Research Areas
Intelligent Design
Topology optimization, data-driven methods, and generative AI for structure and microstructure design.
Intelligent Manufacturing
Additive manufacturing constraints, path planning, and LMPA composite fabrication for manufacturing-oriented design.
Embodied Intelligence
Soft robots and mechanical metamaterials with programmable responses for structural intelligence and self-actuated systems.
Isogeometric Analysis
CAD-integrated finite element framework for exact-geometry shell topology optimization and high-fidelity simulation.
Research Highlights
An Optimized, Easy-to-use, Open-source GPU Solver for Large-scale Inverse Homogenization Problems
Di Zhang, Xiaoya Zhai*, Ligang Liu, Xiao-Ming Fu
We propose a high-performance GPU solver for inverse homogenization problems to design high-resolution 3D microstructures. Practical examples with 512³ ≈ 134.2 million finite elements run in less than 40 seconds per iteration on an NVIDIA GTX 1080Ti, with an easy-to-use framework requiring fewer than 20 lines of user code.
Topology Optimization of Differentiable Microstructures
2D: Xiaoya Zhai, Weiming Wang, Falai Chen, Jun Wu* | 3D: Jiacheng Han, Xiaoya Zhai*, Lili Wang, et al.
Several 2D/3D differentiable microstructures with bulk modulus close to the Hashin-Shtrikman upper bound are proposed for the first time. Discontinuities are avoided via a novel heat-equation-based regularization, enabling continuously parameterized microstructure families across the full density range.
Low-melting-point Alloys Integrated Extrusion Additive Manufacturing
Jingchao Jiang+, Xiaoya Zhai+, Kang Zhang+, et al., Wei-Hsin Liao* (equal contribution)
A novel dual-nozzle extrusion strategy successfully fabricates complex low-melting-point alloy (LMPA) components for the first time, enabling composite parts with improved mechanical properties and integrated circuit manufacturing for smart structures, electromagnetic shielding, and energy harvesting.
Near-isotropic, Extreme-stiffness, Continuous 3D Mechanical Metamaterial Sequences Using Implicit Neural Representation
Yunkai Zhao+, Lili Wang+, Xiaoya Zhai*, Jiacheng Han, et al.
Three near-isotropic, extreme-stiffness, continuous 3D metamaterial sequences are proposed by combining topology optimization and data-driven design, achieving over 98% of the Hashin-Shtrikman upper bounds across densities from 0.2 to 1. Sequences are represented as implicit neural functions for resolution-free continuous density variation.