Automatic Control and Intelligent Management of Mechanical Assembly Process Based on Big Data Technology
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Abstract
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.
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References
A. D. Makwana and G. S. Patange, “Strategic implementation of 5S and its effect on productivity of plastic machinery manufacturing company,” Australian Journal of Mechanical Engineering, vol. 20, no. 1, 2022, pp. 111–120. R. Zhao
M. S. A. Latif, M. Fahmy-Abdullah, and L. W. Sieng, “Determinants factor of technical efficiency in machinery manufacturing industry in Malaysia,” International Journal of Supply Chain Management, vol. 8, no. 6, 2019, pp. 917–928.
H. Angwar, “Understanding the determinants of big data adoption in India: An analysis of the manufacturing and services sectors,” Information Resources Management Journal, vol. 31, no. 4, 2018, pp. 1–22.
H. N. Dai, H. Wang, G. Xu, J. Wan, and M. Imran, “Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies,” Enterprise Information Systems, vol. 14, no. 9–10, 2020, pp. 1279–1303.
E. Raguseo, “Big data technologies: An empirical investigation on their adoption, benefits and risks for companies,” International Journal of Information Management, vol. 38, no. 1, 2018, pp. 187–195.
R. X. Gao, L. Wang, M. Helu, and R. Teti, “Big data analytics for smart factories of the future,” CIRP Annals - Manufacturing Technology, vol. 69, no. 2, 2020, pp. 668–692.
Y. Huang, K. Sheng, and W. Sun, “Influencing factors of manufacturing agglomeration in the Beijing-Tianjin-Hebei region based on enterprise big data,” Journal of Geography (English Edition), vol. 32, no. 10, 2022, pp. 2105–2128.
A. Aliu, “Automation and control applications in developing regions: An industry perspective of emerging technologies and challenges: Surveys of technology projects regarding e-citizen services and smart city approach - ScienceDirect,” IFAC-PapersOnLine, vol. 52, no. 25, 2019, pp. 568–572.
T. Schossig, A. Klien, and F. Steinhauser, “NEW METHODS FOR TESTING AUTOMATION AND CONTROL,” The Journal of Engineering, vol. 2018, no. 15, 2018, pp. 1340–1343.
Y. Cheng, “Analysis of PID model of agricultural electric automation PID control based on fuzzy neural network,” IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association, vol. 30, no. 7, 2018, pp. 845–851.
S. Y. Ding, J. L. Liu, and M. H. Yue, “The Use of ZigBee Wireless Communication Technology in Industrial Automation Control,” Wireless Communications and Mobile Computing, vol. 2021, no. 6, 2021, pp. 1–11.
H. Wang, Z. Zhang, Z. Xiong, T. Liu, K. Long, and X. Quan et al., “An intelligent computation system for precise assembly of large laser optics,” Assembly Automation, vol. 38, no. 4, 2018, pp. 524–535.
C. Cui, “Research on the application of engineering intelligent mechanical construction equipment in the construction of prefabricated Prefabricated building,” Intelligent Building and Engineering Machinery, vol. 002, no. 011, 2020, pp. 62–63.
F. Li, D. Sun, and L. Miao, “Application of intelligent online torque error prevention in construction machinery Assembly line,” Electronic Technology and Software Engineering, vol. 000, no. 011, 2021, pp. 114– 115.
K. S. Gill, “Holons on the Horizon: Re-Understanding Automation and Control,” IFAC-PapersOnLine, vol. 52, no. 25, 2019, pp. 556–561.
M. Akour, “Mobile Voice Recognition Based for Smart Home Automation Control,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 3, 2020, pp. 3788–3792.
W.X.Zeng, L.Wang, B.L Liang, T.M.Zhang, W.D.Luo, and R.H.Huang, “Design of Intelligent Classification Garbage Can Based on S TM32 Control,” Journal of Engineering Studies, vol. 13, no. 05, 2021,p p. 432–441.