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[Industry News]In-Depth AI Empowerment: From "Automatic Control" to "Autonomous Decision-Making", Accelerating Unmanned Full-Process Production

Release time:

Apr 01,2026

[Industry News]In-Depth AI Empowerment: From "Automatic Control" to "Autonomous Decision-Making", Accelerating Unmanned Full-Process Production
[Industry News]In-Depth AI Empowerment: From "Automatic Control" to "Autonomous Decision-Making", Accelerating Unmanned Full-Process Production

In-Depth AI Empowerment: From "Automatic Control" to "Autonomous Decision-Making", Accelerating Unmanned Full-Process Production

1. AI Process Brain: Real-Time Optimization and Adaptive Production

The new generation of equipment is equipped with embedded AI chips + deep reinforcement learning engines, building a closed-loop autonomous system of "perception–analysis–execution–feedback".

  • Core Capabilities: Real-time collection of data such as slurry solid content, ambient temperature and humidity, and mold wear, automatically generating optimal vacuum levels, hot-pressing temperature/pressure, and drying curves. When responding to raw material fluctuations and mold aging, parameter adjustment is done dynamically without manual intervention, with yield stabilized above 98%.
  • Cost-Reduction Effect: Process debugging time reduced from the traditional 4–8 hours to within 30 minutes, line change efficiency improved by 70%.

2. AI Visual Inspection: 0.1mm Defect Detection, 0.8-Second Recognition, Reject Rate Down to 0.3%

AI vision has become a standard configuration for high-end equipment, fully replacing manual inspection.

  • Performance Indicators: Detection accuracy reaches 0.1mm, identifying all types of defects including micro-pores, cracks, under-filling, and deformation. A single camera covers 6–8 mold cavities, with an inspection speed of 0.8 seconds per piece, over 5 times more efficient than manual labor, with an accuracy rate of 99.2%.
  • Practical Results: Projects such as Sichuan Jinzhu Wine Packaging and Zhongchuang Intelligent Smart Factory have reduced high-end wine tray defect rates from 8% to below 0.3% via AI vision + digital twin, perfectly meeting strict standards of leading customers like Wuliangye.

3. Digital Twin + IIoT: Remote O&M + Line Optimization, 90% Fault Prediction Rate

  • Digital Twin: Builds a 1:1 virtual model of the production line, enabling visualized mold switching, process pre-simulation, and capacity simulation. Mold replacement time reduced from 2 hours to 15 minutes, greatly enhancing flexible production capacity.
  • Industrial Internet of Things (IIoT): 78.6% of high-end lines have completed IIoT transformation, with data collection coverage ≥ 90%. Supports remote monitoring, early fault warning, and predictive maintenance, reducing equipment failure rate by 60% and improving operation and maintenance efficiency by 25%.

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