Metamodel-Based Multidisciplinary Design Optimization of Automotive Structures

Ryberg, Ann-Britt

doi:10.3384/diss.diva-140875

Cited by 18 publications

(24 citation statements)

References 38 publications

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“…There are several correlation functions existing in literature for R x k , x k , among them the two commonly applied functions are the exponential and the Gaussian correlation functions [19], i.e.,…”

Section: Kriging Methodsmentioning

confidence: 99%

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Uncertainty analysis of deep drawing using surrogate model based probabilistic method

Huang

Radi

Hami

2016

Int J Adv Manuf Technol

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Deep drawing is an important manufacturing process in industry. In order to obtain high-quality products produced by deep drawing, the set of design variables used in forming operation is designed through deterministic optimization. However, in real forming process, the design variables show variability and randomness which will affect the product quality. These uncertainties are an inherent characteristic of nature and cannot be avoided. This paper focuses on uncertainty analysis of deep drawing with the consideration of uncertainties in material parameters and friction. An uncertainty analysis approach which combines the finite element method (FEM) simulation, surrogate modeling, and Monte Carlo simulation (MCS) is presented in this work. The constructed surrogate models are validated and compared by cross validation and error measures. Then Monte Carlos Simulation is conducted by the use of the constructed surrogate model. The surrogate model based probabilistic method used in this paper is an approach with high-efficiency and sufficient accuracy for uncertainty analysis in deep drawing.

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Section: Kriging Methodsmentioning

confidence: 99%

“…The accuracy of a surrogate model is affected by the type of surrogate model and the quality and quantity of the data set from which it is constructed [19]. Before we use the built surrogate models, the models need to be validated.…”

Section: Surrogate Model Validationmentioning

confidence: 99%

Uncertainty analysis of deep drawing using surrogate model based probabilistic method

Huang

Radi

Hami

2016

Int J Adv Manuf Technol

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“…The meta-modeling process involves model fitting or function approximation to the sampled data of design variables and responses from the detailed model (Ryberg, Bäckryd, & Nilsson, 2012). To demonstrate the idea of our proposed framework, one parametric technique (PR), and five non-parametric techniques (Kriging, SVR, RBF, MARS and ANN) are chosen due to their extensive use in metamodeling.…”

Section: Meta-modelingmentioning

confidence: 99%

A recommendation system for meta-modeling: A meta-learning based approach

Cui

Weir

et al. 2016

Expert Systems with Applications

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Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models are varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: 1) problem module, 2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, 3) meta-learner module which compares the performance of instance-based and model-based learning approaches for optimal framework design, and 4) performance evaluation module which introduces two criteria, Spearman's ranking correlation coefficient and hit ratio, to evaluate the system on the accuracy of model ranking prediction and the precision of the best model recommendation, respectively. To further improve the performance of meta-learning for meta-modeling recommendation, different types of feature reduction techniques, including singular value decomposition, stepwise regression and ReliefF, are studied. Experiments show that our proposed framework is able to achieve 94% correlation on model rankings, and a 91% hit ratio on best model recommendation. Moreover, the computational cost of metamodeling recommendation is significantly reduced from an order of minutes to seconds compared to traditional trial-and-error and ensemble process. The proposed framework can significantly advance the research in meta-modeling recommendation, and can be applied for data-driven system modeling.

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“…Finally, these weights are adjusted so that the estimation error is minimized. For additional details on ANN models, see (Ryberg et al 2012) .…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Optimization of Vehicle Structures under Uncertainties

Shetty¹

2017

Linköping Studies in Science and Technology. Dissertations

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Cover: Schematic illustration of robustness analysis. The histograms indicate the variations in input parameter ‫ݔ‬ and the output parameter ݂ respectively.The front cover picture is reproduced by courtesy of Volvo Car Corporation and illustrates a side impact simulation.Printed by: LiU-Tryck, Linköping, Sweden, 2017 ISBN 978-91-7685-630-7 ISSN 0345-7524 Distributed by: Linköping University Department of Management and Engineering SE-581 83 Linköping, Sweden © 2017 Sandeep ShettyNo part of this publication may be reproduced, stored in a retrieval system, or be transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior permission of the author. Advancements in simulation tools and computer power have made it possible to incorporate simulation-based structural optimization in the automotive product development process. However, deterministic optimization without considering uncertainties such as variations in material properties, geometry or loading conditions might result in unreliable optimum designs.In this thesis, the capability of some established approaches to perform design optimization under uncertainties is assessed, and new improved methods are developed. In particular, vehicle structural problems which involve computationally expensive Finite Element (FE) simulations, are addressed.The first paper focuses on the evaluation of robustness, given some variation in input parameters, the capabilities of three well-known metamodels are evaluated. In the second paper, a comparative study of deterministic, reliability-based and robust design optimization approaches is performed. It is found that the overall accuracy of the single-stage (global) metamodels, which are used in the above study, is acceptable for deterministic optimization. However, the accuracy of performance variation prediction (local sensitivity) must be improved. In the third paper, a decoupled reliability-based design optimization (RBDO) approach is presented. In this approach, metamodels are employed for the deterministic optimization only while the uncertainty analysis is performed using FE simulations in order to ensure its accuracy.In the fifth paper, two new sequential sampling strategies are introduced that aim to improve the accuracy of the metamodels efficiently in critical regions. The capabilities of the methods presented are illustrated using analytical examples and a vehicle structural application.It is important to accurately represent physical variations in material properties since these might exert a major influence on the results. In previous work these variations have been treated in a simplified manner and the consequences of these simplifications have been poorly understood. In the fourth paper, the accuracy of several simple methods in representing the real material variation has been studied. It is shown that a scaling of the nominal stress-strain curve based on the Rm scatter is the best choice of the evaluated choices, when limited material data is avail...

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Metamodel-Based Multidisciplinary Design Optimization of Automotive Structures

Cited by 18 publications

References 38 publications

Uncertainty analysis of deep drawing using surrogate model based probabilistic method

Uncertainty analysis of deep drawing using surrogate model based probabilistic method

A recommendation system for meta-modeling: A meta-learning based approach

Optimization of Vehicle Structures under Uncertainties

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