@inproceedings{discovery10114522,
       booktitle = {Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications},
           month = {January},
           pages = {1016--1021},
         journal = {ADVANCES IN ENGINEERING MATERIALS, STRUCTURES AND SYSTEMS: INNOVATIONS, MECHANICS AND APPLICATIONS},
            note = {This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.},
       publisher = {CRC PRESS-BALKEMA},
            year = {2019},
           title = {Design of thermally deformable laminates using machine learning},
          editor = {A Zingoni},
        abstract = {Recent advances in material science and manufacturing have enabled designers to create objects
which respond to changing environmental conditions by controlled deformation, without external mechanical
or electrical actuation. The design of such smart materials has mostly been done through trial and error due to
their complex nonlinear behavior. This paper will present how this problem is addressed by introducing a novel
inverse design workflow. In this, a desired structural deformation is used as an input to a machine learned model
which subsequently outputs the required geometric and material properties that will produce said deformation
when exposed to an external stimulus. This workflow uses a Generative Adversarial Neural Network (GANN)
trained on pairs of input cut-out patterns of laminate layers and their nonlinear Finite Element Analysis (FEA)
simulation results. The method offers a significant performance speed-up while maintaining acceptable levels
of accuracy, especially at the early design stage. This methodology could be further extended to the design of
any nonlinear mechanical deformation.},
             url = {https://www.routledge.com/Advances-in-Engineering-Materials-Structures-and-Systems-Innovations/Zingoni/p/book/9781138386969},
          author = {Abdel-Rahman, A and Kosicki, M and Michalatos, P and Tsigkari, M}
}