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<p>X-Ray Computed Tomography (CT) is a widely adopted tool in the non-destructive quality assurance of additive manufacturing (AM). Porosity in AM can be assessed via CT without compromising the integrity of the part and without reliance on witness specimen. Reliable pore criticality analysis, essential for AM fatigue assessments, hinges on precise determination of pore dimensions. This work investigates CT data by comparing the pore sizes and shapes from two different data sources (CT and metallography), originating from the same samples. The comparison indicates a pore size underestimation in the CT data by an average of 20%. A subsequent rescaling and smoothing workflow on the CT pore data compensates this underestimation. This workflow reduces the mean pore size deviations between both data sources by up to 50% compared to the original data, allowing a more accurate pore assessment. Additionally the smoothing process reduces errors introduced by the CT reconstruction, lowering the average and scatter in mean curvature between pores. The rescaled and smoothed pores serve as an improved starting point for investigations regarding the effect of porosity on fatigue in AM.</p>

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Enhancing Computed Tomography-Based Pore Mesh Models Through Matching with Microscope Cross-Section Images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月18日

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  • Sebastian Mansky,
  • Malte Becker,
  • Dirk Herzog,
  • Ingomar Kelbassa

摘要

X-Ray Computed Tomography (CT) is a widely adopted tool in the non-destructive quality assurance of additive manufacturing (AM). Porosity in AM can be assessed via CT without compromising the integrity of the part and without reliance on witness specimen. Reliable pore criticality analysis, essential for AM fatigue assessments, hinges on precise determination of pore dimensions. This work investigates CT data by comparing the pore sizes and shapes from two different data sources (CT and metallography), originating from the same samples. The comparison indicates a pore size underestimation in the CT data by an average of 20%. A subsequent rescaling and smoothing workflow on the CT pore data compensates this underestimation. This workflow reduces the mean pore size deviations between both data sources by up to 50% compared to the original data, allowing a more accurate pore assessment. Additionally the smoothing process reduces errors introduced by the CT reconstruction, lowering the average and scatter in mean curvature between pores. The rescaled and smoothed pores serve as an improved starting point for investigations regarding the effect of porosity on fatigue in AM.