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Steel manufacturing is a multi-stage process that begins by smelting iron ore into cast iron in a blast furnace. This initial stage involves layering iron ore with coke, a type of fuel, and crushed limestone within the furnace. The coke is ignited with a high volume of air, leading to the creation of carbon monoxide, which acts to reduce the iron ore to pure iron.
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Updated: Sep 13, 2025

Surrogate Model Development for Digital Experiments in Welding
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Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model.

Shuo Feng1, James Wainwright1, Chong Wang1

  • 1Welding and Additive Manufacturing Centre, School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK40 3AA, UK.

Sensors (Basel, Switzerland)
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

A new semi-automatic annotation tool speeds up video analysis for wire + arc additive manufacturing (WAAM) and welding. This enables faster quality control and feedback systems by leveraging large computer vision models.

Keywords:
deep learningdroplet transfer behaviourvideo segmentationwire + arc additive manufacturing (WAAM)

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Area of Science:

  • Manufacturing Engineering
  • Computer Vision
  • Materials Science

Background:

  • In-process monitoring videos are crucial for quality assurance in wire + arc additive manufacturing (WAAM) and automated welding.
  • Accurate segmentation of video data is needed for feedback control, but is challenging due to fluctuating arc brightness and limitations of conventional methods.
  • Deep learning for WAAM video segmentation is hindered by the high cost and time required for dataset creation.

Purpose of the Study:

  • To develop a semi-automatic annotation tool for WAAM and welding videos to overcome dataset creation challenges.
  • To enable rapid quantitative analysis of WAAM and welding videos with minimal user intervention.
  • To demonstrate the effectiveness of the tool in practical applications.

Main Methods:

  • Development of a semi-automatic annotation tool integrating the foundation model SAM and the video object tracking model XMem.
  • Utilizing the tool for significantly faster video frame annotation compared to manual methods.
  • Demonstrating the tool's application in closed-loop control, droplet transfer analysis, and dataset assembly.

Main Results:

  • The developed tool accelerates video annotation hundreds of times faster than traditional manual methods.
  • The tool facilitates rapid quantitative analysis of WAAM and welding videos.
  • Successful demonstration of the tool's utility in three distinct use cases.

Conclusions:

  • Large computer vision models offer a viable solution to the challenges of WAAM video segmentation.
  • The developed semi-automatic annotation tool significantly reduces the time and cost associated with creating annotated datasets.
  • This approach provides a pathway for broader adoption of deep learning in WAAM and welding process monitoring.