A Fuzzy Modular Reconfigurable Configuration Approach for Personalized Exoskeleton Design

Product customization has been widely applied in manufacture enterprise recently, and with this concept, modularity is considered as an effective development strategy to improve the cost-variety trade-off of complex product. Traditional modular design methods focus on module optimization process, but the fuzzy correlation calculation and module selection for personalized exoskeleton design are rarely discussed. In response, this paper proposes a systematic modular configuration approach to help designers to obtain optimal solution for personalized exoskeleton design. A correlation calculation method based on fuzzy fusion is first put forward to compute the relationship between components. Then a multi-objective mathematic model for module identification is established and an improved particle swarm optimization algorithm (PSO) is introduced to conduct modular optimization. After that, a multi-attribute decision-making method based on VIKOR is used to select the optimal solution from Pareto set. Finally, a case study is demonstrated to validate the effectiveness of the proposed method.