electromechanical integration

Existing SysML model verification approaches lack the capability to ensure physical correctness—particularly adherence to fundamental physical laws such as energy conservation and dynamical constraints—in electro-mechanical coupled systems. To address this, we propose the first SysML verification framework explicitly designed for multi-domain physical consistency, operating across two complementary dimensions: structural views (Block Definition Diagrams and Internal Block Diagrams) and functional views (Activity Diagrams). Our method tightly integrates formal functional semantics with cross-domain physical principles (electromagnetic, mechanical, and thermal), enabling rule-driven constraint solving and logical inference. It supports automated detection of both structural integrity and physical plausibility of functional behavior. We evaluate the framework on four real-world electromechanical systems—coffee machine, vacuum cleaner, hair dryer, and wired speaker—demonstrating its effectiveness in significantly enhancing automated, physics-aware consistency verification during early SysML-based design phases.

This work proposes a modular hybrid-actuation haptic interface architecture to address the challenge of efficiently and flexibly rendering large-scale force feedback in reconfigurable multi-degree-of-freedom systems. The design integrates electric motors and unidirectional brakes within a single compact module, transmitting forces via cables to deliver both smooth active output (up to 6 N) and high-magnitude transient collision feedback (up to 186 N). By enabling arbitrary configuration and supporting high-fidelity force feedback across multiple degrees of freedom, the system significantly expands the dynamic range of haptic rendering while maintaining a compact form factor. This approach facilitates versatile deployment scenarios without compromising the richness or realism of the tactile experience.

This study addresses the limitations of current physical human–robot interaction safety standards, such as ISO/TS 15066, which rely on simplified assumptions without a systematic analysis of their theoretical foundations, underlying premises, or impact on system performance. By modeling safety constraints, conducting energy-based safety analyses, and performing numerical simulations, this work uncovers the implicit assumptions embedded in widely used safety criteria and elucidates their practical consequences, highlighting the central role of energy in safety assessment. The research quantifies the performance degradation induced by oversimplified design choices and introduces tunable parameters to optimize safety-critical control strategies. Furthermore, it offers novel insights into energy-driven safety methodologies, significantly advancing the balance between safety and performance in human–robot collaborative systems.

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

This study addresses the challenge of simultaneously achieving component alignment, system coordination, solution reliability, and computational efficiency in physically interacting interconnected systems within three-dimensional space. To this end, the authors propose a decomposition-based collaborative optimization framework that, for the first time, embeds port-alignment constraints into the SPI² architecture. Treating component positions as design variables, the method employs a penalty function to enforce system-level feasibility and enables automatic generation of initial designs. By integrating gradient-based optimization for enhanced numerical stability and coupling it with NSGA-II for efficient multi-objective search, the approach achieves high-quality coordinated solutions. Demonstrated on automotive powertrain and battery-chassis integration cases, the framework significantly outperforms discrete exhaustive search, delivering superior system-level coordination while substantially reducing computational cost.

This study addresses the challenge of spatial layout optimization for interconnected systems within non-convex design spaces by extending the SPI2 framework. It introduces, for the first time, a geometric representation based on Maximal Disjoint Ball Decomposition (MDBD) combined with differentiable inside-outside tests, enabling component placement under arbitrary non-convex boundaries. The method integrates computations of centroid and moment of inertia and establishes an end-to-end CAD workflow that supports automatic assembly reconstruction. By simultaneously satisfying geometric constraints, routing requirements, and physical performance objectives, the approach guarantees geometric feasibility within numerical precision. The efficacy and practicality of the proposed method are demonstrated through a multi-system co-layout case study of a synthetic aircraft auxiliary unit.

This work addresses the lack of quantitative feedback on structural elements that impede robotic disassembly in current product design, which hinders disassembly optimization. The authors propose a CAD-based method that constructs a contact–connection–constraint graph to analyze robotic disassembly sequences and quantify the influence of individual components. For the first time, this influence is mapped onto the geometric model to generate a 3D heatmap, enabling automatic identification and recommendation of key fasteners that can be eliminated without compromising structural integrity. Experiments on seven household appliances demonstrate that the approach successfully identifies redundant fasteners, removes 8–132 structural constraints, reduces tool changes, and shortens robotic travel distance by 165–1675 mm within allowable structural limits.

This work addresses the complexity and expert dependency of traditional finite element analysis by proposing the first end-to-end automation framework capable of processing both image and text inputs. The approach introduces a multi-agent system grounded in ReAct-style reasoning, integrating vision-language understanding, collaborative task planning, and a verification-first code generation mechanism. To ensure physical validity, the framework incorporates self-debugging and fallback strategies. Evaluated across diverse engineering mechanics scenarios, the method substantially outperforms existing large language model baselines, demonstrating high success rates and robustness in generating complete, correct, and physically consistent simulation models.