Multi-objective optimization of composite kayak paddle shaft through evolutionary deep learning

This paper presents a methodology for multiobjective optimization of a kayak paddle shaft made of Carbon Fiber Reinforced Polymer (CFRP) composite by a filament winding process for the case of individualization of the structure for a single sportperson. Design parameters of a composite layup (orientation of fibers and thickness) and objective functions of the paddle shaft (deflection, strength and weight) are described. These objective functions are based on the individual kayaker's performance and preferences. Data driven evolutionary algorithms, aided by mechanics based theory and experimentation, were used for deep learning and optimization. Due to the mutually contradicting objective functions, the result of such optimization is a Pareto-optimal solution set for an individual sportsperson from which a preferred solution can be picked up by a decision maker. This work shows that such methodology could be successfully used for a personalization of any sport equipment.