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Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using

Amit Kumar Gope1, Yu-Shu Liao1, Chung-Feng Jeffrey Kuo1

  • 1Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.

Polymers
|July 9, 2022
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Summary
This summary is machine-generated.

Artificial intelligence optimizes melt spinning quality by detecting abnormal processing parameters. Machine learning, particularly random forest, accurately identifies settings causing product defects, improving quality control.

Keywords:
artificial intelligencedeep learningmachine learningmelt spinning machineneural networkrandom forest

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Area of Science:

  • Materials Science
  • Polymer Engineering
  • Artificial Intelligence

Background:

  • Melt spinning machine setup is critical for optimal end-product quality.
  • Process parameter control directly influences polypropylene (PP) quality characteristics.
  • Artificial intelligence (AI) offers potential for advanced quality detection and optimization.

Purpose of the Study:

  • To develop an AI system for detecting abnormal melt spinning process parameters.
  • To identify strategies for improving product quality through parameter optimization.
  • To compare machine learning and deep learning methods for diagnosing process abnormalities.

Main Methods:

  • Trained a deep learning neural network using historical data for multi-quality optimization.
  • Applied AI algorithms to detect abnormal processing parameters using polypropylene (PP) as the material.
  • Compared random forest, machine learning, and deep learning methods on training and verification datasets to identify root causes of abnormal quality.

Main Results:

  • Deep learning identified methods for multi-quality optimization of PP melt spinning.
  • The random forest method achieved high accuracy (100% for combined single/double identification) in pinpointing abnormal parameter settings.
  • The study confirmed the effectiveness of the proposed diagnostic method for predicting and identifying causes of product abnormality.

Conclusions:

  • AI-driven systems can effectively monitor and optimize melt spinning processes.
  • Random forest is a highly accurate method for diagnosing process parameter deviations affecting product quality.
  • This research provides a robust framework for enhancing quality control in polymer melt spinning.