Shape optimization of a disc-pad system under squeal noise criteria

Abstract

This paper deals with the parametric shape optimization of a simplified model of brake system under squeal noise criteria. As brake squeal phenomenon induces under-quality perception for industrial structures such as cars and trains, its understanding and management are important challenges for future systems design. Hence, we expose an optimization methodology based on meta-model for a proposed computationally expensive stability criteria representing the squeal noise. Sensitivity analysis is first conducted to assess and validate the chosen geometrical parameters. Then, a Pareto front is obtained through optimization of the system, leading to a set of optimal solutions for the considered multi-objective case.

A review of machine learning methods applied to structural dynamics and vibroacoustic

“…One of the first studies applying BO to experimental design was the article "Efficient global optimization (EGO) of expensive black-box functions" by Jones et al [292]. Since then, BO has been applied to several opti- mization problems, such as to minimize disc-pad shape under squeal noise criteria with EGO in [297], to optimize the modal characteristics of an engine using adaptive hierarchical GPR [298], and to optimize a mechanical metamaterial modeled by RBF-based surrogate in [299]. GPR is often the regressor used in BO as it provides the required probabilistic outputs and performs well with sparse data.…”

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

“…Besides that, several toolboxes implement Bayesian Optimization using GP [233,326,327], although other probabilistic ML methods are suitable. Mohanasundaram et al [175] used the EGO approach in the multi-objective optimization of a disc-pad shape under squeal noise criteria modeled by Kriging, after the previous performance of a variance-based sensitivity analysis. Du et al [176] applied an Adaptive Hierarchical Kriging model to optimize the modal characteristics of an engine.…”