An Efficient Evaluation Method for Automobile Shells Design Based on Semi-supervised Machine Learning Strategy

Abstract

Automobile is one of the important modes of transportation for human travel in today’s society. Batch production in various countries in the world has also promoted the transformation of production concepts. At present, the development of the automobile industry is developing towards the trend of intelligence, personalizat-ion and sharing. Car appearance in a variety of ways, not every design is reasonable. Therefore, the main purpose of this article is to establish a scientific evaluation standard in order to large-scale test the quality of a variety of car shells design. The scientific nature is mainly reflected in combination the fluid-solid coupling knowledge and machine learning in this article, which can analyze the force of different shells in the flow field, and put out the cloud map information such as the stress, pressure and velocity of the shell. At last, analyze the best test samples and store them in the database, and then using semi-supervised heuristic algorithm to perform the sample training, the ultimate goal is to make the evaluation system more robust. The trained model can correctly evaluate each personalized car shape and give a reasonable score, which is convenient for car manufacturers to make best decision with personalized demand and scientific production.