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Abstract

In response to the technical requirements for real-time quality control in the hot pressing process of intelligent plywood production, this study proposes a real-time process control framework driven by edge AI. This framework employs a three-layer edge intelligence architecture. This work shows a practical and efficient boundary node model application scheme for defect detection with multi-level lightweight strategies. In particular, this work builds a decision level data fusion approach for visual detection data and process parameters based on rules for defect-process parameter association mapping. Experimental results have shown that this designed scheme can efficiently detect defects in an edge computing environment. Additionally, with more multi-source fusion being considered in the site environment, the overall detection efficiency might be improved while maintaining a stable closed-loop control system. After that, quality enhancement for products and efficiency improvement for detection were realized. The results provide a feasible method for utilizing engineering processes for enhanced online quality detection for the plywood hot-pressing process based on practical experiences for intelligence upgrades in wood processing.

Keywords

Edge artificial intelligence Real-time process control Plywood manufacturing Industrial Internet of Things Defect detection

Article Details

Author Biography

Lulu Huang, College of Engineering, Technological University of the Philippines, San Marcelino St, Ayala Blvd, Ermita, Manila, 1000, Philippines

Lulu Huang is currently pursuing a Ph.D. at the College of Industrial Education, Technological University of the Philippines in Manila. His research interests include smart manufacturing, information systems, and data science.

How to Cite
Huang, L., & Ferrer, E. (2026). Edge AI-enabled real-time process control in smart plywood production: IoT integration and intelligent automation framework. Future Technology, 5(2), 103–115. Retrieved from https://fupubco.com/futech/article/view/725
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