Data - driven design of a metamaterial unit cell using sparse Gaussian processes

Data - driven design of a metamaterial unit cell using sparse Gaussian processes

Głowacki, Piotr (TU Delft Mechanical, Maritime and Materials Engineering)

Bessa, Miguel (mentor)
Sluiter, Marcel (graduation committee)
Dey, Poulumi (graduation committee)
Popovich, Vera (graduation committee)

Delft University of Technology

Materials Science and Engineering

2019-03-22

Metamaterials are a new class of materials where the properties crucially depend on the design of the unit cell that is periodically repeated in space. In this study a new metamaterial unit cell concept has been proposed, inspired by a class of space structures known as deployable masts. The ability of these structures to contract to a fraction of their size made them suitable candidates for energy absorbing applications. One of the main design targets of energy absorbers is the ability to tune the material response to specific applications. Tunability of the mechanical response of the metamaterial concept requires deep understanding of the influence of design parameters. The prime focus of this study was to gain this understanding via data-driven insights. Conventionally, the design of a new material is carried out by making educated guesses about the design parameters and subsequently performing expensive and time-consuming experiments. In this work computational simulations were utilized to create databases of mechanical responses. These databases are later used to model the relationship between the inputs and the output response. This generated the issue of how and what method to use to effectively determine this relationship. This work explored state-of-the-art machine learning methods to enhance a recently proposed data-driven framework with thegoal of designing a new super-compressible metamaterial with large energy absorption. Importantly, the data-driven design process included the influence of manufacturing imperfections on the mechanical response of the metamaterial.The study revealed that by tuning the design parameters, significantly different mechanical response of the structure was achievable. The proposed learning model has enabled mapping of the influence of design parameters in the design space, moreover the sensitivity to those parameters varied across the design space. The increased energy absorption has been attributed to the resistance to bending of the main load carrying components of the design. It was demonstrated that the number of those components and the elastic modulus were scaling factors for the quantities of interest. Based on the insightsgained, a unit cell metamaterial design with significantly improved energy absorbing capability was proposed.

Metamaterials
Machine Learning
Design of Experiments
Gaussian process
Energy absorption

http://resolver.tudelft.nl/uuid:98ddedb6-eba6-4ffe-be87-11fcd675f809

Student theses

master thesis

© 2019 Piotr Głowacki