Development of advanced part recognition toolkit for automated additive manufacturing post-processing system

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Title
Development of advanced part recognition toolkit for automated additive manufacturing post-processing system

CoPED ID
90e26445-3b7f-4cff-b075-90dd5e4d0269

Status
Active

Funders

Value
No funds listed.

Start Date
Sept. 30, 2021

End Date
Sept. 29, 2025

Description

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Additive Manufacturing Technologies (AMT) is a Sheffield based manufacturer of smart post-processing systems for additive manufacturing (AM). During recent years, AMT has attracted multi-million-pound investment for the development of automatic surface smoothing, de-powdering and colouring machines. AMT's vision is to create a Digital Manufacturing System (DMS), involving smart metrology and AI controlled post-processing modules.

This project aims to develop geometry measurement and machine learning integration into AMT's DMS system. AMT require an analysis toolkit for parts at various states, including powdered, de-powdered, un-processed, post-processed, as well as various geometries and printing methods. This requires research of appropriate machine learning algorithms able to recognise and categorise the surfaces in real time while using limited computational power. Among the tasks for such machine learning is quality control of the geometry of AM part surfaces, involving internal and difficult-to-access areas.

The machine learning will steer the parameter controls for each module of the DMS. Surface processing databases will be generated for smart automated control of post-processing stages. The process will allow smoothing, colouring, functional coating or surface morphology manipulation by iterative self-learning, making it a game-changing technology for AM surface engineering and digital manufacturing. The process will enable a new generation of AM parts to be used in existing applications, e.g. battery/capacitor assemblies, satellite coatings, antimicrobial medical components and creation of new structural fibres.

Ali Ghandour STUDENT_PER

Subjects by relevance
  1. Machine learning
  2. Digital technology
  3. Automation
  4. Manufacturing
  5. Modelling (creation related to information)
  6. Production technology
  7. Geometry
  8. Metrology
  9. Algorithms

Extracted key phrases
  1. Surface processing database
  2. Appropriate machine learning algorithm able
  3. Smart post
  4. Automatic surface smoothing
  5. Processing system
  6. Machine learning integration
  7. Development
  8. Surface morphology manipulation
  9. DMS system
  10. Surface engineering
  11. Processing stage
  12. Recognition toolkit
  13. Colouring machine
  14. Additive manufacturing
  15. Additive Manufacturing Technologies

Related Pages

UKRI project entry

UK Project Locations