Applications of Neural Networks to Metallic Flexor Geometry Optimization of Flat Wipers

Abstract

In recent years, demands of flat wipers have rapidly increased in the vehicle industry due to their simpler structure compared to the conventional wipers. Procedures for evaluating the appropriate metallic flexor geometry, which is one of the major components of the flat wiper, were proposed in the authors’ previous study. However, the computational cost of the aforementioned procedures seems to be unaffordable to the industry. The discrete Winkler model regarding the flexor as the Euler–Bernoulli beam is established as the mathematical model in this study to simulate a flexor compressed against a surface at various wiping angles. The deflection of the beam is solved using a finite difference method, and the calculated contact pressure distributions agree fairly with those based on the corresponding finite element model. Flexor designs are paired with various windshield surfaces to accumulate a sufficiently large simulation database based on the mathematical model. An artificial neural network (ANN) approach is developed to predict contact pressure distributions of the flexor much faster than the mathematical model. Geometry of the curved surface is represented by a shape code obtained via a principal component analysis (PCA) and used in the ANN model. The ANN algorithm is also applied to efficiently evaluate the wiping patterns according to the simulated contact pressure distributions. These patterns are then classified by using a convolutional neural network (CNN) to identify several suitable flexor designs for the specific windshield. The flat wiper suggested by the current procedures is experimentally validated to justify its qualified wiping performances.