Machine learning-based optimization design of bistable curved shell structures with variable thickness

Liu, Junbang; Chang, Jinke; Ji-zhou, YU; Zhang, Wen-Hua; Huang, Shiqing

doi:10.1016/j.istruc.2023.03.124

Cited by 10 publications

(1 citation statement)

References 69 publications

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“…While other bi-stable unit cell components appear rigid, the curved section is first constructed as a curved clamped beam [11]. Their multi-stability allows their curved shell designs to create newly architected metamaterials to trap energy [12]. Due to the instability of the buckling that initiates the bi-stable snap-through behaviour, this structure with negative stiffness characteristics will experience greater deformation, preserve its deformed shape, and trap the crushing energy.…”

Section: Introductionmentioning

confidence: 99%

Design Optimisation of Metastructure Configuration for Lithium-Ion Battery Protection Using Machine Learning Methodology

Fatiha,

Santosa,

Widagdo

et al. 2024

Batteries

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The market for electric vehicles (EVs) has been growing in popularity, and by 2027, it is predicted that the market valuation will reach $869 billion. To support the growth of EVs in public road safety, advances in battery safety research for EV application should achieve low-cost, lightweight, and high safety protection. In this research, the development of a lightweight, crashworthy battery protection system using an excellent energy absorption capability is carried out. The lightweight structure was developed by using metastructure constructions with an arrangement of repeated lattice cellular structures. Three metastructure configurations (bi-stable, star-shaped, double-U) with their geometrical variables (thickness, inner spacing, cell stack) and material types (stainless steel, aluminium, and carbon steel) were evaluated until the maximum Specific Energy Absorptions (SEA) value was attained. The Finite Element Method (FEM) is utilised to simulate the mechanics of impact and calculate the optimum SEA of the various designs using machine learning methodology. Latin Hypercube Sampling (LHS) was used to derive the design variation by dividing the variables into 100 samples. The machine learning optimisation method utilises the Artificial Neural Networks (ANN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to forecast the design that produces maximum SEA. The optimum control variables are star-shaped cells consisting of one vertical unit cell using aluminium material with a cross-section thickness of 2.9 mm. The optimum design increased the SEA by 5577% compared to the baseline design. The accuracy of the machine learning prediction is also verified using numerical simulation with a 2.83% error. Four different sandwich structure configurations are then constructed using the optimal geometry for prismatic battery protection subjected to ground impact loading conditions. An optimum configuration of 6×4×1 core cells arrangement results in a maximum displacement of 7.33 mm for the prismatic battery in the ground impact simulation, which is still less than the deformation threshold for prismatic battery safety of 10.423 mm. It is shown that the lightweight metastructure is very efficient for prismatic battery protection subjected to ground impact loading conditions.

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Section: Introductionmentioning

confidence: 99%

Design Optimisation of Metastructure Configuration for Lithium-Ion Battery Protection Using Machine Learning Methodology

Fatiha,

Santosa,

Widagdo

et al. 2024

Batteries

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Delocalized Deformation Enhanced Reusable Energy Absorption Metamaterials Based on Bistable Tensegrity

Yang,

Zhang,

Wang

et al. 2024

Adv Funct Materials

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Mechanical metamaterials are rationally designed structures possessing exceptional properties that can be manufactured by 3D printing techniques. Mechanical metamaterials provide an unprecedented platform for energy absorption, mitigating damage caused by severe localized impacts within confined areas. However, current designs always reveal deficiencies either in their energy absorption capacities or in their suitability for repetitive utilization. To address such limits, a novel bistable tensegrity structure with superior reusability is derived from a classical tensegrity structure, and a tensegrity‐based assembly strategy is proposed to construct these bistable structures into mechanical metamaterials with a delocalized deformation mechanism. Upon a localized impact on a single loading node, all the elastic components of each reusable bistable structure in the mechanical metamaterials stretch synchronously for energy absorption, exhibiting higher energy‐absorbing capacity. Here, the metamaterials achieve an energy‐absorbing capacity of 26.4 kJ (kg m2)−1 over 10 000 cycles, outperforming other reusable materials by ≈2 orders of magnitude in energy‐absorbing capacity and reusability, respectively. This study provides a novel tensegrity‐based design assembly strategy for developing high‐capacity, reusable energy‐absorbing materials that are suitable for advanced impact protection and engineering systems.

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Machine learning-based design and optimization of double curved beams for multi-stable honeycomb structures

Yu,

Shi,

Feng

et al. 2023

Extreme Mechanics Letters

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Machine learning-based optimization design of bistable curved shell structures with variable thickness

Cited by 10 publications

References 69 publications

Design Optimisation of Metastructure Configuration for Lithium-Ion Battery Protection Using Machine Learning Methodology

Design Optimisation of Metastructure Configuration for Lithium-Ion Battery Protection Using Machine Learning Methodology

Delocalized Deformation Enhanced Reusable Energy Absorption Metamaterials Based on Bistable Tensegrity

Machine learning-based design and optimization of double curved beams for multi-stable honeycomb structures

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