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In recent years, the use of engineering design optimization techniques has grown multifold and formal optimization has become very popular among design engineers. However, the real world problems are turning out to be involved and more challenging. It is not uncommon to encounter problems with a large number of design variables, objectives and constraints. The engineers’ expectation, that an optimization algorithm should be able to handle multi-objective, multi-constrained data is leading them to apply optimization techniques to truly large-scale problems with extremely large number of constraints and objectives. Even as newer and better optimization algorithms are being developed to tackle such problems, more often than not, the optimization algorithms are unable to find a single feasible design that satisfies all constraints.

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.

Need for accounting Robustness and Reliability in engineering design is well understood and being researched. However, the actual practice of applying robustness and reliability methods to high fidelity CAE based simulations, especially during optimization is just starting to gain traction in last few years. Availability of computing power is helping the use of such methods, but, at the same time the demand for modeling stochastic behavior with high fidelity CAE simulations and considering large number of stochastic variables still makes it prohibitive. Typically, Robust Design Optimization (RDO) formulations calculate mean and standard deviation of responses based on sampling. On the other hand Reliability Based Design Optimization (RBDO) formulations have been using methods like First Order Reliability Method (FORM) or Second Order Reliability Method (SORM) which require nested optimization to evaluate joint probability distribution and reliability factor.