InSense3D: Designing Smart 3D-Printed Structures Leveraging Ferromagnetic Filaments for Inductive Deformation Sensing

Paper Title

InSense3D: Designing Smart 3D-Printed Structures Leveraging Ferromagnetic Filaments for Inductive Deformation Sensing

Publication Info

• Topic area: Inductive sensing in 3D-printed deformable structures for tangible interfaces.
• Keywords: Inductive sensing, 3D printing, ferromagnetic cores, deformable lattices, wire-free sensing, tangible interfaces, sensitivity, deformation tracking, passive sensors, user interaction.

Background and Problem

• Problem / challenge: Existing 3D-printed sensing approaches, such as capacitive and resistive methods, suffer from hysteresis, drift, and require post-print assembly of electronic components, making them cumbersome and less reliable.
• Significance: Enabling wire-free, high-sensitivity sensing in deformable 3D-printed structures can simplify fabrication, improve sensing performance, and expand applications in user interaction and prototyping.
• Motivation and related work: Prior research has explored capacitive, resistive, and inductive sensing in 3D-printed structures, but these methods require embedded electronics or wiring. Inductive sensing offers advantages like low hysteresis and stable measurements, yet existing implementations demand complex assembly. This paper addresses the gap by introducing a fully passive, wire-free inductive sensing approach.

Solution

• Proposed approach: InSense3D, a wire-free inductive sensing system using deformable TPU lattices with embedded ferromagnetic cores, read by stationary coils.
• Novelty:
  1. A wiring-free inductive sensing principle for 3D-printed tangibles, eliminating the need for embedded electronics.
  2. A design space and guidelines for core configurations, lattice parameters, and coil–core coupling to achieve multi-axis deformation sensing.
  3. Technical validation showing reduced calibration burden and improved repeatability compared to other sensing methods.
  4. Application examples demonstrating modular, exchangeable interfaces for varied use cases.
• Procedure and key techniques:
  • Design deformable TPU lattice structures with embedded ferromagnetic cores.
  • Position cores near stationary coils to modulate inductance during deformation.
  • Evaluate key design parameters (distance, offset, size, density) influencing sensitivity and responsiveness.
  • Test multi-parameter configurations for richer sensing characteristics.
  • Demonstrate applications such as liquid level measurement, music controllers, stretchable game controllers, and pressure-sensitive shoe soles.

Results

• Concrete findings:
  • Sensitivity: Up to 0.233% change in relative inductance per millimeter of compression.
  • Stability: Inductive response drift remained within 0.05% after 10,000 cycles of compression and decompression.
  • Design parameters: Distance, offset, size, and density significantly influence inductance changes, enabling tailored sensing profiles.
• Advantage over baselines: Improved sensitivity, reduced calibration needs, and long-term stability compared to resistive and capacitive sensing approaches.
• Experiments / evaluation:
  • Systematic testing of design parameters using prototypes with varying core configurations.
  • Validation of sensitivity and stability through controlled compression experiments.
  • Application demonstrations showcasing practical use cases.
• Limitations and future work:
  • Current prototypes rely on planar coil bases, limiting exploration of non-planar geometries.
  • Requires fixed spatial alignment between lattice and coil.
  • Future directions include flexible coils, material-level variations, multi-coil systems for high-resolution sensing, and automated design tools.

Summary

InSense3D introduces a wire-free inductive sensing approach for 3D-printed deformable structures, leveraging embedded ferromagnetic cores within TPU lattices. By systematically studying design parameters, the paper demonstrates how geometric and spatial configurations influence sensitivity and deformation behavior. Application examples highlight the versatility of the approach, enabling modular, passive interfaces for diverse use cases. The system achieves high sensitivity and long-term stability, addressing limitations of prior sensing methods. Future work aims to expand design flexibility and sensing resolution, supporting broader adoption in tangible interaction design.