Robust design: Accounting for uncertainties in engineering

This dissertation involves optimisation of structures taking into account several uncertainties. The particular intent is to find methods to develop solutions which are optimum both in the sense of dealing with the usual load case and reducing the variability of the response, i.e. robust optimal designs. Typically optimized structures may indicate an inclination of being sensitive to small perturbations in the design or loading conditions, which obviously are unavoidable. To produce sturdy designs, it’s important to take into account all conceivable variations (or at best the influencing ones) in the design process. The dissertation is partioned in 2 sections. Part one acts as a theoretical background to the 2nd part, the two appended articles. This 1st part consists of the idea of robust design, basic statistics, optimization theory and meta modelling. The initial appended paper is an application of existing methods on a large industrial example problem…

Contents

Part I Theory and background
1 Introduction
2 Robust design
3 Statistical concepts
3.1 Basic statistics
3.2 Monte Carlo analysis
4 Optimisation
4.1 Deterministic optimisation
4.2 Robust optimisation
5 Meta model approximations
5.1 Response Surface Methodology
5.2 Kriging
5.3 Artificial Neural Networks
5.4 MLS
5.4.1 Local approximations
5.4.2 Global approximations
6 Outlook
7 Review of included papers
Bibliography
Part II Included papers
Paper I
1 Introduction
1.1 The Swedish cab strength test series
1.2 Objective
2 Methodology
2.1 Sub-model
2.2 Responses
2.3 Meta model approximations
2.4 Sensitivity analyses
2.5 Screening
2.6 Effects due to the forming process
2.7 Final sensitivity analysis
3 Results and discussion
3.1 Validation of the complete truck model
3.2 Validation of the sub-model
3.3 First screening
3.4 Second screening
3.5 Third screening
3.6 Effects due to the forming process
3.7 Final sensitivity analysis
4 Conclusions
References
Paper II
1 Introduction
2 Objective
3 Theory
3.1 Random Fields
3.2 Meta modelling
3.3 Artificial Neural Network
3.4 Robust optimisation
4 Methodology
5 Analytical example
5.1 Solution
6 Square aluminium tube example…

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