ABSTRACT The continuous enlargement of wind turbines requires them to withstand a wide range of complex and intense dynamic loads. However, traditional time‐dependent reliability analysis often focuses solely on fixed hotspots, neglecting the potential for failure regions to change under spatiotemporally varying external loads. To address this challenge, this paper proposes a novel spatiotemporal‐dependent reliability analysis (STDRA) framework explicitly designed to track moving failure hotspots over continuous operational periods. To realize this framework efficiently, several established techniques are integrated as supporting components: An Ornstein–Uhlenbeck (OU) stochastic process combined with the expansion optimal linear estimation (EOLE) is developed to characterize and discretize time‐varying wind loads, whereas a reduced‐order model augmented with a residual neural network (NN) is created for the rapid and precise prediction of global spatial structural responses. The proposed STDRA framework is applied to a practical wind turbine tower. Results demonstrate that the framework successfully identifies dynamically evolving critical failure regions over time. Furthermore, it yields failure probabilities consistent with Monte Carlo simulation (MCS) benchmarks but requires significantly less computational time than conventional TRA methods. By revealing these spatiotemporal weak spots, this study provides a robust foundation for artificial intelligence (AI)‐driven and reliability‐based design optimization of critical components, such as long flexible blades and towers, in next‐generation wind turbines.
ABSTRACT With the advancement of high‐resolution optical payloads, the impact of micro‐vibrations on imaging accuracy has become increasingly critical. Micro‐vibrations induce optical axis jitter, leading to the degradation of imaging performance. Optimizing the parameters of the load‐bearing structure helps suppress the transmission of micro‐vibrations, providing a feasible approach for improving imaging performance under micro‐vibration excitation. This paper proposes a novel parameter optimization framework based on a surrogate model to achieve collaborative optimization of lightweight design and minimal image displacement. First, a structural‐performance dataset was prepared, and a multi‐task learning (MTL) surrogate model was established to accurately capture the nonlinear mapping from structural characteristics to performance indicators. Second, an enhanced dynamic weight non‐dominated sorting genetic algorithm (DW‐NSGA), incorporating a dynamic weight mechanism and weighted crowding distance, was proposed to efficiently perform multi‐objective optimization. Results showed that the MTL model achieved high prediction accuracy across all indicators, with R 2 values exceeding 0.9. The DW‐NSGA converged more rapidly and obtained superior optimized solutions, demonstrating excellent performance in evaluation metrics such as hypervolume (HV) and generational distance (GD). Through collaborative optimization, the final design solution effectively balanced the trade‐offs between mass and image displacement. The mass was reduced by 29.2%, the root‐mean‐square vibration response decreased by 29.9%, and the maximum image displacement decreased by 49.10% and 22.78% along two directions. This optimization framework provides an efficient approach for intelligent design and imaging‐stability improvement of optical payloads under micro‐vibration excitation.
Remote center-of-motion (RCM) mechanisms are widely used in robotic palms and wrists, orientation platforms, and medical devices. A spherical mechanism provides a compact RCM realization by enforcing the intersection of all joint axes at a common center. This article introduces a topology-morphing 7R spherical mechanism that switches from a single-loop configuration with compliant motion into a double-loop spherical scissors configuration with substantially increased stiffness. The architecture is modeled using a graph-based description in which the mobility is expressed as a function of the number of links, holes, and tails. A unified kinematic and static formulation is then derived for both topology modes using a consistent joint coordinate set and Jacobian structure. Reconfiguration conditions are established from the pinhole coincidence constraints, and an algorithm is provided to compute admissible reconfiguration configurations. The reachable orientation workspace and manipulability are evaluated by sampling on the unit sphere. Under identical end-effector loading (0-2N), rigid-body simulation predicts an equivalent stiffness increase from 0.3N/mm to 571N/mm. Prototype experiments further confirm compliance with rigid switching, with stiffness increasing from 0.106N/mm to 0.525N/mm. These results indicate that topology-morphing on the sphere can provide a compact mechanism-level strategy for switching mobility and effective stiffness under the same actuation scheme.
Remote center of motion (RCM) mechanisms are widely used in minimally invasive surgery (MIS) due to their ability to provide rotational motion about a fixed spatial point through geometric mechanical constraints. In this article, a family of partially decoupled 2R1T RCM mechanisms is presented, characterized by three distinct features: base-mounted prismatic actuators, unilateral arrangement of branches, and partial motion decoupling between translation and rotation. First, the motion combination principle underlying this family of partially decoupled 2R1T RCM mechanisms is established. Second, based on the Lie group and submanifold method, a systematic type synthesis is performed, yielding numerous mechanism configurations that satisfy the above three characteristics. Third, the kinematics and the unified Jacobian matrix of a representative mechanism are derived. Finally, the singularity and workspace of the selected mechanism are analyzed, with particular attention to verifying the partial decoupling property. The results show that the proposed 2R1T RCM mechanisms meet the kinematic and workspace requirements typical of minimally invasive surgery.
Abstract Communication plays a central role in how engineering design teams generate ideas, negotiate decisions, and make progress. Interaction Dynamics Notation (IDN) offers a structured way to study these interactions, but its reliance on manual, post hoc coding limits how widely and quickly it can be used. This article introduces a system that automatically classifies IDN symbols in real time from live team conversations. The system combines speech transcription with a lightweight language model and a short conversational context window to label interactions as they occur. The approach is evaluated in a professional design sprint and compared against human-coded IDN data. Results show that the real-time system achieves overall accuracy comparable to human inter-coder reliability and to prior automated methods that operate only after a conversation has ended. Hidden Markov model analysis further indicates that the AI-coded data captures the same dominant interaction patterns and transitions observed in human coding, while smoothing some finer distinctions. A sensitivity analysis of context window size highlights practical trade-offs between accuracy and latency. Overall, this work makes IDN more accessible for studying team interactions and supports future tools that help teams understand and reflect on their communication as it unfolds.
