A sustainable solution should holistically optimize all objectives related to the environment and a product’s cost and performance. As such, it should explicitly address material selection, which significantly affects environmental impacts and other objectives of a product design. While Life Cycle Assessment (LCA) provides credible methods to account for environmental impacts, current methods are not efficient enough for use at the early design stages to prune the entire design space without requiring execution of costly LCA analysis for each design scenario. Alternatively, surrogate modeling approaches can facilitate efficient concept selection during early design stages. However, material properties consist of discrete data sets, thus posing a significant challenge in the construction of surrogate models for numerical optimization.
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 modeling approach include achieving manageable dimensionality of LCA with a minimal loss of the important information by the consolidation of significant factors into categorized groups, as well as subsequent efficiency enhancement by a streamlined process that avoids the construction of full LCA. This novel approach combines efficiency of use with a mathematically rigorous representation of any pertinent objectives across an entire design space. To this end, we introduce an adapted two stage sampling approach in surrogate model construction 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.
A knowledge-based surrogate modeling approach for cup drawing with limited data
