Design of a Super-Compressible Metamaterial

Design of a Super-Compressible Metamaterial: An Experimental Investigation Guided by Machine Learning

Houlder, Michael (TU Delft Mechanical, Maritime and Materials Engineering)

Bessa, Miguel (mentor)

Delft University of Technology

Mechanical Engineering | Vehicle Engineering

2019-05-29

Mechanical metamaterials are a new emerging class of materials which achieve properties outside the bounds of conventional materials. A metamaterial consists of a unit cell which is periodically repeated in space. In this study, a new metamaterial unit cell is proposed, derived from a class of space structures known as deployable masts. What makes these masts particularly interesting is their ability to contract to a fraction of their original length. In order to use such a structure as a unit cell, requires a deep understanding of the design parameters impact on material response. To guide this project, a novel data driven approach to design will be implemented. Here, computational simulations are used to create a database of mechanical responses, which in turn is used to model the relationship between input and output responses. This approach essentially flips the conventional approach of mechanical design on its head by using computational simulations to define the design space before manufacturing and testing. This replaces designer intuition with predictive charts, becoming increasingly useful for non-intuitive problems. This study validates the data driven approach through mechanical testing of a metamaterial unit cell. This testing is done at the macroscopic scale, utilizing a hobbyist 3D-printer (Ultimaker 2) to manufacture the structure. This study demonstrates that the material model is capable of accurately predicting the unit cell response. The limitation and possibilities for fused deposition modelling printed parts to be used as functional components is also investigated. Based on the insights gained from the data driven design process and experimental validation, design parameters are proposed for which a metamaterial unit cell exhibits both extreme compressibility and a high compressive strength.

Metamaterials
Machine Learning
Fused Deposition Modeling
Data driven design
mechanical metamaterials
compressible
Additive Manufacturing

http://resolver.tudelft.nl/uuid:3716e88c-ea9a-413d-a310-af6cad5009c3

Student theses

master thesis

© 2019 Michael Houlder