Isogeometric Finite Element Analysis with Machine Learning Integration for Piezoelectric Laminated Shells
Journal Title: Journal of Engineering Management and Systems Engineering - Year 2023, Vol 2, Issue 4
Abstract
Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated.
Authors and Affiliations
Žarko Ćojbašić, Nikola Ivačko, Dragan Marinković, Predrag Milić, Goran Petrović, Maša Milošević, Nemanja Marković
Keywords
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- EP ID EP731770
- DOI 10.56578/jemse020401
- Views 47
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