Evaluation of Human-Bias in Labeling of Ambiguous Features in Electron Microscopy Machine Learning Models

Bruno, Gabriella; Lynch, Matthew; Jacobs, Ryan; Morgan, Dane; Field, Kevin G.

doi:10.1093/micmic/ozad067.767

Microscopy and Microanalysis

2023

DOI: 10.1093/micmic/ozad067.767

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Evaluation of Human-Bias in Labeling of Ambiguous Features in Electron Microscopy Machine Learning Models

Gabriella Bruno

Matthew Lynch

Ryan Jacobs

et al.

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2024

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Cited by 2 publications

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Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Ngo,

Hernandez,

Olatunde

et al. 2024

JOM

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Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

show abstract

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Ngo,

Hernandez,

Olatunde

et al. 2024

JOM

View full text Add to dashboard Cite

show abstract

Accurate quantification of dislocation loops in complex functional alloys enabled by deep learning image analysis

Bilyk,

Goryaeva,

Marinica

et al. 2024

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Evaluation of Human-Bias in Labeling of Ambiguous Features in Electron Microscopy Machine Learning Models

Cited by 2 publications

References 4 publications

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Accurate quantification of dislocation loops in complex functional alloys enabled by deep learning image analysis

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