Scanning Errors Research Articles

Classification of Scanning Errors of Digital Scans Recorded by Using Intraoral Scanners.

The different scanning errors that can be caused by the operator handling an intraoral scanner (IOS) or the intraoral conditions of the patient being scanned have not been described. The purpose of this review was to describe and classify the scanning errors that can be identified in digital scans recorded by using IOSs. The identification of scanning errors in an intraoral scan and understanding the cause of these scanning errors are fundamental procedures for successfully handling an IOS and integrating these digital data acquisition technologies in dental practices. There are two main types of scanning errors: the ones created by the operator and the ones caused by the intraoral conditions of the patient. There are seven operator-related scanning errors: mesh hole, stitching, tissue, reliability, umbrella, implant scan body geometry, and scanning noise errors. Additionally, there are four patient-related scanning errors: humidity, bridge, fuzzy finish line, and scanability noise errors. The identification of scanning errors is fundamental for assessing the quality of an intraoral digital scan. The comprehensive reading of these scanning errors allows the dental professional to understand if the scanning error can be corrected or if it is related to hardware/software limitations of IOSs.

Effect of scanning strategies on trueness and time efficiency of digital impression on paediatric models.

Various scanning methods aim to reduce intraoral scanner errors, yet no specific approach targets paediatric patients. Evaluate continuous (C) and noncontinuous (NC) scanning patterns' impact on the trueness and duration of paediatric digital impressions. A standard pedodontic typodont model was scanned with a Trios 4 Move+ IOS using four scanning strategies. C1 and C2 followed continuous scanning from the right molars, with different directions. NC1 scanned noncontinuously from the right molar, with breaks every four teeth. NC2 began at the right primary incisor, with breaks after every three teeth. Each scan, performed five times by one researcher, was timed. Industrial scanning provided reference data. Deviations were analyzed using the MIXED procedure. NC1 had higher deviations in the lower jaw, whereas C1 and NC1 showed higher deviations in the upper jaw (p < .001). C1 had significantly higher deviations in the upper jaw than the lower jaw (p = .041). NC2 had the longest scanning time in both upper and lower jaws (p = .002). Paediatric digital impressions benefit from starting at the anterior and incorporating breaks, although this increases scanning time.

Effect of digital competence on scan time and scan error in intraoral scanning

Objectives: This study aimed to test whether the digital competence of dental hygienists can affect their intraoral scanning potential in terms of scan time and error. Methods: Dental hygienists and dental hygiene students who had never used an intraoral scanner were surveyed to determine their digital competence. Individual data collected using an intraoral scanner was compared with reference data collected using a model scanner to identify scanning errors, and participants’ scanning times were measured. Results: A significant decrease in scanning time was observed as the overall level of digital competence increased. The increase in digital skills and digital knowledge led to a decrease in scanning time by 3.73 and 3.98 minutes, respectively. Conclusions: This study found that digital competence was associated with reduced scan times, but less so with scan errors. This may be due to recent advances in scanning software, and future studies may need to develop a digital competence assessment tool that is more appropriate for the dental field.

Full surface profile measurement of a small sized ball based on a stitching measurement method by reversal clamping

Full surface profile measurement of a small sized ball based on a stitching measurement method by reversal clamping

Unfamiliar Product and Barcode Scanning Error Leads to Patient Death.

Unfamiliar Product and Barcode Scanning Error Leads to Patient Death.

Autonomous Crack Detection for Mountainous Roads Using UAV Inspection System.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×106) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads.

Comparison of the performances of Statistical and Artificial Neural Network models in the prediction of geometry and density of PLA/wood biocomposite cubes manufactured by FDM

The present study investigates the impact of scanning speed, printing temperature, and layer height on the density, dimensions, errors of parallelism, and surface finish of cubical specimens made of PLA/wood biocomposite and manufactured by Fused Deposition Modelling (FDM). The study examined 64 specimens, each produced with a unique set of process parameters. The Response Surface Methodology (RSM) was employed to evaluate the effects of process parameters on the examined properties of the manufactured cubes. RSM analysis revealed the statistical significance of direct proportion between the layer height, printing temperature, and x-and y-dimensions of the manufactured specimens (with P-values of 0, 0, 0.002, and 0, respectively). Also, the scanning speed and error of parallelism in z-oriented faces were statistically correlated (with a P-value of 0.035). For layer height and cube density, an indirect proportion was observed (with a P-value of 0). Compared to the regression model, ANN exhibited better performance at process parameters effect evaluation. The worse performance of regression models can be attributed to their limited capacity to represent non-linear relationships, while ANN models can capture the complex non-linear nature of the process, leading to better performances (R2 close to 100%). An evaluation of the defects in the specimens was carried out using the go/no-go diagram.

The Influence and Compensation of Microwave Holographic Measurement Errors on Antenna Measurement Accuracy

Microwave holographic measurement technology is a common method used in antenna measurement. This method has high measurement efficiency and high precision. To evaluate and enhance the antenna’s performance, it is crucial to precisely determine the surface deformation. In this paper, the effects of feed offset error and scanning error on the antenna microwave holographic measurement results are investigated, and corresponding error compensation methods are proposed. The relationship between the influence of the error sources on the antenna gain loss and surface deformation accuracy is established. The reasons for holographic measurement errors, their characteristics, and their specific impact on system performance can be better understood. In order to improve the accuracy of the measurement, the compensation methods for the different measurement errors are given. They can provide the theoretical basis for maintaining and enhancing the performance of antenna system.

Exact surface measurement based on phase error insensitive method for white-light scanning interferometer

Phase error induced by the scanning error of piezoelectric transducer and vibration in white-light interference signal (WLIS) impose limitations on measurement accuracy. In this paper, two techniques are investigated to eliminate phase error cooperatively. A monochromatic interferometer owning the same optical path as the white-light scanning interferometer (WLSI) is employed to detect and compensate the phase error. Due to the incomplete consistency between the detected phase error and its actual distribution, an algorithm is introduced to further enhance the measurement accuracy. The simulation is given to prove the proposed method is available in different magnitude of noise. In the experiment, the front surface of wedged window is measured, whose maximum fluctuations are reduced to 28.8 nm and 3.0 nm from 62.7 nm with the gradual application of two technologies, and the repeatability reach 5.0 nm and 0.4 nm from 16.8 nm, respectively. The root mean square height S q and maximum height S z ultimately decrease to 1.3 nm and 1.6 nm. The measurement of step with height of about 3.0 μm also has the good accuracy and repeatability after phase error elimination. Since the different reflectivity of measured surfaces in the wavelength range of light source will also bring the measurement error, its influences are analyzed by simulation. The proposed techniques provide a phase error insensitive method for WLSI that has potential on measurement in the complex environment.

Digital twin-enabled error and uncertainty mapping for 3D scanning

Digital twin-enabled error and uncertainty mapping for 3D scanning

Scanning Error Compensation in Ground-Based ArcSAR Monitoring

Scanning Error Compensation in Ground-Based ArcSAR Monitoring

Fiber Bragg grating scanning spectral distortion caused by vibration in a rotor temperature monitoring system

Fiber Bragg grating scanning spectral distortion caused by vibration in a rotor temperature monitoring system

Dynamic Compensation Strategy for Scanning Error of Contact Probe Based on Damping Ratio and Frequency Mapping

Dynamic Compensation Strategy for Scanning Error of Contact Probe Based on Damping Ratio and Frequency Mapping

Application of the virtual-fit method for fixed complete denture cases designed on intraoral scans: Effect of cement spacing

To validate the virtual-fit alignment, analyze the impact of cement spacing on internal/marginal gaps, and correlate results with conventional trueness measures. Four dental abutment models were scanned using an industrial reference scanner (one time each), Emerald S (three times each), and Medit i700 (three times each) intraoral scanners (IOS). On each IOS scan (n=24), three complete-arch fixed frameworks were designed with 70 or 140µm cement space with no marginal space (groups 70 and 140) and 70µm with an additional 20µm space, including the margin (group 70+20). Two types of alignment were performed by GOM Inspect software. The reference and IOS scans were aligned through a conventional iterative closest point algorithm (ICP) where the penetration of the two scans was permitted into each other (conventional trueness method). Second, the computer-aided designs were superimposed with the reference scan also using an ICP, but preventing the design from virtual penetration into the model (virtual-fit method). The virtual-fit algorithm was validated by non-penetration alignment of the designs with the IOS scans. Internal and marginal gap was measured between the design and the abutments. The difference between spacing groups was compared by Friedman's test. A statistical correlation (Spearman's Rho Test) was computed between the measured gaps and the conventional trueness method. A significant difference was accepted at p<0.05 after the Bonferroni correction. The gaps deviated from the set cement space by 3-13µm on IOS scans (validation of virtual-fit algorithm). The internal gap of the design on the reference scan was not affected by cement spacing (Emerald S, p=0.779; Medit i700, p=0.205). The marginal gap in groups 70 and 70+20 was significantly lower than in group 140 in Emerald S (p<0.05). In Medit i700, it was lower in the 70+20 group than in the group 70 (p<0.01) and in the group 140 (p<0.05). Some Medit i700 scans exhibited high marginal gaps within group 70 but not in groups 70 and 140. The measured gaps correlated significantly (r=0.51-0.81, p<0.05-0.001) with the conventional trueness but were 2.6-4.6 times higher (p<0.001). Virtual-fit alignment can simulate restoration seating. A 20µm marginal and 90µm internal spacing could compensate for scan errors up to several hundred micrometers. However, 140µm internal spacing is counterproductive. The conventional trueness method could only partially predict framework misfit. The virtual-fit method can provide clinically interpretable data for intraoral scanners. Emerald S and Medit i700 intraoral scanners are suitable for fabricating complete-arch fixed tooth-supported prostheses. In addition, a slight elevation of spacing at the margin could compensate for moderate inaccuracies in a scan.

Analysis of scanning systematic errors for airborne laser bathymetry.

For the Palmer mechanical scanning pattern of an airborne laser bathymetry system, the potential errors of the scanning system are analyzed, and the associated error model is derived. The model composes the description of laser rays, water surface fluctuations, and refraction, and introduces certain simplifications concerning the water surface and column. Based on the scanning error model, the impact of each error source on the vertical and horizontal positioning accuracy is investigated and established through a numerical simulation. The quantitative impacts of each inaccuracy on the coordinates of the laser footprints on the sea surface and bottom were calculated, with a height of 100m for the airborne platform and a water depth of 10m. To verify the correctness of the simulation results and the error model based on a theoretical analysis, experiments are utilized with the system that we developed. Both the simulation analysis and experimental results show that this method can effectively obtain the systematic errors. The outcomes of the error model and analysis will give the theoretical foundations for lowering the effect brought on by each error source in the compensation scanning system and improving the point cloud accuracy in the ensuing data processing.

Accurate white light phase-shifting interferometry under PZT scanning error

Accurate white light phase-shifting interferometry under PZT scanning error

Predictability of intraoral scanner error for full-arch implant-supported rehabilitation

ObjectivesThe present study aimed to analyze the behaviors of three intraoral scanners (IOSs): evaluating the interdistance and axial inclination discrepancies in full-arch scans, predictable errors were searched.Materials and methodsSix edentulous sample models with variable numbers of dental implants were used; reference data were obtained with a coordinate-measuring machine (CMM). Each IOS (i.e., Primescan, CS3600, and Trios3) performed 10 scans per model (180 total scans). The origin of each scan body was used as a reference point to measure interdistance lengths and axial inclinations. Precision and trueness of interdistance measurements and axial inclinations were evaluated to address error predictability. Bland–Altman analysis, followed by linear regression analysis and Friedman’s test (plus Dunn’s post hoc correction), was performed to evaluate the precision and trueness.ResultsRegarding interdistance, Primescan showed the best precision (mean ± SD: 0.047 ± 0.020 mm), while Trios3 underestimated the reference value more than the others (p < 0.001) and had the worst performance (mean ± SD: −0.079 ± 0.048 mm).Concerning the inclination angle, Primescan and Trios3 tended to overestimate angle values, while CS3600 underestimated them. Primescan had fewer inclination angle outliers, but it tended to add 0.4–0.6° to the measurements.ConclusionsIOSs showed predictable errors: they tended to overestimate or underestimate linear measurements and axial inclinations of scan bodies, one added 0.4–0.6° to the angle inclination values. In particular, they showed heteroscedasticity, a behavior probably related to the software or the device itself.Clinical significanceIOSs showed predictable errors that could affect clinical success. When performing a scan or choosing a scanner, clinicians should clearly know their behaviors.

Evaluation of SEU impact on convolutional neural networks based on BRAM and CRAM in FPGAs

Evaluation of SEU impact on convolutional neural networks based on BRAM and CRAM in FPGAs

Effect of Three-Dimensional (3D) Scanning Factors on Minimizing the Scanning Errors Using a White LED Light 3D Scanner

The largest problem with scanning real objects involves bearing the huge costs of scanning and the low quality of point cloud data for a scanned object, thereby increasing the cost and lead time. Therefore, a need exists to improve the quality of scanning to save time, costs, and computational resources. In this research, the levels of optimal factors associated with a three-dimensional (3D) scanner were investigated, improving the quality of 3D scanning data. Optimizing the 3D scanner factors could help us acquire errorless digital scanned data that accurately resemble a 3D physical object and which may be further used in various engineering applications, e.g., additive manufacturing and non-engineering applications. For this study, four modes of 3D scanning (A, B, C, and D) were utilized with five crucial 3D scanning factors namely texture, watertightness, simplification, and alternate deployments of smoothness and sharpness. This research was divided into two stages. The former stage involved the 3D scanning of two samples with simple and complex geometrical intricacies and the later stage involved checking the scanned objects for any dimensional errors. A coordinate measuring machine (CMM) was used to measure the dimensional details of the real objects. For virtual metering, Solidworks was utilized. With reference to the limited literature in the current context, 3D scanning errors were highly reduced for the first time up to 0.1% for the complex sample when compared to the errors found for the simple sample.

Bigger babies: what happens in real practice in a non-academic UK center? Detection accuracy and outcomes with induction.

There is emerging evidence of improved outcomes with induction of labour for pregnancies in which the baby is thought to be large. This trial identifies scan accuracy and the effect of intervention for pregnancies complicated by suspected large for gestational age (LGA) on customized chart outside an academic center. This is a retrospective cohort study of 3 groups of induced pregnancies; women with a suspected LGA fetus, women with diabetes (DM) and a control group (C) of women that underwent induction of labour on or after 280 days gestation. Data collection and analysis were prespecified. Scan accuracy and outcomes between the cohorts were compared. Over 1 year there were 845 cases: LGA (128), DM (116) and control cases (601). Mean birthweights differed significantly. PPV of EFW for birthweight >90th centile on GROW chart, WHO chart, and >4 kg was 0.35-0.40. Projected birthweight of >4 kg significantly better predicted itself (AUROC 0.70, 0.74 and 0.80). Mean scan error was -5.2% and +15.6% for DM and LGA. Shoulder dystocia and neonatal morbidity were not increased in LGA despite the significant increase in AVD 28/128, 21.9% vs. 99/601, 16.5%, aOR 2.20 (1.07-4.5). SVD was significantly less likely LGA vs. C at 69/128, 53.9% vs. 413/601, 68.7% aOR 0.38 (95% CI: 0.21-0.70). Third trimester EFW for bigger babies was poorly predictive of macrosomia. Fetal outcomes were good but women selected and induced as LGA had higher rates of hemorrhage and intervention.