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@ARTICLE{Safdar:1047296,
      author       = {Safdar, Mutahar and Kazimi, Bashir and Ruzaeva, Karina and
                      Wood, Gentry and Zimmermann, Max and Lamouche, Guy and
                      Wanjara, Priti and Sandfeld, Stefan and Zhao, Yaoyao Fiona},
      title        = {{A}ccelerated quantification of reinforcement degradation
                      in additively manufactured {N}i-{WC} metal matrix composites
                      via {SEM} and vision transformers},
      journal      = {Materials characterization},
      volume       = {229},
      number       = {Part B},
      issn         = {1044-5803},
      address      = {New York, NY},
      publisher    = {Science Direct},
      reportid     = {FZJ-2025-04211},
      pages        = {115645 -},
      year         = {2025},
      abstract     = {Machine learning (ML) applications have shown potential in
                      analyzing complex patterns in additively manufactured (AMed)
                      structures. Metal matrix composites (MMC) offer the
                      potential to enhance functional parts through a metal matrix
                      and reinforcement particles. However, their processing can
                      induce several co-existing anomalies in the microstructure,
                      which are difficult to analyze through optical
                      metallography. Scanning electron microscopy (SEM) can better
                      highlight the degradation of reinforcement particles, but
                      the analysis can be labor-intensive, time-consuming, and
                      highly dependent on expert knowledge. Deep learning-based
                      semantic segmentation has the potential to expedite the
                      analysis of SEM images and hence support their
                      characterization in the industry. This capability is
                      particularly desired for rapid and precise quantification of
                      defect features from the SEM images. In this study, key
                      state-of-the-art semantic segmentation methods from
                      self-attention-based vision transformers (ViTs) are
                      investigated for their segmentation performance on SEM
                      images with a focus on segmenting defect pixels.
                      Specifically, SegFormer, MaskFormer, Mask2Former, UPerNet,
                      DPT, Segmenter, and SETR models were evaluated. A reference
                      fully convolutional model, DeepLabV3+, widely used on
                      semantic segmentation tasks, is also included in the
                      comparison. A SEM dataset representing AMed MMCs was
                      generated through extensive experimentation and is made
                      available in this work. Our comparison shows that several
                      transformer-based models perform better than the reference
                      CNN model with UPerNet (94.33 $\%$ carbide dilution
                      accuracy) and SegFormer (93.46 $\%$ carbide dilution
                      accuracy) consistently outperformed the other models in
                      segmenting damage to the carbide particles in the SEM
                      images. The findings on the validation and test sets
                      highlight the most frequent misclassification errors at the
                      boundaries of defective and defect-free pixels. The models
                      were also evaluated based on their prediction confidence as
                      a practical measure to support decision-making and model
                      selection. As a result, the UPerNet model with the Swin
                      backbone is recommended for segmenting SEM images from AMed
                      MMCs in scenarios where accuracy and robustness are desired
                      whereas the SegFormer model is recommended for its lighter
                      design and competitive performance. In the future, the
                      analysis can be extended by including higher capacity as
                      well as smaller models in the comparison. Similarly,
                      variations in specific hyperparameters can be investigated
                      to reinforce the rationale of selecting a specific
                      configuration.},
      cin          = {IAS-9},
      ddc          = {670},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.matchar.2025.115645},
      url          = {https://juser.fz-juelich.de/record/1047296},
}