Train Vehicle Nose Design Combining Shape Grammar and Interactive Genetic Algorithm

Abstract

Objective The typical train nose aesthetic design method generally relies on the subjective experience and intuition of designers, and can no longer meet the design and production needs of rapid iteration and mass customization. Therefore, aiming at the aesthetic design problem of train nose, a generative design method of which combining shape grammar and interactive genetic algorithm is proposed. Method The shape feature lines at different levels of the train nose are analyzed and extracted, and the feature lines of the design elements are abstractly created in combination with shape grammar. An interactive genetic algorithm is used to encode design features. Considering the ambiguity of users' aesthetic cognition in the scheme evaluation process, a fuzzy assignment method is used for the fitness to achieve intelligent and diversified selection of the vehicle nose design. The design of maglev train nose is used as an example for verification. Result & Conclusion Compared with the accurate assignment of traditional interactive genetic algorithms, using Gaussian membership functions for fuzzy representation of the fitness values of evolving individuals can reduce the psychological burden of users in the evaluation process. The train nose generative design method combining shape grammar and interactive genetic algorithm can effectively improve conceptual scheme quality and enhance design efficiency. In the interactive evaluation process, with the increase of evolutionary algebra, the average user's interactive evaluation time for the scheme gradually decreases, which reflects the gradual understanding of the scheme by users, and also shows that the interactive generation technology method conforms to the user's cognitive mode.

Keywords

• train
• vehicle nose design
• generative design
• shape grammar
• interactive genetic algorithm