Non-destructive Quality Inspection Research Articles

In-field and non-destructive determination of comprehensive maturity index and maturity stages of Camellia oleifera fruits using a portable hyperspectral imager

To efficiently detect the maturity stages of Camellia oleifera fruits, this study proposed a non-invasive method based on hyperspectral imaging technology. First, a portable hyperspectral imager was used for the in-field image acquisition of Camellia oleifera fruits at three maturity stages, and ten quality indexes were measured as reference standards. Then, factor analysis was performed to obtain the comprehensive maturity index (CMI) by analyzing the change trends and correlations of different indexes. To reduce the high dimensionality of spectral data, the successive projection algorithm (SPA) was employed to select effective feature wavelengths. The prediction models for CMI, including partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), and convolutional neural network regression (CNNR), were constructed based on full spectra and feature wavelengths; for CNNR, only the raw spectra were used as input. The SPA-CNNR model exhibited more promising performance (RP = 0.839, RMSEP = 0.261, and RPD = 1.849). Furthermore, PLS-DA models for maturity discrimination of Camellia oleifera fruits were developed using full wavelength, characteristic wavelengths and their fusion CMI, respectively. The PLS-DA model using the fused dataset achieved the highest maturity classification accuracy, with the best simplified model achieving 88.6 % accuracy in prediction set. This study indicated that a portable hyperspectral imager can be used for in-field determination of the internal quality and maturity stages of Camellia oleifera fruits. It provides strong support for non-destructive quality inspection and timely harvesting of Camellia oleifera fruits in the field.

Super-Resolution Exciton Imaging of Nanobubbles in 2D Semiconductors with Near-Field Nanophotoluminescence Microscopy.

Two-dimensional (2D) semiconductors, such as transition metal dichalcogenides, have emerged as important candidate materials for next-generation chip-scale optoelectronic devices with the development of large-scale production techniques, such as chemical vapor deposition (CVD). However, 2D materials need to be transferred to other target substrates after growth, during which various micro- and nanoscale defects, such as nanobubbles, are inevitably generated. These nanodefects not only influence the uniformity of 2D semiconductors but also may significantly alter the local optoelectronic properties of the composed devices. Hence, super-resolution discrimination and characterization of nanodefects are highly demanded. Here, we report a near-field nanophotoluminescence (nano-PL) microscope that can quickly screen nanobubbles and investigate their impact on local excitonic properties of 2D semiconductors by directly visualize the PL emission distribution with a very high spatial resolution of ∼10 nm, far below the optical diffraction limit, and a high speed of 10 ms/point under ambient conditions. By using nano-PL microscopy to map the exciton and trion emission intensity distributions in transferred CVD-grown monolayer tungsten disulfide (1L-WS2) flakes, it is found that the PL intensity decreases by 13.4% as the height of the nanobubble increases by every nanometer, which is mainly caused by the suppression of trion emission due to the strong doping effect from the substrate. In addition to the nanobubbles, other types of nanodefects, such as cracks, stacks, and grain boundaries, can also be characterized. The nano-PL method is proven to be a powerful tool for the nondestructive quality inspection of nanodefects as well as the super-resolution exploration of local optoelectronic properties of 2D materials.

Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning

AbstractFiber‐reinforced polymer (FRP) composites are widely used in the aerospace and automotive industries due to their high strength‐to‐weight ratio. However, the manufacturing process of FRP composites is intricate, requiring precise control over multiple parameters, which can be challenging. To ensure top‐notch product quality and bolster confidence in the durability of composite materials, it requires a uniform curing evaluation technique and a predictive strength model. This study presents a rapid nondestructive quality inspection technique for composites, involving three distinct studies that employ machine learning techniques in combination with the frequency‐dependent dielectric response of materials. The first study introduces a nondestructive method for swiftly inspecting the cure state (degree of cure) of composite samples. This technique combines broadband dielectric spectroscopy with supervised machine learning algorithms, particularly support vector machines. The second study employs advanced artificial neural networks like multi‐layer perceptron to predict the tensile strength, a measure of mechanical performance, of composite materials. The results demonstrate an accurate classification of the curing state with 96.7% accuracy and a prediction of tensile strength with 87.5% accuracy. The final study explores the application of machine learning in quality monitoring of prepreg (raw materials for FRP) aging at room temperature.Highlights Integration of machine learning with frequency‐dependent dielectric measurement offers efficient method for inspecting and monitoring the curing state and mechanical performance of composite materials. Machine learning and frequency‐dependent dielectric response used to accurately assess the curing state of fiber‐reinforced polymer composites with 96.7% accuracy, which saves the time and effort and also reduces the need for destructive testing. Advanced artificial neural networks, specifically multi‐layer perceptron, employed to predict mechanical performance of composite materials with 87.5% accuracy, enhancing confidence in product reliability. Novel dielectric measurement technique integrated with machine learning algorithms enables prepreg age monitoring at room temperature.

Study on the penetration capability of GPR for the steel-fibre reinforced concrete (SFRC) segment based on numerical simulations and model test

The SFRC segment is gaining attention in shield tunnels due to their better mechanical properties. However, the existence of steel fibres poses a greater difficulty when attempting to utilize ground penetrating radar (GPR) for nondestructive quality inspection compared to conventional reinforced concrete segments. This paper presented a refined numerical model of SFRC segments with gprMax software, to investigate the penetration characteristics of GPR. The numerical model incorporated four different steel fibre contents, namely 0%, 0.5%, 2.5%, and 5%. The model also included an internal void and a surface metal plate to compare and analyze the waveform characteristics of GPR with the normal sections of the segment. The orthogonal test of numerical simulation investigates the imaging characteristics, power attenuation patterns, and imaging features of three different GPR frequency gradients: 200 MHz, 500 MHz, and 1000 MHz. This analysis helps in selecting the appropriate detection frequency based on the observed energy attenuation patterns and imaging characteristics. Then, the model test was conducted at the selected frequency to study the penetration performance of GPR. The research results indicate that the numerical model established in this paper demonstrates good effectiveness in studying the waveform characteristics of steel fibre-reinforced concrete under various conditions. The numerical model established in this paper realized the study of the penetration characteristics and imaging features of different steel fibre contents, GPR frequencies, and detection targets. Analysis of waveform characteristics, power attenuation, and imaging results indicates that GPR at 500 MHz exhibits better detection performance for SFRC segments. The results of the numerical simulation were verified through model tests of real SFRC segments, demonstrating that 500 MHz has good penetration performance for SFRC segments. This paper also proposed a calculation method for equivalent relative dielectric constant (ERDC) based on the model test results and validated its rationality through the analysis of numerical simulation results and real GPR waveforms.

Magnetic signal denoising based on auxiliary sensor array and deep noise reconstruction

Magnetic field measurement technologies are extensively applied in nondestructive testing, quality inspection, and fault diagnosis of machines and equipment. Magnetic signals are highly susceptible to noise interference, and how to effectively conduct signal denoising under strong noise disturbance remains a challenge. This study designs a novel auxiliary sensor array comprising center and distributed satellite sensors for magnetic noise reconstruction and signal denoising. The signals of the center and satellite sensors are highly correlative owing to the propagation of the magnetic noise through space. An attention-based long short term memory model is designed and trained to establish the mapping relationship among the center and satellite signals. The noise of the center sensor can then be reconstructed from those of the satellite sensors in the procedure of magnetic field measurement. Finally, the denoised signal of the center sensor is obtained by subtracting the noisy signal with the reconstructed noise. The effectiveness and superiority of the proposed method are validated by experiments and compared with the conventional methods. The designed auxiliary sensor array and algorithm are easily implemented with remarkable robustness, flexibility, and scalability, thereby showing greatly potential use for magnetic signal denoising in a variety of mechatronic systems.

Spatially Offset Raman Spectroscopy Combined with Attention-Based LSTM for Freshness Evaluation of Shrimp

Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The R2, RMSE, and RPD of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp.

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species.

3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts

X-ray computed tomography (X-CT) plays an important role in non-destructive quality inspection and process evaluation in metal additive manufacturing, as several types of defects such as keyhole and lack of fusion pores can be observed in these 3D images as local changes in material density. Segmentation of these defects often relies on threshold methods applied to the reconstructed attenuation values of the 3D image voxels. However, the segmentation accuracy is affected by unavoidable X-CT reconstruction features such as partial volume effects, voxel noise and imaging artefacts. These effects create false positives, difficulties in threshold value selection and unclear or jagged defect edges. In this paper, we present a new X-CT defect segmentation method based on preprocessing the X-CT image with a 3D total variation denoising method. By comparing the changes in the histogram, threshold selection can be significantly better, and the resulting segmentation is of much higher quality. We derive the optimal algorithm parameter settings and demonstrate robustness for deviating settings. The technique is presented on simulated data sets, compared between low- and high-quality X-CT scans, and evaluated with optical microscopy after destructive tests.

In-field and non-invasive determination of internal quality and ripeness stages of Feicheng peach using a portable hyperspectral imager

In-field and non-invasive determination of internal quality and ripeness stages allows for a selective harvest for Feicheng peach. In this study, a portable hyperspectral imager was used for on-site capturing the images of mid-ripe and ripe peaches on trees, and soluble solids content (SSC) and firmness of the fruit were measured as the reference standards. These samples were split into calibration set and validation set by samples set partitioning based on joint X–Y distances (SPXY) algorithm. Multiple linear regression (MLR) models were established using effective wavelengths selected by competitive adaptive reweighted sampling (CARS) and random frog (RF), respectively. The more promising performances were achieved by RF-MLR model with R v 2 of 0.88 and RMSEV of 0.54 for SSC, and CARS-MLR model with R v 2 of 0.81 and RMSEV of 1.17 for firmness. Furthermore, LIBSVM model was employed to discriminate the ripeness of Feicheng peach using effective wavelengths selected by sequential forward selection (SFS) algorithm, with classification accuracy of 91.7% in the validation set. It can be concluded that portable hyperspectral imager can be applied to determine the internal quality and ripeness stages of Feicheng peach in orchard, providing support for the in-field and nondestructive quality inspection and timely harvesting of Feicheng peach. • In-field capture of hyperspectral images of mid-ripe and ripe Feicheng peaches. • Establish the predictive models for soluble solids content and firmness. • Develop the discrimination model of Feicheng peaches ripeness stages.

Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System

In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LChuv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (huv) ranged from 68.92 to 96.61°, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system.

Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

To preserve the quality of fresh pear fruit after harvest and deliver quality fruit year-round a controlled supply chain and long-term storage are applied. During storage, however, internal disorders can develop due to suboptimal storage conditions that may not cause externally visible symptoms. This makes them impossible to be detected by current commercial quality grading systems in a reliable and non-destructive way. A combination of a Support Vector Machine coupled with a feature extraction algorithm and X-ray Computed Tomography is proposed to successfully detect internal disorders in ‘Conference’ and ‘Cepuna’ pear fruit nondestructively. Classifiers were able to distinguish defective from sound fruit with classification accuracies ranging between 90.2 and 95.1% depending on the cultivar and number of used features. Moreover, low false positive and negative rates were obtained, respectively ranging between 0.0 and 6.7%, and 5.7 and 13.3%. Classifiers trained on ‘Conference’ data were transferred effectively to the ‘Cepuna’ cultivar, suggesting generalizability to other cultivars as well. With continuing developments in both hardware and software to increase inspection speed and reduce equipment costs, the method can be implemented in industrial applications, e.g., inline translational X-ray CT.

Terahertz Quality Inspection for Automotive and Aviation Industries

Nondestructive quality inspection with terahertz waves has become an emerging technology, especially in the automotive and aviation industries. Depending on the specific application, different terahertz systems—either fully electronic or based on optical laser pulses—cover the terahertz frequency region from 0.1 THz up to nearly 10 THz and provide high-speed volume inspections on the one hand and high-resolution thickness determination on the other hand. In this paper, we present different industrial applications, which we have addressed with our terahertz systems within the last couple of years. First, we show three-dimensional imaging of glass fiber–reinforced composites and foam structures, and demonstrate thickness determination of multilayer plastic tube walls. Then, we present the characterization of known and unknown multilayer systems down to some microns and the possibility of measuring the thickness of wet paints. The challenges of system reliability in industrial environments, e.g., under the impact of vibrations, and effective solutions are discussed. This paper gives an overview of state-of-the-art terahertz technology for industrial quality inspection. The presented principles are not limited to the automotive and aviation industries but can also be adapted to many other industrial fields.

A convolutional approach to quality monitoring for laser manufacturing

The extraction of meaningful features from the monitoring of laser processes is the foundation of new non-destructive quality inspection methods for the manufactured pieces, which has been and remains a growing interest in industry. We present ConvLBM, a novel approach to monitor Laser Based Manufacturing processes in real-time. ConvLBM uses a Convolutional Neural Network model to extract features and quality indicators from raw Medium Wavelength Infrared coaxial images. We demonstrate the ability of ConvLBM to represent process dynamics, and predict quality indicators in two scenarios: dilution estimation in Laser Metal Deposition, and location of defects in laser welding processes. Obtained results represent a breakthrough in the 3D printing of large metal parts, and in the quality control of welding processes. We are also releasing the first large dataset of annotated images of laser manufacturing.

Large-scan-area sub-terahertz imaging system for nondestructive food quality inspection

A subwavelength transmission imaging system based on Bessel–Gauss beam focusing in the 140 GHz frequency band (λ = 2.14 mm) was constructed for food quality inspection purposes. The system employed a low-cost beam source for imaging via mechanical raster scanning, and the high resolution (0.75 λ) focusing module had an extended depth of focus (12.6 λ) and supported a large scan area (800 × 400 mm2, 30 min scan time). The performance of the proof-of-concept system was nondestructively investigated by using a variety of food products, such as chocolate bars, dried laver, red ginseng, and walnuts.

Data processing approaches and strategies for non-destructive fruits quality inspection and authentication: a review

Fruit quality inspection and authentication instruments are the essential requirement at the different stages of fruit processing industries from harvesting to market. In recent years, various intelligent analytical methods such as electronic nose, gas chromatography and mass spectroscopy, UV–Vis–NIR spectroscopy, machine vision, hyperspectral imaging and many more have been evolved to access the fruit quality at different stages such as maturity judgement of an on-tree fruit, shelf life measurement of harvested fruit, other quality parameters measurement of various fruit products at processing industries etc. Information extracted from various analytical methods needs to be processed using different data processing approaches and strategies, which plays the major role to bring the intelligence in the analytical instruments. Although, highly promising results have been reported to process data acquired from similar type of sensory panel (gas sensor array in electronic nose) and single sensing technique (impedance measurement) but still there are several challenges to process data acquired from multiple sensing techniques fusion (similar or complementary in nature) to predict better informative results. Recently, there is a growing interest in the direction of multiple sensing techniques fusion to extract better information from fruit samples in a reliable manner and also in less time. This paper presents an extensive review of classical and modern data processing approaches and strategies that have been used for single and multiple non-destructive sensing methods in the area of fruit quality inspection and authentication. Various approaches and strategies for preprocessing, data fusion, feature extraction, model design, multi-modal data processing, training, testing and validation for single and multiple sensing techniques have been briefly explained in the presented review. The presented review also discusses the need, scope, and challenges of data processing methods for multiple sensing techniques fusion. Different commercially available handheld and lab level analytical instruments also have been reviewed based on their intelligence, complexity and quality parameters prediction.

In-line NDT with X-Ray CT combining sample rotation and translation

An alternative acquisition geometry for X-ray computed tomography (CT) is investigated as a solution to in-line non-destructive quality inspection in a high throughput production environment. The sample movement during acquisition combines a translation, typically horizontal, along one axis and a rotation about a second axis perpendicular to the first, and is shown to produce theoretically exact CT reconstructions. A methodology is presented to evaluate the design of a conveyor belt implementation for this acquisition scheme, investigating the trade-off between reconstruction quality and throughput. The methodology was applied in both a simulated version and an experimental mock-up of the conveyor belt implementation for a specific food sample, but can be extrapolated to any type of sample. Throughput, for the food sample, is predicted to be in a practically usable range of up to 5 samples per second. As a general conclusion, higher throughput can be reached with larger inspection stations while maintaining image quality.

New technology for measuring resistance of electrical contact and estimating welding quality

Incomplete penetration is a common defect in the welding gap of large size (welding area is over 100 mm2) electrical contact, which leads to an instability of welding quality. Nevertheless, there was limited research dealing with non-destructive quality inspection of electrical contact. In this research, the resistance of CJ400 electrical contact was measured with the device medium frequency inverter resistance spot welding (MFI-RSW) machine. Multipulse measuring method was designed and proved efficient to reach a higher accuracy by restraining the influence of the contact resistance between electrodes and electrical contact. Moreover, the error of resistance caused by temperature rising in the measuring process is also eliminated effectively. Therefore, more accurate resistance of electrical contact could be extracted compared to the method of single pulse measuring. Then, the relationship between resistance and welding quality was studied and a new quality inspection method was proposed.

A vision-based system for post-welding quality measurement and defect detection

The measurement of the welding bead and the followed evaluation of the weld quality is an important part in the industrial welding. However, the main difficulty in using visual inspection to evaluate the weld quality is the time taken and labor wasted. In order to solve these problems, an automated vision-based system is presented for non-destructive and online post-welding quality inspection. The trouble with respect to vision-based system is the robustness of image processing algorithms. This paper presents a novel image processing method that can automatically extract the weld joint profile and feature points, measure the weld bead size, and detect defects. At the same time, the three-dimensional (3D) profile of the weld surface can be reconstructed with the aim of monitoring the weld quality online. The proposed algorithm is validated through experiment in industrial environment.

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

Spectroscopic Techniques for Nondestructive Quality Inspection of Pharmaceutical Products: A Review

, 2015Spectroscopy is an emerging technology for the quality assessment of pharmaceutical samples, from tablet manufacturing to final quality assurance. The traditional methods for the quality management of pharmaceutical tablets are time consuming and destructive, while spectroscopic techniques allow rapid analysis in a non-destructive manner. The advantage of spectroscopy is that it collects both spatial and spectral information (called hyperspectral imaging), which is useful for the chemical imaging of pharmaceutical samples. These chemical images provide both qualitative and quantitative information on tablet samples. In the pharmaceutics, spectroscopic techniques are used for a variety of applications, such as analysis of the homogeneity of powder samples as well as determination of particle size, product composition, and the concentration, uniformity, and distribution of the active pharmaceutical ingredient in solid tablets. This review paper presents an introduction to the applications of various spectroscopic techniques such as hyperspectroscopy and vibrational spectroscopies (Raman spectroscopy, FT-NIR, and IR spectroscopy) for the quality and safety assessment of pharmaceutical solid dosage forms. In addition, various chemometric techniques that are highly essential for analyzing the spectroscopic data of pharmaceutical samples are also reviewed.Keywords: Applications, Chemometrics, Hyperspectroscopy, Pharmaceutical, Vibrational spectroscopy