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Updated: Jun 27, 2026

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Fast Image Segmentation Toward Automation of 3D Ice Printing.

Andres Garcia1, Akash Garg1, Feimo Yang1

  • 1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15232, United States.

Chemical & Biomedical Imaging
|June 26, 2026
PubMed
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This summary is machine-generated.

Researchers developed machine learning vision techniques to enable real-time control of 3D ice printing. This advancement paves the way for automated, precise additive manufacturing of complex ice structures.

Area of Science:

  • Additive Manufacturing
  • Materials Science
  • Computer Vision

Background:

  • Freeform 3D ice printing is an emerging additive manufacturing technique with broad applications.
  • Current manual design processes for complex ice geometries are time-consuming and empirical.
  • Real-time visualization and control are crucial for improving ice printing precision and efficiency.

Purpose of the Study:

  • To develop vision techniques for real-time monitoring and control of 3D ice printing.
  • To enable closed-loop control for enhanced precision in ice additive manufacturing.
  • To facilitate the automated design and fabrication of complex ice structures.

Main Methods:

  • Implemented convolutional filters for ice segmentation to create training datasets.
Keywords:
3D printingcomputer visioncontrolconvolutional filtersiceoptical flowsegmentation

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  • Utilized a hybrid optical flow algorithm (Farneback-FAST) for video frame segmentation.
  • Trained a neural network (Icenet) using segmented video data for rapid frame analysis.

    • Main Results:

    • Developed a machine learning approach for ice segmentation.
    • Achieved frame segmentation in 25 ms, enabling single and low multi-droplet control.
    • Demonstrated a viable method for generating actionable data from ice printing processes.

    Conclusions:

    • The implemented vision techniques enable future closed-loop control of 3D ice printing.
    • This advancement is critical for automating complex ice structure fabrication.
    • Potential applications include advanced additive manufacturing, organ-on-a-chip systems, and biomanufacturing.