自动化装配在智能工厂中的应用与实践 -电气工程及其自动化专业

摘要

随着制造业向智能化转型，自动化装配技术成为智能工厂建设的关键环节。本文聚焦自动化装配在智能工厂中的应用与实践，旨在探讨其对生产效率、产品质量及柔性制造能力的提升作用。研究基于工业4.0理念，结合物联网、大数据、人工智能等新兴技术，通过构建自动化装配系统模型，分析了从零部件识别、抓取、组装到检测的全流程自动化实现路径。采用案例分析法，选取典型离散制造企业为样本，对其自动化装配线进行实地调研和技术评估。结果表明，自动化装配显著提高了生产效率，平均节拍时间缩短30%，产品合格率提升至99.5%以上，同时实现了多品种小批量生产的快速切换。创新点在于将机器视觉与深度学习算法融入装配过程，解决了复杂工件识别和精密装配难题；开发了基于数字孪生的装配仿真平台，实现了虚拟调试与物理设备的无缝对接。

关键词：自动化装配智能工厂生产效率

Abstract

As manufacturing transitions towards intelligence, automated assembly technology has become a critical component in the construction of smart factories. This paper focuses on the application and practice of automated assembly in smart factories, aiming to explore its enhancement effects on production efficiency, product quality, and flexible manufacturing capabilities. Based on Industry 4.0 concepts and integrating emerging technologies such as the Internet of Things (IoT), big data, and artificial intelligence, this study constructs an automated assembly system model to analyze the full automation implementation path from component recognition, grasping, assembly, to inspection. Utilizing case analysis methodology, a typical discrete manufacturing enterprise was selected as a sample for on-site research and technical evaluation of its automated assembly line. The results indicate that automated assembly significantly improves production efficiency, with average cycle time reduced by 30%, and product pass rates increased to over 99.5%. It also enables rapid switching for multi-variety small-batch production. Innovations include incorporating machine vision and deep learning algorithms into the assembly process, addressing complex workpiece recognition and precision assembly challenges; and developing a digital twin-based assembly simulation platform that achieves seamless integration between virtual debugging and physical equipment.

Keyword:Automated Assembly Intelligent Factory Production Efficiency

1绪论 1

1.1研究背景与意义 1

1.2研究现状综述 1

1.3研究方法介绍 1

2自动化装配技术基础 2

2.1关键技术概述 2

2.2系统架构分析 3

2.3技术发展趋势 3

3智能工厂中的应用实践 4

3.1应用场景分析 4

3.2实施案例研究 4