Vol. 2 No. 5 (2025)

This issue explores deep learning applications in intelligent manufacturing, featuring Xueli Liu's research on intelligent kiln car unstacking robots. The study integrates improved YOLOv8 vision recognition with TD3 path planning algorithms to address automation challenges in ceramics and building materials industries. Experimental results show impressive performance: 95.2% detection accuracy, 96.8% planning success rate, 73.5% efficiency improvement, and 89.2% error reduction compared to manual operations. This research demonstrates deep learning's transformative potential in complex industrial environments, providing valuable insights for Industry 4.0 implementations.