arXiv subjects

Danli Luo

Publications and source records attributed to Danli Luo.

4 recordsLinked to original sources

arXiv2026

FabDreamer: Exploring the Image-to-Physical Workflow Through AI-Assisted Layered Fabrication

Generative AI lets anyone create rich visual content in seconds, yet translating that content into a physically fabricable artifact still demands manual decomposition, occlusion repair, and structural verification that most tools leave entirely to the user. We present FabDreamer, an image-to-physical system that carries an image to fabrication-ready SVGs through three stages with deliberately staged AI initiative: (1) AI leads decomposition into depth-ordered layers, (2) assists on demand during creative editing with realtime 3D preview, and (3) advises on structural integrity before export. We instantiate this workflow for layered laser-cut art and evaluate it through three rounds including a formative analysis, an early prototype user evaluation (N=13), and a cross-domain practitioner study with specialists from 6 fabrication domains (N=6). Our findings show that physical awareness during design opens creative opportunities beyond error prevention, that practitioners appropriate the system's generic geometric operations for their own domains, and that the fabrication agent covers geometry-readable constraints while domain knowledge remains with the maker.

cs.HC↗

arXiv2025

MIDG: Mixture of Invariant Experts with knowledge injection for Domain Generalization in Multimodal Sentiment Analysis

Existing methods in domain generalization for Multimodal Sentiment Analysis (MSA) often overlook inter-modal synergies during invariant features extraction, which prevents the accurate capture of the rich semantic information within multimodal data. Additionally, while knowledge injection techniques have been explored in MSA, they often suffer from fragmented cross-modal knowledge, overlooking specific representations that exist beyond the confines of unimodal. To address these limitations, we propose a novel MSA framework designed for domain generalization. Firstly, the framework incorporates a Mixture of Invariant Experts model to extract domain-invariant features, thereby enhancing the model's capacity to learn synergistic relationships between modalities. Secondly, we design a Cross-Modal Adapter to augment the semantic richness of multimodal representations through cross-modal knowledge injection. Extensive domain experiments conducted on three datasets demonstrate that the proposed MIDG achieves superior performance.

cs.LG↗

arXiv2021

FoamFactor: Hydrogel-Foam Composite with Tunable Stiffness and Compressibility

This paper presents FoamFactor, a novel material with tunable stiffness and compressibility between hydration states, and a tailored pipeline to design and fabricate artifacts consisting of it. This technique compounds hydrogel with open-cell foams via additive manufacturing to produce a water-responsive composite material. Enabled by the large volumetric changes of hydrogel dispersions, the material is soft and compressible when dehydrated and becomes stiffer and rather incompressible when hydrated. Leveraging this material property transition, we explore its design space in various aspects pertaining to the transition of hydration states, including multi-functional shoes, amphibious cars, mechanical transmission systems, and self-deploying robotic grippers.

cs.HC↗

arXiv2020

Material characterization and precise finite element analysis of fiber reinforced thermoplastic composites for 4D printing

Four-dimensional (4D) printing, a new technology emerged from additive manufacturing (3D printing), is widely known for its capability of programming post-fabrication shape-changing into artifacts. Fused deposition modeling (FDM)-based 4D printing, in particular, uses thermoplastics to produce artifacts and requires computational analysis to assist the design processes of complex geometries. However, these artifacts are weak against structural loads, and the design quality can be limited by less accurate material models and numerical simulations. To address these issues, this paper propounds a composite structure design made of two materials - polylactic acid (PLA) and carbon fiber reinforced PLA (CFPLA) - to increase the structural strength of 4D printed artifacts and a workflow composed of several physical experiments and series of dynamic mechanical analysis (DMA) to characterize materials. We apply this workflow to 3D printed samples fabricated with different printed parameters to accurately characterize the materials and implement a sequential finite element analysis (FEA) to achieve accurate simulations. The accuracy of deformation induced by the triggering process is both computationally and experimentally verified with several creative design examples, and the 95% confidence interval of the accuracy is (0.972, 0.985). We believe the presented workflow is essential to the combination of geometry, material mechanism and design, and has various potential applications.

cs.CG↗