Non-contact Method Research Articles

Importance of zero cross-point (Cp = 0) control between the vacancy and interstitial Si atom concentration curves to obtain a perfect Si ingot using Si melt growth

The cross-point (Cp), which appears in the vacancy (CV) and interstitial Si atom (CI) concentration curves, is a simple controlling factor for ingot growth and provides a final goal to obtain a defect-free Si ingot using general Si melt growth. The importance of Cp is demonstrated using the noncontact crucible method (NOC), which has a relatively small temperature gradient due to an ingot grown inside the melt. The control of temperature gradient G at a constant growth rate v is simple for the actual ingot growth; thus, the present simulated results are expressed by G at a constant v. The concentration at the Cp point becomes smaller as G becomes smaller and finally becomes zero (Cp = 0), in which CV and CI are practically zero (CV=CI=0). The estimated G and temperature T at Cp = 0 for v = 0.000144 cm s−1 are 6.3, 12.5, and 18.5 K cm−1 and 1216 K (943 °C), 1354 K (1081 °C), and 1371 K (1098 °C), corresponding to each combination. At 1216 K, G should be precisely controlled within 6.3 ± 3 % K cm−1 in the region substantially close to Cp = 0 to maintain the remaining concentration of point defects substantially lower than 1.0 × 1013 cm−3. G should be changed during growth from larger G to smaller G, such as 6.3 K cm−1, to efficiently grow the ingot within the limited time. The changing point, x of G, is an important key parameter for the timing variation, where x denotes the normalized distance from the growing interface. The initial G is large; thus, the changing point of G approaches the growing interface. The growth condition necessary to obtain a defect-free Si ingot can be demonstrated using the Cp = 0 point, which is fully compatible with the perfect critical point where CV and CI are practically zero. The present results are mainly considered using the CV and CI concentration curves. However, these results largely help understand the approximate trend of G and v for the growth of defect-free ingot and the relation between Cp and the commonly used critical point between CV and CI.

Deep learning-assisted multispectral imaging for early screening of skin diseases

IntroductionMelanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This process is labor-intensive and resource-consuming. Traditional methods for diagnosing skin lesions rely heavily on the subjective judgment of dermatologists, leading to issues in diagnostic accuracy and prolonged detection times. ObjectivesThis study aims to introduce a multispectral imaging (MSI)-based method for the early screening and detection of skin surface lesions. By capturing image data at multiple wavelengths, MSI can detect subtle spectral variations in tissues, significantly enhancing the differentiation of various skin conditions. MethodsThe proposed method utilizes a pixel-level mosaic imaging spectrometer to capture multispectral images of lesions, followed by reflectance calibration and standardization. Regions of interest were manually extracted, and the spectral data were subsequently exported for analysis. An improved one-dimensional convolutional neural network is then employed to train and classify the data. ResultsThe new method achieves an accuracy of 96.82 % on the test set, demonstrating its efficacy. ConclusionThis multispectral imaging approach provides a non-contact and non-invasive method for early screening, effectively addressing the subjective identification of lesions by dermatologists and the prolonged detection times associated with conventional methods. It offers enhanced diagnostic accuracy for a variety of skin lesions, suggesting new avenues for dermatological diagnostics.

Relating the ultrasonic and aerosol filtration properties of filters

Non-contact methods are useful to improve the quality control of particle filtration media. The purpose of this paper is to investigate the correlation between the filtration efficiency of a porous sheet and its ultrasonic properties obtained using a non-contact technique. An air-coupled ultrasonic technique is used to obtain rapid measurements without affecting the integrity of the material. High frequencies (from 0.1 to 2.5 MHz) are used to improve technique sensitivity, and transmitted waves are measured to probe the internal properties of the material. Measurements of transmission coefficient spectra (amplitude and phase) and the corresponding ultrasound velocity and attenuation coefficient at different frequencies are obtained for a set of filtration media with well-characterized properties. Results show that the ultrasonic properties of filtration media vary as a function of basis weight, and therefore filtration efficiency, for a given charge state. However, the effect of electrostatic charge on ultrasonic propagation is almost negligible, as expected. We conclude that ultrasonic transmission may provide a valuable tool for the continuous online monitoring of material quality during fabrication and as a method to tease apart mechanical and electrostatic contributions to particle filtration.

DiffPhys: Enhancing Signal-to-Noise Ratio in Remote Photoplethysmography Signal Using a Diffusion Model Approach.

Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce signals with an inadequate signal-to-noise ratio (SNR) for reliable vital sign measurement due to artifacts like head motion and measurement noise. Another pivotal factor is the overlooking of the inherent properties of signals generated by rPPG (rPPG-signals). To address these limitations, we introduce DiffPhys, a novel deep generative model particularly designed to enhance the SNR of rPPG-signals. DiffPhys leverages the conditional diffusion model to learn the distribution of rPPG-signals and uses a refined reverse process to generate rPPG-signals with a higher SNR. Experimental results demonstrate that DiffPhys elevates the SNR of rPPG-signals across within-database and cross-database scenarios, facilitating the extraction of cardiovascular metrics such as HR and HRV with greater precision. This enhancement allows for more accurate monitoring of health conditions in non-clinical settings.

The study of interface quality in HfO2/Si films probed by second harmonic generation

Time-dependent second harmonic generation (TD-SHG) is an emergent sensitive and non-contact method to qualitatively/quantitively characterize the semiconductor materials, which is closely related to the interfacial electric field. Here, the TD-SHG technique is used to study the interface quality of atomic layer deposited 15 nm HfO2/Si (n-type/p-type) samples, which is compared to the conventional electrical characterization method. A relation between the interface state density and the time constant extracted from TD-SHG is revealed, indicating that TD-SHG is an effective method to evaluate the interface state density. In addition, the dopant type and dopant density can be disclosed by resolving the dynamic process of TD-SHG. The scenario of interfacial electric field between the initial electric field and the laser-induced electric field is proposed to explain the time-dependent evolution of SHG signal. In conclusion, the TD-SHG is a sensitive and non-contact method as well as simple and fast to characterize the semiconductor materials, which may facilitate the semiconductor in-line testing.

Debonding inspection with an enhanced miniature EMAT based on multiple pulse echoes

The present paper proposes a non-contact ultrasonic testing method for the debonding inspection of metal-based composite structures, utilizing an improved electromagnetic acoustic transducer (EMAT). The enhanced EMAT incorporates a carbonyl iron powder coil backplate and a small butterfly coil to enhance the detecting ability for interfacial bonding. A two-dimensional finite element model is developed to quantify the effectiveness of the carbonyl iron powder coil backplate on increasing the eddy current density and the magnetic flux density in the specimen. An experimental setup was designed using the Ritec SNAP 5000 and the improved EMATs to examine an aluminium-epoxy resin composite structure. Ultrasonic C-scan images of the specimen's bonding interface were obtained from the experiment. Both simulation and experimental results show that carbonyl iron powder coil backplates can increase the efficiency of the EMATs. By investigating the influence of echo number on debonding detection, it was revealed that employing higher-order echoes and utilizing carbonyl iron powder as the coil backplate can enhance the debonding detection capability. The C-scan imaging results indicate that the utilization of high-order echoes enhances imaging quality, and the employment of high-quality miniaturized EMATs enhances the capability for quantitative analysis of debonding defects.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy (LIBS) is investigated experimentally. The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness, ranging from 0 to 4.4 mm, by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm. It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases, implying that the method investigated here is feasible. It is also found that, for achieving the inspection of surface flatness within such a wide range, when univariate analysis is applied, a piecewise calibration model must be constructed. This is due to the complex dependence of plasma formation conditions on the surface flatness, which inevitably complicates the inspection procedure. To solve the problem, a multivariate analysis with the help of Back-Propagation Neural Network (BPNN) algorithms is applied to further construct the calibration model. By detailed analysis of the model performance, we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Using unsupervised learning based convolutional neural networks to solve Digital Image Correlation

Digital Image Correlation (DIC) is a robust, non-contact method for full-field deformation measurement widely used in photomechanics. Based on subset image matching and an iterative optimization architecture, traditional DIC deformation field analysis requires manual selection of such calculation parameters as subset size, rendering it challenging to balance spatial resolution and measurement resolution in non-uniform field measurements. The requirement of manual parameter adjustment is lowered by a recently emerged supervised learning-based neural network DIC method, as it establishes a direct correlation between images and deformation fields using deep neural networks (DNN). However, its generalization and accuracy are contingent on the scope and variety of the dataset because it requires training with a large number of simulated speckle images of deformation fields with known standards. This paper proposes an unsupervised learning neural network based DIC measurement method that circumvents the need for standard deformation fields and achieves high precision analysis of deformed fields. It constructs a loss function based on the photometric consistency assumption of speckle images before and after deformation and allows the neural network model to directly extract displacement information from the gray data of the “reference image-deformed image” pair. Meanwhile, the model is enhanced accordingly by further introducing displacement continuity constraints to enhance accuracy and mitigate noise interference. Validation through both simulated and real experiments shows that the proposed approach surpasses traditional subset DIC in measuring non-uniform deformation fields with higher precision. It also offers superior flexibility and adaptability over supervised learning-based neural network DIC methods by eliminating the need for a large training dataset, allowing for immediate application in displacement field analysis.

Assessment of wound dimensions using a smartphone-based application - Imitomeasure in wound assessment

Dimensions of wounds can be measured using several methods such as a photograph, a comparison, a ruler, or a graph. Current generation smartphones have high quality in-built cameras, and they are easily accessible than before and have become affordable. Making use of certain applications built for these smartphones have helped achieve various benefits in medical field. A software called Imitomeasure has been developed for the purpose of measuring wound dimensions without touching the wound. This app has the advantage over clinical measurement using rule or tape, in that, it is a non-contact method, hence less chances of spreading infection to that patient. The effectiveness of Imitomeasure has been assessed, and it has been found to be a very effective application in measuring wound dimensions. In this study, the size of wounds over gluteal region has been measured using Imitomeasure application in a smartphone.

Measurement method of spherical radius size of differential planetary gear tooth surface based on 3D vision

Abstract In order to accurately measure the spherical radius dimensions of planetary gear tooth surfaces, we propose a non-contact measurement method based on 3D vision technology. Firstly, the point cloud data of the planetary gear captured by the laser 3D profiler is preprocessed, and the tooth surface point cloud is extracted. Next, based on the structural characteristics of the planetary gear tooth surface, a uniform slicing and sampling algorithm is used to extract circular contour points where the tooth surface point cloud intersects with the slicing planes. These points are then projected onto a two-dimensional plane, where an improved RANSAC algorithm is used to precisely fit each circular cross-section. Contour points associated with significant fitting errors are removed based on a set tolerance value. Finally, an improved differential evolution algorithm is used to perform a three-dimensional spherical fitting on the contour points that meet the tolerance criteria, achieving accurate measurement of the spherical radius of the planetary gear tooth surfaces. Experimental results demonstrate that the repeatability error of the proposed method is ±0.02 mm, with a maximum absolute error of 0.026 mm and an average measurement time of 4.76 s. The method exhibits high robustness and measurement accuracy, meeting practical engineering measurement requirements.

Efficient Vibration Measurement and Modal Shape Visualization Based on Dynamic Deviations of Structural Edge Profiles.

As a non-contact method, vision-based measurement for vibration extraction and modal parameter identification has attracted much attention. In most cases, artificial textures are crucial elements for visual tracking, and this feature limits the application of vision-based vibration measurement on textureless targets. As a computation technique for visualizing subtle variations in videos, the video magnification technique can analyze modal responses and visualize modal shapes, but the efficiency is low, and the processing results contain clipping artifacts. This paper proposes a novel method for the application of a modal test. In contrast to the deviation magnification that exaggerates subtle geometric deviations from only a single image, the proposed method extracts vibration signals with sub-pixel accuracy on edge positions by changing the perspective of deviations from space to timeline. Then, modal shapes are visualized by decoupling all spatial vibrations following the vibration theory of continuous linear systems. Without relying on artificial textures and motion magnification, the proposed method achieves high operating efficiency and avoids clipping artifacts. Finally, the effectiveness and practical value of the proposed method are validated by two laboratory experiments on a cantilever beam and an arch dam model.

Experimental analysis on the progressive failure mechanism of soft rock subjected to tunnel excavation

The failure of the soft rock can seriously threaten the construction and operation of tunnels, which may suffer from larger deformation and instability. The knowledge of the failure process and mechanism of soft rock can help to optimize the tunnel design and construction. In the present paper, a large-scale physical model test with the use of both contact and non-contact measurement methods was conducted to investigate the progressive failure mechanism of soft rock. The results show that a notable pattern of progressive failure was observed in the soft rock, whereby failure initiates in the proximal regions and propagates to the distal regions within the surrounding rock. The occurrence of the failure intensifies the deformation of the soft rock, and the tunnel suffers from the cross-section flattening finally. The effectiveness of the non-contact measurement method was accessed by the comparison of the strain measurements between embedded sensors and digital photogrammetry analysis. Subsequently, the radius of the failure zone determined by the stress distribution and mechanical analysis is between two and three times the radius of the tunnel. The results of model tests were then verified by numerical analysis. Eventually, the failure modes were evaluated by local relative deformation. The shear failure takes place in the tunnel inverted arch which suffers from large shear stress, while compression failure appears on the tunnel sidewall, and the tension failure occurs at the top of the tunnel arch.

Computer vision–based surface defect identification method for weld images

Welding is the main connection type used in steel structure bridges. As the weld quality significantly affects the performance of a structure, weld defect detection is crucial in the performance evaluation of bridges. With the continuous development of computer vision (CV) techniques and image acquisition equipment, CV-based methods are being increasingly applied to weld defect detection. However, accurate identification of welding defects from several images is challenging. This paper proposes a CV-based method for detecting and quantifying surface porosity defects in welds. CV techniques were used to identify and extract weld defects, and the pixel-level sizes of the weld defects were obtained. The actual sizes of the weld defects were calculated based on a conversion between the image size and actual size. The results show that the proposed image processing method has low complexity and high accuracy in detecting weld defects, providing accurate measurement of the defect size. It is a fast and non-contact detection method for weld defect detection.

Comparison and correction on calculation methods of clothing insulation based on thermal comfort

Clothing insulation is a major factor affecting human thermal comfort, and it is usually a fixed value when the clothing ensemble are determined in the standard. However, real clothing insulation is considerably affected by the environment and activity level (metabolic rate). Thus, the heat flow method for obtaining clothing insulation through thermal equilibrium between the skin layer and environment was proposed. Later, a non-contact method for easy acquisition of clothing insulation was proposed. The method was implemented via infrared imager. In this paper, above methods were compared and analysed based on experiment data. Three activity levels were considered, sedentary (1.0 met), walking at speed of 0.8 m/s (1.8 met), and 1.2 m/s (2.6 met). The feasibility and accuracy of above methods are discussed by comparing the PMV calculated by the clothing insulation with the actual thermal sensation (TSV) of the subjects obtained by questionnaire. The results showed that the non-contact method exhibited the highest prediction accuracy. Due to the sweating effect on skin temperature, the higher the activity levels, the lower the prediction accuracy. In this paper, a correction method of clothing insulation was proposed by using the temperature of face and neck and skin wettedness. After correction, the root means square error between the PMV and TSV decreased by 11 %.

Enhanced interlayer strength in 3D-printed PA12 composites via electromagnetic induction post-processing

Weak interlayer strength remains a critical limitation in the application of material extrusion 3D-printed parts. Herein, a non-contact method, electromagnetic induction post-processing, is proposed to address this issue. This method aims to enhance the weak interlayer strength of 3D-printed PA12 samples by introducing Fe3O4 particles for induced heat generation under alternating magnetic fields. The heat generation characteristics, surface morphology, crystallization behavior, and interlayer performance of 3D-printed samples subjected to induction post-processing are systematically investigated. The results indicate that a remarkable improvement in the interlayer strength of 3D-printed PA12 samples can be achieved through induction post-processing. Tensile strength along the Z-direction (perpendicular to the printing plane) exhibits a distinct increase of 218.3 %, while interlayer tear strength can rise by 328.1 %. Improvement in interlayer strength results from heat generation driving interfacial resin diffusion. Additionally, induction post-processing results in localized surface smoothing and a slight reduction in the crystallinity of the 3D-printed sample. Compared to traditional methods like in-oven heat treatment (annealing), induction post-processing offers higher efficiency by circumventing the initial constraints of heat transfer. Furthermore, the concept of selective induction heating (SIH) is proposed to achieve non-contact, localized heating by adjusting the composition of thermoplastics during printing. SIH holds good promise for applications in 3D-printed continuous carbon fiber-reinforced thermoplastic composites, including mold-free forming, complex surface welding, and repair (particularly in narrow and inaccessible areas).

Vector-autoregressive based vibration monitoring of wind energy turbines using laser scanner profile measurements

Structural health monitoring of wind energy turbines (WET) is crucial due to the potential presence of an imbalanced rotor, leading to adverse centrifugal forces. In this research, non-contact vibration monitoring of a WET is conducted using a terrestrial laser scanner (TLS) of the type Zoller+Fröhlich IMAGER 5016 in a two-dimensional (2D) profile mode, and with a rotation speed of 55 revolutions per second. The TLS profile measurements cover the entire pillar up to a height of 100 meter. Therefore, the challenges imposed by conventional measurement systems, such as accelerometers or inductive displacement transducers, are tackled through the high spatial resolution of the TLS measurements and non-contact measurement methods. Additionally, both time and cost are reduced regarding the sensor installation. In general, the WET is measured in two different directions to account for significant movements, both in the direction of the wind and perpendicular to it. To initiate the analysis, time series are generated from the profile measurements for various positions covering the entire pillar. A robust time-domain modal parameter identification approach based on the Vector-autoregressive (VAR) process with multivariate t-distributed random deviations (VAR-RT-MPI) is proposed to estimate modal parameters, including eigenfrequencies, eigenforms, and modal damping. Besides, it allows estimating unknown auto- and cross-correlation coefficients of the VAR process, the cofactor matrix, and the degrees of freedom of the t-distribution. The VAR-RT-MPI algorithm enables to jointly estimate the eigenfrequencies and damping ratio coefficients considering multiple time series. Additionally, the spatio-temporal model of the pillar is characterised based on the estimates of the amplitudes (in a submillimetre range) and phase shifts at different positions. Vibration monitoring was conducted on a WET located in Hannover, Germany, and the results were compared with a well-known covariance driven stochastic subspace identification (SSI-COV) approach.

Optically controllable deformation and phase change in VO2/Si3N4/Au hybrid nanostructures with polarization selectivity

Optically spatial displacement and material modification hold great potential for the appealing applications in nanofabrication and reconfiguration of functional optical devices. Here, we propose and demonstrate a scheme to achieve simultaneous deformation and phase change in vanadium dioxide (VO2)/Si3N4/Au hybrid nanostructures by laser stimuli. Low triggering threshold and significant deformation characteristics of VO2, based on controllable phase transition, are demonstrated in microscale cantilevers. The plasmonic properties of the nanostructure array are further utilized to achieve a polarization-selective dynamic response. The persistence of deformation and dynamical optical modulation are further demonstrated. Such high-precision fabrication methods and non-contact reconfiguration methods are useful for future applications in dynamic optical manipulation.

SPECIFICS OF DESIGNING AN INFRARED PYROMETER-REFLECTOMETER FOR SEMICONDUCTOR HETEROSTRUCTURE FABRICATION

There are general technical requirements for all types of reactors for chemical vapour deposition technology using AIII- BV metalorganic compounds. Among them, it is worth highlighting the large temperature gradients that cause the origin of convection loops, which in turn, taking into account the high speed of the gas flow, lead to turbulence in the reactor instead of the expected laminar flow. It is also important to take into account the change in parameters of the wafer surface during the growth process and the need for signal separation between the useful signal from the wafer surface and the background signal from the wafer carrier, which rotates at fixed speed for uniform deposition of compounds. To obtain high-quality heterostructures with reproducible parameters, it is important to have a system of precise temperature control on the wafer surface directly in the deposition area, since the deposition process for many complex semiconductor devices (for example, laser diodes, LEDs, photodiodes, transistors on heterojunctions) is very sensitive to temperature changes. The method of optical pyrometry is a non-contact method that allows to precisely determine the temperature of the wafer surface and meets the technical requirements of CVD epitaxy growth reactors. This article is devoted to the analysis of the features of the development of optoelectronic systems for precise temperature measurement during epitaxial growth in order to determine the criteria for the selection or development of components of the optoelectronic system of the pyrometer-reflectometer. The main physical processes, electro-optical characteristics of Si photodiode, AlGaAs/GaAs LED and parameters of bandpass interference filters were investigated. Based on the analysis of the obtained research and measurement results, scientific recommendations have been developed. The recommendations aimed at the selection and optimization of the parameters of the components of the pyrometer-reflectometer (photodetectors, light emitting diodes, optical filters) in order to improve the accuracy and temperature stability of measurements in the pyrometer’s operation conditions, which take into account the compensation of emissivity change from the surface of the wafer.

Energy Efficiency Measurement of Mechanical Crushing Based on Non-Contact Identification Method

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing.

One-shot self-calibrating phase-shifting interferometry by direct normalization of interferograms

Dynamic phase measurement is a valuable technique applied across various scientific and technological domains due to its high accuracy, non-contacting method, and numerous practical applications. In this manuscript, we present a phase retrieval method capable of measuring the object’s phase in real-time. Our proposal is based on a double aperture common-path interferometer adapted for polarization modulation in the input windows, enabling the superposition of two fields with orthogonal circular polarizations. Two interferograms and two intensities are captured in a single shot, showcasing the possibility of obtaining two normalized interferograms directly and straightforwardly. This allows for the derivation of expressions for phase recovery and, additionally, the determination of the arbitrary phase step. We demonstrate that this method is easy to implement, highly accurate, and sufficiently fast for real-time applications. To validate the effectiveness of the method, we conduct numerical error analysis and laboratory experiments.