Designing Negative Poisson’s Ratio Honeycombs

ISEF Category: Engineering Technology: Statics and Dynamics

Subcategory: Computational Mechanics  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Most materials get thinner when you stretch them. Auxetic materials do the opposite. That weird behavior can make parts tougher, lighter, and better at absorbing impact. You can test whether a honeycomb shape can be designed to do it on purpose.

What Is It?

Poisson's ratio tells you how a material changes width when you pull it. If you stretch a rubber band, it gets narrower. That is the usual behavior. A negative Poisson's ratio means the material gets wider instead. Materials with that behavior are called auxetic.

A honeycomb metamaterial gets its properties more from shape than from chemistry. Think of it like folding paper into a clever pattern. The pattern can bend, open, and rotate in ways that make the whole structure act very differently from the plastic or resin it is made of. In this project, you would use simulation to search for a honeycomb shape that pushes Poisson's ratio below zero, then print and test your best design.

The inverse-design part means you do not guess one shape and hope for the best. You tell the computer what you want, then let an optimizer try many geometry options until it finds better ones. Bayesian optimization is one way to do that. It uses past results to choose the next model to test, so you spend less time on bad designs.

Why This Is a Good Topic

This is a strong science fair topic because you can change the geometry, measure the mechanical response, and compare simulation with real prints. That gives you clear variables, repeatable tests, and real engineering tradeoffs. It also connects to impact protection, biomedical devices, flexible structures, and lightweight design. You can learn computational modeling, optimization, 3D printing, and data analysis in one project.

Research Questions

• How does the honeycomb cell angle affect Poisson's ratio in simulation?
• What is the effect of wall thickness on the onset of auxetic behavior?
• Does adding curved struts produce a more negative Poisson's ratio than straight struts?
• To what extent does printed material type change the gap between simulated and measured Poisson's ratio?
• Which geometry variables matter most in a Bayesian optimization search for auxetic response?
• How does the loading direction change the measured Poisson's ratio of the same honeycomb design?

Basic Materials

• Computer with access to Ansys Student or SimScale account.
• Optuna installed in Python.
• 3D printer with slicing software.
• Calipers or digital micrometer.
• Digital kitchen scale with 0.1 g accuracy.
• Phone camera with a tripod or fixed mount.
• High-contrast speckle paint or marker dots for DIC tracking.
• Tensile test frame at school or a shared lab.
• PETG, PLA, or another printable polymer filament.
• Safety glasses.

Advanced Materials

• Access to Ansys Mechanical or a comparable finite element setup.
• Access to SimScale or another cloud simulation platform.
• Optuna and Python environment for Bayesian optimization.
• Strain gauge or extensometer for cross-checking deformation data.
• Digital image correlation software such as ImageJ with a tracking plugin or commercial DIC software.
• High-resolution camera or microscope camera for speckle tracking.
• 3D printer with controlled infill and material settings.
• Tensile testing machine with load cell and displacement control.
• Multiple filament or resin types for comparison.
• Precision balance and metrology tools for specimen characterization.

Software & Tools

• Python: Runs Optuna searches, handles geometry sweeps, and analyzes results.
• Optuna: Chooses the next design to test based on earlier simulation outcomes.
• Ansys Student: Simulates stress, strain, and deformation in your honeycomb model.
• SimScale: Runs cloud-based finite element models if local computing is limited.
• ImageJ: Tracks speckle motion in phone images and estimates strain fields.

Experiment Steps

1. Define the exact auxetic behavior you want to measure, then choose one output metric, such as Poisson's ratio under tension.
2. Pick a small set of geometry variables, so your optimization search stays focused and testable.
3. Build a simulation workflow that can generate one model, run it, and return a single score for the optimizer.
4. Set up a comparison plan between baseline honeycombs and your optimized design, with matching dimensions and loading conditions.
5. Design a print-and-test workflow that keeps the geometry faithful to the digital model, then plan how you will extract strain from phone images.
6. Decide how you will compare simulation, printed test data, and statistical uncertainty before you start collecting results.

Common Pitfalls

• Changing too many geometry variables at once, which makes it hard to know what caused the auxetic response.
• Using a simulation mesh that is too coarse, which can distort stress concentration and Poisson's ratio.
• Printing thin lattice walls that warp or fuse, which changes the real structure away from the CAD model.
• Tracking speckle motion with inconsistent camera angle or lighting, which corrupts digital image correlation measurements.
• Comparing simulation and experiment without matching boundary conditions, which creates fake disagreement.

What Makes This Competitive

A strong version of this project does more than show one auxetic sample. It tests a search strategy, compares it against manual design choices, and reports how well the optimizer generalizes to new geometry. You can also push the project by quantifying uncertainty, checking multiple loading directions, and showing where simulation stops matching print data. That turns the work into a real mechanics and design study, not just a cool demo.

Project Variations

• Test a different lattice family, such as re-entrant cells, rotating squares, or chiral honeycombs, and compare which one reaches the most negative Poisson's ratio.
• Swap the sensing method from phone-based DIC to strain gauges or video tracking, then compare measurement noise and repeatability.
• Optimize for a second target, such as energy absorption or stiffness-to-weight ratio, and see whether the best auxetic design changes.

Learn More

• MIT OpenCourseWare: Search for mechanics of materials, finite element analysis, and design optimization lecture notes.
• NIST Engineering Statistics Handbook: Use the online handbook for uncertainty, regression, and comparing models.
• NIH PubMed: Search for review articles on auxetic materials, metamaterials, and digital image correlation.
• NASA NTRS: Search the technical reports server for lattice structures, structural optimization, and lightweight design.
• Materials Today and Advanced Materials: Read review articles on auxetic metamaterials and find recent design trends through school or library access.