Machine learning technology will be used to make the additive manufacturing (AM) process of metallic alloys for aerospace cheaper and faster, encouraging production of lightweight, energy-efficient aircraft to support net zero targets for aviation.
Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design is led by Intellegens, a
AM is a group of technologies that create 3D objects from computer aided design (CAD) data. AM techniques reduce material waste and energy usage; allow easy prototyping, optimising and improvement of components; and enable the manufacture of components with superior engineering performance over their lifecycle. The global AM market is worth £12bn and that is expected to triple in size over the next five years. Project MEDAL's research will concentrate on metal laser powder bed fusion - the most widely used AM approach in industry - focussing on key parameter variables required to manufacture high density, high strength parts.
The project is part of the National Aerospace Technology Exploitation Programme (NATEP), a £10 million initiative for
"At the AMRC we have experienced first-hand, and through our partner network, how onerous it is to develop a robust set of process parameters for AM. It relies on a multi-disciplinary team of engineers and scientists and comes at great expense in both time and capital equipment," said Hughes. "It is our intention to develop a robust, end-to-end methodology for process parameter development that encompasses how we operate our machinery right through to how we generate response variables quickly and efficiently. Intellegens' AI-embedded platform Alchemite will be at the heart of all of this.
"There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them. With the AMRC's knowledge in AM, and Intellegens' AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster."
Sir
"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
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 and 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 to undertake. Project MEDAL aims to accelerate this process, using Machine Learning (ML) to rapidly optimise AM processing parameters for new metal alloys, making the development process more time and cost efficient.
Pellegrini said 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.
"
"The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80%. 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. 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 aircrafts and improved environmental impact."
Intellegens will produce a software platform with an underlying machine learning algorithm based on its Alchemite platform. It has already been used successfully to overcome material design problems in a
"It targets future integrated structures by accelerating development of new metal alloys and optimising an AM process to create lightweight components; its key driver is to protect the environment by reducing material usage and waste; and it looks to minimise fuel consumption through lightweighting of components for flight controls and potentially landing gear systems," said Brooks.
While this new method is being developed with aerospace in mind, the team believes it will have applications for other sectors too. Brooks said: "The opportunity for this project is to provide end users with a validated, economically viable method of developing their own powder and parameter combinations. 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."
Ends
Notes to editors
Photos:
IMG 1: Close up image of laser melting titanium powder. Picture: ©
IMG 2:
Further images here .
AMRC media contact:
Intellegens media contact:
About the AMRC
www.amrc.co.uk
About Intellegens
Our mission is to help clients accelerate innovation by using our unique deep learning solutions to extract valuable information from existing processes and data. Our technology originated from the work of Dr Gareth Conduit and collaborators at the
.
(C) 2021 M2 COMMUNICATIONS, source