Non-destructive Method Research Articles

Laser ultrasonic characterization of grains in titanium alloy by the inversion method

Abstract The polycrystalline materials have been usually described by the average grain size (AGS). This paper provides a nondestructive method for determination of the AGS in titanium alloy from an inversion of experimental ultrasonic attenuation spectrum (UAS). The detailed analyses of the inverse algorithm and sensitivity of UAS to the AGS are presented, and the sensitivity is identified as a criterion for selecting the experimental data of UAS used in inversion. An example of the inverse method and experimental measurement for UAS comparison was given for the titanium alloy specimen with the AGS of 26 μm. The relative error between the AGS determined by the inversion and scanning electron microscope (SEM) observation was found to be about 1.5 %. The provided method demonstrates the feasibility of laser ultrasonic technology used for the non-destructive evaluation of AGS in polycrystalline materials.

Spectrum radiometric approach: a non-destructive method for measuring the canopy light interception rate in crops

Spectrum radiometric approach: a non-destructive method for measuring the canopy light interception rate in crops

A non-destructive measurement method for thermal barrier coating thickness in a robot polishing process based on point cloud processing

The adhesion state between the bond coat and top coat of thermal barrier coatings (TBCs) is influenced by the surface roughness of the bond coat. Under certain operating conditions, polishing is employed to reduce the surface roughness of the bond coat, thereby enhancing the adhesion strength between the top coat and bond coat and improving the TBC’s resistance to high-temperature oxidation. In order to accurately control the thickness of the bond coat during the polishing process, this study proposes a non-destructive measurement method for coating thickness based on point clouds. By preprocessing, registering, and calculating the distance between the point cloud generated by laser scanning the turbine blade, the thickness of the bond coat was determined. The method exhibits greater accuracy in regions with small curvature variations. Compared to measurements obtained using a metallographic microscope, the maximum thickness deviation between the two methods is 4.004 μm, and the relative error is less than 5%. Experimental results demonstrate that this method can accurately measure coating thickness, offering advantages such as real-time processing, flexibility in application scenarios, high measurement efficiency, and relatively low requirements for coating materials.

Development of a wireless multichannel miniature impedance measurement system and its application for bolt loosening detection

The piezoelectric impedance-based technique is always regarded as one of the most promising structural health monitoring and nondestructive evaluation methods. In recent years, impedance measurement chip AD5933 with the characteristics of high integration and cost-effectiveness makes it possible to address the huge and high-cost problems of the commercialized impedance measurement instrument during the process of structural health monitoring and defect identification. However, it still faces lots of challenges for the chips to be utilized in practical applications due to several limitations, such as short distance for data transmission, single measurement channel and artificial attendant requirement. In this paper, a wireless multichannel miniature impedance measurement system composed by the front-end measurement device and the remote measurement and control platform on the server, is firstly developed and presented with the functions of wireless data transmission, multi-channel acquisition and remote data post-processing. Subsequently, the design concept and composition of the system were introduced in detail. Then, a series of piezoelectric transducers related tests were conducted to validate its impedance measurement performance, especially when comparing with the ones measured by commercialized instruments. In addition, to verify its effectiveness and feasibility of the developed system for the structural damage detection, a bolt loosening detection experiment on the flange connection of a pipeline specimen was investigated for its damage localization and severity quantification. Finally, all the results demonstrated that the developed system provides a great possibility to be used as a convenient and portable impedance measurement tool for the civil structural health monitoring and damage identification in practical applications.

Validation of an Optical Technology for the Determination of pH in Milk during Yogurt Manufacture.

Current systems that allow inline pH control in the fermented dairy industry have drawbacks, such as protein adhesion on the non-glass pH probes, measurement distortion, frequent recalibration needs, and sensitivity to extreme pH conditions encountered during clean-in-place operations. Therefore, the objective of this study was to validate the feasibility of estimating the pH of milk during the yogurt making process by using a NIR light backscatter sensor measuring under different fermentation temperatures and milk protein concentrations using a mathematical model that correlates the light scatter signal with pH. Three replications of the experiment with two protein concentrations (3.5 and 4.0%) and two fermentation temperatures (43 and 46 °C) were used to validate this inline pH prediction model. Continuous and discontinuous measurements of pH were collected as a reference during fermentation, simultaneously with the light backscatter data acquisition. Also, the effect of adjusting the initial voltage gain of the light scatter device on the accuracy of the pH prediction model was evaluated. Temperature and initial voltage were the main factors affecting the fitting accuracy of the model. The adjustment of the initial voltage gain improved the pH prediction model fit. The model has been successfully validated for both continuous and discontinuous measurements of pH, with SEP values < 0.09 pH units and CV < 1.78%. The proposed optical inline and non-destructive method was feasible for inline pH monitoring of milk fermentation, avoiding traditional manual pH measurement.

Automated Tenderness Assessment of Okra Using Robotic Non-Destructive Sensing

The quality of okra is crucial in satisfying consumer expectations, and the tenderness of okra is an essential parameter in estimating its condition. However, the current methods for assessing okra tenderness are slow and prone to errors, necessitating the development of a better, non-destructive method. The objective of the present study is to develop and test a non-destructive robotic sensor for assessing okra freshness and tenderness. A total of 120 pods were divided into two sets and stored under different conditions: 60 pods were kept in a cold chamber for 24 h (considered tender), while the other 60 pods were stored at room temperature for two days. First, the samples were assessed non-destructively using the force sensor of a collaborative robot, where a jamming pad (with internal granular fill) was capable of adapting and copying the okra shapes while controlling its force deformation. Second, the okra pods were evaluated with the referenced destructive tests, as well as weight loss, compression, and puncture tests. In order to validate the differences in the tenderness of the two sets, a discriminant analysis was carried out to segregate the okra pods into the two categories according to the destructive variables, confirming the procedure which was followed to produce tender and non-tender okra pods. After the differences in the tenderness of the two sets were confirmed, the variables extracted from the robotic sensor (maximum force (Fmax), first slope (S1), second slope (S2), the first overshoot (Os), and the steady state (Ss)) were significant predictors for the separation in the two quality categories. Discriminant analysis and logistic regression methods were applied to classify the pods into the two tenderness categories. Promising results were obtained using neural network classification with 80% accuracy in predicting tenderness from the sensor data, and a 95.5% accuracy rate was achieved in distinguishing between tender and non-tender okra pods in the validation data set. The use of the robotic sensor could be an efficient tool in evaluating the quality of okra. This process may lead to substantial savings and waste reduction, particularly considering the elevated cost and challenges associated with transporting perishable vegetables.

Prediction of Mango Quality During Ripening Stage Using MQ-based Electronic Nose and Multiple Linear Regression

In recent years, consumers have shown interest in non-destructive methods to assess the fruit's internal quality during ripening. The objective of this study is to construct and evaluate an E-Nose system using low-cost MQ sensors to evaluate fruit quality, specifically soluble sugar content (SSC) and hardness of mango during ripening. The correlation test was performed to compare sensor readings with SSC and hardness, and multiple linear regression (MLR) was used to establish linear equations for mango quality indices based on sensor variation. Over the storage period, the hardness of mango was decreased from the value of 15.4 kgf/cm² to 12.25 kgf/cm². Similarly, the SSC for mangoes increased from 19.7%Brix to a final value of 24.66%Brix. The sensor values also showed positive correlation with SSC and negative correlation with hardness of mango, respectively. Using the MLR analysis, the hardness and SSC of mango during the ripening stage, the correlation coefficient (r) of 0.847, standard error of 1.49 kgf/cm2 and 0.815, standard error of 1.696 % Brix for hardness and SSC prediction, respectively. These results indicate that MQ-based E-nose is the rapid and non-destructive method for predicting mango qualities during ripening stages.

Detecting Manufacturing Defects on Printed Circuit Boards Using Metamaterial-Based Circular Microstrip Patch Antenna

In this study, a microstrip circular patch antenna is designed having a metamaterial-based (MTM) ground plane to detect manufacturing defects of short circuits and open circuits on printed circuit boards (PCB). In that regard, PCB specimens of FR-4 as the substrate and copper microstrip lines as the conductive wiring lines are designed and manufactured. Five vertical copper lines printed side by side on the top of the substrate are used to demonstrate the working mechanism of the designed antenna sensor. Two different defect scenarios of open and short circuits with controlled locations are studied to determine variations in the return loss data of the proposed structure. MTM cell structures with cross lines enclosed by an octagon ring are periodically placed as part of the ground plane of the proposed antenna to obtain higher sensitivity of the designed and manufactured sensor. Antenna return loss behaviors in terms of the locations of the faults are employed to prepare a database to detect not only the presence of the faults but also determine their locations. Since the samples to be measured are not irreversibly damaged during the testing process, the proposed design can be considered a non-destructive measurement method to provide information about the type and location of defects with real-time measurement data.

Early diagnosis of Cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion

Plant diseases can inflict varying degrees of damage on agricultural production. Therefore, identifying a rapid, non-destructive early diagnostic method is crucial for safeguarding plants. Cladosporium fulvum (C. fulvum) is one of the major diseases in tomato growth. This work presents a method of data fusion using two hyperspectral imaging systems of visible/near-infrared (VIS/NIR) and near-infrared (NIR) spectroscopy for the early diagnosis of C. fulvum in greenhouse tomatoes. First, hyperspectral images of samples at health and different times of infection were collected. The average spectral data of the image regions of interest were extracted and preprocessed for subsequent spectral datasets. Then different classification models were established for VIS/NIR and NIR data, optimized through various variable selection and data fusion methods. The principal component analysis-radial basis function neural network (PCA-RBF) model established using low-level data fusion achieved optimal results, achieving accuracies of 100% and 99.3% for calibration and prediction, respectively. Moreover, both the macro-averaged F1 (Macro-F1) values reached 1, and the geometric mean (G-mean) values reached 1 and 1, respectively. The results indicated that it was feasible to establish a PCA-RBF model by using the hyperspectral technique with low-level data fusion for the early detection of C. fulvum in greenhouse tomatoes.

Fluorescence hyperspectral imaging technology combined with chemometrics for kiwifruit quality attribute assessment and non-destructive judgment of maturity

Dry matter content (DMC), firmness and soluble solid content (SSC) are important indicators for assessing the quality attributes and determining the maturity of kiwifruit. However, traditional measurement methods are time-consuming, labor-intensive, and destructive to the kiwifruit, leading to resource wastage. In order to solve this problem, this study has tracked the flowering, fruiting, maturing and collecting processes of Ya'an red-heart kiwifruit, and has proposed a non-destructive method for kiwifruit quality attribute assessment and maturity identification that combines fluorescence hyperspectral imaging (FHSI) technology and chemometrics. Specifically, first of all, three different spectral data preprocessing methods were adopted, and PLSR was used to evaluate the quality attributes (DMC, firmness, and SSC) of kiwifruit. Next, the differences in accuracy of different models in discriminating kiwifruit maturity were compared, and an ensemble learning model based on LightGBM and GBDT models was constructed. The results indicate that the ensemble learning model outperforms single machine learning models. In addition, the application effects of the ‘Convolutional Neural Network’-‘Multilayer Perceptron’ (CNN-MLP) model under different optimization algorithms were compared. To improve the robustness of the model, an improved whale optimization algorithm (IWOA) was introduced by modifying the acceleration factor. Overall, the IWOA–CNN–MLP model performs the best in discriminating the maturity of kiwifruit, with Accuracytest of 0.916 and Loss of 0.23. In addition, compared with the basic model, the accuracy of the integrated learning model SG-MSC-SEL was improved by about 12%–20 %. The research findings will provide new perspectives for the evaluation of kiwifruit quality and maturity discrimination using FHSI and chemometric methods, thereby promoting further research and applications in this field.

Monitor and classify dough based on color image with deep learning

To address subjective, time-consuming, and labor-intensive manual methods for monitoring and classifying fermenting dough, an automated and non-destructive method is proposed. Deep learning model YOLOv8s and extracted features including dough surface area, contrast, and homogeneity from RGB color images were employed to monitor fermenting dough. The features were input to a stacked ensemble model (SEM) with base models SVM, AdaBoost, KNN, and RF, with AdaBoost as meta-learner to classify fermenting dough into under-fermented, fermented, and over-fermented. SEM demonstrated a high dough classification rate of 83%, with specific rates of 75% for under-fermented, 71% for fermented, and 90% for over-fermented dough. Results reviewed that combining dough surface area and texture features is effective for monitoring dough, and can be used in adjusting chamber conditions. Furthermore, SEM showed great ability in classifying fermenting dough. The proposed method offers a promising solution for improved bread quality and consistency in bread-making.

In Situ Annealing Effect on Thermally Co-Evaporated CsPbI2Br Thin Films Studied via Spectroscopic Ellipsometry.

All-inorganic cesium lead halide perovskites possess excellent thermal stability, a feature that renders them highly favorable for optoelectronic applications with an elevated thermal budget. Employing a coevaporation approach for their deposition holds promise for manufacturing at an industrial level, owing to improvements in device scalability and reproducibility. For unlocking the full potential of vacuum-evaporated perovskite thin films, it is crucial to delve deeper into their crystallization process, which, as a solid-state reaction, has been less investigated compared to the crystallization process of, most commonly used, solution-based methods. In this work, we employ spectroscopic ellipsometry, a nondestructive, high speed, and high accuracy characterization method, to study the real time annealing effect on thermally coevaporated CsPbI2Br thin films in a temperature range between 25 and 300 °C. We achieve this by developing a singular dynamic model that can be fitted in real time as a function of temperature, providing insights into how thermal annealing influences the perovskite film's morphology and optical constants. Based on the latter, we derive the temperature dependence of the thermo-optic coefficient and Urbach energy as well as analyze the interband transition energies via critical point analysis. We demonstrate that the γ- to β-phase transition can be identified through a pronounced shift in the bandgap energy, whereas the β- to α-phase transition can be discerned by a sharp increase in the film's roughness. We corroborate the obtained fit results with additional in- and ex situ measurements, such as in situ grazing incidence wide-angle X-ray scattering, atomic force microscopy, reflectance/transmittance, and profilometry.

Environmental assessment of soluble solids contents and pH of orange using hyperspectral method and machine learning

Progress in non-destructive methods to detect the characteristics of fruits is a new and attractive process for researchers and specialists in this field. On the other hand, these researchers move toward identifying their impacts on their surroundings in line with diagnostic efficiency. One of these essential impacts is the environmental impact of the non-destructive detection process of fruits. Navel oranges are one of the most popular and widely consumed fruits, whose maturity indices such as soluble solids contents (SSC) values and acidity are considered as parameters in determining the quality of this product. This study used the hyperspectral method in the vis-NIR range to evaluate and measure navel oranges' SSC and acidity values. In the following, by applying the life cycle assessment method, the environmental impacts of measuring and evaluating these two parameters of the characteristics of navel oranges were investigated. The Impact2002+ method was used to evaluate the impact of the life cycle list. Based on the findings, the environmental impacts of SSC measurement are about 40, 42, 20, and 18 % higher than those of the environmental impacts of pH measurement from the point of view of endpoint impacts for Human Health, Ecosystem quality, climate change, and resources, respectively. The random forest modeling results showed a suitable and acceptable correlation and relationship (over 90 %) between the wavelengths selected from the feature selection stage and environmental impacts.

Nondestructive diagnosis technique for LiFePO4/graphite cells based on the internal resistance curve analysis

To explain the degradation mechanism of lithium-ion batteries, a nondestructive diagnosis technology to estimate the degree of degradation of the cell components is required. A technology that satisfies that requirement is widely used to analyze the open-circuit voltage (OCV) curve of a cell and estimate the positive-electrode and negative-electrode capacities and the amount of deactivated lithium. However, it has been difficult to estimate the degradation of electrodes composed of active materials with flat electrode potentials, such as lithium iron phosphate (LFP). The novel nondestructive method developed in this study can estimate the degradation state of battery components using LFP by analyzing the “internal resistance curve” of the battery. This method is used to determine the degradation parameters that explain both the measured OCV curve and the measured internal resistance curves of the cell by comparing the internal resistance curves of the cells with those of the positive and negative electrodes. The results of calendar life tests of lithium-ion batteries using LFP at elevated temperature and high voltage were analyzed by using this method, and the results of the analysis show that the degradation of the LFP positive electrode was progressing.

A non-destructive heating method for lithium-ion batteries at low temperatures

Low temperatures seriously affect the performance of lithium-ion batteries. This study proposes a non-destructive low-temperature bidirectional pulse current (BPC) heating method. Different from existing heating approaches, this method not only optimizes heating frequency and amplitude but also considers the optimization of the charge/discharge pulse duration ratio. To optimize the BPC heating strategy, a precise electro-thermal coupled model is established, and a neural network is employed to delineate the relationship among model parameters, temperature, and state of charge (SOC). Additionally, the interplay between the impedance of the graphite anode and that of the full cell is analyzed by constructing a three-electrode battery. Then, a novel full-cell-oriented lithium plating criterion is introduced. Finally, based on the constructed electro-thermal coupled model, lithium plating criterion, and terminal voltage constraint, a novel non-destructive BPC heating method is proposed. The results show a significant improvement in heating efficiency compared to conventional BPC heating. Especially for high SOCs, the heating power is increased at least 8 times. When the battery SOC is below 40 %, the average heating rate from −10 °C to 10 °C is 11.28 °C/min. Even at 90 % SOC, the heating rate remains at 2.88 °C/min. Furthermore, the capacity and impedance of a battery at 50 % SOC exhibit no significant changes after 60 heating cycles using the optimal BPC heating strategy at 100 Hz. These findings show that the optimized method proposed in this study has high heating efficiency and no damage to the battery.

In situ growth of curcumin-loaded cellulose composite film for real-time monitoring of food freshness in smart packaging

Visual pH-responsive packaging material is particularly important in food supply chain safety monitoring due to their non-destructive monitoring method and intuitive result. However, it has always been limited by the instability performance of pH-response components and carriers, which further hinders its wide food safety application. To address these challenges, we selected cellulose with remarkable biocompatibility and mechanical properties as the carrier, and high pH-responsive curcumin to develop a smart packaging material (RC/GC composite film) with real-time food safety monitoring. Compared with pure cellulose film, the RC/GC composite film exhibited excellent mechanical properties (4-fold enhancement) and thermal stability (100 °C increasing). Meanwhile, based on the first reported strategy of curcumin in-situ growth during cellulose film formation, the RC/GC composite film exhibited exceptional antioxidant activity (89.2 %), antimicrobial property (91.6 %), and significant pH-responsive sensitivity (within 15 s). This innovative approach offers a new strategy for easy-to-use and effective monitoring of food spoilage in packaging materials.

基于透射光谱技术的马铃薯种薯黑心病检测研究

Black heart disease is one of the screening indicators of seed potatoes, which has a serious impact on the quality and yield of potato, and at present there are fewer non-destructive testing methods for internal defects of seed potatoes. This paper aims to utilize non-destructive testing technology to quickly identify qualified and black hearted seed potatoes, and then to protect yield and food security. In this paper, transmission spectroscopy system was utilized to collect the spectral data of 104 qualified seed potatoes and 104 black hearted seed potatoes in 450~940 nm band. Subsequently, four algorithms, namely Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and First-order Derivative (FD), were utilized to pre-process the seed potatoes spectral data to improve the data noise ratio. Feature wavelength extraction was made using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) to enhance the sample data characteristics and improve the model interpretability. The construction of classification models for qualified and black hearted seed potatoes relied on two deep learning techniques, Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), which were trained and tested for the feature bands respectively. The experimental results showed that SG-CARS-CNN was the optimal combination of classification algorithms, and the classification accuracies of both the training set and the test set reached 100%, which improved the accuracy of the test set by 3.85% compared with that of the traditional machine learning algorithms, and provided an accurate method for the rapid screening of qualified seed potatoes.

Nondestructive analysis of internal crystallographic structures of Japanese swords using neutron imaging

Crystallographic information and the internal structure of Japanese swords are crucial for understanding their metallurgical characteristics and exploring their making process. By accumulating such information from various swords made in different regions, eras and by different swordsmiths, we believe it will greatly aid in the comprehensive understanding of Japanese swords. We have been investigating Japanese swords using nondestructive analysis methods of neutron Bragg-edge transmission (BET) imaging and neutron tomography. BET provides insights into the crystalline structure of the steel, while the neutron tomography offers cross-sectional views of the sword. In this study, we examined three swords: Kashu Kiyomitsu, Nankaitaro Tomotaka, and Hosokawa Masanori, crafted between the Edo era (ca. 1603–1850) and the Meiji era (1868–1912).Kashu Kiyomitsu, from the early Edo era, exhibited a complex internal structure, with crystallite size of 0.5 µm or less around the cutting tip. Furthermore, other regions with crystallite sizes of 1 µm or larger were observed, extending from the middle area of the blade to the vicinity of the tang. The cross-sectional features of Kashu Kiyomitsu suggest that the sword has a complex structure probably combining low-carbon steel Kawagane and Shingane with a dedicated high-carbon steel for the cutting edge, Hagane. The martensite phase, indicating the quenched area, extended evenly over approximately 4 mm. In contrast, both Nankaitaro Tomotaka, from the late Edo era, and Hosokawa Masanori, from the Meiji era, exhibited relatively uniform crystallographic structures throughout the entire blade. In Nankaitaro Tomotaka, the martensite phase extended over a width of 4–6 mm from the cutting edge, confirming the arch-like boundary between martensite and ferrite/pearlite within the blade. In Hosokawa Masanori, the martensite phase extended over approximately 8–10 mm from the cutting edge, with a wavy pattern along the longitudinal direction. These observations suggest that Nankaitaro Tomotaka and Hosokawa Masanori were made using simpler sword-making techniques compared to Kashu Kiyomitsu.

Automatic segmentation of microporous defects in composite film materials based on the improved attention U-Net module

ABSTRACT Composite film materials offer several advantages, such as high strength-to-weight ratios, flexibility, durability, or cost-effectiveness, which make them ideal for a wide range of applications across various industries. However, microporous defects in the material compromise its durability and air-tightness. Thus far, few works have been performed on inquiring non-destructive testing methods intended for the detection of microporous defects in composite film materials. In this study, the automatic detection of microporous defects is achieved by using a thermal imaging inspection system developed on the basis of a laser infrared system and the U-Net. Innovatively, an improved attention module is added to U-Net to boost its detection performance. While using a laser as a heat source, a fibre laser beam shaper is chosen as the laser beam expansion to ensure a uniform intensity distribution of the laser beam within a small heating area. Experimental results show that the proposed method can reliably detect microporous defects in the composite film materials with sizes in the order of millimeters/micrometers.

Dynamical and optical properties of rare phenakite mineral: A combined experimental and computational study

The electronic structure, optical and dynamical properties of the natural phenakite Be2SiO4, are studied using ab initio calculations completed by Raman spectroscopy experiments. The calculated structural properties are in excellent agreement with experimental data and are accompanied by chemical bonds analysis using Maximally Localized Wannier Functions method. The theoretical analysis of projected density of states reveals the nature of the valence band as composed by electronic states of oxygen atoms, while conducting band is composed by electronic states of Si-atoms. Using linear response method the optical properties are calculated and compared with optical properties of quartz. It was shown the similarity of rare phenakite crystal and quartz. The calculated phonon dispersion structure reveals mostly delocalized nature of phonon states with quasilocalized phonons in high-frequency range, where the most intense band in Raman spectrum is observed. The band is ambiguously assigned to the breathing mode in SiO4 tetrahedra. Basing on such peculiarity in the spectra the rapid nondestructive diagnostic method of the sample is purposed.