Measurement Error Research Articles

A robot in-place geometric model reconstruction method for additive manufacturing parts by fusing 3D scanner measuring data

Abstract Geometric model reconstruction of additive manufacturing (AM) parts based on in-place measurement (IPM) is meaningful in realizing the robotic post-processing of AM parts. However, the low motion accuracy of the robot and the low efficiency of the IPM system restrict the development of the in-place model reconstruction. This paper presents a reconstruction method that integrates the 3D scanner and the robotic IPM system to improve the accuracy and efficiency of the model reconstruction. First, considering the measurement errors in the robotic IPM system, an error correction model is developed by measuring the calibration ball (CB) at the boundary of the measurement space. For each CB, a modified correction matrix (MCM) is constructed based on measurement data and geometric parameters of the CB to compensate for measurement errors. Second, a CB in the measurement space is measured, and the MCMs of each CB at the boundary are combined using the neural network model. Based on the measurement data of the CB, a geometric error correction model for the entire measurement space is developed, which corrects the spatial distribution of geometric errors in the robotic IPM system and enhances its accuracy within the measurement space. Finally, a local motion algorithm based on IPM data is proposed to calibrate the large volume of point cloud data from a 3D scanner. A high-precision model of AM parts is quickly reconstructed by fusing the few high-accuracy robotic IPM data with the large volume of low-accuracy point cloud data obtained by the 3D scanner. In order to verify the effectiveness of the method proposed in this paper, the measurement experiments on the CB are conducted. The results show that the model reconstruction accuracy can reach 0.06 mm compared with point cloud data obtained by the 3D scanner. The AM concave and convex parts are measured and the manufacturing geometric accuracy is analyzed by the proposed method. Compared to the digital model, the maximum deviation of the AM concave part is 1.3864 mm, and the maximum deviation of the AM convex part is 1.6802 mm.

Direct assessment of the influence of pose repeatability on the accuracy of dimensional measurements for computed tomography systems with high degrees of freedom

Abstract Industrial x-ray computed tomography (CT) systems with high geometric flexibility are increasingly utilized for large-scale measurement objects or challenging measurement tasks. To maintain high accuracy when deviating from the established circular scan trajectory, trajectory calibration methods using multi-sphere reference objects with known marker positions are commonly employed. These multi-sphere objects can either be scanned together with the measurement object (online trajectory calibration) or in a separate scan (offline trajectory calibration). While offline calibration increases machine time, it generally results in higher scan quality. However, a sufficient pose repeatability is necessary to ensure comparable or even superior accuracy to online calibration. In this contribution, we present a straightforward procedure to compare both types of trajectory calibration in a way that the differences of the results can directly be traced back to the influence of the pose repeatability. The multi-sphere reference object is not only used for trajectory calibration, but simultaneously as a measurement object for repeated measurements. The methodology is tested on both a twin robotic CT system and a conventional CT system that is additionally equipped with a hexapod manipulator for adaptive object tilting. Results showed, independent from the type of trajectory calibration, systematic measurement errors in the order of 10−5–10−4 of measured sphere distances and sphericity values below 50 μ m . For sphere distances, random errors were increased by a factor of 5 due to the offline trajectory calibration, but were still low ( < 1 μ m ) in comparison to systematic errors and the spread of different measurement features. Overall, both investigated systems demonstrated sufficient positioning repeatability for offline trajectory calibration. The method is in general also applicable to any other types of manipulator systems used for CT devices. It provides a workflow for the decision which type of trajectory calibration is preferable for a given CT system.

Electrochemical Impedance based Characterization of Membrane Separators

Abstract Separators used in classical alkaline electrolyzers have to prevent gas crossover while keeping the potential drop due to the separator to a minimum. Impedance measurements are an important way of characterizing resistivity of membrane separators. A variety of hardware has been used in the literature for such characterization. The objective of this study is to compare the relative merits and demerits of different kinds of cell hardware for such measurements. We propose the use of coin cells in membrane characterization. Here, we have compared a conventional conductivity cell with a coin cell and a T-cell, by electrochemical impedance spectroscopy studies on commercial Zirfon® membranes in 0.1M NaOH electrolyte. In the latter two cases, several configurations of membrane-electrode assembly have been studied in order to establish the reproducibility and reliability of measurements. The coin cell and T–cell hardware are seen to improve the reproducibility and reliability significantly over the conductivity cell for electrochemical characterization of membranes. Pros and cons along with possible sources of error in measurement have been analyzed in each case. The impedance data for Zirfon® in both coin and T-cell setup was observed to be reproducible and internally consistent.

Simultaneous measurement of particle size distribution and mixing ratio based on Monte Carlo ultrasonic attenuation model

Abstract Mixed particle systems are commonly employed in industrial processes, where the characterization of particle size parameters and mixing ratio can frequently serve as key indicators in process control and production optimization. A Monte Carlo (MC) model was developed to numerically predict and study the ultrasonic attenuation spectrum characteristics in the polymethyl methacrylate (PMMA)-glass aqueous suspension, and together with the particle swarm optimization (PSO) algorithm, to handle the inverse problem in solving the particle size, distribution width, and mixing ratio. The results of the numerical simulations indicate that there exists a linear relationship between the attenuation coefficient and the mixing ratio, with the particle size exerting a significant influence. Furthermore, the multi-parameter simultaneous inversion also yielded calculation deviations of less than 1%, 3%, and 6% for the mixing ratio, characteristic diameter, and distribution width, respectively, in comparison to their given values. Afterward, a series of experiments were conducted to quantify the particle size and mixing ratio through the analysis of ultrasonic spectra. In spherical PMMA-glass aqueous suspensions, the measurement error for the mixing ratio and particle size parameters are found to be less than 7% and 10%, respectively, when compared to the image method and the given values. Nevertheless, the measurement errors are slightly increased in a non-spherical mixed particle system, where the volume median diameter and mixing ratio are still less than 10%. The MC modeling and PSO algorithm offer the potential to characterize particle size and mixing ratio for mixed particle systems in industrial applications.

ROAR-CAT: Rapid Online Assessment of Reading ability with Computerized Adaptive Testing

The Rapid Online Assessment of Reading (ROAR) is a web-based lexical decision task that measures single-word reading abilities in children and adults without a proctor. Here we study whether item response theory (IRT) and computerized adaptive testing (CAT) can be used to create a more efficient online measure of word recognition. To construct an item bank, we first analyzed data taken from four groups of students (N = 1960) who differed in age, socioeconomic status, and language-based learning disabilities. The majority of item parameters were highly consistent across groups (r = .78–.94), and six items that functioned differently across groups were removed. Next, we implemented a JavaScript CAT algorithm and conducted a validation experiment with 485 students in grades 1–8 who were randomly assigned to complete trials of all items in the item bank in either (a) a random order or (b) a CAT order. We found that, to achieve reliability of 0.9, CAT improved test efficiency by 40%: 75 CAT items produced the same standard error of measurement as 125 items in a random order. Subsequent validation in 32 public school classrooms showed that an approximately 3-min ROAR-CAT can achieve high correlations (r = .89 for first grade, r = .73 for second grade) with alternative 5–15-min individually proctored oral reading assessments. Our findings suggest that ROAR-CAT is a promising tool for efficiently and accurately measuring single-word reading ability. Furthermore, our development process serves as a model for creating adaptive online assessments that bridge research and practice.

Translation and psychometric evaluation of the Danish version of the knee outcome survey - activities of daily living scale in patients with anterior cruciate ligament injuries

BackgroundThe Knee Outcome Survey – Activities of Daily Living Scale (KOS-ADLS) is a patient-reported outcome measure (PROM) developed to assess symptoms and functional limitations in patients with various knee disorders. The aim of this study was to translate and culturally adapt the KOS-ADLS to Danish and to evaluate the psychometric properties of the Danish version (KOS-ADLS-DK) in patients with anterior cruciate ligament (ACL) injury.MethodsThe KOS-ADLS was translated and culturally adapted to Danish in accordance with recommended guidelines. To evaluate psychometric properties in a test-retest design 115 Danish patients with ACL injury completed KOS-ADLS-DK and other knee specific PROMs at baseline and after 14 days. A sub-population of 79 patients completed the KOS-ADLS-DK before and after 3 months of rehabilitation. Structural validity (factor analysis), Internal consistency (Cronbach`s alpha), construct validity (hypothesis testing), test-retest reliability (Intraclass Correlation Coefficient [ICC]), test-retest agreement (Bland-Altman plots with 95% Limits of Agreement), Standard Error of Measurement (SEM), Smallest Detectable Chance (SDC), responsiveness (construct approach with hypothesis testing) and floor/ceiling effects were assessed.ResultsNo major problems were revealed in the cross-cultural adaptation process. The KOS-ADLS-DK was uni-dimensional and showed a high internal consistency (Chronbach’s alpha = 0.90). Construct validity was not perfect as only five of seven hypotheses were confirmed, but there was a good reliability (ICC = 0.88) and test-retest agreement showed equal distribution of measurement error across the scale and the SEM was 4.9% and SDC was 13.6%. However, hypotheses testing on change scores revealed the KOS-ADLS-DK to be responsive and there were no floor/ceiling effects.ConclusionOverall, the Danish version of KOS-ADLS is considered a valid, reliable and responsive PROM for assessing symptoms and functional limitations in patients with ACL injury but may show some limitations in its construct validity.

Using causal diagrams and superpopulation models to correct geographic biases in biodiversity monitoring data

Abstract Biodiversity monitoring schemes periodically measure species' abundances and distributions at a sample of sites to understand how they have changed over time. Often, the aim is to infer change in an average sense across some wider landscape. Inference to the wider landscape is simple if the species' abundances and distributions are similar at sampled to non‐sampled locations. Otherwise, the data are geographically biased, and some form of correction is desirable. We combine causal diagrams with ‘superpopulation models’ to correct time‐varying geographic biases in biodiversity monitoring data. For a given time‐period, expert‐derived causal diagrams are used to deduce the set of variables that explain the geographic bias, and superpopulation models adjust for these variables to produce a corrected estimate of a landscape‐wide mean of for example abundance or occupancy. Estimating a time trend in the variable of interest is achieved by fitting models for multiple time‐periods and, if the drivers of bias are suspected to change over time, by constructing per period causal diagrams. We test the approach using simulated data then apply it to real data from the UK Butterfly Monitoring Scheme (UKBMS). If the variables that explain the geographic bias are known and measured without error, our method is unbiased. Introducing measurement error reduces the method's efficacy, but it is still an improvement on using the sample mean. When applied to data from the UKBMS, the approach gives different results to the scheme's current method, which assumes no geographic bias. Where the goal is to estimate change in some variable of interest at the landscape level (e.g. biodiversity indicators), models that do not adjust for geographic bias implicitly assume it does not exist. Our approach makes the weaker assumption that there is no geographic bias conditional on the adjustment variables, so it should yield more accurate estimates of time trends in many circumstances. The method does require assumptions about the drivers of bias, but these are codified explicitly in the causal diagrams. Operationalising our approach should be less costly than full probability sampling, which would be needed to satisfy the assumptions of conventional approaches.

Measurement error in remotely sensed fractional snow cover datasets: implications for ecological research

Measurement error in remotely sensed fractional snow cover datasets: implications for ecological research

Evaluation of four different standard addition approaches with respect to trueness and precision.

This work provides a statistical analysis of four different approaches suggested in the literature for the estimation of an unknown concentration based on data collected using the standard addition method. These approaches are the conventional extrapolation approach, the interpolation approach, inverse regression, and the normalization approach. These methods are compared under the assumption that the measurement errors are normally distributed and homoscedastic. Comparison is done with respect to the two most important characteristics of every estimator, namely trueness (bias) and precision (variability). In addition, the authors supply, if not already available, mathematical formulas to approximate both quantities. Also, a real-world data set is used to illustrate the performance of all four methods. It turns out, that, given that all assumptions underlying the use of the standard addition method apply, the common extrapolation method is still the most recommendable method with respect to bias and variability. Nonetheless, if additional concerns come into play, other methods like, for example, the normalization approach in the case of increased problems with outliers might also be of interest for the practitioner.

Raman-based in situ concentration measurement of boric solution with high accuracy and stability

In this paper, a quantitative analysis of boric acid solutions’ concentration with high accuracy is demonstrated by Raman spectroscopy. The in-situ concentration measurement model is developed by an elastic net against Raman spectral data. The R 2 of the elastic net model in the test set prediction can reach 0.9960. Compared with the partial least squares method used in traditional quantitative model, the method proposed in this paper shows excellent performance with low measurement error and high stability. One hundred and twenty groups of Raman spectra of boric solution with different concentrations and flow rates are used for training and validation of the model. The mean relative prediction error of the elastic net model was reduced from 7.1 to 1.9% in comparison with the models based on linear regression and the partial least squares. The verification test of various concentrations of boric acid solution shows that the detection error is <2% when the concentration is below 2000 mg/L. The study provides a reference for improving the in-situ quantitative detection accuracy by using Raman spectroscopy.

Error Characteristic Analysis and Filtering Algorithm for GNSS Time-Series Data

Under regional environmental conditions such as open-pit mines and construction sites, there are usually fixed GNSS measurement points. Around these fixed stations, there are also mobile GNSS measurement modules. These mobile measurement modules offer advantages such as low power consumption, low cost, and large data volume. However, due to their low accuracy, these modules can only provide approximate positions as monitoring data, such as for vehicle management in open-pit mines. To extract more information from the existing large volume of low-accuracy data, it is necessary to process these low-accuracy data. Under conditions of the same time and space in a small area, factors affecting measurement accuracy can be comprehensively considered. By analyzing the temporal GNSS data within the same spatiotemporal small region and understanding the variation patterns of measurement errors, a general equation for measurement error variation can be formulated. Using filtering methods, the data quality can be improved. Through the analysis of the experimental data in this study, it was found that the variation patterns of measurement data obtained by devices of the same accuracy during the same time period are generally consistent. After applying filtering methods, the measurement accuracy of each station improved by up to approximately 95.9%, with a minimum improvement of approximately 84.4%. Under the condition of a 95% confidence level, the reliability increased by up to approximately 73.2%, with a minimum improvement of approximately 58.2%. These experimental results fully demonstrate that under regional spatiotemporal conditions, the temporal data obtained by GNSS measurement devices with similar accuracy exhibit similar error distribution patterns. Applying the same filtering method can significantly improve the accuracy and reliability of measurement data.

Computerized adaptive testing for PRWHE measurements using domains of pain and motor function.

The Patient-Rated Wrist and Hand Evaluation is an outcome measure for patients with conditions affecting the wrist or hand. We evaluated the structural validity of the Patient-Rated Wrist and Hand Evaluation using psychometric techniques, then developed computerized adaptive testing algorithms. Factor analysis found two health constructs consistent with 'Pain' and 'Motor Function'. Two computerized adaptive tests were developed, which reduced the number of items from five to a median of one ('Pain'), and from 10 to two ('Motor Function'). Both computerized adaptive tests achieved high levels of precision (standard error of measurement <0.3) and similar scores to the full-length measure, demonstrated by Bland-Altman analysis. Dividing the Patient-Rated Wrist and Hand Evaluation into two subscales could reduce the response burden, improve standardization of outcome measurement for clinicians and provide precise insight into patient symptoms.Level of evidence: N/A.

A stochastic process-based degradation modeling framework considering measurement errors: a perspective of dual non-Gaussian assumptions

The stochastic processes are a natural choice for describing the randomness in degradation processes caused by inherent randomness and environmental factors. This article proposes a new modified skew-normal distribution to capture measurement uncertainty, and then establishes a stochastic process-based degradation modeling framework, in which both degradation increments and measurement errors do not follow the Gaussian distributions. Taking the IG process as an example, the basic reliability indicators and the alarm probabilities caused by measurement errors are derived. In addition, a multi-stage parameter estimation algorithm based on moderate particle sizes and comparative tolerances is developed for this stochastic process-based degradation model under dual non-Gaussian assumptions. Finally, the effectiveness and advantages of the proposed model along with the parameter estimation algorithm are demonstrated by a simulation study and a case application, and it is particularly pointed out that the relative size of measurement errors has a significant impact on the precision of degradation reliability assessment.

Long-range trajectory reconstructions using the point mass model.

In shooting incident reconstructions, forensic examiners usually deal with scenes involving short-range trajectories, typically ≤30 m. Insituations such as this, a linear trajectory reconstruction model is appropriate. However, a forensic expert can also be asked to estimate a shooter's position by reconstructing a long-range trajectory where the bullet's path becomes arced as a result of gravity and the greater time in flight. In this study, the point mass model (PMM) was used, because it is accessible and considered sufficiently accurate. A computer program using PMM can perform long-range trajectory reconstructions starting from an impact point. The reconstruction results in an area where the shot is expected to be fired from, not a single location. This is caused by varying the input parameters of the PMM. The aim of this study is to assess the accuracy of the method and discuss the influence of the most relevant parameters. The model has been validated by comparing its performance with 20 handgun bullet trajectories that were determined using Doppler radar measurements over long ranges, i.e. from 500 m to 1800 m. Comparison between the area calculated using the model and the actual shooter position demonstrates the limits of these reconstructions, particularly at high incident angles. The differences between the reconstructed deflections and the deflections measured by the tracking radar are rather large. This phenomenon is caused by either measurement errors in the cross wind as a function of height or inaccuracy of the radar's deflection measurements.

Large errors in soil carbon measurements attributed to inconsistent sample processing

Large errors in soil carbon measurements attributed to inconsistent sample processing

Estimating Sharpe ratio function in the presence of measurement error

In financial econometrics, the Sharpe ratio function serves as a gold standard to measure the return-to-risk ratio for comparing different assets or trading strategies. In the recent literature, several methods have been developed to directly or indirectly estimate the Sharpe ratio function, yet none of them apply to the scenario where the covariates are measured with error. To handle this problem, we propose a new method by incorporating the local polynomial smoothing and SIMEX to simultaneously estimate the Sharpe ratio function and the negative log-volatility function in the presence of measurement error. The asymptotic bias and variance of the proposed estimators are also derived under some regularity conditions. We further conduct Monte Carlo simulations to evaluate the finite sample performance, and apply two real data examples to illustrate the usefulness of our new method.

A Method for Determining the Minimum Thickness of the Cut Layer in Precision Milling.

The minimum cutting thickness is a key value in machining processes, as below this value the material will only undergo elastic and plastic deformation without chip removal. Existing measurement methods require time-consuming preparation and complicated procedures. This work focuses on the development of a new, simplified method for determining the minimum cutting thickness (hmin) using a contact profilometer that can be used in industry. The use of the contact measurement method has made it possible to directly determine the value of the hmin parameter, to determine the length of the characteristic zones of interaction of the tool with the surface of the specimen, and to measure the angle of inclination of the working plane of the specimen. Measurement using a profilometer allows for the obtainment of results with high resolution, which greatly facilitates the identification of zones of tool interaction with the workpiece material during the cutting test and reduces the value of the measurement error. The proposed method simplifies the specimen preparation process by using rectangular specimens positioned on an inclined plane, which allows the depth of the cut to be varied smoothly. This paper presents experimental results and statistical analysis. Tests were carried out on C45 steel, and an ANOVA analysis was carried out to evaluate the effect of the grinding parameters on the hmin parameter. It was found that the feed rate had the largest effect on hmin (93%), while cutting speed had a smaller effect. A mathematical model was developed to predict values based on selected technological parameters such as cutting speed and feed per tooth.

The digit triplet test for school-age hearing screening: comparing languages, school grades and scoring methods in a large sample

Objective To investigate the reliability of a bilingual school-age hearing screening in four school grades based on the Digit Triplet Test (DTT) in two languages and to investigate three calculation methods for referral values in their ability to detect hearing losses and avoid false-positive results. Design and study sample 3255 children, aged between 10 and 17 years old, were tested during a systematic hearing screening program in a bilingual, French-German area in Belgium. French speaking children were tested with a French DTT, German children were tested with a German DTT. The SRT-values, their stability and measurement error were investigated per grade and language. The number of false-positive results was studied for three referral methods, using additional audiometric data of 71 children. Results Our data showed that reliable results with high stability and a small measurement error can be obtained in only around two minutes per ear. Differences between languages are minimal and grade-specific referral values were necessary. A referral method considering the lowest SNRs in the adaptive staircase reduces the number of false-positive results substantially. Conclusion The DTT versions of different languages can be used reliably in a bilingual school-age hearing screening program when grade-specific, alternative referral methods are implemented.

Eye posture and screen alignment with simulated see-through head-mounted displays.

When rendering the visual scene for near-eye head-mounted displays, accurate knowledge of the geometry of the displays, scene objects, and eyes is required for the correct generation of the binocular images. Despite possible design and calibration efforts, these quantities are subject to positional and measurement errors, resulting in some misalignment of the images projected to each eye. Previous research investigated the effects in virtual reality (VR) setups that triggered such symptoms as eye strain and nausea. This work aimed at investigating the effects of binocular vertical misalignment (BVM) in see-through augmented reality (AR). In such devices, two conflicting environments coexist. One environment corresponds to the real world, which lies in the background and forms geometrically aligned images on the retinas. The other environment corresponds to the augmented content, which stands out as foreground and might be subject to misalignment. We simulated a see-through AR environment using a standard three-dimensional (3D) stereoscopic display to have full control and high accuracy of the real and augmented contents. Participants were involved in a visual search task that forced them to alternatively interact with the real and the augmented contents while being exposed to different amounts of BVM. The measured eye posture indicated that the compensation for vertical misalignment is equally shared by the sensory (binocular fusion) and the motor (vertical vergence) components of binocular vision. The sensitivity of each participant varied, both in terms of perceived discomfort and misalignment tolerance, suggesting that a per-user calibration might be useful for a comfortable visual experience.

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.