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Smart asset assessment for life-extension engineering using artificially intelligent feature recognition

  • Full or part time
  • Application Deadline
    Monday, September 30, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

This application is for a 3.5 year PhD to research into the area of reverse engineering. This PhD will look specifically at the reverse engineering of end of life assets, such as subsea components for oil and gas etc.. There exists a number of technologies for digitising components such as these through laser scanning, 3D image reconstruction or structured light systems. These systems create a highly accurate point cloud in 3 dimensions that can then be used to take measurements. It will be the focus of this project to take such a dataset and reverse engineer from the point cloud a usable CAD type dataset and then model in a format compatible with CAD/CAM software. This will include automatically recognising surfaces, edges, radii and other physical features and creating nominal definitions of each feature.

This process is currently very labour intensive therefore this PhD will seek to investigate artificial intelligence techniques by adapting and customising them as much as possible to reduce the human intervention requirement as low as possible with a target of zero. The impact of this PhD will be far reaching with the oil and gas sector having many subsea assets, with no digital definition, needing replaced or remanufactured. If successful, this research will make the broad theme of remanufacturing more attainable and in line with the effort level of new manufacture.

The primary objective of the PhD will be to 3D scan a component, retrieve the resulting point cloud in its raw format and have an artificial intelligence robot convert the point cloud into a 3D model of a standard expected of an as designed geometry.

The work content of this studentship will be in the following areas:
• Literature review of current state of the art in reverse engineering software
• Point cloud dataset manipulation and acquisition
• Analysis of the knowledge required to convert point cloud data into a usable CAD model
• AI algorithm development for key feature recognition
• Automation of software based conversion of point cloud into CAD format
• Analysis of the consistency of this conversion/AI decision making

Funding Notes

Home/EU funded only

References

2 written references to be provided

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