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Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
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Feature-based volumetric defect classification in metal additive manufacturing.

Arun Poudel1,2, Mohammad Salman Yasin1,2, Jiafeng Ye3

  • 1National Center for Additive Manufacturing Excellence (NCAME), Auburn University, Auburn, AL, 36849, USA.

Nature Communications
|October 26, 2022
PubMed
Summary

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This study analyzed volumetric defects in 3D-printed titanium using X-ray computed tomography. A new method using multiple defect features accurately classifies lack of fusions, gas pores, and keyholes.

Area of Science:

  • Materials Science
  • Additive Manufacturing
  • Non-Destructive Testing

Background:

  • Volumetric defects in additively manufactured parts arise from various formation mechanisms.
  • Understanding defect morphology is crucial for quality control in laser powder bed fusion (LPBF).
  • Titanium alloys like Ti-6Al-4V are widely used in demanding applications where defect-free components are essential.

Purpose of the Study:

  • To analyze and quantify the morphological features of common volumetric defects in LPBF Ti-6Al-4V.
  • To develop a robust methodology for classifying defect types based on their geometric characteristics.
  • To assess the effectiveness of machine learning algorithms for automated defect classification.

Main Methods:

  • High-resolution X-ray computed tomography (HRXCT) was employed to capture defect geometries.

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  • Nine morphological parameters (e.g., maximum dimension, roundness, aspect ratio) were used for defect quantification.
  • Machine learning models, including decision trees and artificial neural networks, were utilized for classification.

    • Main Results:

    • Three common defect types (lack of fusion, gas-entrapped pores, keyholes) exhibit overlapping morphological parameter ranges.
    • Single or dual parameter analysis is insufficient for unique defect type identification.
    • The proposed multi-parameter classification methodology achieved high accuracy (>98% for decision trees, >99% for ANNs).

    Conclusions:

    • A multi-parameter approach is necessary for accurate classification of volumetric defects in LPBF Ti-6Al-4V.
    • Machine learning models integrated with this methodology provide a reliable solution for automated defect detection and characterization.
    • This work contributes to improving the quality and reliability of additively manufactured metal components.