•Non-destructive evaluation techniques in measurement applications require transferring complex signals to usable quantities.•Equivalent circuit models for eddy current sensors evaluated for their ...voltage transfer functions are those of lossy transformers.•Splitting transformer coupling and eddy-current losses enables simple models, which can be automatically constructed.•Implementation of parameter identification in a real-time soft sensor is possible.•This soft sensor can be used to measure material properties in a repeatable fashion.
The use of eddy-current techniques as a measurement method for material characterization instead of simple fault detection requires transformation of the electrical response signals to quantifiable physical parameters that correlate with material properties. Apart from calibration-curve methods, exact approaches that correctly represent all interactions between excitation field and response field are computationally intensive and not well suited to real-time application. In this work, an equivalent circuit network model is constructed and its analytical equation for the voltage transfer function is derived. This model is then implemented in a soft sensor system. Finally, measurement results from various materials with different electromagnetic properties are presented.
The global fruit industry is continually confronted with new technological challenges to meet people's material quality of life expectations. Fruit maturity has a strong association with the ...receiving time, transportation technique, and storage method of the fruit, and it has a direct impact on the fruit's quality. How to undertake rapid and non-destructive fruit quality testing has become a prominent topic in recent years. Because of its high repeatability, ease of operation, pollution-free, and measurement stability, visible and near-infrared (Vis/NIR) spectroscopy has become the most advanced non-destructive quality assessment technique in terms of equipment, applications, and data analysis methods in the field of non-destructive monitoring. An overview of the use of Vis/NIR optical biosensors in fruit internal quality monitoring and variety identification is presented. The benefits and drawbacks of various types of optical biosensors, as well as the practicality of various measurement modalities, are explored. Commonly used spectral biosensor data processing methods are summarized, including preprocessing, variable selection, calibration, and validation. Finally, the transition of pricey handheld NIR equipment to more cost-effective photodiode-based fruit maturity estimate devices was indicated as an issue for further investigation.
•Vis/NIR optical biosensors applications for fruit quality monitoring were presented.•Spectral biosensor data processing methods were summarized.•More economical photodiode fruit maturity assessment equipment was presented.•Optical biosensors characteristics and practiaclly measurement modes were explored.
•Lamb wave propagation method can be used for the detection of matrix cracking.•The Lamb wave velocity and amplitude decreased with matrix crack density.•Artificial intelligence methods helped the ...classification of matrix cracking.•Employing the linear discriminant analysis enhanced the classification accuracy.
The guided wave propagation and artificial intelligence (AI) approaches were used to propose an intelligent model for automatic detection and classification of the matrix cracking in composites. Glass/epoxy cross-ply laminated composites were fabricated and the matrix cracking with several densities was induced in 90° layers. A non-destructive testing procedure using the fundamental antisymmetric Lamb wave propagation was performed on the intact specimens and those with 0.05, 0.15, and 0.25 matrix cracking density. The velocity of propagated Lamb wave, wave amplitudes in four different distances from the actuator, and the ratio of these amplitudes to the first received wave amplitude, in three frequencies of 100, 200, and 330 kHz were extracted from the acquired signals. The extracted sensory features were used to train three types of supervised machine learning models for the crack density classification. The linear discriminant analysis (LDA) was performed for dimensional reduction to find a linear combination of features that can better discriminate the classes. Support vector machines (SVM), linear vector quantization (LVQ) neural network (NN), and multilayer perceptron (MLP) NN were used for the classification. It was shown that SVM accounted for the highest classification accuracy (91.7%) followed by LVQ NN (88.9%) and MLP NN (77.8%), respectively.
•Non-destructive measurement of volumetric microstructure evolution is demonstrated.•Bulk grain evolution with strain is examined in ∼5000 3D tracked grains.•Orientation gradients are strongly ...correlated with grain size.•Lattice reorientation suggests substantial influence of local neighborhood.•3D evolution data is invaluable for input/validation of crystal plasticity models.
We present a non-destructive in-situ measurement of three-dimensional (3D) microstructure evolution of 99.995% pure polycrystalline copper during tensile loading using synchrotron radiation. Spatially resolved three-dimensional crystallographic orientation fields are reconstructed from the measured diffraction data obtained from a near-field high-energy X-ray diffraction microscopy (nf-HEDM), and the evolution of about 5000 3D bulk grains is tracked through multiple stages of deformation. Spatially resolved observation of macroscopic texture change, anisotropic deformation development, and the correspondence of different crystallographic parameters to defect accumulation are illustrated. Moreover, correlations between different crystallographic parameters, such as crystal rotation evolution, short- and long-range orientation gradient development, microstructural features, and grain size effects are investigated. The current state of data mining tools available to analyze large and complicated diffraction data is presented and challenges associated with extracting meaningful information from these datasets are discussed.
The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations ...of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.
•The state-of-the-art of DL-based ultrasonic NDE is reviewed.•The axioms that DL methods should satisfy to be fully applicable to NDE are identified.•A series of automation levels are proposed as a potential NDE automation roadmap.•Key remaining challenges are noted for each automation level.
This study delves into the impact of voids within CFRP materials on dark-field images obtained through an X-ray Talbot-Lau interferometer (TLI). Detecting voids in CFRP holds excellent significance ...because these voids can significantly influence material properties, such as strength and elastic modulus. TLI presents a promising approach for void detection, given its ability to capture a wide area rapidly and effectively identify voids. However, the relationship between voids and dark-field images has been inadequately assessed. Quantitative assessment of the impact of voids on the dark-field signal is imperative to predict the characteristics of voids in CFRP based on dark-field images. This study introduced unimpregnated areas, representative of voids in CFRP, within the material. Subsequently, we photographed the CFRP using TLI and verified that these unimpregnated areas influenced the dark-field signal. This result was used to thoroughly investigate the relationship between the geometric properties of unimpregnated areas and the extent of their impact on the dark-field signal. Our findings revealed a correlation between the geometric properties of these void-like areas and the magnitude of their effect on the dark-field signal.
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