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Rotation-invariant (RI) point cloud models aim to reduce sensitivity to viewpoint changes, but their performance still drops noticeably in real-world settings when local geometry is degraded by noise, occlusion, and uneven sampling. Once these disturbances propagate through deeper layers, they can lead to significant robustness degradation, especially for high-capacity RI backbones. To address this problem, we propose AGSM-CPA (Adaptive Geometric Signal Modulation with Cross-Perturbation Alignment), a lightweight and plug-and-play framework that enhances the robustness of RI models without altering their core convolutional operators. It integrates two complementary modules: the Geometric Signal-to-Noise Ratio (G-SNR) modulation mechanism, which adaptively suppresses unreliable neighborhoods based on local coordinate variance, and the Cross-Perturbation Semantic Consistency Alignment (CP-SCL) module, which enforces prediction consistency between weakly augmented inputs and strongly corrupted ones. We evaluate AGSM-CPA on ModelNet40, ScanObjectNN, and ShapeNetPart. Across standard corruption protocols, AGSM-CPA consistently improves robustness while maintaining competitive clean accuracy with negligible computational overhead. These results indicate that AGSM-CPA offers a practical, reliability-aware adapter for robust rotation-invariant point cloud learning.

Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) Nature 75:7] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks. In the experiment, subjects could reconsider their response to factual questions after having received average or full information of the responses of other subjects. We compare subjects' convergence of estimates and improvements in accuracy over five consecutive estimation periods with a control condition, in which no information about others' responses was provided. Although groups are initially \"wise,\" knowledge about estimates of others narrows the diversity of opinions to such an extent that it undermines the wisdom of crowd effect in three different ways. The \"social influence effect\" diminishes the diversity of the crowd without improvements of its collective error. The \"range reduction effect\" moves the position of the truth to peripheral regions of the range of estimates so that the crowd becomes less reliable in providing expertise for external observers. The \"confidence effect\" boosts individuals' confidence after convergence of their estimates despite lack of improved accuracy. Examples of the revealed mechanism range from misled elites to the recent global financial crisis.

Existing precision design methods cannot directly guide the tolerance design. Therefore, in this study, an optimization design method of machine tool static geometric accuracy based on tolerance modeling is proposed. In this methodology, the mapping relationship between the geometric error of machine tools and tolerance design is established using the small displacement torsor to represent the tolerance information and the Monte Carlo simulation method is used to establish the response model of the torsor parameters and the tolerance variation bandwidths. An assembly accuracy model is then established by combining a machine tool topology analysis and the forming mechanism of the joint surface error. To calculate the tolerances of the component joint surface, a tolerance response model related to the component joint surface tolerance and torsor parameters is developed. Finally, according to the state function of assembly accuracy reliability, a function response model of the assembly accuracy, reliability, and tolerance is developed. Combining the assembly’s processing cost model with the accuracy, reliability, and tolerance principles, a tolerance optimization model of the static geometric accuracy of a CNC machine tool, a linear axis motion guide, is constructed as a case study. Using a simulated annealing genetic algorithm to solve the tolerance optimization model, the tolerance optimization value is obtained, thereby verifying the effectiveness of the proposed method.

Engineering design evaluation of complex spatial products—such as vehicle interiors, building environments, and aircraft cabins—frequently demands high-fidelity digital representations that couple geometric accuracy with semantic interpretability. Yet a persistent methodological gap exists between raw 3D data acquisition and computable, performance-evaluable parametric models: conventional approaches either produce geometrically precise but semantically opaque point clouds, or rely on idealized CAD models that deviate from as-built reality. This paper proposes a general-purpose semantic-geometric digital twin framework that bridges this gap, enabling automated conversion of physical environments into structured, parameterized virtual models that directly support multi-dimensional design performance evaluation and optimization. The framework comprises four methodological layers applicable across engineering domains: (1) a multi-sensor fusion acquisition layer with microsecond-level time synchronization for efficient spatial data capture; (2) a globally consistent 3D reconstruction layer combining error-state Kalman filtering with factor graph optimization to suppress cumulative drift in elongated or repetitive environments; (3) a semantic-geometric hybrid modeling layer integrating deep learning segmentation with parametric geometric reconstruction to automatically identify components and extract design-critical parameters; and (4) a model-driven performance evaluation layer that quantifies multi-dimensional design metrics (comfort, safety, economics) and supports Pareto-optimal design decision-making. The framework is validated through a full-scale civil aircraft cabin case study (C919 simulator), where it achieves a 6× improvement in modeling efficiency over stationary scanning, sub-centimeter geometric accuracy (RMSE < 1.5 cm), and measurement precision better than 8 mm for key human factors dimensions. The case study demonstrates the framework’s capacity for airworthiness compliance verification, ergonomic heatmap analysis, and layout optimization. Beyond aviation, the proposed methodology generalizes to any engineering design domain where physical-to-digital conversion, semantic decomposition, and performance-driven evaluation of spatial layouts are required—such as automotive interiors, hospital operating rooms, factory floor planning, and architectural space design.

Geometric accuracy is a critical performance factor for parallel robots, and regardless of error compensation, accuracy design or tolerance allocation is another way to ensure the pose accuracy of a robot at design stage. A general method of both geometric error modeling and accuracy design of lower-mobility parallel mechanisms is presented. First, a general approach for error modeling of lower-mobility parallel mechanism is proposed based on screw theory, and then the geometric errors affecting the compensatable and uncompensatable accuracy of the end-effector are separated using the properties of dual vector space. The pose error aroused by compensatable geometric errors can be compensated via kinematic calibration, while the uncompensatable geometric errors should be minimized during the manufacturing and assembly processes. Based on that, the tolerance allocation method is presented, giving each uncompensatable geometric error a proper tolerance by the use of reliability theory. Compared with the traditional tolerance allocation method, the advantages of the proposed method are as follows: the number of geometric errors to be allocated is greatly reduced; the results of serialized tolerance allocation can be obtained according to different reliability indices of pose accuracy of end-effector for designers to choose; on the premise of guaranteeing the same pose accuracy of end-effector, the allocated tolerances are loose and easy to realize. Finally, the proposed methods are successfully applied to an R(2-RPS&RP)&UPS lower-mobility parallel robot, and the effectiveness and practicability of the proposed method are verified.

In this paper, a new single component repairable system model with a repairman is proposed. Assume that the successive working time interval of the component and the successive repair time interval after repair is described by the extended geometric process. The repairman has multiple vacation when the component is working, and component is repaired delayed with a given probability when it fails. The component will work again when it repaired. Under the assumption, the explicit expression of the long-run average cost rate function of the system based on the failure number of the component is derived. Numerical cases are designed to illustrate the long-run average cost rate function of the proposed model. Finally, sensitive analysis of parameters is carried out.

Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric workflows remains not well understood. This study provides a controlled quantitative evaluation of learning-based super-resolution for video-based 3D reconstruction. Low-resolution video frames are enhanced using two representative methods: an open-source real-world SR model (Real-ESRGAN ×4) and a commercial solution (Topaz Video AI ×4). All datasets are processed with the same Structure-from-Motion and Multi-View Stereo pipelines and tested against terrestrial laser scanning (TLS) reference data. Results show that super-resolution significantly increases reconstruction density and improves the recovery of fine-scale surface details, while also leading to greater local surface variability compared with reconstructions from the original video; photogrammetric stability remains consistent despite these changes. The findings highlight a fundamental trade-off between reconstruction completeness and local geometric accuracy and clarify when enhanced video imagery via super-resolution can be a reliable source for 3D reconstruction. These results are especially important for spatial data science workflows and AI-powered 3D modeling and digital twin applications.

The rapid development of digital manufacturing and robotic systems places increased demands on the accuracy and reliability of industrial manipulators. Traditional time-based reliability metrics do not reflect the robot’s ability to consistently achieve the desired position and orientation within process tolerances or the probability of the end-effector falling into a given area of permissible poses. The proposed framework integrates a deterministic kinematic model, a stochastic representation of Denavit–Hartenberg parameters and control variables, analytical methods for estimating probabilities, and numerical modeling using the Monte Carlo method. The methodology has been tested on the widely used industrial robot FANUC LR Mate 200iD/7L. The results demonstrate a significant dependence of geometric reliability on the kinematic configuration of the manipulator, with maximum reliability in compact poses and a significant reduction in elongated configurations near singularities. Comprehensive validation was carried out, including numerical experiments on a planar prototype, high-precision physical measurements on a real robot and analysis of operational data, which confirmed the adequacy of the proposed model. The developed approach provides a powerful tool for designing, optimizing and predicting the reliability of robotic cells in high-precision automation environments.

Advances in 3D printer technology are increasing rapidly, enabling the creation of many products. However, there are problems in ensuring dimensional integrity in parts produced using additive manufacturing. Dimensional integrity becomes even more important in the production of thin-walled parts, especially those used in the aerospace, aviation, and biomedical fields. For this reason, the production parameters should be controlled and the deviations from the actual dimensions minimized. This study was carried out under the conditions of three different geometries (square, round, and elliptical), three different wall thicknesses (1, 2, and 3 mm), and three different layer thicknesses (0.1, 0.2, and 0.3 mm). As a result of the study, the dimensional integrity, wall thickness accuracy, and surface roughness of different thin-walled geometrically shaped parts produced by 3D printer were determined. The highest dimensional accuracy was obtained in the square parts and the lowest in the round and elliptical parts. As the wall thickness was increased, the dimensional accuracy decreased, whereas the wall thickness accuracy increased. When the wall thickness was kept constant, as the layer thickness was increased, the dimensional accuracy increased, whereas the wall thickness accuracy decreased. Besides, the surface roughness was evaluated and it was determined that the layer thickness was the most important parameter affecting the surface quality of the samples. The starting point where the nozzle begins to form the layers and the gap formed between the wall layers were also determined to have an important effect on the geometric accuracy.

Machining accuracy is critical for the quality and performance of a mechanical product, and the reliability of a multi-axis NC machine tool reflects the ability to reach and maintain the required machining accuracy. The objective of this study is to propose a general methodology that will simultaneously consider geometric errors and thermal-induced errors to allocate the geometric accuracy of components, for improving machining accuracy reliability under certain design requirements. The multi-body system (MBS) theory was applied to develop a comprehensive volumetric error model, showing the coupling relationship between the individual errors of the components of this machine tool and their volumetric accuracy. Additionally, a thermal error model was established based on the neural fuzzy control theory and was compared to the common thermal error modeling method called BP neural network. Based on the traditional cost model and the reliability analysis model, a geometric error-cost model and a geometric error-reliability model were established, taking the weighted function principle into consideration. Then, an allocation approach of the geometric errors, for optimizing total cost (manufacture and QLF) and reliability, subject to the geometrical and operational constraints of the machine tool, was proposed and formulated into a mathematical model, in order to perform the optimization process of accuracy allocation by using the advanced NSGA-II algorithm. A case study was also performed in a five-axis machine tool, and the traditional NSGA algorithm was used for comparison. The optimization results for the five-axis machining center showed that the proposed approach is effective and able to perform the optimization of geometric accuracy and improve the machining accuracy and the reliability of the machine tool.