Depth Error Research Articles

The Impact of Occlusion on Depth Perception at Arm's Length.

This paper investigates the accuracy of Augmented Reality (AR) technologies, particularly commercially available optical see-through displays, in depicting virtual content inside the human body for surgical planning. Their inherent limitations result in inaccuracies in perceived object positioning. We examine how occlusion, specifically with opaque surfaces, affects perceived depth of virtual objects at arm's length working distances. A custom apparatus with a half-silvered mirror was developed, providing accurate depth cues excluding occlusion, differing from commercial displays. We carried out a study, contrasting our apparatus with a HoloLens 2, involving a depth estimation task under varied surface complexities and illuminations. In addition, we explored the effects of creating a virtual "hole" in the surface. Subjects' depth estimation accuracy and confidence were assessed. Results showed more depth estimation variation with HoloLens and significant depth error beneath complex occluding surfaces. However, creating a virtual hole significantly reduced depth errors and increased subjects' confidence, irrespective of accuracy enhancement. These findings have important implications for the design and use of mixed-reality technologies in surgical applications, and industrial applications such as using virtual content to guide maintenance or repair of components hidden beneath the opaque outer surface of equipment. A free copy of this paper and all supplemental materials are available at https://bit.ly/3YbkwjU.

Addressing Actuator Saturation during Fault Compensation in Model-Based Underwater Vehicle Control

Robust control systems are a necessity for autonomous underwater vehicle (AUV) systems due to the challenges they face during operation. Many AUV control-design methods have been developed for different actuator configurations, with robustness against model parameter uncertainties, environmental disturbances, and system faults. Actuator faults can reduce the physical capabilities of a system, which can be compensated for through control re-allocation. However, the increased control allocation to the remaining actuators may cause actuator saturation and reduce controller performance. In this work, we present a depth-pitch model-based nonlinear control law that directly considers actuator saturation, and a fault-tolerant control allocation method for a hybrid AUV actuator configuration. Two types of actuator faults are considered for an underwater vehicle with a hybrid actuator configuration. The proposed controller is implemented in a simulated system, and its trajectory tracking performance is compared with a baseline system without fault or saturation tolerance. To determine the utility of the proposed saturation and fault tolerance control methods, the tracking performance in these simulations is quantified in terms of the settling time, post-fault peak values, and root mean square of the depth and pitch errors.

Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records

The use of unmanned aerial vehicles (UAVs) is steadily increasing due to their capacity to capture terrain elevation data with remarkable precision and cost-effectiveness. Nonetheless, their application for estimating water surface elevations and submerged terrain, such as channel bathymetry, remains constrained. Consequently, the development of a digital terrain model that relies on UAV data during low-water periods assumes a more extensive dry channel surface area, thus alleviating the information gap regarding submerged terrain. The objective of this brief report is to validate a hydraulic model for flood calculation. To this end, a 1D steady-state hydrological model of the Ñuble River based on a UAV survey in the low-water period of 2016 was constructed in HEC-RAS v.5.0.3 and compared to water surface elevation observations of the flood on 24 June 2023. The model tends to overestimate the flood, but the errors are considered tolerable for flood calculation (on average, a 10.6% depth error was obtained for a 30-year return period flood); therefore, the hydraulic model derived from remote sensing seems to be an effective alternative for the construction of hydraulic models for flood studies.

Numerical investigation of mixing process of high-pressure jet-cutting clay by water–air coaxial nozzle considering soil rheological properties

Simulation and accurate modeling of the mixing process of the high-pressure jet-cutting clay by the water–air coaxial nozzle is significantly important for the performance optimization of the triple fluid jet grouting. In this paper, a numerical model considering the soil rheological properties is proposed to investigate the mixing process of the high-pressure jet-cutting clay. The cohesive force model of clay is obtained based on the solution of the power law index and consistency factor by coupling the Herschel-Bulkley and soil logarithmic models. The interaction model among the gas phase, the liquid phase, and the clay medium is further established through use of the drag force model. A laboratory device of high-pressure jet-cutting transparent clay is developed to prove the feasibility of the proposed model for the mixing process of the high-pressure jet-cutting clay. Finally, using the validated numerical model, the mixing process of the high-pressure jet-cutting clay by the water–air coaxial nozzle with varying radial spacings between the air nozzle and water nozzle is numerically investigated, and the axial stability of the jet, the width of the cross-sectional profile, and the variation of the central axis velocity field of the mixing process are analyzed. Results demonstrated that the variation trend of the jet in both simulations and experiments is consistent, and the maximum error in jet depth is better than 3.3%, validating the accuracy of the numerical model of the mixing process of the high-pressure jet-cutting clay. The optimal radial spacing size for a water–air coaxial nozzle in high-pressure jetting of clay medium is 1.4 mm, which provides the best axial stability, the narrower jet cross-section, and the slowest decay of jet velocity along the central axis.

Searching for Unknown Material Properties for AM Simulations

Additive manufacturing (AM) simulations are effective for materials that are well characterized and published; however, for newer or proprietary materials, they cannot provide accurate results due to the lack of knowledge of the material properties. This work demonstrates the process of the application of mathematical search algorithms to develop an optimized material dataset which results in accurate simulations for the laser directed energy deposition (DED) process. This was performed by first using a well-characterized material, Ti-64, to show the error in the predicted melt pool was accurate, and the error was found to be less than two resolution steps. Then, for 7000-series aluminum using a generic material property dataset from sister alloys, the error was found to be over 600%. The Nelder–Mead search algorithm was then applied to the problem and was able to develop an optimized dataset that had a combined width and depth error of just 9.1%, demonstrating that it is possible to develop an optimized material property dataset that facilitates more accurate simulation of an under-characterized material.

An Experimental Study of GPR Near-Field Problem in Depth Ranging of Buried Objects

We evaluate the often neglected ranging (or cover depth) error of an object located within the Fresnel Region (or called radiating near-field) and reactive near-field region in ground penetrating radar (GPR). The experiments were conducted in concrete specimens with various rebar cover depths from 10 to 80 mm, which imitates any object imaged by GPR from reactive near-field, Fresnel region to far-field region. Velocities of each individual data point of the hyperbolic reflections of the embedded reinforcement in radargram are calculated by developed algorithms based on common offset antenna and multiple trilaterated ray paths. Two different velocity algorithms, based on semi- and full-trilaterated ray paths, were applied to estimate the wave travelling velocity as a prerequisite of cover depth measurement. The algorithms were firstly validated using a high-frequency 2-GHz antenna to verify the travelling speed of radar waves in air, which is equal to the speed of light. Then, the same antenna was used to estimate the cover depth by measuring the time of flight and velocity based on the two velocity algorithms. The results reveal that the ranging accuracy is highly in doubt in reactive near-field and Fresnel regions but is largely improved when the object locates in far-field region where GPR wave propagation becomes plane-type. The measurements, riding on accurate modelling of ray-path’s trilateration models, provide evidence that the normal linear scale of GPR time-to-depth conversion overlooks the effects of near-field/Fresnel region. We therefore suggest that the GPR near-field/Fresnel region and far-field boundary must be taken into account before any attempt of time-to-depth conversion and depth estimation of objects by GPR are carried out.

Estimating backfat depth, loin depth, and intramuscular fat percentage from ultrasound images in swine.

Fast, accurate, and reliable estimates of backfat depth, loin depth, and intramuscular fat percentage in swine breeding stock are used to increase genetic improvement and farm profitability. The objective of this study was to develop an equation-based model for the estimation of swine backfat depth, loin depth, and intramuscular fat percentage estimates obtained from longitudinal ultrasound images. Images were collected from purebred Duroc (n=230), purebred Large White (n=154), and commercial (n=190) pigs born in January 2021 at three farms located in North Carolina. An Exapad ultrasound machine captured longitudinal images across the 10th to 13th ribs at 182 (±12.8 SD) days of pig age. The total number of images processed for Duroc, Large White, and commercial pigs was 1385, 928, and 1168 images, respectively. To establish a standard measurement for model comparison, trained personnel following standard company procedures using the BioSoft Toolbox (v4.0.1.2; Biotronics Inc., Ames, IA) obtained backfat and loin depth measurements from the images. Longissimus muscle intramuscular fat percentage was predicted using near-infrared spectroscopy at approximately 22h postmortem. Backfat and loin depth estimation were conducted only for commercial pigs (n=190) while intramuscular fat estimation was conducted on all pigs (n=574). Average backfat depth, loin depth, and intramuscular fat percentage were 14.6 (±2.6 SD) mm, 63.7 (±5.5 SD) mm, and 2.21 (±0.82 SD) %. Image analysis and estimation model development were conducted in MATLAB R2021a. Edge detection via the image gradient was applied to segment ultrasound images into backfat, loin, and rib regions. Segmented images were used to estimate backfat depth, loin depth, and loin intramuscular fat percentage. After image quality control and filtering, the image inclusion rate for each breed-trait combination ranged from 76 to 97%. All Duroc and commercial pigs and 97% of Large White pigs were represented by at least one image for trait estimation. Coefficient of determination of models for the estimation of backfat depth, loin depth, and intramuscular fat percentage were 0.58, 0.57, and 0.56, respectively. Root mean square error of backfat depth, loin depth, and intramuscular fat estimation were 1.65mm, 3.58mm, and 0.54%, respectively. Results demonstrate the feasibility of using ultrasound image gradient and an equation-based approach to estimate swine backfat and loin depth, and intramuscular fat percentage. This equation-based approach to estimate carcass traits in live swine can enhance genetic improvement.

Postoperative intraocular lens stability following cataract surgery with or without primary posterior continuous curvilinear capsulorrhexis: an intra-individual randomized controlled trial.

To evaluate the effect of primary posterior continuous curvilinear capsulorrhexis (PPCCC) on the positional stability of IOLs. This study is a prospective intra-individual comparative randomized controlled trial including 31 patients (62 eyes). Eyes of the same patient were randomly assigned to the PPCCC group (18 right eyes and 13 left eyes) or group without PPCCC (NPCCC group). Eyes in both groups were implanted with a one-piece foldable hydrophobic acrylic IOL via routine cataract surgery. Patients in the PPCCC group underwent additional manual PPCCC before IOL implantation. Examinations were performed 1day, 1week, 1month and 3months postoperatively. IOL tilt (x, y), decentration (x, y), anterior chamber depth (z) and refractive prediction error data were collected and analyzed with Pentacam. Postoperatively, the range of IOL position change over 3months in PPCCC group was comparable to NPCCC group, which indicated smaller value in every tilt and decentration index. PPCCC eyes showed comparable tilt and decentration with NPCCC eyes in this study endpoint: mean tilt (x, y), decentration (x, y) and anterior chamber depth (ACD) were 1.04 ± 0.56°, 0.90 ± 0.64°, 0.239 ± 0.140mm, 0.233 ± 0.133mm and 4.01 ± 0.32mm, respectively, in the PPCCC group vs. 1.09 ± 0.76°, 1.10 ± 0.82°, 0.252 ± 0.153mm, 0.244 ± 0.155mm and 4.01 ± 0.38mm, respectively, in the NPCCC group. Refractive prediction error in the PPCCC group demonstrated a mild hyperopic shift vs. the NPCCC group (0.13 ± 0.50 vs. 0.05 ± 0.39; p = 0.208), and corrected distance visual acuity (CDVA) did not differ between the two groups (0.027 ± 0.014 vs. 0.059 ± 0.185; p = 0.377). Comparable IOL tilt, decentration, ACD and refractive prediction error were observed in PPCCC eyes with that underwent routine cataract surgery. Little IOL position fluctuation and good visual acuity were shown in PPCCC group over time. The study was registered at the Chinese Clinical Trial Register Center on May 27th, 2020 (protocol code ChiCTR2000033304, 27/05/2020).

Simulation of runoff process based on the 3-D river network

Many rivers originating from mountainous areas cannot be adequately measured in the field to obtain sufficient basic data due to the challenging terrain, which poses difficulties for conducting relevant research such as hydrodynamic and sediment transport simulations, fluvial material fluxes. This study utilized a combination of 71 river cross sections extracted from multisource remote sensing data and 34 cross sections measured by hydrological stations between 2010 and 2018 to generate generalized cross sections for all river orders in the middle reaches of the Yellow River. By integrating the generalized cross sections, river surfaces, and traditional DEM river network, a comprehensive 3-D river network has been established to provide a more accurate representation of the morphological changes along the river channel. To tackle the challenge of simulating runoff in data-deficient areas, the Digital Yellow River Integrated Model (DYRIM), a potent distributed hydrological model, was enhanced by incorporating geometric parameters derived from a 3-D river network into almost all input parameters that can be obtained from multi-source remote sensing. This enhancement enabled the model to accurately simulate runoff in representative data-deficient rivers of the Loess Plateau. The Nash Efficiency coefficients for the simulated runoff exhibited improvement of 23% compared to previous studies, surpassing 0.8 for the period between 2010 and 2018. Additionally, the simulated errors for flow velocity and water depth were 7% and 9% respectively. This approach can provide essential data for the simulation of hydrological processes and improving hydrological models, as well as technical methodologies for related research such as sediment transport simulations and river carbon emissions.

Fabrication of randomly distributed V grooves on the great circles of a spherical surface using ultra-precision five-axis milling

Multi-axis ultra-precision milling is a powerful technology which enables the manufacturing of various complicated curved surfaces. However, in the fabrication of an undetermined and random micro-structured surface, the conventional manufacturing mode from a given geometry model to the final surface machining is no longer applicable. Therefore, this work presents an integrative numerical control (NC) code generation method for fabricating randomly distributed V grooves on the great circles of a spherical surface firstly, in which the tool path generation is integrated into the geometry design process. The randomness of the V grooves refers to the geometric feature that the normal vectors of all the planes of the great circles are randomly generated, which are uniformly distributed towards the corresponding points on a unit sphere. Besides, the singular movements in five-axis milling of the randomly distributed V grooves on the great circles are analyzed. It is revealed that there are two distinct mechanisms of the singularity problem. One is the singular configuration of the machine tool, and the other is the discretization of the generated tool path. Subsequently, a general post-processing algorithm is contributed to solve the singularity problem, which enables the generation of NC code with error control. Furthermore, in five-axis milling of the randomly distributed V grooves on the great circles, the position error of tool virtual center can also significantly influence the machining accuracy. Therefore, a two-point calibration method is proposed to accurately determine the position of tool virtual center. Finally, a high-precision random V groove-structured sphere is machined by using the proposed NC code generation method and the tool virtual center calibration method. The maximum depth error of the V grooves is controlled less than 3.3 μm. The proposed methods in this work enable five-axis milling of high-precision random micro-structures on a spherical surface.

Robotic compliant grinding of curved parts based on a designed active force-controlled end-effector with optimized series elastic component

High-accuracy, fast-response, and low-overshoot force control are important to guarantee the material removal accuracy and surface quality of robotic compliant grinding. In order to achieve the above target, this study develops an active compliant force-controlled end-effector based on a series elastic actuator for the robotic grinding of curved parts. Firstly, a decoupled robotic grinding system composed of an industrial robot and an end-effector is developed, and a novel force-controlled end-effector is designed by adding an elastic component between the servo motor and the load to improve compliance. Secondly, the influences of the elastic component and grinding tool stiffness on the stability of the force-controlled end-effector system are analyzed by establishing a contact model of the compliant end-effector and the transfer function of the entire force control system. Then, the stiffness of the grinding tool and the elastic component are optimized with full consideration of the system cutoff frequency and the end-effector compliance. To improve the force tracking accuracies of the developed compliant end-effector, a proportional-integral (PI) controller with first-order differential force feedforward control is designed. Finally, grinding experiments are conducted for verification. The effects of spring stiffness and grinding tool stiffness on the force control system are tested, which match well with the theoretical analysis. Grinding results show that the maximum force control error of the compliant force-controlled end-effector is decreased by 70% compared to that of the rigid force-controlled end-effector, and the overshoot of the force control is reduced from 30% to almost 0 under the same response speed. The maximum and average absolute grinding depth errors using the designed end-effector are reduced by 57.2% and 58.6%, respectively, compared with those of the traditional rigid end-effector, and the average surface roughness of the finished part is reduced by 19.2%. Which demonstrates the effectiveness and advantages of the proposed compliant force-controlled end-effector.

Feature information extraction method for narrow gap U-type groove based on laser vision

The precise measurement of weld groove position and geometric dimensions serves as the foundation for predicting and planning welding strategies and process parameters. The steep sidewalls of narrow gap U-type grooves pose challenges for sensing and detection during the welding process. This paper presents a feature information extraction method for narrow gap U-type grooves based on laser vision technology. Firstly, the image acquisition system is calibrated using the incident plane of the laser beam. Subsequently, a digital image processing technique is employed to enhance the collected images, combining a 3D spatial domain enhancement algorithm with a laser stripe image refinement method based on Euclidean distance transformation. Finally, the transverse and vertical positions of the groove, as well as its geometric features such as width, depth, assembly gap, and misalignment, are computed and measured using feature point positioning. The proposed algorithm is experimentally verified for its accuracy, robustness, and real-time performance. The results indicate that the method effectively suppresses unevenness in laser stripe brightness and mitigates random noise in the captured images. The average measurement errors for transverse position, vertical position, width, depth, assembly gap, and misalignment of the groove are 0.0857 mm, 0.0987 mm, 0.1664 mm, 0.2216 mm, 0.0935 mm, and 0.0997 mm, respectively. Furthermore, the average image processing time per frame is 58 ms, meeting the requirements of pre-weld detection in arc welding.

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs.

Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano- and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi-layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning-based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross-talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8mm, depth errors of 0.22mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter-element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.

Noise-suppression algorithm of GM-APD lidar based on Markov random field

Background light easily affects a Geiger-mode avalanche photodiode (GM-APD) laser imaging, detection, and ranging (Lidar), and its detection ability is significantly reduced in strong light environments. Improving imaging performance under a low signal-to-back ratio has become critical. A GM-APD lidar noise-suppression algorithm based on a Markov random field is proposed. The field objective function and prior model are established using similar centre and adjacent pixel features. Both enhance the feature difference between a strong background light and a weak echo signal and improve the ability of weak echo signal reconstruction under an ultra-low signal-to-background ratio (SBR). Imaging experiments of long-distance building targets under varying illumination verified the noise-suppression ability of the algorithm. When the SBR is 0.0055, target recovery can reach 19.40%, and recovery of the whole array image can reach 60.94%. Compared with the sparse Poisson intensity reconstruction algorithm, the image signal-to-noise ratio is improved by 83 dB, and the average depth error was reduced by 36.14 cm. The proposed algorithm improves the imaging performance under ultra-low SBR; it significantly promotes the development of GM-APD lidar all-time applications.

Mechanical behavior of asphalt pavement on steel-concrete composite beam bridge under temperature-load coupling

Over the past few decades, steel–concrete composite beam (SCCB) bridges have been gaining popularity all over the world. However, distresses such as rutting, delamination and cracking may occur on the corresponding bridge pavement structure soon after open traffic. To alleviate pavement distresses and clarify the applicability of different pavement structures and materials, in this study, a targeted research method for the mechanical properties of SCCB bridge deck pavement under temperature-load coupling was proposed. The research elements and procedures of the SCCB bridge deck pavement were structurally analyzed and synthesized. In addition, a temperature-load coupling model of SCCB bridge deck pavement was established. The permanent deformation under different cumulative axle load action times was calculated and fitted, and then compared with the actual detected data. Finally, the mechanical response of pavement structures commonly used for SCCB bridges was analyzed. The results show that the absolute error of rutting depth between the field test and fitting result was small, which proved the rationality of the proposed method. Besides, for SCCB bridges, “AC + SMA” and “double-layer SMA” have better road performance. The conclusion of this paper provides reliable support for the maintenance and distress causes analysis of SCCB bridge deck pavement.

Method for determining of shallow water depths based on data recorded by UAV/USV vehicles and processed using the SVR algorithm

Bathymetric measurements in waters shallower than 1 m are necessary to monitor seafloor relief changes in the coastal zone. This is especially important for ensuring the safety of navigation, navigation efficiency, as well as during the design and monitoring of hydrotechnical structures. Therefore, the aim of this article is to present a method for determining of shallow water depths based on data recorded by Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vehicle (USV), as well as processed using the Support Vector Regression (SVR) algorithm. Validation studies of this method was carried out on two selected waterbodies (inland and sea) using the DJI Phantom 4 Real Time Kinematic (RTK) UAV and the AutoDron USV. Then, the geospatial data recorded by a drone was applied to determine depths of shallow waterbodies using the SfM algorithm. Subsequently, the determined depths were compared with the depths measured by a Global Navigation Satellite System (GNSS) RTK receiver or a Single Beam Echo Sounder (SBES), in order to assess the accuracy of the proposed method. The research showed that it is able to determine shallow waterbody depths with the accuracy requirements specified for the International Hydrographic Organization (IHO) S-44 special order (depth error ≤ 0.25 m (p = 0.95)), but only to depths not exceeding 1 m. In the inland waterbody, the depth accuracy was 0.22–0.24 m (p = 0.95), while in the sea waterbody the measurement accuracy was higher, amounting to 0.16 m (p = 0.95). However, for the depth range up to 2 m, the results are radically different, because the vertical position error with a 95% confidence level exceeds the value of 0.3 m in the analysed waterbodies.

Pseudo-Mono for Monocular 3D Object Detection in Autonomous Driving

Current monocular 3D object detection algorithms generally suffer from inaccurate depth estimation, which leads to reduction of detection accuracy. The depth error from image-to-image generation for the stereo view is insignificant compared with the gap in single-image generation. Therefore, a novel pseudo-monocular 3D object detection framework is proposed, which is called Pseudo-Mono. Particularly, stereo images are brought into monocular 3D detection. Firstly, stereo images are taken as input, then a lightweight depth predictor is used to generate the depth map of input images. Secondly, the left input images obtained from stereo camera are used as subjects, which generate enhanced visual feature and multi-scale depth feature by depth indexing and feature matching probabilities, respectively. Finally, sparse anchors set by the foreground probability maps and the multi-scale feature maps are used as reference points to find the suitable initialization approach of object query. The encoded visual feature is adopted to enhance object query for enabling deep interaction between visual feature and depth feature. Compared with popular monocular 3D object detection methods, Pseudo-Mono is able to achieve richer fine-grained information without additional data input. Extensive experimental results on the datasets of KITTI, NuScenes, and MS-COCO demonstrate the generalizability and portability of the proposed method. The effectiveness and efficiency of Pseudo-Mono have been demonstrated by extensive ablation experiments. Experiments on a real vehicle platform have shown that the proposed method maintains high performance in complex real-world environments.

Predicting the postoperative intraocular lens position based on IOL Master 700 biometry, compared with results from the anterior segment analysis system.

Predict intraocular lens position after cataract surgery using the IOL Master 700 and explore the associated ocular parameters compared with the results obtained from the anterior segment analysis system (Sirius, CSO Inc, Florence, Italy). A total of 98 patients (106 eyes) were included in the retrospective study. The postoperative intraocular lens position was obtained using the IOL Master 700 and measured using Adobe Illustrator software. Correlation analysis and linear regression analysis were applied to study the correlation between the actual position of the postoperative intraocular lens (ALP) and the ocular parameters. In addition, Bland-Altman consistency analysis was used to compare the consistency between any two among the predicted intraocular lens position (ALPi) obtained using IOL Master 700 biometry, the predicted artificial lens position (ALPs) calculated using the anterior segment analysis system, or the ALP. Ocular parameters, including preoperative anterior chamber depth, lens thickness, axial length, white-to-white, and postoperative refractive error were all correlated with ALP after cataract surgery (P < 0.05) using univariate analysis. However, in multivariate linear regression, only the first three variables were correlated with ALP. Compared with the equation obtained by the anterior segment analysis, the equation from IOL Master 700 biometry provided a better fit. The results of the consistency analysis showed that ALP, ALPi, and ALPs were in good agreement. IOL Master 700 biometry can help predict intraocular lens position after surgery, and its accuracy is better than that provided by the anterior segment analysis system.

Sensors-based discharge data acquisition and response measurement in ultrasonic assisted micro-EDM drilling

Micro-electrical discharge machining (μEDM) suffers from the problem of low material removal rate (MRR) and poor surface finish, especially when there are recurring abnormal sparks. Such sparks are mainly due to the accumulation of unflushed debris in the discharge zone resulting in a frequent arcing phenomenon. Vibration assistance to μEDM has proven contributions to the MRR and surface finish enhancement, but the intrinsic information on discharge pulse modification due to the vibration is unclear. The present work proposes a pulse categorization strategy to understand the nature of discharge pulses during the conventional and ultrasonic vibration assisted μEDM. The discharge pulses were acquired at a sufficiently high sampling rate using NI LABVIEW-based data acquisition through an in-house developed pulse discrimination system (PDS). The developed PDS is simultaneously used to estimate the real-time discharge energies of individual pulses and their histograms with the machining progress. The acquired information from the developed PDS is used to justify the variations in discharge energy, discharge frequency, surface roughness and accuracy with increasing machining depth. Vibration assistance to the μEDM process was found beneficial, with an 18% increase in the discharge energy, and a 7.14% reduction in the percentage error in depth. The surface roughness at high-energy settings reduces from 3.2 μm to 0.45 μm because of ultrasonic vibration assistance.

Study on anisotropic heat transfer and thermal damage in nanosecond pulsed laser processing of CFRP

AbstractNanosecond pulsed laser processing is an efficient method of processing carbon fiber reinforced plastic(CFRP), which is widely used in the manufacturing field of aviation, medical, and auto industries. The anisotropic heat transfer of carbon fiber is a key factor degrading the surface integrity of laser processing CFRP. So it is urgent to explore the significant mechanism of anisotropic heat transfer of laser interacting with CFRP. A novel numerical model considering anisotropic heat transfer was established to analyze heat transfer and thermal damage of CFRP laser machining. By comparison of simulation and experiments, the average error of ablation depth and width ranges from 4.71% to 15.09%, so the model could precisely simulate the CFRP laser machining. It demonstrates that the heat conductivity along the axial carbon fiber is higher than that along the radial, resulting in an elliptical ablation morphology. And when the laser scanning direction is parallel to the direction of the carbon fiber, deeper and narrower microgrooves can be obtained. In addition, laser processing at the edge of the CFRP plate causes serious heat accumulation, leading to severe thermal damage. The simulation and experiments were conducted to explore the effect of parameters including laser energy, scanning speed, and scanning space on the depth and width of laser ablation. A deeper micro‐channel with a smooth machined surface and small thermal damage was obtained when laser energy E = 0.275 mJ, scanning speed v = 630 mm/s, and scanning space h = 21 μm.