Product portfolios need to present the widest coverage of user requirements with minimal product diversity. User requirements may vary along multiple performance measures, comprising the objective space, whereas the design variables constitute the design space, which is usually far higher in dimensionality. Here we consider the set of possible performances of interest to the user, and use multi-objective optimization to identify the non-domination or the pareto-front. The designs lying along this front are mapped to the design space; we show that these “good designs” are often restricted to a much lower-dimensional manifold, resulting in significant conceptual and computational efficiency. These non-dominated designs are then clustered in the design space in an unsupervised manner to obtain candidate product groupings which the designer may inspect to arrive at portfolio decisions. With help of dimensionality reduction techniques, we show how these clusters in low-dimensional manifolds embedded in the high-dimensional design space. We demonstrate this process on two different designs (springs and electric motors), involving both continuous and discrete design variables.

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