Optimization of lattice structures using neural networks and numerical simulations based on FEM

• Published in: Russian physics journal Vol. 68; no. 3; pp. 532 - 539
• Main Authors: Podpruzhnikov, I. A., Vershinin, A. V., Levin, V. A., Zingerman, K. M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2025; Springer Nature B.V
• Subjects: Acoustic insulation; Acoustic waves; Algorithms; Condensed Matter Physics; Finite element method; Hadrons; Heavy Ions; Lasers; Mathematical analysis; Mathematical and Computational Physics; Neural networks; Nuclear Physics; Optical Devices; Optics; Parameters; Photonics; Physics; Physics and Astronomy; Programming languages; Python; Theoretical; Neural networks; Frequency filtering of waves; Machine learning; Finite-element method; Data science; Gradient structures; Elastic waves; Additive manufacturing; Lattice structure
• ISSN: 1064-8887; 1573-9228
• DOI: 10.1007/s11182-025-03461-9

The effects occurring in the cases where the acoustic waves generated by a pulsed source are applied to linearly elastic solids formed by a periodic lattice structure are examined. For a series of numerical experiments based on the finite element method, various types of lattice structures are modeled in the CAE Fidesys. In these lattice structures, the parameters of the undulation of the bars forming the lattice structure, the frequency of the applied pulse, and the distance at which the sound insulation level is measured, are varied. A number of points forming the cells of the lattice structure are also varied. The presence of frequency filtering is established for some of these variable parameters. An analysis of the relationships of variable parameters and sound insulation level is made. Using a series of virtual experiments, a model predicting the level of sound insulation is built based on a neural network. This algorithm is configured using the Python programming language and the scikit-learn library. The use of this algorithm allows reduce the time for calculating the sound insulation level by a factor of hundreds.