Noncontact Defect Detection Method of Automobile Cylinder Block Based on SVM Algorithm

Abstract

Based on the problems encountered in the gateway system, this article developed the SVM algorithm support vector machine auxiliary system based on the function of the hybrid network gateway to help the gateway dynamically adjust its operating status to ensure the stability of the gateway system and the real-time internal data. This paper studies the noncontact defect detection system of automobile cylinder block on the basis of the SVM algorithm. There are many thin holes in the wall of automobile engine cylinder block, the structure is complex, and the processing is easy to deform. In order to solve the problem of deformation of the automobile engine cylinder block during the milling process, this paper uses a combination of simulation and experiment to analyze the clamping deformation and milling deformation during the machining of the top surface of the automobile cylinder block and proposes the control of the milling deformation profile error measure. Aiming at the problems of backward detection technology, low detection efficiency, and low detection accuracy for the detection of defects on the inner surface of the master cylinder of automobile brakes, this paper uses the machine vision technology to choose the noncontact method of measurement. The use of laser nonultrasonic contact systems is not limited to workpieces having regular shapes, such as planes and cylinders. It is limited to workpieces with regular shapes such as planes and cylinders. In the automotive and other industrial fields, for various curved parts with free-form surfaces, nondestructive testing techniques of laser nonultrasonic contact systems are generally required to find defects. In the research of the noncontact defect detection method of the automobile cylinder block based on the SVM algorithm, this paper hopes that the detection efficiency and detection accuracy of the inner surface of the brake cylinder can be improved, and the defect location can be accurately found.