The competitive pressure to shorten product development time has necessitated the automotive industry to rely more on Computer Aided Engineering (CAE) for analyzing and proving product reliability and robustness. The challenge of this approach is the incorporation of product variability, due to manufacturing and customer usage variations in the analysis, requires a massive computation process which may be prohibitive even with today's advanced computers. In this paper, we demonstrate the use of an efficient computational procedure based on optimal Latin Hypercube Sampling (LHS) and a "cheap-to-compute" nonlinear surrogate model using Multivariate Adaptive Regression Splines (MARS) to emulate a computationally intensive complex CAE model. The result of the analysis is the identification of sensitivity of design parameters, in addition to a computationally affordable reliability assessment. Fatigue life durability of automotive shock tower is presented as an example to demonstrate the methodology.

No abstract

No abstract

No abstract