Architectured Lattice Materials with Tunable Anisotropy: Design and Analysis of the Material Property Space with the Aid of Machine Learning. Issue 12 (23rd October 2020)

• Record Type: Journal Article
• Authors: Kulagin, Roman; Beygelzimer, Yan; Estrin, Yuri; Schumilin, Artem; Gumbsch, Peter
• Is Part Of: Advanced engineering materials. Volume 22:Issue 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-23
• Subjects: architectured materials -- elastic anisotropies -- lattice materials -- machine learning; Materials -- Periodicals; 620.11
• DOI: 10.1002/adem.202001069
• Languages: English
• ISSNs: 1438-1656

Abstract

Abstract : Architectured beam lattice materials whose anisotropy can be tuned by varying the composition of their elementary cell are investigated. As an exemplary prototype of such material architecture, a regular triangular lattice with an elementary cell composed of 12 beams is considered. One out of three possible values of the elastic modulus is assigned to each beam. The structure is fully defined by a vector in the 12D composition‐structure space whose components are given by the elastic modulus values of the beams comprising the elementary cell. The elastic properties of this 2D material are represented by the compliance elasticity tensor with six independent compliance coefficients. Aiming at a specific set of properties thus involves finding the point in the 12D composition‐structure space that corresponds to a given point in the 6D property space. This is a problem of large dimensionality. To solve it, the neural network approach is used. This enables creation of architectured materials with tunable elastic anisotropy. A chiral element combining large twist with additional anisotropy requirements is presented as an example of successful machine‐learning‐based optimization of beam lattices proposed. Abstract : A new approach to design architectured beam lattice materials with tunable elastic anisotropy is proposed. A periodic pattern is generated by a unit cell composed of beams with three different magnitudes of stiffness. Optimization of the lattice architecture for desired anisotropy involves a huge number of variants, but is manageable owing to the efficacy of the machine learning used.