FluxLab: Creating 3D Printable Shape-Changing Devices with Integrated Deformation Sensing

2025-12-02

AI Summary

To address the challenge of simultaneously achieving programmable deformation, embedded sensing, and low-cost fabrication in soft devices, this paper introduces FluxLab—a unified design and fabrication system for silicone-based 3D-printed shape-changing devices. Methodologically, it integrates actuation, sensing, and structural support within a single physical substrate via a nested multi-layer architecture comprising shape memory alloy (SMA) actuation channels, lattice-based mechanical supports, and helical conductive traces. It further incorporates an interactive deformation editor and inductive sensing for user-defined deformation modeling, alongside a lightweight machine learning classifier enabling real-time, high-accuracy deformation recognition. Fabricated using consumer-grade stereolithography (SLA) printers and elastic silicone resin, FluxLab demonstrates feasibility through functional prototypes—including a steam-cooker clamp, a remote gripper, and an interactive desk lamp—achieving superior manufacturability, sensing accuracy (94.2% mean classification accuracy), and functional versatility.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

We present FluxLab, a system comprising interactive tools for creating custom 3D-printable shape-changing devices with integrated deformation sensing. To achieve this, we propose a 3D printable nesting structure, consisting of a central SMA channel for sensing and actuation, lattice-based padding in the middle for structural support and controllable elasticity, and parallel helix-based surface wires that preserve the overall form and provide anchoring struts for guided deformation. We developed a design editor to embed these structures into custom 3D models for printing with elastic silicone resin on a consumer-grade SLA 3D printer and minimal post-printing assembly. A deformation authoring tool was also developed for users to build a machine learning-based classifier that distinguishes desired deformation behaviors using inductive sensing. Finally, we demonstrate the potential of our system through example applications, including a self-deformable steamer bowl clip, a remotely controllable gripper, and an interactive desk lamp.

Problem

• Develops 3D printable shape-changing devices with embedded sensing
• Integrates sensing and actuation into a single printable structure
• Enables user creation of custom deformable objects with machine learning control

Innovation

• Integrated SMA channel for sensing and actuation
• Lattice padding and helix wires for structural control
• ML classifier with inductive sensing for deformation recognition