Generating tyre tread designs using a sensory evaluation regression model and a generative model.

This study introduces a method for generating tyre tread images that are highly rated in terms of 'slip resistance' sensory evaluations. It involves constructing a regression model to predict 'slip resistance' sensory evaluations in tyre tread images and subsequently developing an image generation model informed by this regression model. The regression model was developed using a machine learning framework based on ensemble learning and SHapley Additive exPlanations. The model was then used to filter training data for the image generation process, leading to the creation of a tyre tread image generation model aimed at producing images with high 'slip resistance' sensory evaluations, using StyleGAN. The proposed method was applied in a case study using real data, and its effectiveness was validated by comparison with a multiple regression analysis approach. The results revealed that more accurate results were achieved with the random forest (RF) regression model than with the multiple regression model due to the RF regression model's emphasis on non-linear features. Furthermore, a significant difference was observed between the 'slip resistance' sensory evaluation values of the images generated by StyleGAN trained with training data screened by the RF regression model and those from the training data. [ABSTRACT FROM AUTHOR]