Fundamental Study on Optimal Design Method of Tread Patterns Simultaneously Realizing Functional Properties and Aesthetics

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 functional properties and aesthetics. First, the dataset of aesthetics evaluation is prepared based on the semantic differential method, and it is transformed into a convolutional neural network model using a machine learning technique. It enables the quantitative evaluation of the aesthetics of respective patterns. The physical performance indices are evaluated by the finite elements methods as well. Then we solve multi-objective optimization problems using a data-driven topology design method with physical and sensory indices as objective functions. Finally, we apply the proposed method to an example problem about tire tread patterns to verify its fundamental validity and effectiveness.