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Decision making in engineering design is complicated, especially when dealing with high-dimensional data. Modern software tools are able to produce a large amount of data while performing optimization studies. A typical optimization problem with many objectives may produce 100s or even 1000s of Pareto Optimal solutions. It is a challenge to analyze this data and make a decision about which design/s to choose for further testing or as a final design. To tackle the problem, two data analysis techniques are used in this paper. Partitive Clustering (PC) is used to locate groups of similar designs in the dataset while Principal Component Analysis (PCA) is used to reduce the dimensionality of the data and visualize it in two and three dimensions. Although these techniques can be used independently, when used together, they prove to be a tremendous help in decision making. This paper underlines the benefit of using these two methods together.

Robust Design Optimization (RDO) using traditional approaches such as Monte Carlo (MC) sampling requires tremendous computational expense. Performing a RDO for problems involving time consuming CAE analysis may not even be possible within time constraints. In this paper a new stochastic modeling technique based on chaos collocation method is used to measure the mean and standard deviation (σ) for uncertain output parameters. For a given accuracy, chaos collocation method requires far less sample evaluations compared to MC. The efficient evaluation of mean and std. deviation terms using chaos collocation method makes it quite attractive to be used with RDO methods. In this work the RDO of an automotive engine design is performed employing chaos collocation method.