A Computational Design Pipeline to Fabricate Sensing Network Physicalizations.

Area of Science:

• Human-Computer Interaction
• Data Visualization
• Computational Design
• 3D Printing

Background:

• Designing interactive physicalizations is complex, requiring expertise in visualization, fabrication, and electronics.
• Current methods often result in unique, non-reusable solutions for specific datasets or problems.
• Network data's complex geometry poses challenges for intuitive physical interaction.

Purpose of the Study:

• To introduce a computational design pipeline for producing 3D-printed network physicalizations with integrated sensing.
• To enable intuitive touch-based interaction, specifically selection, for exploring network data.
• To establish a new design paradigm that unifies form and interactivity in a single fabrication workflow.

Main Methods:

• Development of a computational design pipeline for 3D printing.
• Integration of capacitive sensing and computational inference for touch interaction.
• Evaluation through computational analysis, usage scenarios, and expert interviews.

Main Results:

• The pipeline successfully generates network physicalizations with integrated sensing capabilities.
• The system supports touch-based selection, a critical interaction for data exploration.
• Evaluations confirm the approach's effectiveness and usability for visualization tasks.

Conclusions:

• The computational design pipeline offers a novel and cohesive approach to creating interactive physicalizations.
• This paradigm lowers barriers to the research, creation, and adoption of physicalization technologies.
• The method facilitates the exploration of complex network data through intuitive physical interaction.