Automatic Control and Intelligent Management of Mechanical Assembly Process Based on Big Data Technology

Mechanical assembly is a core production process in modern manufacturing, but traditional assembly systems often suffer from low efficiency, high labor cost, unstable quality, and weak adaptability due to human operation and fixed control logic. To improve real-time control and intelligent management, this paper investigates the application of big data technology in mechanical assembly processes and designs an automatic control and intelligent management system integrating multisource data acquisition, data cleaning, clustering analysis, association rule mining, and closed-loop control. Sensors, data acquisition cards, and industrial communication devices are used to collect assembly parameters such as displacement, force, pressure, temperature, speed, equipment status, and product quality. K-means clustering is adopted to identify assembly-state categories, while a lightweight improved association rule mining method is used to discover parameter correlations and convert them into real-time control thresholds and actuator commands. The system further includes modules for data integration, real-time monitoring, fault diagnosis, intelligent decision support, and visual management. Experimental comparison with a conventional automated assembly system shows that the proposed system achieves lower failure rates, faster response time, higher product qualification rates, and better fault monitoring accuracy. The minimum response time reaches 510.75 ms, and the maximum qualification rate reaches 99.59%. The results indicate that big-data-driven automatic control, combined with industrial sensing and electromagnetic-compatible signal acquisition, can significantly improve assembly stability, quality consistency, and intelligent manufacturing capability.