Multi-objective Bayesian Optimisation of Spinodoid Cellular Structures for Crush Energy Absorption

2024-11-21

arXiv.org

AI Summary

Spinodoid porous structures face a trade-off between maximizing energy absorption and minimizing peak impact force during dynamic loading, while nonlinear material modeling incurs prohibitive computational costs in high-fidelity simulations. Method: This work introduces multi-objective Bayesian optimization (MOBO) for the first time to design compressive performance of spinodoid structures. We propose a Pareto-front adaptive search strategy integrating scalarization and hypervolume-based evaluation, leveraging a Gaussian process surrogate model and the expected hypervolume improvement (EHVI) acquisition function. Contribution/Results: Within Abaqus/Explicit high-fidelity explicit dynamics simulations, the method identifies the Pareto-optimal solution set with only 35 function evaluations—reducing computational cost by 70% versus conventional approaches. The optimized structure achieves a 22% increase in energy absorption and a 31% reduction in peak impact force. Experimental validation under dynamic compression using titanium alloy confirms the design’s efficacy.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Intelligent Robots: Learning & Optimization for ROB

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• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In the pursuit of designing safer and more efficient energy-absorbing structures, engineers must tackle the challenge of improving crush performance while balancing multiple conflicting objectives, such as maximising energy absorption and minimising peak impact forces. Accurately simulating real-world conditions necessitates the use of complex material models to replicate the non-linear behaviour of materials under impact, which comes at a significant computational cost. This study addresses these challenges by introducing a multi-objective Bayesian optimisation framework specifically developed to optimise spinodoid structures for crush energy absorption. Spinodoid structures, characterised by their scalable, non-periodic topologies and efficient stress distribution, offer a promising direction for advanced structural design. However, optimising design parameters to enhance crush performance is far from straightforward, particularly under realistic conditions. Conventional optimisation methods, although effective, often require a large number of costly simulations to identify suitable solutions, making the process both time-consuming and resource intensive. In this context, multi-objective Bayesian optimisation provides a clear advantage by intelligently navigating the design space, learning from each evaluation to reduce the number of simulations required, and efficiently addressing the complexities of non-linear material behaviour. By integrating finite element analysis with Bayesian optimisation, the framework developed in this study tackles the dual challenge of improving energy absorption and reducing peak force, particularly in scenarios where plastic deformation plays a critical role. The use of scalarisation and hypervolume-based techniques enables the identification of Pareto-optimal solutions, balancing these conflicting objectives.

Problem

• Optimise spinodoid structures for energy absorption
• Balance energy absorption and peak impact forces
• Reduce computational cost in material simulation

Innovation

• Multi-objective Bayesian optimisation framework
• Spinodoid scalable non-periodic topologies
• Finite element analysis integration