Stefanie Hahmann (University Grenoble INP)
Zoom Lecture: 91344952941
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Recent advances in digital manufacturing, where computational design, materials science and engineering meet, offer whole new perspectives for tailoring mechanical properties and fabrication of material with applications as diverse as product design, architecture, engineering and art. Auxetic materials are characterized by a negative Poisson’s ratio. This means that they do not behave as usual materials. When stretched in one direction, they do not shrink in the other directions, in contrary they expand. In comparison to standard materials, auxetics are therefore characterized by enhanced mechanical properties such as energy absorption, indentation resistance and acoustic absorption.
This presentation is devoted to our recent work on a category of metamaterials called auxetic structures, or auxetic networks. Whereas regular auxetic networks are well studied, our focus is on irregular, also called disordered auxetic networks. In particular, we are exploring geometrical strategies to generate 2-dimensional disordered auxetic structures.
Starting from an irregular dense network, we seek to reduce the Poisson's ratio, by pruning bonds (edges) based solely on geometric criteria. To this end, we first deduce some prominent geometric features from regular auxetic networks and then introduce a strategy combining a pure geometric pruning algorithm followed by a physics-based testing phase to determine the resulting Poisson's ratio of our networks. We provide statistical validation of our approach on large sets of irregular networks, and we additionally show real auxetic networks laser-cut using sheets of rubber. The findings reported here show that it is possible to reduce the Poisson's ratio by geometric pruning, and that we can generate disordered auxetic networks at lower processing times than a physics-based approach.
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