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Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering.

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This study developed a smart manufacturing model for synthetic rubber vulcanization, enabling early defect detection by analyzing input patterns without manual chemical testing.

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Chemical Engineering

Background:

  • The need for smart manufacturing environments drives innovation in industrial processes.
  • Traditional manufacturing methods often lack real-time defect detection capabilities.
  • Synthetic rubber vulcanization presents complex challenges for process optimization.

Purpose of the Study:

  • To develop and embed an anomaly detection model into a programmable logic controller (PLC) system.
  • To investigate the innovation process for smart manufacturing in synthetic rubber production.
  • To enable real-time sensing and pre-detection of defects in the vulcanization process.

Main Methods:

  • Developed a predictive model for anomaly detection.
  • Integrated the model into an existing programmable logic controller (PLC) system.
  • Investigated real-time sensing technologies and cloud-based infrastructure.

Main Results:

  • Analysis of input variable patterns effectively detects anomalies without manual chemical property testing.
  • The developed model successfully pre-detects defects in the synthetic rubber vulcanization process.
  • Cloud-based infrastructure facilitates the practical implementation of smart manufacturing.

Conclusions:

  • Input variable pattern analysis is a viable method for anomaly detection in smart manufacturing.
  • The integration of predictive models enhances defect pre-detection in industrial processes.
  • The study provides a practical framework for realizing smart manufacturing environments.