mechanical design

To address the conflicting challenges of low kinematic fidelity, insufficient rotational stiffness, and significant parasitic motion in large-angle (±15°) flexible crossed-hinge mechanisms, this paper proposes a static-dynamic-driven multi-objective optimization design methodology. We innovatively integrate rapid Euler–Bernoulli beam modeling with high-fidelity 3D ANSYS finite-element refinement to establish an interpretable hybrid modeling framework. Coupled with the NSGA-II algorithm, this approach efficiently explores the high-dimensional design parameter space and yields a Pareto-optimal solution set. The optimized configuration achieves motion error <0.5° over ±15° rotation, enhances rotational stiffness by 3.2×, and suppresses parasitic displacement by 87%, substantially outperforming conventional designs. This work provides both theoretical foundations and an engineering paradigm for high-performance compliant mechanisms.

Existing topology optimization methods rely on post-processing—such as mesh smoothing and feature extraction—to obtain manufacturable, smooth boundaries, thereby increasing computational cost and workflow complexity. This paper proposes a Gaussian Function Integration (GFI) topology optimization framework: structural geometry is explicitly represented via superposition of anisotropic Gaussian functions, coupled with a Heaviside-type level-set projection for mesh-independent parametric modeling. To the best of our knowledge, this is the first work to introduce explicit Gaussian function integration into topology optimization; the representation inherently ensures curvature continuity and manufacturing compatibility, while enabling flexible control over smoothness, discreteness, and geometric complexity through tunable parameters. Numerical experiments—including 2D/3D stiffness maximization and compliant mechanism design—demonstrate performance competitive with state-of-the-art Moving Morphable Components (MMC) methods, while yielding designs with sharper boundaries and superior geometric consistency—entirely eliminating the need for post-processing.

In human–robot collaborative scenarios involving soft robots, motion components must simultaneously satisfy mechanical performance requirements and collision-free motion constraints. Method: This paper proposes the first multi-objective optimization framework that unifies structural compliance design and motion planning. It integrates gradient-enhanced topology optimization, nonlinear contact modeling, model predictive control (MPC), and real-time collision detection to jointly generate task-driven stiffness distributions and motion trajectories. Contribution/Results: The framework innovatively couples physical properties (e.g., stiffness/compliance) with kinematic constraints—including dynamic collision avoidance—at the optimization level, enabling online co-regulation of stiffness and trajectory. Experimental validation—spanning simulation and physical hardware—demonstrates a 62% reduction in collision impact force, a task success rate of 98.3%, and an end-to-end response latency under 50 ms.

Existing CAD systems suffer from a fundamental disconnect between feature-based parametric modeling and B-rep–based direct modeling, hindering cross-paradigm collaborative editing of geometry, topology, and parametric constraints. To address this, we propose a unified constraint graph model and a hybrid modeling kernel interface—enabling, for the first time, bidirectional, seamless integration of both paradigms. Our approach extends the constraint solver, introduces a topology-event–driven mapping mechanism, and designs parameter semantic extraction and incremental synchronization algorithms to support real-time, cross-mode collaboration. Evaluated on mainstream CAD platforms, our system achieves sub-80-ms editing latency, 99.2% constraint fidelity, and efficient handling of complex assemblies. This work breaks down longstanding paradigm barriers in CAD modeling and establishes a foundational architectural framework for next-generation intelligent CAD systems.

This work addresses the lack of physical plausibility in single-image 3D reconstruction. We propose the first physics-compatible reconstruction framework that enforces static equilibrium as a hard constraint. Methodologically, we explicitly decouple and jointly optimize material stiffness, external loading forces, and the static equilibrium geometry; deformation responses are modeled via differentiable physics simulation, enabling gradient-based joint optimization of all variables. Our approach breaks from conventional simplifications—such as rigid-body assumptions or neglect of external forces—by embedding real-world physical constraints directly into the single-image reconstruction pipeline. Evaluated on Objaverse, our method yields reconstructions with significantly improved mechanical stability, suitable for downstream dynamic simulation and 3D printing. Physical validation via real-world force testing further confirms the structural robustness of the generated models.

Although topology optimization has matured, its reliance on manual intervention—such as modeling, meshing, and boundary condition specification—hinders accessibility for non-experts. This work proposes the first conversational framework based on a large language model (LLM) agent that enables end-to-end topology optimization through natural language instructions and optional inputs (e.g., images, geometry, or meshes), automatically invoking finite element solvers and optimization tools. The approach integrates multi-load structural and thermal optimization, handles stress constraints, and employs few-shot prompting strategies, successfully reproducing benchmark cases while solving complex engineering problems and autonomously generating optimized structures, field distributions, and convergence curves. Ablation studies confirm that prompt design critically enhances system robustness, substantially lowering the usability barrier without compromising numerical reliability.

This study addresses the limitations of conventional robotic construction systems, which decouple material design from robotic manipulation, hindering efficient grasping, self-alignment, reversible connections, and structural performance. To overcome this, the authors propose a geometrically intelligent composite nested lattice module composed of truncated octahedron and octahedron units. The module integrates dedicated grasping surfaces, screw-releasable snap-fit joints, and a triaxial interlocking mechanism, enabling robotic-efficient handling, three-dimensional interlocking assembly, and structural reconfiguration. By synergistically combining material-level geometric intelligence with robotic assembly, the approach successfully realizes structures spanning from furniture to architectural scales. The modules exhibit a compressive stiffness of 4,556 N/mm, a maximum load capacity of 3,445 N, an elastic modulus of 17.5 MPa, and support repeated disassembly and reassembly for cyclic, multi-configuration construction.

This work addresses the limitations of conventional thermomechanical topology optimization, which typically relies on small-strain linear elasticity and temperature-independent material properties, rendering it inadequate for accurately capturing the mechanical behavior of multimaterial compliant devices at elevated temperatures. To overcome this, the authors propose a thermomechanical topology optimization framework that integrates geometric nonlinearity and temperature-dependent material properties. The approach employs an additive decomposition of thermal eigenstrains in logarithmic strain space, utilizes a finite-strain constitutive model based on quadratic Hencky strain, and incorporates temperature-dependent thermal conductivity, coefficient of thermal expansion, and elastic modulus. Coupled with a physics-informed simultaneous analysis and design methodology, the framework successfully optimizes thermal actuators and grippers in a titanium–copper–steel system. Results demonstrate that the proposed fully physical model substantially enhances structural strength and thermal robustness with only a modest increase in computational cost.

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.

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.