Profile Sensor Research Articles

A measurement method for the axial bending deformation of parts with circumferential cycling element based on laser profile sensor

Abstract The cross-section of the CPP (circumferential periodic parts) is composed of fixed shaped elements regularly arranged in the circumferential direction, examples include cylinders, positive prisms, lead screws, gear shafts, and spline shafts, it has applications in many fields. Existing research has not yet proposed a universal method for measuring the axial bending of CPP. This paper presents a non-contact measurement method using a laser profile sensor that is capable of measuring axial bending of CPP. The axis of a shaft part is determined by connecting the geometric center of each cross-section. This method is suitable for measuring the bending deformation of most CPS (circumferential periodic shaft parts). To verify the validity of this method, numerical simulation calculations are conducted on cylinders, hexagonal prisms, and lead screws. Additionally, comparative experiments are performed on lead screws using self-built experimental platform and a measuring projector to investigate the effectiveness and repeatability.

Application of a Laser Profile Sensor for the Full-Field Measurement of the Continuous Icing Process of Rotating Blades.

With the advancing energy transition, icing is a growing problem in the wind turbine sector. The development of systems to detect and mitigate icing makes it necessary to understand its basic behavior and characteristics. This paper proposes a method for the continuous and full-field measurement of the icing process of rotating blades, using a single line laser profile scanner. Inside of a climate chamber, a rotor is driven by a motor, while a system of nozzles provides a fine water dust, which leads to ice accumulating on simple NACA blades, which in turn is measured by a triangulation laser. The measurement data are cleared from outliers and presented as a surface in 3D space. An alpha shape is used to reconstruct and extract the volume of the ice between a reference and a measurement surface, using the corresponding Matlab function. Appropriate input parameters for the function and offsetting of the reference surface to improve the results are compared and discussed. The resulting system is able to detect small changes in the ice layer thickness in the sub-millimeter range.

Turbulent Channel Flow Diagnostics By Means Of Shake-The-Box (STB): Flow Structures And Sublayer Profiles

Time-resolved Shake-the-Box (STB) experiments have been conducted in the channel-flow facility at the Institute of Fluid Mechanics (ISTM) at f = 30 kHz repetition rate to study the near-wall velocity field at a viscous Reynolds number of Reτ = 350 (centerline velocity Ucl = 8.4 m/s, channel half-height h = 12.6 mm, viscous unit δv ≈ 36 μm) to testify the STB method for the applicability to provide high-resolution velocity information of the given flow field including the sublayer region of the channel flow. In addition to the extraction of Lagrangian particle tracks and corresponding Eulerian iso-surfaces of vortical structure the major emphasis of the study centers around an evaluation of the resulting near wall velocity profile. Particularly, it is discussed how the choice of data-processing steps and strategies affects the resulting velocity estimates in the immediate vicinity of the wall, where the major impact for the current study has been identified to result from image-arithmetic efforts during data pre-processing. The achieved results are discussed and further compared with direct numerical simulations (DNS) of similar Reτ and earlier experimental efforts at ISTM of the same flow configuration by means of stereoscopic particle image velocimetry (stereo PIV) and a laser-Doppler velocimetry profile sensor (LDV-PS). Finally, future perspectives of the experimental efforts for near-wall shear flow measurements are outlined on the grounds of the achieved insights.

An Improved Multi-point Chord Reference Measurement Device and Error Correction Method for Corrugation Measuring in Sharp Curves

Background: The traditional chord reference method and its device have faced challenges matching the sharp curve track. Thus, it is of utmost importance to research and develop a measurement device and method that can obtain accurate and comprehensive information regarding rail running band corrugation. Aims: It provides a solution for the measurement of the multi-point chord reference method on sharply curved rails and, at the same time, meets the demand for data richness and accuracy in theoretical studies on the causes of rail corrugation and wheel-rail contact relationships. Objective: This paper aims to research and design a measurement device based on the multi-point chord reference method for the rail running band corrugation. Meanwhile, the error in measuring the sharp curve rail is corrected and verified by simulation. Methods: The lateral information of rail running band is obtained by using a line laser profile sensor; the rail corrugation measurement model and error correction model of the rail running band area are established based on multi-point reference system; the simulation of the system model is carried out using Matlab; and the mechanical structure is built for static testing. Results: The measurement model based on the profile sensor and multi-point chord reference system can accurately obtain the rail corrugation waveform of any longitudinal measurement line in the running band area. The measurement results can be accurately corrected for a radius of 200m- 400m, the error parameters can be effectively improved, and the mechanical mechanism of the measurement system runs stably. Conclusion: The results show that the measurement model can accurately measure the rail corrugation of the sharp curve section, and the multi-waveform in the rail running band area has good measurement performance, but the mechanical structure can be optimized and improved in the light weight.

A waste separation system based on sensor technology and deep learning: A simple approach applied to a case study of plastic packaging waste

Plastic waste pollution is a challenging and complex issue caused mainly by high consumption of single-use plastics and the linear economy of “extract-make-use-throw”. Improvements in recycling efficiency, behaviour changes, circular business models, and a more precise waste management system are essential to reduce the volume of plastic waste. This paper proposes a simplified conceptual model for a smart plastic waste separation system based on sensor technology and deep learning (DL) to facilitate recovery and recycling. The proposed system could be applied either at the source (in a smart waste bins) or in a centralised sorting facility. Two smart separation systems have been investigated: i) the one utilising 6 sensors (near-infrared (NIR), humidity, temperature, CO2, CH4, and a laser profile sensor) and ii) the one with an RGB camera to separate packaging materials based on their composition, size, cleanliness, and appearance. Simulations with a case study showed that for a camera-based sorting, Inception-v3, a DL model based on convolution neural networks (CNN), achieved the best overall accuracy (78%) compared to ResNet-50, MobileNet-v2, and DenseNet-201. In addition, the separation resulted in a higher number of misclassified items in bins, as it focused solely on appearance rather than material composition. Sensor-based sorting faced limitations, particularly with dark colouration and organic matter entrapment. Combining the information from sensors and cameras could potentially mitigate the limitations of each individual method, thus resulting in higher purity of the separated fractions.

A biological ocean data reformatting effort

Biological ocean data collected from ships find reuse in aggregations of historical data. These data are heavily relied upon to document long term change, validate satellite algorithms for ocean biology and are useful in assessing the performance of autonomous platforms and biogeochemical models. Existing aggregate products have largely been restricted to the surface ocean, omit physical data or have limited biological data. We present the first version of a BIOlogical ocean data reforMATting Effort (BIO-MATE) to begin to fill a gap in subsurface bio-physical data aggregates in a reproducible way. BIO-MATE uses open-source R software that reformats openly sourced published datasets from oceanographic voyages. These reformatted biological and physical data from underway sensors, profiling sensors, pigments analysis and particulate organic carbon analysis are stored in an interoperable BIO-MATE data product for easy access and use. Specific QA/QC protocols can now be easily applied to the BIO-MATE data product to support a variety of surface and subsurface applications.

A density-controllable 3D measurement system based on linear profile sensor and universal manipulator

In order to offer a simple and efficient solution for obtaining point clouds, a modular 3D measurement system that incorporates an LPS mounted on a universal manipulator is developed. Unlike the requirement for electronic connections between the LPS and the robot to achieve hard synchronization, we employ a high-low-frequency collaborative strategy for soft synchronization, thereby eliminating the need for complex hardware synchronization modules. Since soft synchronization lacks the high-speed synchronous data acquisition of hard synchronization, we have also proposed an interpolation method on low density data to generate uniform higher-density point clouds, thus enabling a density-controllable point clouds acquisition approach. Experimental results demonstrate the effectiveness and versatility of the system.

Learning-based defect inspection of laptop covers with a laser profile sensor

Quality inspection is a crucial process in the manufacturing industry, which significantly impacts the productivity and profits of a factory. To reduce costs and enhance quality control, companies are increasingly driving automation of this process due to the limitations of manual labour. However, the lack of flexible solutions that can adapt to the changing environment in factories has been a challenge. Computer-vision-based inspection methods often require hefty mechanisms and strict lighting conditions, which limit the investment in such solutions. Furthermore, with increasing demands for precision, traditional algorithms are struggling to cope with the problem, while data-driven solutions like deep learning require significant effort in acquiring and labelling data. To address these problems, this paper proposes an eye-in-hand system, i.e., a sensor attached to a robotic manipulator, integrated with unsupervised learning algorithms. One typical product, laptop bottom covers, is tested and discussed with regard to the system to validate our method since it has tiny defects and light-absorbing coatings. With only nine samples of the product, our system solved the case and demonstrated high-precision results and a flexible workflow. In this paper, our main contributions are: 1) we propose a flexible quality inspection system and workflow that needs extremely small volumes of data with unsupervised learning algorithms; and 2) we first propose using a laser profile sensor for collecting 2D intensity images, that are resilient to ambient light disturbance, of matt laptop cover data without lighting equipment.

Measurements and Model Improvement: Insight into NWP Model Error Using Doppler Lidar and Other WFIP2 Measurement Systems

Abstract Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

New Approach for Processing Recycled Carbon Staple Fiber Yarns into Unidirectionally Reinforced Recycled Carbon Staple Fiber Tape.

This study describes a novel process in which staple fiber yarns made from recycled carbon fibers (rCFs) and polyamide 6 (PA6) fibers are further processed into semi-finished tape products in a modified impregnation and calendaring process. In this process, the staple fiber yarns are heated above the melting temperature of the polymer, impregnated, and stretched to staple fiber tapes (SF tapes) in the calendaring unit. SF tapes with different degrees of stretching and/or repasses were produced. The individual width and thickness were measured in line by a laser profile sensor. From these tapes, preforms were manually laid and processed into laminates in an autoclave. The important physical properties of the unidirectionally reinforced laminates made of the tapes were compared with organic sheets wound from staple fiber yarns. With increasing stretching, both the fiber orientation and mechanical properties improved compared to the organic sheets made from unstretched staple fiber yarns. An improvement in fiber orientation relative to the process direction from 66.3% to 91.9% (between ±10°) and 39.1% to 71.6% (between ±5°), respectively, was achieved for a two-stage stretched tape. The tensile and flexural moduli were increased by 15.2% and 14.5%, respectively.

Measurements in a Turbulent Channel Flow by Means of an LDV Profile Sensor

Spatially and temporally resolved velocity measurements in wall-bounded turbulent flows remain a challenge. Contrary to classical laser Doppler velocimetry (LDV) measurements, the laser Doppler velocity profile sensor (LDV-PS) allows the combined measurement of tracer particle position and velocity, which makes it a promising tool. To assess its feasibility a commercial LDV-PS is employed in a turbulent channel flow at Reτ=350\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Re_\ au =350$$\\end{document}. Additionally, the measurement and signal-processing accuracies of velocity and location are evaluated for various tracer-object sizes and velocities. On this basis, the turbulent channel flow measurements are evaluated and compared to reference data from direct numerical simulations. Thus, potentials of the LDV-PS are investigated for different regions of the flow and various data processing routines as well as the experimental practice are discussed from an application perspective.

Magnetic crawler for welds Visual Testing, based on 3D profilometry and 2D image processing

To ensure the satisfactory performance of a welded structure, the quality of the welds must be determined by adequate testing procedures. During Visual Testing (VT), the weld is examined through the eyes of an inspector to determine surface discontinuities. Although this is commonly considered the easiest, quickest, and least expensive type of inspection, it has some limitations: a certified inspector shall always be on site to perform the test, making it strongly dependant on his experience, knowledge and current environmental conditions. In addition, there is no possibility to record inspection data except standard pictures and comments to be added to inspection reports. Modern technology brings novel solutions for quality assurance. This article describes a powerful tool which can improve the reliability of VT that combines precise laser measurements, image processing and data cloud computing. A portable magnetic crawler has been developed using a Raspberry Pi SBC, a smart profile sensor and a 5 MPix industrial colour camera in order to gather both weld 3D point cloud and surface pictures. Laser triangulation and processing power directly integrated on board allow an easy weld profile measurement. Undercuts, reinforcement excess, spatters, hi-low are precisely detected and sized by the laser sensor while integrated 2D camera records and analyses surface features such contaminations, corrosion, and weld discoloration. The point cloud weld reconstruction is realized stitching together 2D profile data at a fixed interval that can range up to a hundredth of a millimetre with a spatial resolution up to 50um. Inspected weld is fully digitalized in the form of 3D object together with a set of numerical features, data are securely stored in a local flash memory and automatically synchronized with cloud platform for remote data visualization. Automatic weld assessment can also be applied using cloud computing and artificial intelligence (AI) algorithms together with deep learning tools. The combination of images, 3D models, measurements, and other evaluable features all together in the same digital platform definitely enriches the weld assessment making it quicker and reliable with the support of AI, it gives the possibility of remote visual inspection, it ensures a flawless workflow where visual inspection data are stored in a dedicated platform and can be accessed at any moment by stakeholders.

Evaluation of Three Soil Moisture Profile Sensors Using Laboratory and Field Experiments.

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m3 m-3 per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m3 m-3) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future.

Recognition and robot grasping of disordered workpieces with 3D laser line profile sensor

Disorderly grasping of mixed or stacked workpieces are widely used in the manufacturing industry, especially in customized assembly lines. The mutual occlusion between workpieces causes geometrical information loss, thus making it difficult to obtain precise poses when grasping the workpieces. To provide a better robotic disorderly grasping solution, we firstly use a 3D laser line profile sensor to acquire the point clouds of the workpieces. Then the point clouds are inputted into a relation-shape convolutional neural network (RS-CNN) for instance-level recognition. According to the recognition results, coarse point cloud registration as well as fine point cloud registration are employed to estimate the pose of each workpiece. The disordered workpieces will be picked up by a 6-axis industrial robot in priority order. Experimental results show that the proposed recognition and grasping system can achieve a higher grasping success rate on a set of four types of common workpieces with adversarial geometry.

Research on the line point cloud processing method for railway wheel profile with a laser profile sensor

Wheels are crucial components of railway trains, measuring their parameters is extremely significant to the routine maintenance of train operation. In this study, a line point cloud data processing method is suggested for measuring the wheel profile with a laser profile sensor. Aiming at two common types of noise point in line point cloud data, the steps of the method are divided into two parts: 1)Establishing a discriminant function based on the Angle-Distance algorithm to remove defective points; 2)Applying a segmental filter to process the noise data: in the small radius of curvature segment of the wheel profile curve, using improved SG filter which takes into account the distance-based Gaussian weight (DGSG), and in the large radius of curvature segment and linear segment are processed by Gaussian filter and median filter respectively. For lab and in-service wheels, the measuring procedures are both used. The geometric parameters are measured from the processed data, which are then compared with standard data to verify the accuracy of measurements. The comparable experiment results show that the geometric parameters measured from processed data have higher accuracy than the original data.

Narrow Weld Joint Recognition Method Based on Laser Profile Sensor

Intelligent manufacturing technology has recently experienced rapid development, where robot welding systems are an essential component. A laser profile sensor is commonly utilized in intelligent robot welding systems. However, when the laser stripe is projected onto a narrow weld joint, the deformation of the laser stripe is not sufficiently apparent, and the traditional weld joint recognition methods based on the laser profile sensors cannot detect the narrow weld joint position. Meanwhile, the strong arc and spatter noise during welding and the expansion of weld joint width caused by welding thermal expansion are likely to interfere with weld joint recognition. In addition, many common weld seam recognition methods can only recognize certain weld joints and have limited flexibility and robustness for other types of weld joints. This study proposes a wide range of weld joint recognition algorithms based on the laser profile sensor to overcome these challenges. The gray value histogram analysis method, the adaptive threshold method, the internal propulsion center and boundary extraction (IPCBE) method, the coordinate analysis method, and the stripe end center detection method proposed in this study are used primarily in the algorithm. The algorithm solves the problem of identifying narrow weld joints and allows for the identification of larger conventional weld joints. Furthermore, the algorithm has good a anti-spatter and anti-arc interference ability and a fast speed, allowing it to meet the demand for real-time welding identification.

Application of optical velocity measurements including a novel calibration technique for micron-resolution to investigate the gas flow in a model experiment for crystal growth

An important step in the fabrication of many modern semiconductor materials and devices is the crystal growth process. These processes take place in high-temperature furnaces using inert gas and other atmospheres. The flow in the gas phase influences the transport of crystal components, dopants and impurities and hence has a significant impact on the grown crystals. In this work, we study the flow in a simplified model of a crystal growth furnace. This model is made of PMMA filled with a Diesel mixture as a model fluid to match the refractive index of PMMA and to allow for measurements in the complex geometry. The comparability to the flow in a real furnace is ensured by matching the Reynolds number. Two optical measurement methods, Particle Image Velocimetry (PIV) and the Laser Doppler Velocity Profile Sensor (LDV-PS) are used to investigate the global flow field as well as the small-scale flow structures. A calibration model is developed for the LDV-PS to reduce systematic measurement errors caused by the refractive index of the model fluid from up to 1% to less than 0,1%. The results obtained in this study improve the understanding of the gas flow behavior inside a crystal growth furnace and provide reference data for simulation. The first analysis shows a highly unsteady flow with unexpected flow direction along the crystal and melt surfaces. The near-surface flow patterns are of particular relevance in crystal growth because of their large influence on the local heat and mass transport during the crystallization process.

Temperature compensated and soil density calibrated soil moisture profiling sensor with multi-sensing point for in-situ agriculture application

In this work, we have fabricated a dielectric based soil moisture sensor with multi-sensing points (SMSMSP), which comprises of five interdigitated electrodes on both side of printed circuit board (PCB). Thickness of the fabricated SMSMSP is about 1.6 mm and light weight (0.1 kg), hence easy to carry for the field measurements. Further, we calibrated the sensor for different soil dry bulk density and observed that the time taken by the soil moisture to penetrate from soil surface till 25 cm is around 10 mins and 57 mins when density is maintained at 1.1 gm/cc and 1.3 gm/cc, respectively. We observed that the SMSMSP output changes only by 2.7 % when the ambient temperature varies from 20 °C to 60 °C. Both lab and field deployments results indicate that measured soil water content using SMSMSP at different depths is within ± 3% when compared with the gravimetric method.

On the applicability of LDV profile-sensors for periodic open wet clutch flow scenarios

The present experimental study revolves around the applicability of a Bragg-shifted laser Doppler velocimetry profile sensor (LDV-PS) in use for open wet clutch flow scenarios, where sub-millimeter gap height and textured surfaces are present. It is shown that the LDV-PS is capable to determine angular-resolved 1D3C velocity information, with all complex flow structures, depicted properly that are present in a radial groove. For the flow measurements the sensor is tilted to pm 30^{circ } compared to the axial orientation to enable the opportunity to reconstruct angular-resolved 1D3C velocity fields from two consecutively conducted runs. This facilitates measurement results with high axial and angular resolution for the complete open clutch flow and proves for the first time, that a profile sensor is capable to extract 3C information with the mentioned method. The results show that all characteristic flow structures occurring in the investigated sub-millimeter rotor-stator gap flow can be recorded properly. This insight renders the LDV-PS a promising and straight-forward applicable means to support industry-relevant research so as to uncover formerly hidden flow features and thus contribute to advanced development approaches for the respectively considered applications.Graphical abstract

Inter-comparison of atmospheric boundary layer (ABL) height estimates from different profiling sensors and models in the framework of HyMeX-SOP1

Abstract. This paper reports results from an inter-comparison effort involving different sensors and models used to measure the atmospheric boundary layer height (ABLH). The effort took place in the framework of the first Special Observing Period of the Hydrological Cycle in the Mediterranean Experiment (HyMeX-SOP1), with the Raman lidar system BASIL deployed in Candillargues (southern France) and operating in almost continuous mode over the time period September–November 2012. ABLH estimates were obtained based on the application of the Richardson number technique to Raman lidar and radiosonde measurements and to ECMWF-ERA5 reanalysis data. In the effort we considered radiosondes launched in the proximity of the lidar site, as well as radiosondes launched from the closest radiosonde station included in the Integrated Global Radiosonde Archive (IGRA). The inter-comparison effort also includes ABLH measurements from the wind profiler, which rely on the turbulence method, as well as measurements obtained from elastic backscatter lidar signals. The Richardson number approach applied to the on-site radiosonde data is taken as reference. Measurements were carried out throughout the month of October 2012. The inter-comparison is extended to both daytime and night-time data. Results reveal a very good agreement between the different approaches, with values of the correlation coefficient R2 for all compared data pairs in the range 0.94–0.98. Values of the slope of the fitting line in the regression analysis are in the range 0.91–1.08 for daytime comparisons and in the range 0.95–1.03 for night-time comparisons, which testifies to the presence of the very small biases affecting all five ABLH estimates with respect to the reference ABLH estimate, with slightly smaller bias values found at night. Results also confirm that the combined application of different methods to the sensors and model data allows us to get accurate and cross-validated estimates of the ABL height in a variety of weather conditions. Correlations between the ABLH measurements and other atmospheric dynamic and thermodynamic variables, such as CAPE (convective available potential energy), friction velocity and relative humidity, are also evaluated to infer possible mutual dependences.