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Boeing to accelerate additive manufacturing for aerospace using machine learning

Boeing is taking part in a collaborative research project that will use machine learning to reduce the cost and increase the speed of metal additive manufacturing (AM) for aerospace.

In doing this the partners of project MEDAL (Machine Learning for Additive Manufacturing Experimental Design) will accelerate the product development lifecycle of aerospace components, while at the same time encouraging the production of lightweight, energy-efficient aircraft to support net-zero aviation targets.

In addition to Boeing, project parters include AI firm Intellegens and the University of Sheffield’s Additive Manufacturing Research Centre (AMRC).

Intellegens, a University of Cambridge spin-out and completer of the ATI Boeing Accelerator, will lead the project.

Lengthy and expensive certification cycles

Aerospace components have to withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace material mix. 

One of the main drawbacks of AM is the limited material selection currently available. In addition, the design of new materials – particularly in the aerospace industry – requires expensive and extensive testing and certification cycles, which can take longer than a year to complete and cost as much as £1 million. 

MEDAL aims to accelerate this process using machine learning to rapidly optimise laser powder bed fusion processing parameters for new metal alloys, making the development process more time and cost-efficient. 

Optimisation through machine learning

Experimental design techniques are extremely important to develop new products and processes in a cost-effective and confident manner. The most common approach is Design of Experiments (DOE), a statistical method that builds a mathematical model of a system by simultaneously investigating the effects of various factors.

‘DOE is a more efficient, systematic way of choosing and carrying out experiments compared to the Change One Separate variable at a Time (COST) approach,’ said Ben Pellegrini, CEO of Intellegens. ‘However, the high number of experiments required to obtain a reliable covering of the search space means that DOE can still be a lengthy and costly process, which can be improved.’

The MEDAL machine learning solution will significantly reduce the need for many experimental cycles by around 80 per cent, according to Pellegrini.

‘The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties,’ he explained. ‘The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircraft and improved environmental impact.’

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Intellegens will produce a software platform for MEDAL with an underlying machine learning algorithm based on its Alchemite platform. Alchemite was already used successfully to overcome material design problems in a University of Cambridge research project with a leading OEM, where a new alloy was designed, developed, and verified in 18 months rather than the expected 20-year timeline, saving about $10m.

While the new method is being developed with aerospace in mind, the team believes it will have applications for other sectors too: ‘Research findings from this project and the project output will have applications for other sectors including automotive, space, construction, oil and gas, offshore renewables, and agriculture,’ confirmed Ian Brooks, AM technical fellow at the AMRC.

MEDAL is part of the National Aerospace Technology Exploitation Programme, a £10 million initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy, and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK.

Sir Martin Donnelly, president of Boeing Europe and managing director of Boeing in the UK and Ireland, said the project shows how industry can successfully partner with government and academia to spur UK innovation.

'We are proud to see this project move forward because of what it promises aviation and manufacturing, and because of what it represents for the UK’s innovation ecosystem,' he said. 'We helped found the AMRC two decades ago, Intellegens was one of the companies we invested in as part of the ATI Boeing Accelerator and we have longstanding research partnerships with Cambridge University and the University of Sheffield. We are excited to see what comes from this continued collaboration and how we might replicate this formula in other ways within the UK and beyond.'

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