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Development of a multi-sensor system for defects detection in additive manufacturing.

Xing Peng, Lingbao Kong

    Optics Express
    |October 15, 2022
    PubMed
    Summary

    A new multi-sensor defect detection system (MSDDS) improves additive manufacturing quality control. This system fuses visible, infrared, and polarization data for accurate defect identification, overcoming limitations of single-sensor methods.

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    Area of Science:

    • Materials Science
    • Manufacturing Engineering
    • Optical Engineering

    Background:

    • Traditional single-sensor defect detection in additive manufacturing (AM) suffers from low accuracy and limited information.
    • Maintaining product quality in AM processes necessitates robust defect monitoring and maintenance strategies.

    Purpose of the Study:

    • To develop and evaluate a multi-sensor defect detection system (MSDDS) for enhanced AM quality control.
    • To fuse data from visible, infrared, and polarization sensors for comprehensive defect analysis.
    • To optimize imaging quality assessment and demonstrate the feasibility of sensor module integration.

    Main Methods:

    • Development of a Multi-Sensor Defect Detection System (MSDDS) integrating visible, infrared, and polarization sensors.

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  • Optimization and evaluation of imaging quality assessment criteria.
  • Tolerance sensitivity and Monte Carlo analysis for sensor module feasibility.
  • Application of multi-sensor image fusion, super-resolution reconstruction, and feature extraction.

    • Main Results:

    • The MSDDS achieved high contrast and clear key information for defect detection.
    • High-quality images of AM defects like cracking, scratches, and porosity were effectively extracted.
    • Simulation and experimental studies validated the system's performance.

    Conclusions:

    • The developed MSDDS offers a significant improvement over traditional methods for AM defect detection.
    • This system provides a potential solution for defect detection and process parameter optimization in AM, including Selective Laser Melting.