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Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion.

Xing Peng1,2,3, Lingbao Kong1,4, Wei Han1

  • 1Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, China.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

This study introduces a novel multi-sensor image fusion technique combining visible and infrared data for enhanced defect detection in powder bed fusion (PBF) processes. The method significantly improves image quality and defect visibility for better quality control.

Keywords:
defect detectioninfrared imagingmulti-sensor image fusionpowder bed fusionvisible imaging

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Area of Science:

  • Materials Science and Engineering
  • Additive Manufacturing
  • Non-Destructive Testing

Background:

  • Powder Bed Fusion (PBF) processes are critical in additive manufacturing, but monitoring and defect detection remain challenging.
  • Conventional optical inspection methods struggle to identify defects not easily visible in standard images.
  • High-quality defect detection is vital for ensuring the integrity and reliability of PBF parts.

Purpose of the Study:

  • To develop and evaluate a multi-sensor image fusion technique for improved defect detection in PBF.
  • To integrate visible and infrared imaging for capturing defects missed by conventional methods.
  • To enhance the characterization and analysis of defects in PBF components.

Main Methods:

  • Designed a multi-source image acquisition system for simultaneous visible and infrared data capture.
  • Proposed a multi-sensor image fusion method utilizing finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and improved pulse-coupled neural networks (PCNN).
  • Evaluated fusion performance using metrics like information entropy, average gradient, and structural similarity, comparing with other algorithms.

Main Results:

  • The proposed fusion method demonstrated satisfactory performance across various image quality indices.
  • Achieved significant improvements in image contrast, edge detail, and texture information.
  • Effectively retained and fused essential information from both visible and infrared source images.
  • Outperformed other fusion algorithms in enhancing defect visibility.

Conclusions:

  • The developed multi-sensor image fusion technique offers a robust solution for defect detection in PBF.
  • This approach enhances the capability to identify subtle defects, improving PBF part quality assurance.
  • The research provides a valuable tool for defect information fusion and characterization in advanced manufacturing.