Non-contact Measurement Research Articles

Recent developments in time domain NIRS for non-destructive fruit quality assessment: non-contact measurements and device miniaturization

Recent developments in time domain NIRS for non-destructive fruit quality assessment: non-contact measurements and device miniaturization

Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry.

Monitoring oxygen saturation ( ) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions. We aim to develop and validate a contactless measurement approach using 3D convolutional neural networks (3D CNN) and 3D visible-near-infrared (VIS-NIR) multimodal imaging, to offer a convenient, accurate, and robust alternative for monitoring. We propose an approach that utilizes a 3D VIS-NIR multimodal camera system to capture facial videos, in which is estimated through 3D CNN by simultaneously extracting spatial and temporal features. Our approach includes registration of multimodal images, tracking of the 3D region of interest, spatial and temporal preprocessing, and 3D CNN-based feature extraction and regression. In a breath-holding experiment involving 23 healthy participants, we obtained multimodal video data with reference values ranging from 80% to 99% measured by pulse oximeter on the fingertip. The approach achieved a mean absolute error (MAE) of 2.31% and a Pearson correlation coefficient of 0.64 in the experiment, demonstrating good agreement with traditional pulse oximetry. The discrepancy of estimated values was within 3% of the reference for of all 1-s time points. Besides, in clinical trials involving patients with sleep apnea syndrome, our approach demonstrated robust performance, with an MAE of less than 2% in estimations compared to gold-standard polysomnography. The proposed approach offers a promising alternative for non-contact oxygen saturation measurement with good sensitivity to desaturation, showing potential for applications in clinical settings.

Multi-verse optimizer for thermal error modeling approach of spindle system based on thermal image

Since the spindle thermal error of CNC machine tools has a significant impact on machining precision, this paper introduces a unique approach for modeling spindle thermal error. Several key steps are involved in the proposed approach. First, the Fluke thermal imaging camera is employed for acquiring thermal image information of the spindle system. Second, the Gaussian filter is employed to denoise the thermal image sequence. Next, the temperature values at the measurement points are extracted from the thermal image sequence according to the mapping relationship between the grayscale value and the temperature value. Subsequently, critical temperature points are identified from thermal images using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the correlation coefficient method. Finally, the multi-verse optimized NARX neural network is employed to investigate the nonlinear prediction of thermal deformation. The research is conducted on the VMC-850E vertical machining center as the subject of study. The performance of the model is validated under conditions of idle spindle and 5000 r/min, comparing prediction results against traditional algorithms. The findings demonstrate that the non-contact measurement method based on thermal imaging successfully establishes the thermal error model, achieving a prediction accuracy of 0.1517 μm for the MVO-NARX model.

Magnetic and acoustic sensors in the age of digitalization for industrial production

The challenges of digitalization are giving rise to new generations of measurement technology. This does not necessarily involve the use of new physical principles; rather, the importance of handling and generating valuable data has increased. We present a new measuring system that samples and digitizes analogue signals at 50 MHz with 24-bit conversion. Every 43 µs, a Fourier step is generated for 1,024 data points via an FPGA-based measurement card. The resulting spectral data diagram can then be electronically filtered to improve the signal-to-noise ratio. With this procedure, further possibilities have been created to improve data analysis and data handling, one of which is a parametric pattern recognition, which transfers selectable signals to a library based on the spectral data diagram and searches for them in future data using a similarity algorithm. The use of Python is another building block. With Python, machine learning methods can be transferred from training directly to the measurement system and can run there directly using the extensive library standards. Sensor-fusion is a module that allows analog data to be analyzed via the measurement card, but at the same time also links digital data with analog data by connecting DAQ modules from a third source as virtual ports and transmitting values to shared databases. This article shows the application of the described measurement concept using magneto-inductive sensors and piezoelectric sensors. Magnetic sensors react sensitively to material changes and can reliably determine mechanical parameters such as hardness, residual stresses and grain size. The mentioned software functions enable non-contact measurement of ferromagnetic materials at high throughput speeds. So far limited to laboratory applications, magnetic sensors can also be used safely in harsh industrial conditions. Applications include rapid differentiation of heat treatment conditions, detection of grinding burn and hardness measurements. Acoustic sensors are used in areas that are difficult to access or whose environmental conditions make the use of optical systems in particular difficult. The sensors can mainly detect cracks, frictional behaviour and wear quantities. Applications are in particular straightening processes of hardened steel components, wire drawing, welding, deep drawing and machining. In general, this method can also be used to monitor other sensors and thus raise them to a new level of evaluation technology and data analysis.

An Experimental Investigation Into Non-Contact Measurements for Leak Detection Using a Commercial Camera to Measure Vibration

An Experimental Investigation Into Non-Contact Measurements for Leak Detection Using a Commercial Camera to Measure Vibration

Evaluation of the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to ultrasonic measurements

The use of reinforced concrete is foundational to modern infrastructure. Acknowledging this, it is imperative that health monitoring techniques be in place to study corrosion within these structures. By using a non-destructive method for detecting the early formation of cracks within reinforced concrete, the method presented in this paper seeks to improve upon traditional techniques of monitoring corrosion, within reinforced concrete structures. In this paper, the authors present a method to evaluate the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to a set of ultrasonic measurements, which constitutes a novel approach to the problem of observing the impact of corroding rebars and resulting concrete damage. A non-contact ultrasonic transducer is operated at a carrier frequency of 500 [kHz], with a layer of saltwater separating the sensor from the concrete surface. Following this non-contact measurement collection of the surface and rebar echo responses, a poro-elastic model is used to model the sound propagation, through an adapted version of the Biot-Stoll model. At first, a set of default parameters, obtained from the physical characteristics of the reinforced concrete, are used to match experimental and simulated acoustic signature of the sample. Performing statistical averaging along the corroding rebar within three samples over a period of nearly nine months, a small but monotonous increase in the distance between the concrete surface and the top of the rebar, indicating gradual corrosion of the rebar. Next, a non-linear optimization algorithm is used to optimize the match between measured and simulated echoes. Through the implementation of this model parameter optimization, the root mean square error between measured and simulated responses was reduced by 63.7% for the full signal, and 62.6% for the rebar echo.

A novel optical thermometry strategy: Based on the combined effects of red-shift charge transfer band and thermal coupling

The non-contact temperature measurement method based on the fluorescence intensity ratio of thermally coupled energy levels has emerged as a focal point of research in recent years due to its characteristics of short response time, suitability for extreme environments, and high sensitivity. The enhancement of relative sensitivity (Sr) is a key objective in refining ratio-metric optical thermometry. However, the Sr is limited by the thermometry strategies. Regarding excitation and monitoring methods, ratio-metric temperature measurement strategies typically adopt two distinct approaches: single-excitation dual-emission (S-D), and dual-excitation single-emission (D-S). In this study, we developed a dual-excitation dual-emission (D-D) thermometry strategy that theoretically and experimentally demonstrates higher Sr and reduced temperature uncertainty (δT). This advancement was achieved by analyzing thermal coupling energy levels (TCELs) and the red-shift charge transfer band (CTB) properties. YVO4:Er3+ was selected for experimental validation due to its pronounced thermal coupling and red-shift CTB effects. The experimental results show that the Sr(D-D) equal to the sum of Sr(S-D) and Sr(D-S), and 1/δT(D-D) equal to the sum of 1/δT(S-D) and 1/δT(D-S), which well verifies the theoretical expectations. Thus, the D-D strategy emerges as a novel and effective method for ratio-metric thermometry, promising enhanced precision in temperature measurements.

Pixel Screening in Lifetime-Based Temperature Mapping Using β-NaYF4:Nd3+,Yb3+ by Time-Gated Near-Infrared Fluorescence Imaging on Deep Tissue in Live Mice.

Near-infrared fluorescence (NIRF) thermometry is an emerging method for the noncontact measurement of in vivo deep temperatures. Fluorescence-lifetime-based methods are effective because they are unaffected by optical loss due to excitation or detection paths. Moreover, the physiological changes in body temperature in deep tissues and their pharmacological effects are yet to be fully explored. In this study, we investigated the potential application of the NIRF lifetime-based method for temperature measurement of in vivo deep tissues in the abdomen using rare-earth-based particle materials. β-NaYF4 particles codoped with Nd3+ and Yb3+ (excitation: 808 nm, emission: 980 nm) were used as NIRF thermometers, and their fluorescence decay curves were exponential. Slope linearity analysis (SLA), a screening method, was proposed to extract pixels with valid data. This method involves performing a linearity evaluation of the semilogarithmic plot of the decay curve collected at three delay times after cutting off the pulsed laser irradiation. After intragastric administration of the thermometer, the stomach temperature was monitored by using an NIRF time-gated imaging setup. Concurrently, a heater was attached to the lower abdomens of the mice under anesthesia. A decrease in the stomach temperature under anesthesia and its recovery via the heater indicated changes in the fluorescence lifetime of the thermometer placed inside the body. Thus, NaYF4:Nd3+/Yb3+ functions as a fluorescence thermometer that can measure in vivo temperature based on the temperature dependence of the fluorescence lifetime at 980 nm under 808 nm excitation. This study demonstrated the ability of a rare-earth-based NIRF thermometer to measure deep tissues in live mice, with the proposed SLA method for excluding the noisy deviations from the analysis for measuring temperature using the NIRF lifetime of a rare-earth-based thermometer.

Radar-Enabled non-contact speed estimation for rotating electrical machinery

Electric motors play a pivotal role in contemporary industrial processes, prompting the necessity for their continuous monitoring through an array of sensors in various applications. Among the crucial parameters under scrutiny, the operational speed of electric motors holds a prominent position. Current methods for speed detection conventionally entail the utilization of physically connected sensors or the measurement of some electrical variables. Nonetheless, a non-contact speed measurement approach with a mobile device promises low cost, efficiency, and even safety in some specific cases. This research delves into the remote speed detection of electric motors employing Continuous Wave radar technology. To this end, phase extraction techniques, which have demonstrated robust performance in diverse application domains, have been assessed for their efficacy in motor speed detection. First, five distinct methods have been utilized to extract the phase of baseband signals obtained from a 24 GHz IQ-demodulated radar system directed toward an asynchronous motor. These methodologies have undergone comprehensive evaluation in experimental measurements and simulation encompassing ideal and noisy conditions. Complex Signal Demodulation, one of the phase extraction methods, exhibits a noteworthy superiority over alternative approaches in motor speed measurement experiments. These experiments have entailed three different load scenarios and encompassed ten distinct speeds, equally spaced between 500 and 1400 rpm. The Complex Signal Demodulation method has delivered exceptional results, successfully detecting motor speeds across thirty experiments with a mean absolute percentage error of merely 0.08. Radar-based non-contact speed detection holds immense promise, particularly in fault diagnosis and predictive maintenance.

First noncontact millimeter-wave radar measurement of heart rate in great apes: Validation in chimpanzees (Pan troglodytes).

Heart rate is a crucial vital sign and a valuable indicator for assessing the physical and psychological condition of a target animal. Heart rate contributes to (1) fundamental information for cognitive research, (2) an indicator of psychological and physical stress, and (3) improving the animal welfare of captive animals, especially in nonhuman primate studies. Heart rate has been measured using a contact-type device; however, the device burdens the target animals and that there are risks associated with anesthesia during installation. This study explores the application of heartbeat measurement techniques using millimeter-wave radar, primarily developed for humans, as a remote and noninvasive method for measuring the heart rate of nonhuman primates. Through a measurement test conducted on two chimpanzees, we observed a remarkable correspondence between the peak frequency spectrum of heart rate estimated using millimeter-wave radar and the mean value obtained from electrocardiograph data, thereby validating the accuracy of the method. To the best of our knowledge, this is the first demonstration of the precise measurement of great apes' heart rate using millimeter-wave radar technology. Compared to heart rate measurement using video analysis, the method using millimeter-wave radar has the advantage that it is less susceptible to weather and lighting conditions and that measurement techniques for multiple individuals have been developed for human subjects, while its disadvantage is that validation of measurement from long distances has not been completed. Another disadvantage common to both methods is that measurement becomes difficult when the movement of the target individual is large. The possibility of noncontact measurement of heart rate in wild and captive primates will undoubtedly open up a new research area while taking animal welfare into consideration.

Interferometric-scale full-field vibration measurement by a combination of digital image correlation and laser vibrometer

Digital Image Correlation (DIC) is a crucial noncontact full-field optical measurement method used in various fields. However, in practical applications, DIC is affected by systematic and random noises, leading to experimental resolutions lower than theoretical ones. In this study, we proposed a laser Doppler vibrometer guided DIC to perform vibration measurements. 3D-DIC obtains a sequence of out of displacement field initially. A three-dimensional frequency domain collaborative filtering (3D-FDCF) method that utilizes Laser Doppler vibrometer (LDV) single-point data to assist in processing of the displacement field sequence pixel-wise is adopted. The 3D-FDCF method combines lowpass filtering in the spatial frequency domain with LDV-guided bandpass filtering in the temporal frequency domain. The effectiveness of the 3D-FDCF method is demonstrated through a comparison among DIC data, the filtered DIC data, and continuously scanning LDV data. The experiment results demonstrate the 3D-FDCF method's capability in measuring vibration amplitudes of several hundred nanometers with the size of a test sample of about 100 mm × 100 mm, supporting the statement of interferometric scale full-field vibration measurement by DIC with the guidance of the LDV data.

Magnetic-Dielectric Cantilevers for Atomic Force Microscopy.

Atomic force microscopy (AFM) is a technique that relies on detecting forces at the nanonewton scale. It involves using a cantilever with a tiny tip at one end. This tip interacts with the short- and long-range forces of material surfaces. These cantilevers are typically manufactured with Si or Si3N4 and synthesized using a lithography technique, which implies a high cost. On the other hand, through simple chemical methods, it is possible to synthesize a magneto-dielectric composite made up of artificial SiO2 opals infiltrated with superparamagnetic nanoparticles of Fe3O4. From these materials, it is possible to obtain tipless cantilevers that can be used in AFM analysis. Tipless cantilevers are an alternative tool in nanoscale exploration, offering a versatile approach to surface analysis. Unlike traditional AFM probes, tipless versions eliminate the challenges associated with tip wear, ensuring prolonged stability during measurements. This makes tipless AFM particularly valuable for imaging delicate or soft samples, as it prevents sample damage and provides precise measurements of topography and mechanical and electromechanical properties. This study presents the results of the characterization of known surfaces using magneto-dielectric cantilevers and commercial cantilevers based on Si. The characterization will be carried out through contact and non-contact topography measurements.

Development of Macrotexture Test Method for Dense-Graded Asphalt Mixtures

The design and construction of pavements necessitate careful consideration of longevity, cost-effectiveness, sustainability, and safety, with a focus on enhancing the durability and lifespan of dense-graded asphalt (DGA) surface mixtures. This paper discusses a research project the Federal Highway Administration (FHWA) initiated to assess the macrotexture characteristics of DGA mixture pavement—particularly concerning safety at higher speeds. The project aims to develop an efficient testing procedure for measuring macrotexture during mixture design and field-testing processes, striking a balance between longevity, cost-effectiveness, sustainability, and safety in pavement design and construction. The study began with a comprehensive review of existing practices, leading to the selection and testing of a unique laser texture scanner capable of noncontact macrotexture measurement. The subsequent phase involved validating the suggested macrotexture measurement system through data collected from State-sponsored projects under the FHWA Mobile Asphalt Technology Center program. Macrotexture measurements were collected from field cores and laboratory-compacted specimens from various State departments of transportation projects and analyzed. Preliminary analysis of the limited dataset to date revealed a positive correlation between average macrotexture measurements from gyratory specimens’ top surface and field core macrotexture measurements. The FHWA developed a draft test method for measuring macrotexture on laboratory specimens and field cores, thereby aiming to reduce procedural variability. Interlaboratory macrotexture test results demonstrated good reproducibility, with an average coefficient of variation of approximately 20%. Overall, this research project seeks to advance the understanding and measurement of macrotexture in DGA mixture pavements and thereby contribute to the development of safe pavements.

Accurate and non-contact measurement of volume percentages of oil-water fluids using microstrip sensors independent of the volume of sample using artificial neural network

Due to their moderate sensitivity, extremely cheap cost, ease of fabrication method, and, more crucially, the fact that they are non-invasive, planar microwave sensors have attracted a lot of interest from both industries and academics over the past years. These intriguing properties drive this field's research toward opening up a wide range of applications that go beyond oil and gas to include biological, material sensing, pollution monitoring, and other industrial uses. The main focus of this research is on the simulation and fabrication of a high-sensitivity, very small, and repeatable microwave sensor to measure volume fractions of oil and water in real-time. This sensor is designed by Ansys HFSS software and is made on the RT/Duroid 5880 (with εr = 2.2, thickness = 0.787 mm, loss tangent of 0.0009). In a polylactic acid (PLA) box made using a 3D printer, oil and water with different volume percentages will be placed on the microwave sensor in non-contact conditions. To determine volume percentages independent of the volume of the samples, different samples were analyzed in volumes of 5 ml, 10 ml, and 15 ml. The developed sensor includes two passing bands, and when exposed to crude oil with varying amounts of water, the frequencies of these bands, their insertion loss, and their prominence in these frequencies change. Due to the non-linear variations in the insertion loss, frequency, and prominence value of the two passbands, the MLP neural network is used in this study over other approaches for identifying the objective parameter. The MLP neural network's output was the water volume percentage, and its inputs were variations in the frequency, insertion loss, and prominence of the two passbands in the transmission response. Thanks to microwave sensors and artificial neural networks, volume fractions could be detected with high accuracy, independent of the volume of samples. The suggested microwave sensor could be a highly effective way to measure volume percentages in the oil sector because of its high accuracy, compact size, simplicity of transportation, non-contact feature, etc.

Simultaneous Reconstruction of Gas Concentration and Temperature Using Acoustic Tomography.

Acoustic tomography utilizes sensor arrays to collect sound wave signals, enabling non-contact measurement of physical parameters within an area of interest. Compared to optical technologies, acoustic tomography offers the advantages of low cost, low maintenance, and easy installation. Current research in acoustic tomography mainly focuses on reconstruction algorithms for temperature fields, while monitoring the composition and concentration of gases is significant for ensuring safety and improving efficiency, such as in scenarios like boiler furnaces and aviation engine nozzles. In excitable gases, the speed of sound exhibits an S-shaped curve that changes with frequency, a characteristic that could be potentially useful for acoustic tomography. Therefore, this study primarily discusses the quantitative calculation of gas concentration and temperature based on the dispersion of the speed of sound. By employing graphic processing and pattern matching methods, a coupled relationship of the dispersion of the speed of sound with gas concentration and temperature is established. The projection intersection method is used to calculate the concentration and temperature of binary and ternary gas mixtures. Combined with the inversion method, a joint reconstruction method for gas concentration fields and temperature fields based on the dispersion of the speed of sound is developed. The feasibility of the proposed simultaneous reconstruction method for temperature and concentration fields is validated using numerical simulations. Additionally, an acoustic tomography experimental system was set up to conduct reconstruction experiments for binary gas concentration fields and temperature fields, confirming the effectiveness of the proposed method.

Speed of sound measurement and mapping in transparent materials by impulsive stimulated Brillouin microscopy

Impulsive stimulated Brillouin scattering (ISBS) is a variant of stimulated Brillouin scattering, which can overcome the shortcomings of the long acquisition time of traditional Brillouin microscopy. We introduce the difference between ISBS and other Brillouin microscopies in calculating longitudinal modulus. The Brillouin frequency shift obtained by ISBS is only related to the system parameters and the speed of sound (SOS) in the sample, not to the refractive index. Non-contact SOS measurement of homogeneous samples is an important application of Brillouin scattering, used in the early study of Brillouin spectroscopy and the mechanical properties of liquids. However, the measurement requires prior knowledge of the sample refractive index, which limits the measurement of the unknown refractive index sample. Here, we propose a method to measure the SOS based on ISBS, which in principle avoids the need for refractive index parameters. The SOS of several liquids are measured and compared with the standard values. The mean relative standard deviation is 1.13%. Moreover, we measure the SOS of a mixture of ethanol and water to demonstrate an application of measuring SOS without refractive index information. We also demonstrate the high spatial resolution of ISBS with a methanol-filled PDMS sample.

Impact of coating and microstructure on wear resistance of tool steels for wood cutting: a novel approach to quantification and analysis of wear-related damages

Squaring, a wood transformation process, is an operation which consists of introducing the logs into a squaring machine which then uses sharp tools to cut the wood into pieces with high surface quality. Tool steels used in this process experience significant wear, damaging the wood surface and hence leading to substantial scrape rate. This study investigates the wear resistance of three tool steels specifically designed for wood cutting applications: modified AISI A8, modified steels with 0% and 1% tungsten, and powder metallurgy prepared W360 steel. Furthermore, the influence of a PVD coating on the wear resistance of the three alloys was investigated. ASTM G65 abrasive wear tests were conducted using the dry sand/rubber wheel abrasion test. A methodology using a non-contact 3D measurement system and specialized software was developed, allowing for a thorough quantitative assessment of the wear of these steels. The results revealed that the coated A8mod + 1%W steel exhibits the best resistance among the coated steels. Despite the excellent intrinsic resistance of W360 steel, the coating did not provide a significant improvement for this steel, showing only a 10% reduction in wear. Microstructural analysis revealed that the predominant wear mechanisms were abrasion and impact. The relative performance of each steel was quantified and is reported. Field trials conducted under actual cutting conditions, indicate the superiority of W360 steel in terms of resilience to wear and impacts compared to other tested alloys, while confirming the effectiveness of surface treatments in mitigating material wear. However, A8 steel modified with 1% tungsten exhibits increased wear under coating.

Temperature-Switch-Controlled Second Harmonic Mode Sensor for Brain-Tissue Detection.

Identifying brain-tissue types holds significant research value in the biomedical field of non-contact brain-tissue measurement applications. In this paper, a layered metastructure is proposed, and the second harmonic generation (SHG) in a multilayer metastructure is derived using the transfer matrix method. With the SHG conversion efficiency (CE) as the measurement signal, the refractive index ranges that can be distinguished are 1.23~1.31 refractive index unit (RIU) and 1.38~1.44 RIU, with sensitivities of 0.8597 RIU-1 and 1.2967 RIU-1, respectively. It can distinguish various brain tissues, including gray matter, white matter, and low-grade glioma, achieving the function of a second harmonic mode sensor (SHMS). Furthermore, temperature has a significant impact on the SHG CE, which can be used to define the switch signal indicating whether the SHMS is functioning properly. When the temperature range is 291.4~307.9 Kelvin (K), the temperature switch is in the "open" state, and the optimal SHG CE is higher than 0.298%, indicating that the SHMS is in the working state. For other temperature ranges, the SHG CE will decrease significantly, indicating that the temperature switch is in the "off" state, and the SHMS is not working. By stimulating temperature and using the response of SHG CE, the temperature-switch function is achieved, providing a new approach for temperature-controlled second harmonic detection.

Non-Contact Evaluation of Deformation Characteristics on Automotive Steel Sheets

Non-Contact Evaluation of Deformation Characteristics on Automotive Steel Sheets

Anomaly detection in structural dynamic systems via nonlinearity occurrence analysis using video data

Dynamic loads in disaster events, such as earthquakes, often cause structural damages that severely affect serviceability and even reduce the safety of civil structures or render them unsafe. Most of these damage scenarios can cause the transition from a linear to nonlinear behavior in structural dynamics. Therefore, extracting such nonlinear behaviors is an effective strategy for structural anomaly detection. In recent years, the rapid development of media devices has led to the widespread use of video data, primarily because of the ease of acquisition and lack of contact measurement. This study developed a novel method for detecting anomalies due to structural nonlinearity from video data that could capture target dynamic behaviors via non-contact measurement. The method is based on optical flow, extended repulsive force networks, and morphological operations for extracting singularities due to nonlinear events in the estimated motion vector field in video data. Subsequently, shaking table tests on a three-story shear building model containing a novel controllable hinge bearing verified the effectiveness of the developed method. The results of the feature enhancement process demonstrate the accurate localization of anomalous regions and effective mitigation of unwanted interference using the proposed method. The proposed method facilitates the use of video-based technology to evaluate rapid damage conditions post-earthquake.