Article2009

Finite element based robustness study of a truck cab subjected to impact loading

David Lönn · Michael Öman · Larsgunnar Nilsson · Kjell Simonsson

16 citations0/0 files storedInternational Journal of Crashworthiness

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Probabilistic and Robust Engineering Design Advanced Multi-Objective Optimization Algorithms Transportation Safety and Impact Analysis

Abstract

Optimised designs have a tendency of being sensitive to variations. It is therefore of great importance to analyse this sensitivity to assure that a design is robust, i.e. sufficiently insensitive to variations. To analyse robustness, variations are introduced in model parameters, and their influences on simulation responses are studied. This is usually achieved using the Monte Carlo method. Though, due to the large number of simulations needed, the Monte Carlo method is very costly for problems requiring a long computing time. Therefore, in this work, a meta model-based Monte Carlo method is used to evaluate the robustness of a vehicle structure. That is, the Monte Carlo analysis is performed on a surface approximation of the true response, over the domain of interest. The methodology used is to first identify the variables that influence the response the most, referred to as a screening, using simple linear response surfaces. This is followed by a more detailed sensitivity analysis using only the identified variables and a quadratic response surface, thereby incorporating second order effects. A truck cab model exposed to a pendulum impact load is used as an evaluation of this method, and the important variables and their influence on the response are identified. The effect of including results from forming simulations is also evaluated using the truck cab model. Variations are introduced before forming simulations, thereby taking forming effects into account in the sensitivity analysis. The method was found to be a good tool to identify important dispersion variables and to give an approximate result of the total dispersion, all with a reasonable amount of simulations.

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References

1996Response Surface Methodology 1992Screening, Predicting, and Computer Experiments 1993Taguchi on Robust Technology Development 1994Taguchi on Robust Technology Development: Bringing Quality Engineering Upstream 2003The use of metamodeling techniques for optimization under uncertainty 2001Optimisation and robustness for crashworthiness of side impact 2004Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty 2005Automotive crashworthiness design using response surface‐based variable screening and optimization

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