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Details of Grant 

EPSRC Reference: EP/X024288/1
Title: ATLAS: Assurance through layer-wise anomaly sensing
Principal Investigator: Hooper, Dr PA
Other Investigators:
Davies, Dr CM Huthwaite, Dr P
Researcher Co-Investigators:
Project Partners:
AWE Domin Fluid Power Ltd Osstec Ltd
Renishaw Rolls-Royce Plc (UK)
Department: Mechanical Engineering
Organisation: Imperial College London
Scheme: Standard Research
Starts: 01 May 2023 Ends: 30 April 2025 Value (£): 608,553
EPSRC Research Topic Classifications:
Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Nov 2022 Digital Manufacturing Full Announced
Summary on Grant Application Form
We will develop technology for the real-time detection of defects in metal additive manufacturing processes. We envision a future where every part made will come with a digital copy of itself containing a 3D map of defects. This will enable manufacturers to accelerate certification and quality assurance of high-integrity parts through virtual testing and also provide online feedback for the rapid optimisation of process parameters. This project addresses multiple digital manufacturing research challenges across data analytics, real-time optimisation, virtual testing, and model verification.

Additive manufacture (AM), also known as 3D printing, of metallic materials is transforming manufacturing supply chains across the energy, transport, healthcare, and defence sectors. It stimulates design innovation and through lighter, better performing and more reliable products, it can help us meet our future net zero and sustainability goals. However, use of AM parts in safety critical industries is limited by concerns around material property consistency. These concerns present a considerable challenge for quality assurance, slowing further adoption of AM processes and constraining much needed innovation.

We aim to solve this challenge using in-process sensing, where cameras and other sensor types observe the manufacturing process in real-time, in combination with data-driven machine learning models to predict when defects occur. To do this we will design and build part geometries representative of common industrial designs and collect in-processing monitoring data across several sensor modalities (i.e. co-axial melt pool imaging, surface temperature, melt track morphology sensor systems) from our unique in-process monitoring platform. The parts will then be micro-CT scanned post-build to establish porosity truth data, creating a suite of pristine, spatially registered, data sets. The builds will cover various industrially relevant manufacturing parameters and common machine issues such as dirty lens, clogged filter, contaminated powder, worn wiper blade, etc. These data sets will be used for the training and validation of data-driven machine learning models to predict part porosity. Robust non-destructive evaluation methodologies will be used to characterise model performance. We will then implement online layer-wise feedback to dynamically adjust processing parameters and repair defects through selective remelting. This approach will address fundamental challenges in model robustness, data reduction, real-time processing, optimisation, and feedback.

Ultimately, this project will enhance metal additive manufacturing part quality and enable accelerated virtual certification. Combined, these outputs will reduce the risk involved in developing innovative new products, removing a significant barrier to the widespread adoption of metal additive manufacturing technology.

Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.imperial.ac.uk