Complex free-form surface machining plays a critical role in biomedical and customized manufacturing, where high manufacturing accuracy is essential for ensuring functional and anatomical performance. Traditional iso-scallop tool path planning methods control scallop height to enhance surface quality. However, these methods often face challenges in balancing struggle to balance machining efficiency with and precision, leading to resulting in redundant tool paths or uncut regions, particularly when applied to triangular mesh models. This study addresses these limitations by introducing To address these limitations, this study introduces a geodesic-based framework that constructs curvature-adaptive contours as tool paths using the heat flow method. A novel strategy based on direction field singularities and a Minimal Envelope Line is developed to accurately detect uncut regions, which are subsequently resolved through globally optimized Clean-Paths. Numerical experiments on both synthetic and anatomical surfaces confirm the effectiveness of the proposed method. In the Molar Crown case, the method achieves a 68.37

The camera imaging model reflects the mapping relationship between three-dimensional spatial coordinates and two-dimensional image plane coordinates, serving as a key factor in assessing the geometric accuracy of camera imaging. In practical engineering applications, optical distortion causes camera imaging to deviate from the pinhole imaging principle, necessitating correction to improve geometric accuracy. The scale of the stationary orbit large-area array camera detector has significantly increased, and traditional distortion correction methods are limited by the high-precision angle measurement range of the two-dimensional turntable, making it impossible to achieve high-precision sampling of the global range measurement points of the array detector; thus, the distortion correction accuracy cannot be guaranteed. This paper proposes an extrapolation distortion correction method based on the measurement data of the sub-area of the area array detector, taking into account the practical situation of the area array camera engineering and the good extrapolation characteristics of the polynomial model. The experimental results show that the method proposed in this paper can effectively achieve distortion correction of area array cameras, and the average and standard deviation of the correction results reach the sub-pixel level, which are 0.4930 pixels and 0.1833 pixels, respectively. Compared with traditional distortion correction methods, the correction accuracy of our proposed method is significantly better than that of traditional models. The average and standard deviation of the correction results are improved by 40.22% and 71.91%, respectively.

Accurate measurement of parts with high-dynamic-range (HDR) surfaces remains challenging in industrial scenes. Recently, four main strategies have been developed for effective HDR surface measurement, such as multi-exposure fusion, projection intensity modulation, hardware-assisted methods, and multi-view stereo (MVS). However, these methods cannot simultaneously provide single-shot, fast and precise measurement. Current learning-based measurement methods overlook restricted receptive fields and coarse initialization on HDR surfaces, reducing measurement precision. In this paper, we propose a learning-based measurement method for HDR surfaces using speckle projection profilometry with a conditional diffusion MVS model to address these limitations. Differing from prior methods, depth refinement is modeled with conditional diffusion and combined with multi-view stereo to improve HDR surface measurement robustness. To enhance feature representation on HDR surfaces, a multi-scale feature extraction module with deformable convolution is employed for adaptive receptive field adjustment. A conditional diffusion model is developed for integrating matching information, image context, and depth cues to iteratively denoise and progressively recover more accurate depth maps while mitigating local minima. Finally, a depth map fusion module combines the depth estimates to output a high-precision point cloud. Quantitative experiments on HDR datasets and real workpieces demonstrate that the method achieves accuracy of 0.03-0.06 mm, outperforming state-of-the-art learning-based approaches. The inference time is 0.353 s with 7.80 GB GPU usage, indicating its potential for lightweight deployment.

Multiple refractions between different media lead to camera refractive distortions, thereby making it difficult to ensure underwater measurement accuracy. Consequently, precise modeling and calibration of underwater multilayer imaging systems is essential to address these distortions. In this paper, employing a perspective projection scale factor along the light propagation direction, a universal forward multilayer refractive model is presented. On this basis, the refractive model incorporates a general rotation matrix, which not only enriches application diversity but also enhances solution precision. Allowing for direct underwater camera calibration without prior calibration in air, the model simplifies the calibration process and enhances its suitability for practical applications. Through underwater calibration experiments, the refractive model enhances solution efficiency, improves identification precision and distributes evenly and mediately in terms of reprojection error. In addition, it increases the number of identifiable parameters, demonstrating its robustness and superiority in complex parameter identification scenarios. Finally, underwater measurement experiments indicate that the absolute accuracy calibrated and measured by the refractive model is better than 0.25 mm. Overall, combining all the benefits of forward and backward compensation methods, the forward refractive model offers versatility in handling multiple refractions without limited refractive layers.

Aqueous zinc ion batteries (ZIBs) have gained significant traction in recent years owing to their inherent advantages, including high safety, low cost, and environmental friendliness. Nevertheless, the emergence of detrimental side reactions and dendrite formation has impeded their practical utilization. People are increasingly focusing on designing advanced functional hydrogel electrolytes to address these challenges. Herein, we have developed a hydrophilic polyacrylamide hydrogel decorated with NH4H2PO4. The NH4+ within this hydrogel create a "shielding effect", which prevent direct contact between active water and zinc, thereby effectively inhibiting dendrite growth and reducing surface side reaction. The H2PO4- increases the wall thickness of modified polyacrylamide hydrogel, thus achieving the commendable mechanical properties of tensile and torsional resistance, as well as consistent electrochemical stability. The hydrophilic properties and 3D porous structure of the doping polyacrylamide (PAM-NH) hydrogel mitigate the desolvation energy of zinc ions, and facilitate uniform deposition. Notably, employing PAM-NH hydrogel electrolyte prolongs a remarkable cycle life to 1400 h with a high coulombic efficiency of 99.03 %. Furthermore, the full cell of Zn//((NH4)2V3O8/rGO)) exhibit a higher discharge specific capacity of 203mAh g- 1 and 81.4 % capacity retention after 500 cycles. The flexible battery maintains a stable voltage of 1.162 V and consistent discharge specific capacity of 155 mAh g- 1 at the various bending angles, underscoring the promising prospects of this electrolyte in advancing aqueous zinc ion battery technology.

Extracting geometric features from 3D point clouds is widely applied in many tasks, including registration and recognition. We propose a simple yet effective method, termed height-azimuth image based transformation-invariant net (HA-TiNet), to learn a distinctive, general and rotation-invariant 3D local descriptor. HA-TiNet is composed of a height-azimuth image generator and a feature extraction net. Based on a local reference axis (LRA), the height-azimuth image generator first partitions local region along the plane-radial direction, and then implements a statistic of height and azimuth information in each divided space to generate a set of height-azimuth images. The generated height-azimuth images are invariant in the rotation around x- and y-axes and have high accuracy due to the high repeatability of an LRA. Besides, they can be easily embedded in 2D convolutional neural networks (CNNs). Our feature extraction net learns the information on the height-azimuth images using a ResNet-based backbone and a rotation-invariant layer. The ResNet-based backbone is lightweight while very effective. The rotation-invariant layer removes the rotation-variance around z-axis, making our descriptor have full rotation-invariance. Extensive experiments on indoor and outdoor datasets show that our method presents superior overall performance, and exhibits strong descriptiveness and generalization ability compared to the state-of-the-art descriptors.

The optical 3D shape measurement method with digital speckle pattern projection is widely applied in dynamic measurement and industrial applications. When the surface to be measured is complex, existing speckle projection profilometry (SPP) methods face challenges such as low accuracy and efficiency due to the use of a fixed window size, occlusion, and high computational cost. Although existing multi-view reconstruction algorithms can address occlusion issues and enhance measurement robustness, they are not applicable to industrial fields. Additionally, existing stereo matching algorithms also have several limitations in terms of window size adaptability and efficiency. In this paper, a multi-view measurement method by single shot with curvature-aware adaptive window size selection is proposed. A multi-view stereo matching pipeline based on photometric and geometric consistency is proposed for initial reconstruction, with depth map refinement using the adaptive window. A sequential circular sampling strategy is proposed to obtain high-consistency neighborhood point clouds for curvature-aware scoring. Subsequently, curvature-aware composite scoring is performed based on plane fitting error, normal vector angle, and matching cost difference to enable adaptive window size selection. After iterating for multi-view consistency under the adaptive window, multiple depth maps are fused to generate the final point cloud. A four-camera measurement system, has been developed for experimental validation. Extensive experiments show that the proposed method achieves a surface measurement accuracy of approximately 0.04 mm, reducing the standard deviation by 9.9% to 26.5% compared to state-of-the-art methods. It maintains comparable runtime and computational complexity, and meets the industrial application requirements.

In the context of global drive towards decarbonisation, the significance of marine hydrodynamics is expanding. The focus has shifted beyond traditional offshore structures, such as ships and offshore platforms, to include emerging areas such as marine renewable energy devices, offshore aquaculture infrastructure, side-by-side offloading/bunkering of clean fuel, metamaterial, etc. These novel applications introduce new challenges in numerical modelling, including considerations for multibody dynamics, artificial dissipation, Morison element, perforated structures, hydroelasticity, interconnected bodies, etc. To effectively address these challenges, there is a need for an update in the wave radiation and diffraction theory and numerical code. This paper presents a comprehensive survey of the advancements in the boundary element method for water wave interactions with floating marine structures, covering both conventional and emerging areas. All numerical examples provided are validated through experiments or existing analytical solutions, and are reproducible with sufficient details. Moreover, this study lays a theoretical foundation for understanding wave interactions with different types of floating structures adapted to novel applications.

Due to the limited dynamic range of cameras, reconstruction tasks on high dynamic range surfaces often exhibit overexposed and underexposed regions in the captured images, compromising measurement accuracy and completeness. Existing high dynamic range measurement techniques perform 3D reconstruction directly using image intensity or multi-exposure fusion methods. These techniques cannot simultaneously meet the requirements of single-shot measurement, high precision, high completeness, and ease of use. In this article, we propose a novel measurement method with single shot to address these challenges. Different from existing methods, a multi-view stereo pipeline is proposed to fully utilize the properly exposed areas of different views. By multi-view matching based on photometric and geometric consistency, the depth and normal vector for each pixel in every view will be obtained. The depth and normal vector can be combined into a spatial slanted plane, initializing the first-order shape function parameters. A multi-view digital speckle correlation method is proposed to optimize the plane parameters. Finally, the plane parameters are integrated to produce the depth map fusion. A four-camera measurement system with a photolithographic speckle projection module is developed for experimental validation. Extensive quantitative and qualitative experiments demonstrate that the proposed method achieves the measurement accuracy of 0.04-0.07 mm, meeting the requirements of industrial applications.

As a huge heat source, the fuel assembly directly causes its surrounding dynamic temperature field and subsequently induces the irregular real-time variations of refractive index. Consequently, thermal turbulence phenomena inevitably occur, affecting the measurement accuracy when measuring the bow and twist deformation of fuel assembly. To mitigate the impact brought by the thermal turbulence, an improved U-Net network that boasts exceptional restoration efficiency and precision is presented. Utilizing a linear activation function, corresponding experimental datasets are captured and preprocessed for further training. The principle of thermal turbulence phenomenon is elaborated and the corresponding existence is demonstrated by field turbulence experiments. Compared to thermal turbulence conditions, field restoration experiments demonstrate that our restoration model significantly reduces the impact of image fluctuations and temporal fluctuations, prominently enhances the accuracy of center coordinate extraction, and subsequently provides a strong guarantee for the field deformation measurement of fuel assembly.

Accurate positioning of surface geometric features is essential for quality inspection of automotive components. While vision-based robotic measurement systems are extensively employed, residual errors arising from nonlinear factors, kinematic constraints, and environmental influences pose significant challenges. This article introduces a novel compensation method leveraging a joint error propagation deep neural network (JEP-DNN) to overcome these limitations. First, we develop a comprehensive error model integrating both kinematic and nonkinematic factors, including geometrical parameters and gravity-induced deformations. Then, to address the residual model error, we establish a joint error propagation model to describe error propagation across different parts of the robotic system. The JEP-DNN utilizes the error propagation model to perform residual error compensation, combining corrections in joint space and Cartesian space to achieve high interpretability and precision. The experimental results demonstrate the effectiveness of the proposed method. In a laser tracker calibration experiment, the JEP-DNN reduced average position errors by 30.4%, while in a vision-based inspection task for an automotive cornering light, it achieved a 12.4% improvement in positional accuracy compared to uncompensated results. Finally, accuracy and repeatability evaluation of automotive sheet metal parts illustrate that the robotic vision system achieved a maximum position error of 0.143 mm, with high repeatability (less than 0.066 mm).

When measuring fuel assemblies that have just been removed from the reactor core, residual heat in fuel rods causes the uneven temperature gradient field, which subsequently leads to irregular variations of refractive index. Consequently, inevitable thermal turbulence occurs, impacting the measurement accuracy of bow and twist deformation. A multi-layer refractive model is adopted for direct underwater calibration to accurately identify camera parameters. Field simulated turbulence experiments are carried out to qualitatively and quantitatively demonstrate the presence of thermal turbulence. Compared to normal conditions, these image processing experiments show that underwater thermal turbulence induces image fluctuations and temporal fluctuations, affecting image positions and image distributions and ultimately impacting the accuracy of optical line laser center extraction.

This work deals with the linear surface waves generated by a vessel advancing at a constant forward speed. These waves, known as ship waves, appear stationary to an observer on the vessel. Rather than exploring the well-studied stationary ship waves, this work delves into the physical properties of ship waves measured at Earth-fixed locations. While it might have been expected that analysing these waves in an Earth-fixed coordinate system would be a straightforward transformation from existing analytical theories in a moving coordinate system, the reality proves to be quite different. The properties of waves measured at fixed locations due to a passing ship turn out to be complex and non-trivial. They exhibit unique characteristics, being notably unsteady and short crested, despite appearing stationary to an observer on the generating vessel. The analytical expressions for the physical properties of these unsteady waves are made available in this work, including the amplitude, frequency, wavenumber, direction of propagation, phase velocity and group velocity. Based on these newly derived expressions and two-point measurements, an inverse method has been presented for determining the advancing speed and the course of motion of the moving ship responsible for the wave generation. The results from this study can be used in a wide range of applications, such as interpreting data from point measurements and assessing the roles of ship waves in transporting ocean particles.

Three-dimensional (3D) local feature descriptor plays an important role in 3D computer vision because it is widely used to build point-to-point correspondences in many 3D vision applications. However, existing descriptors are difficult to have both high descriptiveness and strong robustness to various nuisances (e.g., noise and occlusion). To address this problem, a descriptor named Fully Attribute-pairs Statistical Histogram (FApSH) is proposed. FApSH is constructed on a local reference axis (LRA), and fully encodes the relevancy information of five attributes at each neighbor point by ten attribute-pair statistics. In this process, a priori distribution-based partition strategy is proposed for evenly distributing the attribute values of all neighbor points, and a radial distance-based histogram assignment method is proposed to improve the robustness to noise and outliers. The proposed methods are rigorously evaluated on six benchmark datasets with different application scenarios and nuisances. The results show that FApSH has high descriptiveness and strong robustness. It obviously outperforms the existing handcrafted descriptors, and is comparable to some superior learning-based descriptors. The results also show that the proposed priori distribution-based partition strategy significantly reduces the length and also improves the descriptiveness of FApSH, and the proposed radial distance-based histogram assignment method improves the robustness of FApSH on various datasets.

The local reference frame (LRF), as an independent coordinate system generated on a local three-dimensional (3D) surface, is widely used in 3D local feature construction and 3D transformation estimation which are two key steps in the local method-based 3D registration. Numerous LRF methods are proposed in literatures. In these methods, the x- and z-axis are commonly generated by different methods or strategies, and some x-axis methods are conducted on the basis of a z-axis being given. In addition, the weight and disambiguation methods are usually used in these LRF methods. Existing evaluations test each LRF method in a complete form. However, the merits and demerits of z-axis, x-axis, weight and disambiguation methods in LRF construction are unclear. This paper comprehensively analyzes the z-axis, x-axis, weight and disambiguation methods in constructing LRFs, obtaining six z-axes, eight x-axes, three weight and two disambiguation methods where some axis methods are firstly designed in this paper. The performance of these methods are comprehensively evaluated on six datasets with various application scenarios and nuisances. Based on the results, the merits and demerits of the weight, disambiguation, z- and x-axis methods are analyzed and summarized. The results also show that several firstly designed axis methods in this paper have superior performance compared with the state-of-the-art ones.

The collection of large volumes of temporal data during the production process is streamlined in a cyber manufacturing environment. The ineluctable abnormal patterns in these time series often serve as indicators of potential manufacturing faults. Consequently, the presence of effective analytical methods becomes essential for monitoring and recognizing these abnormal manufacturing patterns. However, the extensive process data may contain various minor abnormal patterns, typically reflecting changes in production status influenced by multiple anomalous causes. This study introduces an approach for recognizing abnormal manufacturing patterns through multi-scale time series classification (TSC). Long-term process signals undergo slicing using dynamically sized observation windows and subsequent classification at multiple scales employing our proposed TSC model, the distance mode profile-multi-branch dilated convolution network (DMP-MDNet). DMP-MDNet comprises two key modules aimed at bypassing complicated feature engineering and enhancing generalization capability. The first module, DMP, uses similarity measurement to encode scale- and magnitude-invariant temporal properties. Subsequently, the MDNet, equipped with multi-receptive field sizes, effectively leverages multi-granularity data for accurate classification. The effectiveness of our method is demonstrated through the analysis of a real-world body-in-white production dataset and various widely used public TSC datasets, showing promising applicability in actual manufacturing processes.

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

Industrial robots require high-precision calibration to perform tasks with accuracy and reliability in real-world environments. This study presents a novel rotational laser vision sensor designed to enhance the calibration accuracy of industrial robots. The key contribution of this paper is the high-precision modeling and calibration method for the proposed sensor, which effectively improves accuracy and repeatability. The sensor combines a rotating laser scanning mechanism with a monocular vision system, offering a compact design, high-efficiency area scanning, and laser resistance to interference. The sensor’s performance was evaluated through a series of experiments using a four-sphere calibration object for robot calibration. The Levenberg-Marquardt optimization algorithm was applied to identify the robot’s Denavit-Hartenberg (DH) parameters and external transformation matrices. The results show a substantial reduction in positioning errors, with the maximum error decreasing from 2.7468 mm to 0.1345 mm and the standard deviation from 0.5629 mm to 0.0296 mm after calibration.