摘 要
随着汽车工业的快速发展和消费者对产品质量要求的不断提高，汽车制造过程中的质量控制与缺陷检测技术成为保障产品性能和可靠性的重要环节。本研究旨在探讨现代汽车制造中质量控制的关键技术和缺陷检测的有效方法，并提出一种基于多传感器融合与人工智能算法的综合检测方案。通过分析传统质量控制方法的局限性以及新兴技术的优势，研究引入了机器视觉、激光扫描和深度学习等先进技术，以实现对制造过程中潜在缺陷的高效识别与精准定位。实验采用模拟生产线环境，结合实际案例验证了所提方法在复杂工况下的适用性和准确性。结果表明，该方案能够显著提升缺陷检测的灵敏度和效率，同时降低误检率和漏检率。此外，研究还开发了一套实时监控系统，用于动态跟踪生产流程中的质量变化趋势，为优化工艺参数提供了数据支持。本研究的主要创新点在于将多源信息融合技术与智能化算法相结合，突破了单一检测手段的限制，实现了全方位、多层次的质量评估。这一成果不仅为汽车行业提供了先进的质量控制工具，也为其他制造业领域推广智能化检测技术奠定了基础。总体而言，本研究为提高汽车制造质量水平和推动智能制造发展做出了积极贡献。关键词：质量控制；缺陷检测；多传感器融合；人工智能算法；机器视觉
Abstract
With the rapid development of the automotive industry and the continuously increasing demands of consumers for product quality, quality control and defect detection technologies in automobile manufacturing have become crucial for ensuring product performance and reliability. This study aims to explore key technologies in modern automotive quality control and effective methods for defect detection, proposing an integrated inspection scheme based on multi-sensor fusion and artificial intelligence algorithms. By analyzing the limitations of traditional quality control methods and the advantages of emerging technologies, this research incorporates advanced techniques such as machine vision, laser scanning, and deep learning to achieve efficient identification and precise localization of potential defects during the manufacturing process. Experiments were conducted in a simulated production line environment, combined with real-world case studies, to validate the applicability and accuracy of the proposed method under complex conditions. The results indicate that this scheme significantly enhances the sensitivity and efficiency of defect detection while reducing false-positive and false-negative rates. Additionally, a real-time monitoring system was developed to dynamically track quality variation trends in the production process, providing data support for optimizing process parameters. A major innovation of this study lies in integrating multi-source information fusion technology with intelligent algorithms, overcoming the limitations of single detection methods and enabling comprehensive, multi-level quality evaluation. This achievement not only provides advanced quality control tools for the automotive industry but also lays a foundation for promoting intelligent inspection technologies in other manufacturing sectors. Overall, this research makes a positive contribution to improving the quality level of automobile manufacturing and advancing the development of smart manufacturing..Key Words：Quality Control；Defect Detection；Multi-Sensor Fusion；Artificial Intelligence Algorithm；Machine Vision
目 录
摘 要 I
Abstract II
第1章 绪论 2
1.1 汽车制造质量控制的研究背景 2
1.2 质量控制与缺陷检测的意义分析 2
1.3 国内外研究现状综述 3
1.4 本文研究方法与技术路线 3
第2章 质量控制的关键技术与理论基础 4
2.1 质量控制的基本概念与原则 4
2.2 统计过程控制在汽车制造中的应用 4
2.3 六西格玛管理与质量改进方法 5
2.4 数据驱动的质量控制模型构建 6
2.5 理论基础对实践的指导作用 6
第3章 缺陷检测技术及其实施路径 8
3.1 缺陷检测技术的分类与特点 8
3.2 视觉检测技术在车身装配中的应用 8
3.3 声学检测技术对零部件质量的评估 9
3.4 无损检测技术的发展与创新 9
3.5 缺陷检测技术的集成与优化策略 10
第4章 质量控制与缺陷检测的综合应用研究 11
4.1 智能制造环境下的质量控制体系 11
4.2 数字孪生技术在质量监控中的作用 11
4.3 基于大数据的质量预测与决策支持 12
4.4 实时监测系统的设计与实现方案 13
4.5 质量控制与缺陷检测的协同优化 13
结 论 14
参考文献 16
致 谢 17
