Abstract
Tread patterns are grooves or notches carved into shoe soles and tire surfaces. They significantly affect physical performance, such as ease of running and stopping. In addition, the complexity of their design and the fact that they form the external shape of a product also demands their visual beauty. This study proposes a multi-objective optimal design method that simultaneously considers functionality and aesthetics. First, the dataset of aesthetic evaluation is prepared based on the semantic differential method, and the sensory evaluation model is constructed using a convolutional neural network. This enables the quantitative aesthetic evaluation of each pattern generated in the optimization process. The physical performance indices are evaluated using the finite elements method. Then we solve multi-objective optimization problems using a data-driven topology design method with the physical and sensory indices as objective functions. Typical designs are extracted by deep clustering of the obtained patterns, and various design candidates are suggested to the designer. We apply the proposed method to an example problem about tire tread patterns to investigate its validity and effectiveness. The results demonstrate that the proposed method can generate a wide variety of aesthetic patterns with compromising functionality.
