Journal of Engineering Research

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.

Machine vision; Automated assembly line; Precision improvement; Optimization methods; Case analysis