自动化装配中的零部件识别技术研究 -电气工程及其自动化专业

摘要

自动化装配是现代制造业的重要组成部分，随着工业4.0的推进，对零部件识别技术提出了更高要求。传统方法难以满足复杂多变的生产环境需求，亟需开发高效、精准的识别系统。为此，本文聚焦于自动化装配中的零部件识别技术研究，旨在构建一套适用于多种场景的智能识别方案。通过融合深度学习与计算机视觉技术，提出基于卷积神经网络的多模态特征提取算法，能够有效处理不同材质、形状和尺寸的零部件。实验采用工业级数据集进行验证，结果表明该方法在识别准确率上较传统方法提升15%以上，平均响应时间缩短至20毫秒以内。特别地，针对遮挡、光照变化等实际工况，引入注意力机制优化模型鲁棒性，使系统具备更强的适应能力。此外，设计了轻量化网络结构以降低计算资源消耗，确保算法在嵌入式设备上的实时运行。本研究不仅为自动化装配提供了可靠的技术支持，也为智能制造领域的发展奠定了坚实基础，具有重要的理论意义和应用价值。

关键词：自动化装配零部件识别卷积神经网络

Abstract

Automated assembly is a critical component of modern manufacturing, and the advancement of Industry 4.0 has imposed higher demands on part recognition technology. Traditional methods struggle to meet the requirements of complex and dynamic production environments, necessitating the development of efficient and precise recognition systems. This study focuses on part recognition technology in automated assembly, aiming to construct an intelligent recognition solution adaptable to various scenarios. By integrating deep learning with computer vision techniques, a multimodal feature extraction algorithm based on convolutional neural networks (CNNs) is proposed, which effectively handles parts of different materials, shapes, and sizes. The method was validated using industrial-grade datasets, demonstrating an improvement of over 15% in recognition accuracy compared to traditional methods, with the average response time reduced to within 20 milliseconds. Specifically, to address practical conditions such as occlusion and varying lighting, an attention mechanism was introduced to enhance model robustness, thereby improving the system's adaptability. Additionally, a lightweight network architecture was designed to minimize computational resource consumption, ensuring real-time operation on embedded devices. This research not only provides reliable technical support for automated assembly but also lays a solid foundation for the development of smart manufacturing, possessing significant theoretical and practical value.

Keyword:Automated Assembly Component Recognition Convolutional Neural Network

1绪论 1

1.1自动化装配中的零部件识别背景 1

1.2零部件识别技术的研究意义 1

1.3国内外研究现状综述 1

1.4本文研究方法与创新点 2

2零部件识别的关键技术 2

2.1图像处理技术在识别中的应用 2

2.2机器视觉系统的构建与优化 3

2.3特征提取与匹配算法分析 3

2.4深度学习在识别中的作用 4

3零部件识别系统设计 4