Multi-objective constrained robust design of a metamaterial vibration isolator with a limited budget

Abstract

Many real-world engineering design optimisation problems encounter input uncertainties, which may introduce undesired fluctuations in design performance or even make the design solution infeasible. Robust design optimisation (RDO) is widely studied due to its ability to deal with uncertainties. RDO methods require many evaluations generally, which is prohibitive when relying on expensive simulations. Surrogate-assisted optimisation can help to reduce the number of simulation calls. An improved constrained multi-objective efficient robust global optimisation method (CMO-ERGO) is proposed in this work, which combines the robust expected improvement of ERGO with the probability of feasibility. CMO-ERGO improves the ERGO framework and quantifies the robustness of the objective and the constraint functions using an analytical uncertainty quantification based on the Kriging surrogate. The proposed method is tested on three numerical benchmark functions and applied to the robust design of a metamaterial vibration isolator with a honeycomb structure to explore its performance in solving engineering applications. The robustness of optimal solutions is demonstrated using the Monte Carlo Method. Moreover, comparisons between CMO-ERGO and several existing RDO methods illustrate the effectiveness and efficiency of CMO-ERGO.

KEYWORDS:

• Robust design
• multi-objective optimisation
• metamodeling
• sequential sampling
• uncertainty analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research receives funding from the Flemish Government under the ‘Onderzoeksprogramma Artifciele Intelligentie (AI) Vlaanderen’ programme and the ‘Fonds Wetenschappelijk Onderzoek (FWO)’ programme, and Chinese Scholarship Council [grant number 202106160005].