Exploration of precision improvement methods for automated assembly lines based on machine vision
基于机器视觉的自动化装配线精度提升方法探讨

摘要

机器视觉技术在自动化装配线中发挥着重要作用，但其精度提升仍面临诸多挑战，如环境干扰、零部件复杂性、系统集成难度等。为此，提出了一系列优化策略，包括改善光学条件、应用深度学习算法、强化系统集成和升级硬件设备等。这些策略在实际应用中显著提高了装配线的精度和效率，减少了次品率，降低了生产成本。未来，随着技术的不断进步，机器视觉有望在更复杂的生产环境中实现更高精度的装配，为制造业的智能化发展提供更有力的支持。

Abstract

Machine vision technology plays an important role in automated assembly lines, but its precision improvement still faces many challenges, such as environmental interference, component complexity, and system integration difficulties. To address these issues, a series of optimization strategies have been proposed, including improving optical conditions, applying deep learning algorithms, strengthening system integration, and upgrading hardware equipment. These strategies have significantly improved the precision and efficiency of the assembly line in practical applications, reduced the defect rate, and lowered production costs. In the future, with continuous technological progress, machine vision is expected to achieve higher-precision assembly in more complex production environments, providing stronger support for the intelligent development of the manufacturing industry.