We present a compact yet highly expressive design space for shellular metamaterials that support both interactive exploration and inverse design. With only a few dozen charges, our representation generates a wide family of periodic shells, spanning from simple planar configurations to complex TPMS-like morphologies. To enable rapid evaluation, we introduce an efficient GPU-based homogenization pipeline that computes the effective elastic tensor of a candidate design in near real time ( 0.4), making interactive shellular design practical. Across a large set of synthesized structures, our design space exhibits geometric diversity and spans a broad spectrum of mechanical responses, covering a wide range of effective material properties. This fast evaluation further enables inverse design for target macroscopic properties. In the low-solid-volume regime, the resulting shellular structures achieve performance competitive with state-of-the-art shell-based metamaterials in multiple material properties. Finally, we validate manufacturability by fabricating tiled prototypes via additive manufacturing, demonstrating the potential of our approach for real-world engineering applications.

Endowing robots with tool design abilities is critical for enabling them to solve complex manipulation tasks that would otherwise be intractable. While recent generative frameworks can automatically synthesize task settings—such as 3D scenes and reward functions—they have not yet addressed the challenge of tool-use scenarios. Simply retrieving human-designed tools might not be ideal since many tools (e.g., a rolling pin) are difficult for robotic manipulators to handle. Furthermore, existing tool design approaches either rely on predefined templates with limited parameter tuning or apply generic 3D generation methods that are not optimized for tool creation. To address these limitations, we propose **RobotSmith**, an automated pipeline that leverages the implicit physical knowledge embedded in vision-language models (VLMs) alongside the more accurate physics provided by physics simulations to design and use tools for robotic manipulation. Our system (1) iteratively proposes tool designs using collaborative VLM agents, (2) generates low-level robot trajectories for tool use, and (3) jointly optimizes tool geometry and usage for task performance. We evaluate our approach across a wide range of manipulation tasks involving rigid, deformable, and fluid objects. Experiments show that our method consistently outperforms strong baselines in both task success rate and overall performance. Notably, our approach achieves a 50.0\% average success rate, significantly surpassing other baselines such as 3D generation (21.4\%) and tool retrieval (11.1\%). Finally, we deploy our system in real-world settings, demonstrating that the generated tools and their usage plans transfer effectively to physical execution, validating the practicality and generalization capabilities of our approach.

We introduce TetSphere Splatting, a Lagrangian geometry representation designed for high-quality 3D shape modeling. TetSphere splatting leverages an underused yet powerful geometric primitive -- volumetric tetrahedral meshes. It represents 3D shapes by deforming a collection of tetrahedral spheres, with geometric regularizations and constraints that effectively resolve common mesh issues such as irregular triangles, non-manifoldness, and floating artifacts. Experimental results on multi-view and single-view reconstruction highlight TetSphere splatting's superior mesh quality while maintaining competitive reconstruction accuracy compared to state-of-the-art methods. Additionally, TetSphere splatting demonstrates versatility by seamlessly integrating into generative modeling tasks, such as image-to-3D and text-to-3D generation.

We propose the Medial Skeletal Diagram, a novel skeletal representation that tackles the prevailing issues around skeleton sparsity and reconstruction accuracy in existing skeletal representations. Our approach augments the continuous elements in the medial axis representation to effectively shift the complexity away from the discrete elements. To that end, we introduce generalized enveloping primitives, an enhancement over the standard primitives in the medial axis, which ensure efficient coverage of intricate local features of the input shape and substantially reduce the number of discrete elements required. Moreover, we present a computational framework for constructing a medial skeletal diagram from an arbitrary closed manifold mesh. Our optimization pipeline ensures that the resulting medial skeletal diagram comprehensively covers the input shape with the fewest primitives. Additionally, each optimized primitive undergoes a post-refinement process to guarantee an accurate match with the source mesh in both geometry and tessellation. We validate our approach on a comprehensive benchmark of 100 shapes, demonstrating the sparsity of the discrete elements and superior reconstruction accuracy across a variety of cases. Finally, we exemplify the versatility of our representation in downstream applications such as shape generation, mesh decomposition, shape optimization, mesh alignment, mesh compression, and user-interactive design.

We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming rigidity or neglecting external forces. Consequently, the reconstructed objects fail to withstand real-world physical forces, resulting in instability or undesirable deformation -- diverging from their intended designs as depicted in the image. Our optimization framework addresses this by embedding physical compatibility into the reconstruction process. We explicitly decompose the three physical attributes and link them through static equilibrium, which serves as a hard constraint, ensuring that the optimized physical shapes exhibit desired physical behaviors. Evaluations on a dataset collected from Objaverse demonstrate that our framework consistently enhances the physical realism of 3D models over existing methods. The utility of our framework extends to practical applications in dynamic simulations and 3D printing, where adherence to physical compatibility is paramount.

We study the design of transfer functions for volumetric rendering of magnetic resonance imaging (MRI) datasets of human hands. Human hands are anatomically complex, containing various organs within a limited space, which presents challenges for volumetric rendering. We focus on hand musculoskeletal organs because they are volumetrically the largest inside the hand, and most important for the hand's main function, namely manipulation of objects. While volumetric rendering is a mature field, the choice of the transfer function for the different organs is arguably just as important as the choice of the specific volume rendering algorithm; we demonstrate that it significantly influences the clarity and interpretability of the resulting images. We assume that the hand MRI scans have already been segmented into the different organs (bones, muscles, tendons, ligaments, subcutaneous fat, etc.). Our method uses the hand MRI volume data, and the geometry of its inner organs and their known segmentation, to produce high-quality volume rendering images of the hand, and permits fine control over the appearance of each tissue. We contribute two families of transfer functions to emphasize different hand tissues of interest, while preserving the visual context of the hand. We also discuss and reduce artifacts present in standard volume ray-casting of human hands. We evaluate our volumetric rendering on five challenging hand motion sequences. Our experimental results demonstrate that our method improves hand anatomy visualization, compared to standard surface and volume rendering techniques.

The progress in generative AI, particularly large language models (LLMs), opens new prospects in design and manufacturing. Our research explores the use of these tools throughout the entire design and manufacturing workflow. We assess the capabilities of LLMs in various tasks: converting text prompts into designs, generating design spaces and variations, transforming designs into manufacturing instructions, evaluating design performance, and searching for designs based on performance metrics. We identify and discuss the current strengths and limitations of LLMs, suggesting areas for potential enhancements. Additionally, we examine the ethical implications and propose strategies to mitigate risks associated with employing generative AI in design and manufacturing.

The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design. We investigate the application of this tool through sequential steps of the computational design and manufacturing workflow. In particular, we examine how LLMs can aid in tasks including: converting a text-based prompt into a quantitative design specification, transforming a design into manufacturing instructions, producing a design space and variations within that space, computing the performance of a given design, and optimizing for designs predicated on performance goals. Through a series of examples, we highlight overarching capabilities and limitations of the current LLMs. By exposing these aspects, we aspire to catalyze the continued improvement and progression of these models, providing a roadmap to build on their strengths and reduce their weaknesses."How Can Large Language Models Help Humans in Design And Manufacturing?" is a two-part article. Part 2, "Synthesizing an End-To-End LLM-Enabled Design and Manufacturing Workflow" can be read here .

Modeling arbitrarily large deformations of surfaces smoothly embedded in three-dimensional space is challenging. We give a new method to represent surfaces undergoing large spatially varying rotations and strains, based on differential geometry, and surface first and second fundamental forms. Methods that penalize the difference between the current shape and the rest shape produce sharp spikes under large strains, and variational methods produce wiggles, whereas our method naturally supports large strains and rotations without any special treatment. For stable and smooth results, we demonstrate that the deformed surface has to locally satisfy compatibility conditions (Gauss-Codazzi equations) on the first and second fundamental forms. We then give a method to locally modify the surface first and second fundamental forms in a compatible way. We use those fundamental forms to define surface plastic deformations, and finally recover output surface vertex positions by minimizing the surface elastic energy under the plastic deformations. We demonstrate that our method makes it possible to smoothly deform triangle meshes to large spatially varying strains and rotations, while meeting user constraints.

The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design. We investigate the application of this tool across the entire design and manufacturing workflow. Specifically, we scrutinize the utility of LLMs in tasks such as: converting a text-based prompt into a design specification, transforming a design into manufacturing instructions, producing a design space and design variations, computing the performance of a design, and searching for designs predicated on performance. Through a series of examples, we highlight both the benefits and the limitations of the current LLMs. By exposing these limitations, we aspire to catalyze the continued improvement and progression of these models.

We introduce a compact, intuitive procedural graph representation for cellular metamaterials, which are small-scale, tileable structures that can be architected to exhibit many useful material properties. Because the structures’ “architectures” vary widely—with elements such as beams, thin shells, and solid bulks—it is difficult to explore them using existing representations. Generic approaches like voxel grids are versatile, but it is cumbersome to represent and edit individual structures; architecture-specific approaches address these issues, but are incompatible with one another. By contrast, our procedural graph succinctly represents the construction process for any structure using a simple skeleton annotated with spatially varying thickness. To express the highly constrained triply periodic minimal surfaces (TPMS) in this manner, we present the first fully automated version of the conjugate surface construction method, which allows novices to create complex TPMS from intuitive input. We demonstrate our representation’s expressiveness, accuracy, and compactness by constructing a wide range of established structures and hundreds of novel structures with diverse architectures and material properties. We also conduct a user study to verify our representation’s ease-of-use and ability to expand engineers’ capacity for exploration.

Precision modeling of the hand internal musculoskeletal anatomy has been largely limited to individual poses, and has not been connected into continuous volumetric motion of the hand anatomy actuating across the hand's entire range of motion. This is for a good reason, as hand anatomy and its motion are extremely complex and cannot be predicted merely from the anatomy in a single pose. We give a method to simulate the volumetric shape of hand's musculoskeletal organs to any pose in the hand's range of motion, producing external hand shapes and internal organ shapes that match ground truth optical scans and medical images (MRI) in multiple scanned poses. We achieve this by combining MRI images in multiple hand poses with FEM multibody nonlinear elastoplastic simulation. Our system models bones, muscles, tendons, joint ligaments and fat as separate volumetric organs that mechanically interact through contact and attachments, and whose shape matches medical images (MRI) in the MRI-scanned hand poses. The match to MRI is achieved by incorporating pose-space deformation and plastic strains into the simulation. We show how to do this in a non-intrusive manner that still retains all the simulation benefits, namely the ability to prescribe realistic material properties, generalize to arbitrary poses, preserve volume and obey contacts and attachments. We use our method to produce volumetric renders of the internal anatomy of the human hand in motion, and to compute and render highly realistic hand surface shapes. We evaluate our method by comparing it to optical scans, and demonstrate that we qualitatively and quantitatively substantially decrease the error compared to previous work. We test our method on five complex hand sequences, generated either using keyframe animation or performance animation using modern hand tracking techniques.

针对航空发动机压气机叶片在实际工况下的超高周疲劳断裂问题,研究了三种锻造温度下TC4钛合金三点弯曲-轴向拉伸复合加载的疲劳破坏行为.试验结果表明,S-N曲线呈直线下降型和双平台型,采用985℃近锻造时疲劳性能最好.随着应力幅值降低,裂纹由表面萌生向次表面萌生转变,断口形貌呈现准解理断裂特征.表面裂纹萌生于α晶界或α-β相界,由位错滑移堆积导致;而次表面裂纹萌生于刻面,由初生α相解理导致.疲劳寿命由裂纹萌生阶段主导,且所占比例随总寿命的增加而变大.双态组织中初生a含量和尺寸均小于等轴组织,且β转变组织含量更高,从而具备更好的疲劳性能.轴向拉伸改变了试件的轴向应力分布,有利于提高裂纹萌生于次表面的概率,使裂纹起源点向内部迁移.

The ultra-high cycle fatigue performance of TC4 titanium alloy specimens under different laser power densities was carried out by an ultrasonic fatigue testing machine. The fatigue fracture mechanism was analyzed by scanning electron microscope (SEM), and the reliability of ultra-high cycle fatigue life was analyzed. The results show that the specimen fatigue performance is obviously improved after laser shock peening, and the fatigue fracture surface shows a transgranular quasi-cleavage cracking mode with facets on the surface or subsurface, corresponding to high cycle fatigue and ultra-high cycle fatigue, respectively. At the same time, the position of the crack origin zone shifts sideways. The ultra-high cycle fatigue life obeys both lognormal distribution and three-parameter Weibull distribution, and the influence of reliability and confidence on reliability life depends on the sensitivity of unilateral tolerance confidence factor to both. According to the p-γ-S-N fitting curves, it was concluded that the fatigue life is higher and the scatter is smaller under the power density of 3.42 GW/cm2, so it is the best impact parameter.

Modeling arbitrarily large deformations of surfaces smoothly embedded in three-dimensional space is challenging. The difficulties come from two aspects: the existing geometry processing or forward simulation methods penalize the difference between the current status and the rest configuration to maintain the initial shape, which will lead to sharp spikes or wiggles for large deformations; the co-dimensional nature of the problem makes it more complicated because the deformed surface has to locally satisfy compatibility conditions on fundamental forms to guarantee a feasible solution exists. To address these two challenges, we propose a rotation-strain method to modify the fundamental forms in a compatible way, and model the large deformation of surface meshes smoothly using plasticity. The user prescribes the positions of a few vertices, and our method finds a smooth strain and rotation field under which the surface meets the target positions. We demonstrate several examples whereby triangle meshes are smoothly deformed to large strains while meeting user constraints.

Objective Laser shock peening is an advanced surface technology which uses laser-induced plasma shock waves to strengthen metal materials. Compared with mechanical shot peening, low plastic rolling and other traditional technologies, it has many technical advantages, such as better strengthening effect, stronger controllability, and better applicability. The plastic deformation of the material surface layer occurs at an ultra-high strain rate under the action of shock waves, which not only forms high numerical residual stress, but also changes the microstructure and even produces the nanocrystal structure. The formation mechanism of nanocrystals is discussed by many researchers, but there is still a controversy at present. In order to further study the formation mechanism of surface nanocrystals induced by laser shock peening on TC4 titanium alloys, the surface microstructure evolution is systematically analyzed by electron backscattered diffraction (EBSD) and transmission electron microscope (TEM) , and the continuous dynamic recrystallization mechanism of original grains and the surface temperature rise effect are revealed. The work done plays a positive role in the application of laser shock peening in the anti-fatigue design of aero-engine blades and other key components. Methods The test material is aviation grade forged TC4 titanium alloy bimodal microstructure. The plate forgings are processed into 32 mm x 10 mm x 4 mm blocks, and the YD60-R200B laser shock peening equipment is used for a double-sided shock. The impact energy is 3.6 J, the spot diameter is 2.2 mm, and the lap rate is 50% . During the processing, the laser beam is fixed with a trigger frequency of 1 Hz. The clamping specimen moves according to the unidirectional serpentine path with a speed of 1. 1 mm/s, the restraint layer is industrial pure water, and the absorption layer is black tape. The EBSD samples are prepared by cutting, inlaying, grinding, and polishing, and observed by the Zeiss Merlin field emission scanning electron microscope with the Nordlysnano probe. The range of surface center area is 100 mu m X 100 mu m and the scanning step size is 0.3 mu m. The TEM surface samples are prepared by a combination of nail thinning and ion thinning, and the cross-sectional samples are first bonded with M-bond 610 adhesive and then prepared by thinning. The Tecnai G2 F30 transmission electron microscopy is used to observe the film samples, and the change of the TC4 titanium alloy surface layer microstructure after laser shock peening is analyzed. Results and Discussions The distribution and content of alpha and beta phases do not change significantly in the impact zone. The average grain size and the coefficient of variation decrease (Fig. 4) . The high-level strain field is more uniform, and the number of grain boundaries increases significantly and the distribution is denser ( Fig. 5) . The content of small-angle misorientation decreases, and the degree of non-correlated misorientation deviating from Mackenzie distribution weakens (Fig. 7) . The polar density of {0001} , {11-20} , and {10-10} basal plane texture decreases, and the polar distribution is polytropic and tends to be normal. The EBSD analysis results show that there is no phase transformation during impact, and the original grains are refined and homogenized due to the severe plastic deformation on the surface layer. At the same time, the small-angle subgrain boundaries gradually change to the large-angle ones driven by a shock wave, and the misorientation is more random. After laser shock peening, a uniform nanocrystal structure with a thickness of about 710. 4 nm is formed on the surface layer, and the electron diffraction pattern shows a sharp and continuous ring (Fig. 9) . The cross-sectional areas at 20, 50 and 100 mu m away from the surface show the distribution of dislocation cells, high-density dislocations, and low-density dislocations, respectively (Fig. 10) . The TEM analysis result shows that the influence depth of laser shock peening on the surface microstructure is less than 100 mu m, and the nanocrystals evolve from dislocation cells to high density dislocations. Combined with the EBSD and TEM analysis results, it is concluded that the formation of nanocrystals accords with the continuous dynamic recrystallization mechanism ( Fig. 11) . The driving force is the crystal distortion energy stored in the deformation zone, the surface is affected by the maximum strain and maximum strain rate, the dislocation nucleation points are the most, the crystal distortion energy is the largest, and the recrystallization process is most fully carried out. In addition, the transient temperature rise caused by laser shock peening reaches 755 degrees C , and the actual temperature is 780 degrees C which is higher than the recrystallization temperature and provides a thermal driving force for continuous dynamic recrystallization. Conclusions In this paper, the surface microstructure of the laser shocked TC4 titanium alloy is analyzed by EBSD and TEM, respectively. The microstructure evolution law and the nanocrystal formation mechanism are revealed. Laser shock peening does not change the microscopic material composition, but it can refine and homogenize the original grains, transform the small-angle grain boundaries to the large-angle ones, reduce the texture pole density, and make the grain orientation be more random. The surface layer microstructure consists of nanocrystals, dislocation cells, high-density dislocations, and original coarse grains, in which the thickness of the nanocrystal layer is about 710.4 nm, and the overall influence depth is less than 100 mu m. The surface nanocrystals are formed through the complex dislocation movement, which conforms to the continuous dynamic recrystallization mechanism. The surface crystal distortion energy is the largest and the temperature rise effect is the most prominent, so the recrystallization process is the most fully carried out and the grain refinement is the highest.

The composite sheet layup process involves stacking several layers of a viscoelastic prepreg sheet and curing the laminate to manufacture the component. Demands for automating functional tasks in the composite manufacturing processes have dramatically increased in the past decade. A simulation system representing a digital twin of the composite sheet can aid in the development of such an autonomous system for prepreg sheet layup. While Finite Element Analysis (FEA) is a popular approach for simulating flexible materials, material properties need to be encoded to produce high-fidelity mechanical simulations. We present a methodology to predict material parameters of a thin-shell FEA model based on real-world observations of the deformations of the object. We utilize the model to develop a digital twin of a composite sheet. The method is tested on viscoelastic composite prepreg sheets and fabric materials such as cotton cloth, felt and canvas. We discuss the implementation and development of a high-speed FEA simulator based on the VegaFEM library [29]. By using our method to identify sheet material parameters, the sheet simulation system is able to predict sheet behavior within 5 cm of average error and have proven its capability for 10 fps real-time sheet simulation.

The ultrasonic fatigue tests under two three-point bending loading modes were carried out on TC4 specimens with the equiaxed, bimodal, and lamellar microstructure in the range of 10(5) to 10(9) cycles. The S-N curves with different shapes were obtained, and it was found that the crack initiation in very high cycle regime changed from surface to subsurface. The crack initiation mechanism with different microstructures was revealed by fracture analysis. The axial tension influence mechanism is that it changes the axial stress distribution on the specimen cross section and makes the crack origin migrate to the interior.

As aero engine life increases, blade fatigue has become one of the key factors restricting the high reliability and long service life of aero engine. In this paper, TC4 bar was treated by α+β forging process. The microstructure and mechanical properties of the treated material were observed and tested at room temperature, and the three point bending ultrasonic fatigue test was carried out. The results show that the S-N curve of the α+β forging material presents a double platform shape. The high cycle showed surface cleavage mode, the very high cycle showed internal cleavage mode, and the very high cycle cracks originated from the primary phase cleavage plane.