and safeguarding consumer rights. Therefore, the goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. A total of 210 mixed samples of 21 different proportions of cashmere and wool are prepared in the experiment, and data are collected in the NIR spectral band of 1,000–2,500 nm. Convolution Savitzky–Golay (S–G) combined with the second-order derivative is then used for spectral preprocessing. The variable iterative space shrinkage approach (VISSA) optimizes the characteristic wavelengths, and 339 wavelength points are selected. The prediction model of the least squares support vector machine (LSSVM) is established by particle swarm optimization (PSO), fast positioning, and analysis of key information related to the target in complex spectral data. Finally, the training set and the prediction set are divided according to the ratio of 8 : 2. Experiments show that in terms of modeling and prediction, the PSO-LSSVM model based on the wavelength selected by VISSA has a prediction determination coefficient R-squared of 0.9821, a prediction root mean square error of 1.1263, and an mean absolute error of 0.6527. The hybrid modeling method of VISSA, PSO, and LSSVM based on NIR spectroscopy (VISSA–PSO–LSSVM) can provide a more accurate and stable method for the non-destructive detection of cashmere and wool blended fiber content.

The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning.

Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900-1800 nm range, six preprocessing methods-Savitzky-Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)-were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears.

Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-contaminated and non-contaminated mussels were collected in the range of 950-1700nm. The model based on a robust energy-based least squares twin support vector machine (RELS-TSVM) was established to detect Cd-contaminated mussels. The influence of parameters on the RELS-TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS-TSVM model in detecting Cd-contaminated mussels reached 99.92%, which was better than other twin support vector machine-derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS-TSVM model had a high robustness for noise disturbance. The results show that near-infrared spectroscopy combined with the RELS-TSVM model can realize the detection of Cd-contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish. PRACTICAL APPLICATION: The method of detecting Cd-contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.

Detection of early bruises in apples using hyperspectral imaging and an improved MobileViT network.

Apples are susceptible to postharvest bruises, leading to a shortened shelf life and significant waste. Therefore, accurate detection of apple bruises is crucial to mitigate food waste. This study proposed an improved lightweight network based on MobileViT for detecting early-stage bruises in apples, utilizing hyperspectral imaging technology from 397.66 to 1003.81nm. After acquiring hyperspectral images, the Otsu threshold algorithm was employed for mask extraction, and principal component analysis was used for feature image extraction. Subsequently, the improved MobileViT network (iM-ViT) was implemented and compared with traditional algorithms, utilizing depthwise separable convolutions for parameter reduction and integrating local and global features to enhance bruise detection capability. The results demonstrated the superior performance of iM-ViT in accurately detecting apple bruises, showing significant improvements. The F1 score and test accuracy for detecting apple bruises using iM-ViT reached 0.99 and 99.07%, respectively. The fivefold cross-validation strategy was used to assess the stability and robustness of iM-ViT, and ablation experiments were performed to explore the effects of depthwise separable convolutions and local features on parameter reduction and classification accuracy improvement for early-stage bruise detection in apples. The results demonstrated that iM-ViT effectively reduced parameters and improved the ability to detect early bruises in apples. PRACTICAL APPLICATION: This study proposed an improved lightweight network to detect early bruises in apples, providing a reference for quick detection of bruises caused in the production process. Potential insights into the nondestructive detection of apple bruises using lightweight networks have been presented, which might be applied to mobile or online devices.

Dendritic copper nanostructures for ultrasensitive detection of rhodamine 6G and Congo red

Surface-Enhanced Raman Scattering (SERS) is a fast-responding and non-destructive ultra-sensitive detection technique that can replace traditional chromatographic and spectroscopic analysis techniques. In this experiment, dendritic copper nanostructures were prepared by the solid-state ionics method and the vacuum hot evaporation process with an applied current of I=9μA. Using them as a SERS substrate, Raman detection was performed on the dye molecules Rhodamine 6G (R6G) and Congo Red (CR) to determine SERS enhancement performance. It is found that the growth of the prepared copper nanostructures has a top-end dominant phenomenon. The fractal dimension of the copper nanostructure was calculated using fractal theory, and it was revealed to be 1.560. Scanning electron microscopy (SEM) results showed that a large number of 60-80 nm regularly arranged copper nanoparticles were attached to the surface of the dendritic copper nanostructure. This is beneficial to increase the surface roughness of the substrate and improve the detection level of dye molecules. Subsequently, copper nanostructures can sensitively detect R6G and CR solutions as low as 1×10-13 mol/L and 1×10-7 mol/L, reaching single-molecule levels. The Raman mapping experiment showed that the mean relative standard deviation (RSD) values of R6G and CR were 12.3% and 12.9%, indicating high uniformity and reproducibility of the structure. This structure effectively achieves the fusion of uniformity, repeatability, and sensitivity, and shows broad application prospects in food detection.

Defect focused Harris3D & boundary fine-tuning optimized region growing: Lithium battery pole piece defect segmentation

As a vital component of lithium battery, the pole piece is prone to defects during the production process, which adversely affects performance and even causes safety accidents. Therefore, conducting quality inspections for lithium battery pole pieces is crucial. In this research, an optimized region growing algorithm based on 3D point cloud data is proposed to automatically detect pole piece defects. Specifically, a potential defect-focused Harris3D method (DFHarris3D) for keypoint detection and a boundary fine-tuning algorithm (BFT) are introduced. Comparative experimental results prove that our method outperforms others in terms of completeness, accuracy, and robustness. Therefore, this novel non-destructive detection technique demonstrates the potential to accurately segment surface defects in lithium battery pole pieces, which can be integrated into smart factories for fully automated defect detection.

Flexible label-free SERS substrate with alginate-chitosan@silver nanocube for in situ nondestructive detection of thiram on apples

Flexible label-free SERS substrate with alginate-chitosan@silver nanocube for in situ nondestructive detection of thiram on apples

Failure behavior study of EB-PVD TBCs under CMAS corrosion and thermal shock cycles

Abstract Calcia-magnesia-alumino-silicate (CMAS) erosion has become a major obstacle, limiting the operating temperature and service life of Thermal barrier coatings (TBCs) in aircraft engines. Constructing simulation environments that replicate TBCs’ working conditions and exploring online, non-destructive detection techniques are reliable approaches to studying coatings’ failure, representing both a global research hotspot and a challenge in this field. The paper presents an initial endeavor to establish a simulation experiment for TBCs in aviation-engine within a CMAS environment. Experimental results show that electron beam physical vapor deposition (EB-PVD) Y2O3-stabilized ZrO2 (YSZ), one of the mainstream TBCs technologies, produced 20% surface spallation after 50 thermal-shock cycles under simulated CMAS corrosion conditions. Testing and analysis of the macroscopic and microscopic structures of the failed samples, combined with SEM, EDS, and XRD findings, revealed significant physical and chemical interactions between the ceramic layer and CMAS deposits, as well as phase transformation within the coatings, leading to substantial alterations in mechanical properties and ultimately causing the failure of EB-PVD YSZ.