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Vision based feedback control of weatherstrip coextrusion with predictive dimension modeling
Donguk Lee1,2, Thong Phi Nguyen1, Hye-Jin Lee1
1Autonomous Manufacturing & Process R&D Department, Korea Institute of Industrial Technology, 143, Hanggaul-ro, Sangnok-gu, Ansan-si, Gyeonggi-Do, 15588, Republic of Korea.
Scientific Reports
|May 21, 2026
Summary
This study introduces a data-driven control strategy for weatherstrip co-extrusion, enhancing geometric accuracy. The system uses predictive models for startup and vision-based feedback for steady-state production, ensuring precise dimensions.
Area of Science:
- Manufacturing Engineering
- Control Systems
- Materials Science
Background:
- Weatherstrip co-extrusion faces challenges in geometric accuracy due to complex process dynamics and time delays.
- Conventional methods struggle to integrate dimensional inspection into closed-loop feedback control systems.
Purpose of the Study:
- To develop an integrated data-driven control framework for regulating product dimensions in weatherstrip co-extrusion.
- To manage both process initialization and steady-state production phases effectively.
Main Methods:
- A Random Forest-based prediction model was used to optimize initial screw speeds during the startup phase.
- A smart vision-based feedback system with an artificial neural network was implemented for steady-state control, compensating for transport delays.
Main Results:
- The Random Forest model minimized trial-and-error adjustments during process startup.
- The vision-based feedback system successfully compensated for dimensional deviations, maintaining errors within ±0.1 to ±0.2 mm.
- Field validation demonstrated the system's effectiveness in a mass-production environment.
Conclusions:
- The proposed integrated control strategy is practically feasible for autonomous quality management in industrial weatherstrip co-extrusion.
- This data-driven approach addresses the limitations of conventional methods by accounting for complex process variables and time delays.
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