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<p>Direct Ink Writing (DIW) enables the fabrication of complex, architected elastomeric parts, but ensuring filament-scale geometric fidelity during production remains a critical bottleneck. Existing inspection relies on post-print X-ray computed tomography (CT), constrained by volume-resolution tradeoffs, or surface-only optical methods that lack internal spatial awareness. We present a scalable software pipeline for on-machine inspection of DIW polymer lattices that combines layerwise camera imaging, a compact convolutional segmentation network, and a computer-vision skeletonization algorithm to extract filament diameter. Across face-centered tetragonal, helicoidal, and simple cubic geometries, it achieves robust segmentation (Dice ≈ 0.973) and reliable measurements, with a mean absolute percentage error of 2.6%, a root mean squared error of 3.4 μm, and high correlation with human labels. We further inspect a production-scale cushion whose dimensions exceed the practical limits of CT: processing ~2.4k images per layer yields continuous spatial diameter maps that diagnose macroscopic anomalies such as substrate tilt entirely in situ. The workflow provides a practical, on-machine metrology stream for DIW, demonstrated on a single platform, suitable for quality control and as a foundation for future closed-loop control; code and curated datasets are provided to enable reproducibility.</p>

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Scalable on-machine inspection of direct ink write additive manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月1日

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  • Brian T. Weston,
  • Michael E. Zelinski,
  • Hamed Ziad Ammar,
  • Aldair E. Gongora,
  • Brian Au,
  • Robert Cerda,
  • Joshua R. DeOtte,
  • William Smith,
  • Brian Giera

摘要

Direct Ink Writing (DIW) enables the fabrication of complex, architected elastomeric parts, but ensuring filament-scale geometric fidelity during production remains a critical bottleneck. Existing inspection relies on post-print X-ray computed tomography (CT), constrained by volume-resolution tradeoffs, or surface-only optical methods that lack internal spatial awareness. We present a scalable software pipeline for on-machine inspection of DIW polymer lattices that combines layerwise camera imaging, a compact convolutional segmentation network, and a computer-vision skeletonization algorithm to extract filament diameter. Across face-centered tetragonal, helicoidal, and simple cubic geometries, it achieves robust segmentation (Dice ≈ 0.973) and reliable measurements, with a mean absolute percentage error of 2.6%, a root mean squared error of 3.4 μm, and high correlation with human labels. We further inspect a production-scale cushion whose dimensions exceed the practical limits of CT: processing ~2.4k images per layer yields continuous spatial diameter maps that diagnose macroscopic anomalies such as substrate tilt entirely in situ. The workflow provides a practical, on-machine metrology stream for DIW, demonstrated on a single platform, suitable for quality control and as a foundation for future closed-loop control; code and curated datasets are provided to enable reproducibility.