Jikai Liu

The growing demand for heat dissipation in aerospace, electronics cooling, and advanced energy systems is becoming increasingly critical, where maintaining lightweight design is equally vital for their high efficiency. To satisfy both requirements, lattice structures have emerged as promising candidates due to their high surface-area-to-volume ratio and lightweight nature. This study introduces a novel lightweight shell lattice structure with enhanced heat dissipation performance. The skeleton-driven parametric modeling approach is adopted to realize flexible adjustment of structural topology and shape. The generation parameters are obtained through a thermo-fluid topology optimization process. The optimized fluid channels are embedded into three-dimensional shell lattice structures via parameter mapping. Efficient heat dissipation lattice structures with directional fluid channels are generated. Three-dimensional conjugate heat transfer simulations demonstrate that the optimized design significantly reduces average and peak temperatures while simultaneously lowering pressure drop compared to the initial design. Comparative analysis with other shell lattice structures (e.g., triply periodic minimal surfaces) demonstrates superior flow guidance and mitigation of localized hot spots. Additionally, the skeleton-driven modeling method can be readily extended to conformal designs, while preserving excellent flow control and thermal performance in curved space. Physical experiments have demonstrated the effectiveness of the optimization design method. This work provides a systematic framework for developing high-efficiency, lightweight thermal management structures with broad applicability in advanced engineering systems.

Metamaterials with a negative Poisson’s ratio (NPR) exhibit unique auxetic deformation mechanism that enables superior energy absorption and mechanical resilience. Topology optimization (TO) can effectively generate microstructures with NPR characteristics, but conventional optimized designs often suffer from sharp corners and stress concentrations, which compromise durability and limit multicycle energy absorption. To address this issue, we introduced a boundary-fitting derivable geodesics-coupled TO (B-DGTO) framework to construct explicit curvature constraints into the optimization process, ensuring smooth boundaries and more uniform stress distribution with optimal NPR properties. In numerical example and experiment, we provided different types of 2D/3D NPR microstructures under curvature control to demonstrate the versatility of the proposed approach. These results confirm that the curvature constraint significantly improves the stress distribution of NPR microstructures and enhances their robustness and reliability under repeated loading. This study highlights curvature-constrained TO as a general and practical strategy for developing durable NPR metamaterials with superior energy dissipation performance.

Density method and level set method (LSM) stand as the two most prevalent topology optimization approaches. The former boasts strong robustness but suffers from ambiguous boundary geometric information, while the latter describes structural boundaries via implicit functions, enabling accurate high-order boundary information, but suffers from issues of initial guess dependency and incompatibility to standard optimizers. These two approaches have long been isolated with rare mutual compensations. In this paper, we proposed a novel approach that starts from the density field and achieves its transformation to the signed distance function (SDF) by solving design-dependent transient diffusion equation and Poisson equation. The derived SDF maintains geometric equivalence with the original density field. On this foundation, we further developed two B-DGTO (Boundary-fitting Derivable Geodesics-coupled Topology Optimization) frameworks: the density-based B-DGTO and the SDF-based B-DGTO, in supporting the density-level set co-topology optimization. The efficacy of these frameworks is validated through addressing mean curvature constraint and perimeter constraint on the L-brackets and thermal conduction structures. The proposed method provides a systematic framework integrating density method and LSM, holding profound implications for future development.

Remanufacturing effectively extends component lifespans by restoring used or end-of-life parts to like-new or even superior conditions, with an emphasis on maximizing reutilized material, especially for high-cost materials. Hybrid manufacturing technology combines the capabilities of additive and subtractive manufacturing, with the ability to add and remove material, enabling it to remanufacture complex shapes and is increasingly being applied in remanufacturing. How to effectively plan the process of additive and subtractive hybrid remanufacturing (ASHRM) to maximize material reutilization has become a key focus of attention. However, current ASHRM process planning methods lack strict consideration of collision-free constraints, hindering practical application. This paper introduces a computational framework to tackle ASHRM process planning for general shapes with strictly considering these constraints. We separate global and local collision-free constraints, employing clipping planes and graph to tackle them respectively, ultimately maximizing the reutilized volume while ensuring these constraints are satisfied. Additionally, we also optimize the setup of the target model that is conducive to maximizing the reutilized volume. Extensive experiments and physical validations on a 5-axis hybrid manufacturing platform demonstrate the effectiveness of our method across various 3D shapes, achieving an average material reutilization of 69% across 12 cases. Code is publicly available at https://github.com/fanchao98/Waste-to-Value.

The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditional forward design methods are constrained by their inability to explore the vast combinatorial design space, inverse design offers a compelling alternative by directly generating structures that fulfill predefined performance criteria. However, achieving precise control over both geometry and material properties remains a significant challenge due to their intricate interdependence. Existing approaches, which typically rely on voxel or parametric representations, often limit design flexibility and structural diversity. In this work, we present a novel generative model that integrates latent diffusion with Holoplane, an advanced hybrid neural representation that simultaneously encodes both geometric and physical properties. This combination ensures superior alignment between geometry and properties. Our approach generalizes across multiple microstructure classes, enabling the generation of diverse, tileable microstructures with significantly improved property accuracy and enhanced control over geometric validity, surpassing the performance of existing methods. We introduce a multi-class dataset encompassing a variety of geometric morphologies, including truss, shell, tube, and plate structures, to train and validate our model. Experimental results demonstrate the model's ability to generate microstructures that meet target properties, maintain geometric validity, and integrate seamlessly into complex assemblies. Additionally, we explore the potential of our framework through the generation of new microstructures, cross-class interpolation, and the infilling of heterogeneous microstructures. The dataset and source code will be open-sourced upon publication.

This paper investigates the impact of varying the part geometric complexity and 3D printing process setup on the resulting structural load bearing capacity of fiber composites. Three levels of geometric complexity are developed through 2.5D topology optimization, 3D topology optimization, and 3D topology optimization with directional material removal. The 3D topology optimization is performed with the SIMP method and accelerated by high-performance computing. The directional material removal is realized by incorporating the advection-diffusion partial differential equation-based filter to prevent interior void or undercut in certain directions. A set of 3D printing and mechanical performance tests are performed. It is interestingly found that, the printing direction affects significantly on the result performance and if subject to the uni direction, the load-bearing capacity increases from the 2.5D samples to the 3D samples with the increased complexity, but the load-bearing capacity further increases for the 3D simplified samples due to directional material removal. Hence, it is concluded that a restricted structural complexity is suitable for topology optimization of 3D-printed fiber composites, since large area cross-sections give more degrees of design freedom to the fiber path layout and also makes the inter-layer bond of the filaments firmer.