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A defect recognition method based on ITLPP and multi-feature fusion matrix.

Biting Lei1, Pengxing Yi2

  • 1School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China. leibiting@dgut.edu.cn.

Scientific Reports
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a new framework for identifying metal component defects, distinguishing between surface and subsurface flaws with high accuracy. The method uses improved tensor locality preserving projection (ITLPP) and feature fusion for reliable structural integrity monitoring.

Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Non-Destructive Testing

Background:

  • Structural integrity monitoring of metal components relies on accurate defect recognition.
  • Distinguishing surface from subsurface defects is critical but challenging due to depth interference.
  • Current methods struggle with the complexity of defect classification.

Purpose of the Study:

  • To propose a novel defect recognition framework for enhanced structural integrity assessment.
  • To overcome limitations of single-feature approaches in defect detection.
  • To accurately discriminate between surface and subsurface defects in metal components.

Main Methods:

  • Developed a multi-feature fusion defect recognition framework.
  • Integrated Improved Tensor Locality Preserving Projection (ITLPP) for dimension reduction.
Keywords:
Defect recognitionImproved tensor locality preserving projection (ITLPP)K-nearest neighbors (KNN)Multi-feature fusion

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  • Employed a fusion matrix mechanism and improved k-nearest neighbors (KNN) for classification.

    • Main Results:

    • Achieved a 98.1% recognition accuracy for surface/subsurface defects.
    • Demonstrated superior performance compared to conventional single-eigenvalue methods.
    • Successfully identified defects with varying cross-sectional shapes, indicating suitability for natural defects.

    Conclusions:

    • The proposed ITLPP-based multi-feature fusion method offers effective defect classification.
    • This framework enhances the accuracy and reliability of structural integrity monitoring.
    • The method shows promise for real-world applications in detecting natural defects.