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Fabric tearing performance state perception and classification driven by multi-source data.

Jianmin Huang1,2, Qingchun Jiao3, Yifan Zhang3

  • 1Zhejiang Institute of Standardization (Zhijiang Standardization Think Tank), Hangzhou, China.

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Summary

This study introduces a new method for monitoring fabric tear strength testing. It accurately classifies testing states, improving workflow and traceability in textile quality control.

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Area of Science:

  • Textile Engineering
  • Materials Science
  • Quality Control

Background:

  • Textile tear strength is vital for product quality but is susceptible to variations from equipment, environment, and human factors during laboratory testing.
  • Current testing methods lack traceable records of influencing factors and effective classification of testing activities, hindering process optimization.
  • The need for a robust system to monitor and classify fabric tear performance testing is evident to ensure reliable and reproducible results.

Purpose of the Study:

  • To propose a state-awareness and classification approach for fabric tear performance testing using multi-source data.
  • To develop a system capable of real-time monitoring of electrical parameters, operational environment, and operator behavior during testing.
  • To enhance the traceability and classification accuracy of fabric tear testing processes.

Main Methods:

  • Systematic design of fabric tear performance testing activities with real-time monitoring capabilities.
  • Multi-source data collection including electrical parameters, environmental conditions, and operator actions.
  • Data preprocessing and classification using a Decision Tree Support Vector Machine (DTSVM) with ten-fold cross-validation.

Main Results:

  • The developed system effectively perceives fabric tear performance testing processes.
  • High accuracy, exceeding 98.73%, was achieved in classifying different fabric testing states.
  • The system demonstrates robust performance in monitoring and classifying testing activities.

Conclusions:

  • The proposed state-awareness and classification approach significantly enhances the perception of fabric tear performance testing.
  • The system contributes to continuous improvement in the workflow and traceability of fabric tear performance testing.
  • Widespread application of this system can lead to more reliable textile quality assessment.