基于智能算法的汽车装配件缺陷检测研究  

Research on Defect Detection of Automotive Parts Based on Intelligent Algorithms

作　　者：邹字 陈时法 Zi Zou;Shifa Chen(Nanjing University of Posts and Telecommunications,Nanjing,Jiangsu 210003)

机构地区：[1]南京邮电大学,江苏南京210003

出　　处：《人工智能研究》2024年第4期31-40,共10页

摘　　要：随着汽车工业的快速发展,汽车装配件的质量控制变得日益重要。据统计,汽车装配缺陷可能导致高达30%的汽车召回事件,给制造商带来巨大的经济损失和品牌信誉损害。因此,基于智能算法的汽车装配件缺陷检测研究具有深远的意义。通过引入先进的机器学习和深度学习技术,可以显著提高缺陷检测的准确性和效率,减少人工检测的主观性和误差。例如,利用卷积神经网络(CNN)对装配件图像进行分析,可以实现对微小缺陷的高精度识别。此外,智能算法的应用不仅限于视觉检测,还包括声音、振动等多种传感器数据的综合分析,以实现全方位的质量监控。对此,通过阐述汽车装配件缺陷检测类型,进面研究探索智能算法在汽车装配件缺陷检测上的应用,以期为汽车装配件的质量控制提供更为高效和精确的解决方案,推动汽车制造业的高质量发展。With the rapid development of the automotive industry,quality control of automotive components has become increasingly important.Statistics show that assembly defects in automobiles may lead to as many as 30%of vehicle recalls,causing signifcant economic losses and damage to manufacturers'brand reputation.Therefore,research on defect detection in automotive components based on intlligent algorithms is of profound significance.By introducing advanced machine learning and deep learning technologies,the accunacy and eficiency of defect dection can be significandly improved,reducing the subjectivity and errors of manual inspection.For example,using Convolutional Neural Neworks(CNN)to analyze images of components can achieve high-precision identification of minor defecs.Morcover,the application of intelligent algorithms is not limited to visual detection but also includes comprehensive analysis of various sensor data such as sound and vibration,to achieve all-around quality monitoring.In this regard,by describing the types of defects in automotive components,this paper explores the application of intelligent algorithms in the detection of these defects,aiming to provide more fficient and accurate solutions for the quality control of automotive components,and to promote high-quality development of the automotive manufacturing industry.

关 键 词：智能算法 汽车装配件 缺陷检测 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]