A predictive modelling-based material selection method for sustainable product design

Material selection significantly affects environmental impacts and other objectives of a product design. Life cycle assessment (LCA) methods are not efficient enough for use at the early design stages to prune a design space. Material properties consist of discrete data sets, which are further complicated when LCA data are included, thus posing a significant challenge in the construction of surrogate models for prediction of all relevant behaviours and numerical optimisation. In this work, we address the unique challenges of material selection in sustainable product design in some important ways. Salient features of the robust surrogate modelling approach include achieving manageable dimensionality of LCA with a minimal loss of the important information by the consolidation of significant factors into categorised groups, as well as subsequent efficiency enhancement by a streamlined process that avoids the construction of full LCA. This approach combines efficiency of use with a mathematically rigorous representation of any pertinent objectives across a design space. To this end, we adapt a two-stage sampling approach in surrogate model construction for sustainability considerations based on a feasible approximation of a Latin Hypercube design at the first stage. The development and implementation of the method are illustrated with the aid of an automotive disc brake design, and the results are discussed in the context of robust optimal material selection in early sustainable product design.