Critical Analysis of the Suitability of Surrogate Models for Finite Element Method Application in Catalog-Based Suspension Bushing Design

Cernuda, Carlos; Llavori, Iñigo; Zăvoianu, Alexandru-Ciprian; Aguirre, Aitor; Zabala, Alaitz; Plaza, Jon

doi:10.1109/etfa46521.2020.9212166

Cited by 3 publications

(1 citation statement)

References 18 publications

(12 reference statements)

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“…In this study, at given design parameters, the stiffness of bushings was predicted using ANNs. Cernuda [13] has investigated the usability of ANNs in the vehicle development process. The dataset was generated by FEM as well.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Design Optimization of Chassis Bushings

Töpel,

Fuchs,

Büttner

et al. 2023

Vehicles

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In this work, a method is developed for the component design of chassis bushings with contoured inner cores, aided by artificial neural networks (ANNs) and design optimization. First, a model of a physical chassis bushing is generated using the finite element method (FEM). To determine the material parameters of the material model, a material parameter optimization is conducted. Based on the bushing model, different samples for a design study are generated using the design of experiments method. Due to invalid areas of the geometrical model definitions, constraints are established and the design parameter space is cleaned up. From the cleaned design parameter space, a database of several design parameter samples and three associated quasi-static stiffnesses, calculated with FEM simulations, is generated. The database is subsequently used for the training and hyper-parameter optimization of the ANN. Subsequently, the feed-forward ANN is employed in a design study, where stiffnesses are prescribed and design parameters identified. The design process is inverted with the help of a constrained design parameter optimization (DO), based on particle swarm optimization (PSO). Two usecases are defined for the evaluation of the design accuracy of the entire method. The design parameters found are validated by corresponding FEM simulations.

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Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Design Optimization of Chassis Bushings

Töpel,

Fuchs,

Büttner

et al. 2023

Vehicles

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Recent advances and applications of surrogate models for finite element method computations: a review

2022

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Surrogate Model-Based Parameter Tuning of Simulated Annealing Algorithm for the Shape Optimization of Automotive Rubber Bumpers

Huri

Mankovits

2022

Applied Sciences

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A design engineer has to deal with increasingly complex design tasks on a daily basis, for which the available design time is shrinking. Market competitiveness can be improved by using optimization if the design process can be automated. If there is limited information about the behavior of the objective function, global search methods such as simulated annealing (SA) should be used. This algorithm requires the selection of a number of parameters based on the task. A procedure for reducing the time spent on tuning the SA algorithm for computationally expensive, simulation-driven optimization tasks was developed. The applicability of the method was demonstrated by solving a shape optimization problem of a rubber bumper built into air spring structures of lorries. Due to the time-consuming objective function call, a support vector regression (SVR) surrogate model was used to test the performance of the optimization algorithm. To perform the SVR training, samples were taken using the maximin Latin hypercube design. The SA algorithm with an adaptive search space and different cooling schedules was implemented. Subsequently, the SA parameters were fine-tuned using the trained SVR surrogate model. An optimal design was found using the adapted SA algorithm with negligible error from a technical aspect.

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Critical Analysis of the Suitability of Surrogate Models for Finite Element Method Application in Catalog-Based Suspension Bushing Design

Cited by 3 publications

References 18 publications

Machine-Learning-Based Design Optimization of Chassis Bushings

Machine-Learning-Based Design Optimization of Chassis Bushings

Recent advances and applications of surrogate models for finite element method computations: a review

Surrogate Model-Based Parameter Tuning of Simulated Annealing Algorithm for the Shape Optimization of Automotive Rubber Bumpers

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