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A Comprehensive Dataset for Image Segmentation in Custom Manufacturing Environments.

Martell Bell1, Rachel V Vitali2

  • 1University of Iowa, Department of Mechanical Engineering, Iowa City, 52242, US. martell-bell@uiowa.edu.

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|October 31, 2025
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Summary
This summary is machine-generated.

This study introduces a new image dataset for automating sprue and riser removal in high-mix, low-volume (HMLV) foundries using machine learning. The open-source dataset aids in developing robust industrial imaging models for process automation.

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Area of Science:

  • Industrial Automation
  • Computer Vision
  • Materials Science

Background:

  • High-mix, low-volume (HMLV) foundries face automation challenges.
  • Industry 4.0 offers potential solutions, especially in process automation.
  • Automating post-processing tasks like sprue and riser removal is crucial for efficiency.

Purpose of the Study:

  • To develop a comprehensive image dataset for training neural networks.
  • To automate sprue and riser removal in sand-cast parts.
  • To address the variability in HMLV environments through diverse image data.

Main Methods:

  • Creation of a novel image dataset including real, synthetic, and augmented images.
  • Systematic evaluation of image quality metrics and their impact on model training.
  • Development of an image segmentation neural network for automated post-processing.

Main Results:

  • The dataset captures diverse part representations and scene complexity.
  • Image quality metrics were evaluated for their impact on model performance.
  • The approach addresses variability inherent in HMLV manufacturing.

Conclusions:

  • The developed dataset is a valuable resource for machine learning in industrial imaging.
  • The approach enhances the robustness of models in complex industrial settings.
  • The complete dataset and code are publicly available as an open-source resource.