A Machine Learning Approach to The Inverse Problem of Self-Morphing Composites

Gal Kapon, Arielle Blonder, Guy Austem

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Composite materials are valued in architecture for their remarkable strength-to-weight ratio and ability to shape intricate structures. However, conventional methods relying on single-use molds raise environmental concerns. Recent advancements in moldless fabrication, particularly self-morphing techniques’ leverage geometric frustration— internal stresses gemrated by material architecture. Uniaxial shrinkage in composites, traditionally seen as distortions’ can be harnessed to create a self-shaping mechanism, enabling the achievement of complex geometries by varyingfiber orientations. This paper addresses the inverse problem of self-morphing composites, aiming at the generation of production plans from desired designs for morphing. We propose leveraging machine learning, notably Convolutional Neural Networks (CNNs), topredictfiber layouts using 2D data matrices. The paper outlines the use of simulations to construct a dataset for training CNN models to predict the fiber layouts required to achieve design geometry. The contribution of this work is to advance digital design and simulation methods and tools towards the implementation of self-morphing matter in architecturalfabrication.

Original languageEnglish
Title of host publicationProceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2024
EditorsOdysseas Kontovourkis, Marios C. Phocas, Gabriel Wurzer
PublisherEducation and research in Computer Aided Architectural Design in Europe
Pages293-302
Number of pages10
ISBN (Print)9789491207372
DOIs
StatePublished - 2024
Externally publishedYes
Event42nd Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2024 - Nicosia, Cyprus
Duration: 9 Sep 202413 Sep 2024

Publication series

NameProceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe
Volume1
ISSN (Print)2684-1843

Conference

Conference42nd Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2024
Country/TerritoryCyprus
CityNicosia
Period9/09/2413/09/24

Bibliographical note

Publisher Copyright:
© 2024, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.

Keywords

  • CNN
  • composite materials
  • digitalfabrication
  • geometric frustration
  • inverse design
  • machine learning
  • moldless fabrication
  • Self-morphing

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