Quantitative analysis of pit defects in an automobile engine cylinder cavity using the radial basis function neural network–genetic algorithm model

Abstract

In the automotive remanufacturing movement, the inspection of the corrosion defects on the engine cylinder cavity is a key and difficult problem. In this article, based on the ultrasonic phased array technology and the radial basis function neural network–genetic algorithm model, a new quantitative analysis method is proposed to estimate the size of the pit defects on the automobile engine cylinder cavity. Echo signals from the small pit defects with different sizes are acquired by an ultrasonic phased array transducer. According to the ultrasonic signal characteristics, the feature vectors are extracted using wavelet packet, fractal technology, peak amplitude method, and some routine extract methods. The radial basis function neural network–genetic algorithm model is investigated for the quantitative analysis of the pit defects, which can obtain an optimal quantitative model. The results show that the proposed model is effective in the corrosion estimation work.