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Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach

Haemi Lee1, Yoonjae Lee1, Minho Jo1

  • 1Department of Mechanical Design and Production Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05030, Republic of Korea.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

This study introduces a new feature selection method, feature partial density, to improve diagnostic models for roll-to-roll manufacturing defects. The proposed method enhances defect detection accuracy and efficiency in production lines.

Keywords:
feature selectionfunctional filmmachine learningroll-to-roll manufacturing systemrotating element diagnosis

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Area of Science:

  • Manufacturing Engineering
  • Data Science

Background:

  • Roll-to-roll manufacturing offers cost-effective, eco-friendly mass production using flexible substrates.
  • Defects in rotating components (rollers, bearings) critically impact functional layer quality.

Purpose of the Study:

  • To develop an intelligent diagnostic model for identifying rotating component defects in roll-to-roll systems.
  • To propose a quantitative feature-selection method, feature partial density, for high-efficiency diagnostic models.

Main Methods:

  • Extracted feature combinations from measured signals.
  • Evaluated features using partial density and Mahalanobis distance for classification performance.
  • Constructed ranked model groups and compared with existing methods.

Main Results:

  • The proposed feature-selection algorithm successfully identified high-performing feature combinations.
  • The high-ranking group selected by the algorithm showed improved training time, accuracy, and positive predictive value.
  • The top feature combination outperformed existing methods across all performance indicators.

Conclusions:

  • The feature partial density method is effective for developing high-efficiency diagnostic models in roll-to-roll manufacturing.
  • This approach significantly improves the accuracy and efficiency of detecting rotating component defects.