Non-contact Measurement Method Research Articles

A non-contact method for weighting of aggregate pile based on binocular vision

Concrete is indispensable in contemporary industry, and the weighing of aggregates directly determines the quality of concrete preparation. In response to the difficulty of traditional methods in meeting the industrial requirements for accuracy, efficiency, and cost of measurement, this paper proposes a non-contact bone pile weight measurement method based on binocular vision. Firstly, the on-site image is obtained through a binocular camera, and the camera parameters are used to improve the image edge information and preprocess the image. Then, the improved semi global matching (SGM) algorithm is used to improve computational efficiency, obtaining three-dimensional information of the aggregate pile for volume calculation. Based on the watershed algorithm and T-S inference, the aggregate clearance rate is calculated, and the initial volume is corrected. Finally, a linear relationship is established between the density of the aggregate pile and the clearance rate, and the weight of the aggregate pile can be obtained by the density formula. The experimental results on different sizes of aggregate piles shows that the average error of the experiment is less than 4%. Efficiency of improved SGM matching algorithm has increased several times compared to the original algorithm, providing a low-cost and efficient measurement method for industrial aggregate weighing.

An effective cross-scenario remote heart rate estimation network based on global-local information and video transformer.

Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.

Особенности расчета температуры резания при высокоскоростном фрезеровании алюминиевых сплавов без применения СОЖ

Introduction. The calculation of temperature during high-speed milling of aluminum alloys is of interest, since temperature can act as one of the main limiting factors in choosing rational milling modes. This is especially important when milling thin-walled products used in aircraft construction, since its high values can lead to local warping of the structure. It is not possible to control the temperature factor in production conditions, which makes it necessary to develop a mathematical model for calculating temperature. The purpose of the work is to develop a methodology for predicting the cutting temperature during high-speed milling of aluminum alloy workpieces for cutting conditions, in which it is not possible to use cutting fluid. Methods. This paper presents experimental studies of the cutting temperature during high-speed milling of aluminum alloy workpieces without the use of cutting fluid using non-contact temperature measurement methods. The results obtained were used to determine the coefficients substituted into formulas for calculating temperatures on the front and back surfaces of the cutting blade. Results and discussions. Based on the results of experimental tests and theoretical modeling, a temperature graph is drawn up. A comparison of experimental studies of milling of aluminum alloy D16T, with changing cutting conditions (the cutting speed changed) with theoretical data, gave a satisfactory result. The average relative error when comparing experimental data with theoretical one is 6.05 %. Based on experimental data, it can be concluded that the comparison of experimental data for measuring cutting temperatures is in satisfactory agreement with the proposed method of theoretical calculation of temperatures. The advantage of this technique is that it allows, without time-consuming and costly experimental studies, theoretically calculate (forecast) the temperatures on the front and back surfaces of the cutting blade, as well as the cutting temperature, for those narrow milling conditions, where effective heat removal from the cutting zone is impossible. It can also be used for milling aluminum alloys, the mechanical and thermophysical properties of which differ.

Research on Coaxiality Measurement Method for Automobile Brake Piston Components Based on Machine Vision

Aiming at addressing the problem of the online detection of automobile brake piston components, a non-contact measurement method based on the combination of machine vision and image processing technology is proposed. Firstly, an industrial camera is used to capture an image, and a series of image preprocessing algorithms is used to extract a clear contour of a test piece with a unit pixel width. Secondly, based on the structural characteristics of automobile brake piston components, the region of interest is extracted, and the test piece is segmented into spring region and cylinder region. Then, based on mathematical morphology techniques, the edges of the image are optimized. We extract geometric feature points by comparing the heights of adjacent pixel points on both sides of the pixel points, so as to calculate the variation of the spring axis relative to the reference axis (centerline of the cylinder). Then, we extract the maximum variation from all images, and calculate the coaxiality error value using this maximum variation. Finally, we validate the feasibility of the proposed method and the stability of extracting geometric feature points through experiments. The experiments demonstrate the feasibility of the method in engineering practice, with the stability in extracting geometric feature points reaching 99.25%. Additionally, this method offers a new approach and perspective for coaxiality measurement of stepped shaft parts.

Multi-View consistency-based point cloud registration method with low overlap rate

Accurate and efficient workpiece measurement is crucial for workpiece processing and quality monitoring. Non-contact optical measurement methods have gained more attention due to their simplicity, efficiency, and flexibility compared to complicated and inefficient contact measurement methods. Multi-view registration of measurement data is a key issue in workpiece measurement, as it relies on the system’s geometric accuracy and motion stability, presenting challenges such as the insufficient overlap of multi-viewpoint cloud data and cumulative error. To address these challenges, this paper proposes a multi-view planning and registration algorithm with a low overlap rate. The multi-view planning algorithm employs a greedy method to plan the scanning viewpoints of the workpiece to obtain complete point cloud data efficiently. The multi-view registration algorithm extracts features using a multi-scale geometric feature extraction network, matches the features based on the Hungarian algorithm, builds a graph, and optimizes workpiece positions based on the G2O algorithm for multi-view registration, effectively reducing cumulative error. Measurement experiments on blade workpieces confirm the feasibility of the proposed algorithms.

Non-contact measurement method of displacement of spherical joints in long-span space steel structures

For long-span space steel structures after a long period of operation, it is crucial to quickly and accurately measure the deformation for structural safety evaluation. To address the challenges in deformation measurement of space steel structures, this paper presents a non-contact, three-dimensional coordinate measurement method for key ball joints in their spatial positions. A high-precision Laser Total Station is utilized to measure the surface of the ball joints at multiple points. Three-dimensional coordinates of the center of the ball joints are then accurately determined by using three-dimensional analytical method and the least square error method. Subsequently, the deformation of key joints of the long-span space structures is obtained by comparing the determined coordinates with their initial coordinates. An engineering application by adopting the propose method to measure the deformation of a large-span steel structure gymnasium in Guangdong Province is presented. This method not only can be used to evaluate the safety of existing space steel structures, but also can be applied to rapid measurements and positioning of new long-span steel structures to improve the measurement efficiency and personnel safety.

Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview

The integration of magnetorheological (MR) semi-active suspension systems in all-terrain vehicles (ATV) has garnered significant attention due to their ability to enhance damping performance and off-road capabilities. However, traditional control strategies result in poor control accuracy and limited vibration reduction effects when facing complex road excitations and impact disturbances. With technological advancements, enhanced vehicle environmental perception and road sensing capabilities have made it possible to implement model predictive control (MPC) for vehicle suspensions. Nevertheless, traditional MPC is limited in vehicle suspension applications due to its high computational complexity. To address these issues, this study introduces an explicit model predictive control based on road preview (EMPC-P). Firstly, road data obtained through a non-contact measurement method enables the system to perceive road excitation information in advance. Subsequently, a 7 Degree-of-Freedom (7-DOF) suspension model incorporating road excitations is constructed. By adhering to system constraints and employing a multiparameter optimization method, the control problem based on rolling optimization is transformed into an explicit polyhedral system. The offline precomputation of control state relations enhances the computational efficiency of the control system. Through this approach, the designed EMPC allows the vehicle suspension system to make optimal control decisions quickly and accurately in response to complex driving conditions, thus improving the damping effect of the system. Through a combined approach of simulation and experimental validation, the designed EMPC-P controller is compared with the Skyhook controller under preview and non-preview states, respectively. Empirical testing confirms that the EMPC-P exhibits superior damping effects, significantly improving vehicle ride comfort and handling stability.

Design and Implementation of a Linear Active Disturbance Rejection Control-Based Position Servo Control System of an Electromotive Valve for Exhaust Gas Recirculation.

An exhaust gas recirculation (EGR) valve is used to quickly and dynamically adjust the amount of recirculated exhaust gas, which is critical for improving engine fuel economy and reducing emissions. To address problems relating to the precise positioning of an electromotive (EM) valve under slowly varying plant dynamics and uncertain disturbances, we propose a servo control system design based on linear active disturbance rejection control (LADRC) for the EGR EM valve driven by a limited angle torque motor (LATM). By analyzing the structure of the LATM and the transmission, the dynamic model of the system is derived. In addition, to solve the problems caused by slowly varying plant dynamics and uncertain disturbances, we combine the effects of uncertain model parameters and external disturbances as the total disturbance, which is estimated in real time by an extended state observer (ESO) and then compensated. In addition, accurate angular information is obtained using a non-contact magnetic angle measurement method, and a high-speed digital communication channel is established to help implement a closed-loop position control system with improved responsiveness and accuracy. Simulation and experimental results show that the proposed servo system design can effectively ensure the precision and real-time performance of the EM valve under slowly changing plant dynamics and uncertain disturbances. The proposed servo system design achieves a full-stroke valve control accuracy of better than 0.05 mm and a full-stroke response time of less than 100 ms. The controlled valve also has good robustness under shock-type external disturbances and excellent airflow control capability. The repeatability of the airflow control is generally within 5%, and the standard deviation is less than 0.2 m3/h.

Measurement and assembly methods for bolted flange joints using three-dimensional digital image correlation

Bolted flange joints are extensively used in pressure vessels and piping equipment. The assembly coaxiality accuracy and bolt pre-tightening force are particularly important in causing the risk of seal and leakage failure. This paper proposed a novel non-contact measurement and assembly adjustment method to reduce the coaxiality angle deviation efficiently. The flange joint is first bolted according to standard flange joint assembly guidelines and then adjusted using the proposed method. In the proposed method, the surface deformation and the coaxiality of the flange are evaluated using a 3D-DIC (three-dimensional digital image correlation) technique; RMS (root mean square) of the surface deformation is used to drive the correction of the bolts to further reduce the coaxiality angle deviation. Compared with existing bolting patterns, the established measurement and assembly adjustment method, which combines 3D-DIC measurement and adjustment based on RMS, can effectively reduce the coaxiality angle deviation. The experiments using a correction strategy demonstrated that the coaxiality angle deviation is reduced by 48.9 % and 60.1 % on average compared with the well-known star bolting pattern and modified star bolting pattern provided by ASME PCC-1, respectively, and even by 30.6 % and 51.9 % compared with their rotational pass patterns. Further experiments with an iterative strategy showed that the coaxiality angle deviation could be reduced by around 70 % and 75 %, tending to be stable around 0.01°–0.035°, after only 5 iterative adjustments.

Non-Contact Current Measurement for Three-Phase Rectangular Busbars Using TMR Sensors.

This paper proposes a non-contact current measurement method for three-phase rectangular busbars based on TMR (tunneling magneto-resistance) sensors, due to their advantages of large dynamic range, wide bandwidth, light weight, and easy installation. A non-contact current sensor composed of only three TMR sensors is developed and the TMR sensors are respectively placed at a location with a certain distance from the surface of each rectangular busbar to measure the magnetic fields generated by the busbar currents. To calibrate the developed current sensor, i.e., to establish the relationship between the magnetic fields measured by the TMR sensors and the currents flowing in the three-phase rectangular busbars, we designed a thyristor-controlled resistive load as a calibrator, which is connected to a downstream branch of the distribution cabinet. By switching the resistive load, a calibration current, which can be identified from the background current, is generated in one rectangular busbar and its value is measured at the location of the calibrator, and transmitted wirelessly to the location of the TMR sensors. A new and robust method is proposed to extract the voltage components, corresponding to the calibration current, from the voltage waveforms of the TMR sensors. By calculating the proportional coefficients between the calibration currents and the extracted voltage components, online calibration of the current sensor is achieved. We designed and implemented a current measurement system consisting of a current sensor using TMR sensors, a thyristor-controlled resistive load for current sensor calibration, and a data acquisition circuit based on a multi-channel analog-to-digital converter (ADC). Current measurement experiments were performed in a practical distribution cabinet installed in our laboratory. Compared to the measurement results using a commercial current probe with an accuracy of 1%, the relative error of the measured currents in RMS is less than 2.5% and the phase error is less than 1°, while the nonlinearity error of the current sensor is better than 0.8%.

Research on deformation and temperature characteristics of micro negative Poisson's ratio anchor rod

This study aims to investigate the global deformation, temperature characteristics, and their correlation during the quasi-static tensile process of micro-NPR anchor rods. The LEW-500 static tensile testing system was used to carry out the uniaxial tensile testing with three deformation rates, and the deformation and temperature of the anchor rods were studied through the DIC system and IRT system. The mechanical results indicate that the micro-NPR anchor rods have the characteristics of high elongation, high constant resistance, no yielding platform, and no obvious necking. The DIC results show that the deformation of anchor rods can be divided into the initial and accelerated differentiation stages. In the initial differentiation stage, the statistical dispersion of deformation is low and increases slowly with time. In the accelerated differentiation stage, a relatively concentrated local deformation appears, shown by the sharp rise and decline of statistical dispersion. The IRT results indicate that the surface temperature of the anchor rods also increases with deformation, and the upward trend exhibits distinct characteristics in two differentiation stages. The faster the deformation rate, the higher the temperature of the anchor rods’ surface. The correlation coefficient of axial strain and temperature difference in the anchor rods is calculated, and the correlation coefficient is maintained at a high level in the accelerated differentiation stage. The linear fitting of the standard deviation of axial strain and the temperature difference is perfect. This study provides a foundation and a new idea for future deformation patterns and monitoring analysis in on-site applications of micro-NPR anchor rods through the joint analysis of two non-contact measurement methods.

Robust Method for Non-Contact Vital Sign Measurement in Videos Acquired in Real-World Light Settings From Skin Less Affected by Blood Perfusion

Robust Method for Non-Contact Vital Sign Measurement in Videos Acquired in Real-World Light Settings From Skin Less Affected by Blood Perfusion

Research on Noncontact Voltage Measurement Method for Three-Phase Busbar of Low-Voltage Distribution Cabinets

Research on Noncontact Voltage Measurement Method for Three-Phase Busbar of Low-Voltage Distribution Cabinets

Non-contact three-dimensional emissivity distribution measurement method of M8 LiDAR echo

Non-contact three-dimensional emissivity distribution measurement method of M8 LiDAR echo

An optimized fuzzy K-means clustering method for automated rock discontinuities extraction from point clouds

Recently, non-contact measurement methods such as laser scanning, have gained popularity in collecting discontinuous data due to their ability to generate high-resolution point clouds containing detailed information about rock surface. However, quickly and accurately extracting discontinuities from massive point clouds faces challenges. In this study, we propose an optimization algorithm based on fuzzy clustering and region growth that enables swift extraction of discontinuity information from point clouds. The proposed method employed a composite indicator to evaluate the similarity and dissimilarity of the points in a clustering group basing on the membership function matrix and the optimal clustering number could be estimated without predefining. Additionally, region growing is difficult to deal with increasingly enormous point clouds, a faster way is estimating a possible range as a search radius to avoid meaningless time-consuming in region growing. Further, the proposed methodology was implemented in Matlab to extract discontinuities from high-resolution point cloud, includes data pre-processing, optimized fuzzy clustering, and optimized region growing. Finally, particular attention was given to the sensitivity of automatic extraction in point cloud resolution and cluster number, these parameters are always special in different objects. The results showed that the optimized method performed excellent in clustering without a priori assumption of cluster number, and provided optional range of resolutions without losses of accuracy to cater to diverse requirements. The proposed method gave a new way to estimate the optimal clustering number as same as manually separated without predefining, to collect all orientations of discontinuity quickly, and to meet different needs with appropriate resolution.

Depth-aware pose estimation using deep learning for exoskeleton gait analysis

In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human–machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human–machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS) is designed in the network framework. The degree of correlation between the various joints of the human body is used as a detection target to effectively overcome prediction difficulties due to partial joint occlusion during walking. Finally, a post processing algorithm is designed to describe patients’ walking motion by combining the pixel coordinates of each joint point and leg length. Our advantage is that we provide a non-contact measurement method with strong universality, and use depth estimation and JGS to improve measurement accuracy. Conducting experiments on the Walking Pose with Exoskeleton (WPE) Dataset shows that our method can reach 95.77% PCKs@0.1, 93.14% PCKs@0.08 and 3.55 ms runtime. Therefore our method achieves advanced performance considering both speed and accuracy.

Image uncertainty components in video strain measurement of mortar destructive compression testing

This paper describes a video strain measurement solution developed for application in mortar destructive compression testing. Knowledge about the mechanical behavior of this type of construction material, namely up to its fracture, is still superficial due to the less common use of non-contact measurement methods. The performed research was focused on the determination of image coordinate accuracy, based on the experimental quantification of the identified main uncertainty components, using traceable reference patterns, and validated computational toolboxes dedicated to camera parameterization and digital image processing. The obtained results show the following uncertainty contributions: lens distortion - negligible; re-projection errors - 0.21 pixel; 0.10 pixel - spatial resolution; and digital image processing operations - 0.28 pixel. The combination of these uncertainty components resulted in an image coordinate standard uncertainty equal to 0.36 pixel, which was propagated (in addition to the scale coefficient measurement uncertainty) to the selected camera model – orthographic projection with uniform scaling – which supports the video strain measurement.

Application of Profilometric Measurements in an Assessment of Woven Fabrics Surface Geometry

ABSTRACT The surface roughness of the textile materials is one of the crucial features influencing the appearance and functionality of fabrics and products made of them. The surface roughness of textile materials can be measured using the contact or non-contact methods. In the presented work, the non-contact method using the MicroSpy® Profile profilometer was applied to assess the quality of surface of the cotton woven fabrics of plain and 3/1S twill weave. Determination of surface geometry parameters and functions was done using the Mark III software according to the DIN EN ISO 4287 standard. Results showed that the twill woven fabric is characterized by greater roughness than the plain woven fabric. The profiles created along the warp and weft have different shapes dependably on fabric weave. The autocorrelation function reflects the weave pattern of the woven fabrics. Performed investigations confirmed that the non-contact method of measurement of fabric surface geometry by means of the profilometer provides very detailed and complex data about the fabrics’ surface quality.

A Y-shaped network based single-shot absolute phase recovery method for fringe projection profilometry

Fringe projection profilometry (FPP) is a widely used non-contact 3D measurement method. Though maturing in the last decade, it remains a significant challenge when facing the phase unwrapping of measured object surfaces in a single-shot measurement setting. With the rapid development of deep learning techniques, the adoption of a data-driven approach is gaining popularity in the field of optical metrology. This study proposes a new absolute phase recovery method based on the devised single-stage deep learning network. The aim is to ensure high-quality absolute phase recovery from a single-shot fringe projection measurement. Unlike most existing approaches, where the numerators and denominators of the wrapped phases and the fringe orders are predicted in various stages, the proposed method acquires the wrapped phases and the corresponding fringe orders within a single network, i.e. it can predict both wrapped phases and the corresponding fringe orders directly and simultaneously from the single fringe pattern projected in the single-shot mode based on a unified Y-shaped network. Experiments on benchmark datasets and models have demonstrated the effectiveness and efficiency of the technique, especially in terms of high-quality recovery of absolute phase information by using the lightweight single-stage network, and enabling the FPP-based phase 3D measurements in an online manner.

Non-Contact PIM Measurement Method Using Balanced Transmission Lines for Impedance Matched PIM Measurement Systems

Non-Contact PIM Measurement Method Using Balanced Transmission Lines for Impedance Matched PIM Measurement Systems