Optimal design of the S-rail family for an automotive platform with novel modifications on the product-family optimization process

The genetic algorithm as a successful procedure that has been widely used in previous works to optimally design a product family. In this paper, to improve optimization procedure, a novel modification on the augmented chromosomes after performing genetic operations is proposed. Some drawbacks of product family penalty function (PFPF), such as the inability to detect the level of platform commonality, are illustrated and fully discussed by counterexamples. To overcome this drawback, a commonality index based on the augmented chromosome is employed as a further modification to select trade-off optimum design points using multi-criteria decision-making methods. The proposed modifications are employed to optimally design a family of S-rails for an automotive platform. The S-rails are the main components in front and rear of the automotive underbody with significant effects on crashworthiness capability of a vehicle. Therefore, to find S-rails with high commonality and crashworthiness capability, experimentally verified S-rail models are implemented through a non-dominated sorting genetic algorithm with four-objective optimization, namely remaining energy, peak crushing force, mass, and PFPF. Results show that there exists a set of S-rails with acceptable crashworthiness capability and 100% commonality, which can be used in all products within the product family.