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.

A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation

“…Traditionally, machine learning algorithms [23][24][25], such as K-nearest neighbors (KNN), support vector machine (SVM), and K-means clustering methods, and some regression algorithms were used to solve the problems with classification and clustering. In fact, as a perfect integration approach of K-neighbors filtering algorithm and Euclidean clustering segmentation, 3D data processing could solve complex identification systems in a manner of high-defect processing efficiency, fine local characteristics, and better generalization performance compared with traditional machine vision detection.…”

A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

“…Traditionally, machine learning algorithm [19][20][21] such as K-nearest neighbors (KNN), Support vector machine (SVM), K-means clustering methods, and some regression algorithms were used to solve the problems with classi cation and clustering. In fact, as a perfect integration approach of K-neighbors ltering algorithm and Euclidean clustering segmentation, 3D data processing could solve complex identi cation systems in a manner of high-defect processing e ciency, ne local characteristic and better generalization performance compared with traditional machine vision detection.…”

A Study on the Application of Machine and Deep Learning Using the Impact Response Test to Detect Defects on the Piston Rod and Steering Rack of Automobiles

“…Therefore, with the development of various intelligent approaches, data-driven approaches have become increasingly attractive. The most common intelligent defect diagnosis methods are machine learning (ML) approaches such as support vector machines (SVM), artificial neural networks (ANN), and self-organizing map (SOM) networks [11,12]. The majority of intelligent defect diagnosis methods for mechanical parts are based on the processing and analysis of vibration signals, with discriminant models serving as inputs for artificial features extracted from the acquired raw signals.…”