RESEARCH NEWS
Hybrid approach to titanium manufacturing
A new hybrid manufacturing method could boost capability in the UK and increase manufacturing efficiency for key aerospace components , which could in future be used by global manufacturers for aircraft production . Shropshire-based SME , SDE Technology ( a lightweight engineering business ), is part of the initiative to develop a new superplastic forming process , enabling the company to expand into the aerospace market .
The project forms part of the National Aerospace Technology Exploitation Programme ( NATEP ) and is supported by the Advanced Forming Research Centre ( AFRC ) within the National Manufacturing Institute Scotland ( NMIS ) Group , operated by the University of Strathclyde and part of the High Value Manufacturing Catapult ( HVMC ). Boeing and Timet UK are industry collaborators , supporting the project with technical and business activities .
Superplastic forming is a near net-shape manufacturing method for producing thin-sheet metallic components and is typically used to create complex-shaped titanium parts used by the aerospace sector .
Shortening the forming cycle
The new hybrid technique is estimated to shorten the forming cycle time by over 50 % and cut the manufacturing cost by as much as 25 %, when compared to traditional superplastic forming . Applying this process will enable SDE Technology to enter the aerospace market , strengthening the UK supply chain in high-value manufacturing of titanium parts . Manufacturing with this new approach uses innovative new tooling which enables a reduced process time .
Evgenia Yakushina , forming team lead at the Advanced Forming Research Centre , said : “ This work has the potential to unlock opportunities for manufacturers to offer improved , quicker methods of producing key parts for aircraft . So far , the research has demonstrated huge potential with important parallels between the new hybrid method and the traditional approach already evident .”
The AFRC team has previously investigated the hybrid technique , but the latest collaboration explores how it could be scaled up for the industrial needs associated with the aerospace sector . At the end of the 18-month research project , funded by the Aerospace
Technology Institute ( ATI ) programme through NATEP , the team aims to prove that complex-shaped titanium components can be manufactured to the same specification , tolerances and quality compared to traditional superplastic forming .
Further funding has also been secured to evaluate the carbon footprint of the new process , which could be cut significantly because of shortened heating and forming times , as well as using lower temperatures of around 800 ° C .
In addition , when exposed to high temperatures during superplastic forming , an oxide layer is formed on titanium components ( known as alpha case ) which requires powerful acids to remove . The new approach uses less heat and , therefore , also reduces the layer thickness and associated time spent to remove the oxide layer .
Richard Homden , CEO of SDE Technology , said : “ This project has huge potential for not only us as a business but also the whole aerospace sector . It is fantastic to be working collaboratively to explore new manufacturing techniques . Hot forming was not previously our area of expertise , but with the technical support and knowledge base from the AFRC we can see it becoming a core element of our business plans moving forward . We ’ re especially excited by the opportunity to become part of the supply chain for aircraft and provide Boeing with UK-manufactured components .” n
AI project for critical infrastructure security
Using artificial intelligence ( AI ) to support human decisions , increasing efficiency and security in the operation of critical infrastructure is the aim of the European Horizon Europe project AI4REALNET ( AI for REAL-World network operation ).
Led by the Portuguese research institute INESC TEC and involving the Department of Electronics , Information and Bioengineering and the Department of Management , Economics and Industrial Engineering of Politecnico di Milano and other partners from France , Germany , the Netherlands , Switzerland , Sweden and Austria , the project promotes collaboration between humans and artificial intelligence . The goal is to support the decisions of human operators and to create the conditions for the decarbonisation of crucial sectors such as energy and transport .
“ It is not about replacing humans with AI , but rather ensuring that AI emerges
as a support for faster decisions and even planning specific tasks autonomously . The project aims to reduce the operators ’ workload in areas where human intervention prevails , offering opportunities to address critical infrastructure challenges . AI4REALNET developments will be validated in six industrial use cases , demonstrating tangible added value ,” explained the consortium partners .
“ The ultimate goal ,” explained Prof . Marcello Restelli , project coordinator for Politecnico di Milano , “ is to improve the security and resilience of critical infrastructure , which is becoming increasingly complex due to the increase in information and the challenges imposed by decarbonisation . The AI4REALNET consortium believes that AI can increase operational effectiveness and reduce errors .”
Industry involvement will promote awareness of the benefits of reinforcement learning and explainable machine learning . The project will also use open-source AI-friendly digital environments such as Grid2Op , Flatland and BlueSky to foster and advance a global artificial intelligence community .
The project , which is funded with around four million euros by the European Union through the Horizon Europe programme and two million euros by the Swiss State Secretariat for Education , Research and Innovation ( SERI ), emphasises international collaboration to tackle crucial societal challenges .
The project ’ s core elements are AI algorithms mainly composed by supervised and reinforcement learning ; human-in-theloop decision-making for co-learning between AI and humans ; and Autonomous AI systems relying on human supervision . n
https :// ai4realnet . eu /
16 | ismr . net | ISMR June 2024