Materials Processing Institute launches AI initiative
A £600,000 research collaboration aims to develop an artificial intelligence (AI) tool that is capable of creating greater efficiencies within the additive manufacturing (AM) sector.
The project is being led by the UK's Materials Processing Institute (MPI).
It aims to provide a versatile, commercial, predictive, material reuse management tool that will enable AM to expand by introducing greater cost efficiencies.
The project is a partnership with Additive Manufacturing Solutions Ltd and AMFG.
Known as SMART-APP, its aim is to enable world-class production of AM components using Laser Powder Bed Fusion through the introduction of smart predictive models for resource efficiency and waste reduction.
SMART-APP aims to predict the quality change of the powder after each process and proposes alternative process parameters on used powder, to extend its lifespan with a minimal or an in-specification impact on product quality.
One area of interest is the growth of metal AM, which is not yet cost-effective due to a development gap in the level of powder waste and length of processing time.
The research will feature materials characterisation and mechanical testing, investigating shelf life and the processability envelope of environmentally affected common stainless steel, titanium and superalloy base feedstock.
It will also examine methods of reclaiming the powders and the effect on the final product.
The resulting outputs will be fed into an advanced database linking powder input properties against AM part performance to provide a predictive tool that will be available for industry to use.
The project is funded by Innovate UK, part of UK Research and Innovation.
Nick Parry, Industrial Digitalisation Group Manager at the Teesside-based MPI, says, ‘SMART-APP is the next logical step to continue the work the Institute has already undertaken in powder characterisation. By developing an AI tool that can help AM users create faster and cheaper ways of maximising powder reuse, the AM industry, especially those needing to maximise the operational effectiveness of their machines.'