Altair has joined forces with Aston Martin, Sarginsons Industries, and other key partners in the £5.8 million Performance Integrated Vehicle Optimisation Technology (PIVOT) project. Backed by Innovate UK and the Advanced Propulsion Centre (APC), the initiative seeks to revolutionise vehicle manufacturing by creating lightweight, environmentally friendly components.
The PIVOT project focuses on utilising 100% recycled aluminium to produce cast vehicle structures that are up to 30% lighter and significantly reduce carbon emissions. Altair’s advanced artificial intelligence (AI) and simulation technologies will be central to this innovation, enabling the development of components optimised for weight, strength, and manufacturability.
Sam Mahalingam, Chief Technical Officer at Altair, stated “Our AI-powered engineering technology is at the forefront of developing efficient vehicle components. This partnership is a prime example of how technology can drive sustainable advancements in automotive manufacturing.”
The project, which also includes Brunel University London and metal recycling experts GESCRAP, aims to reduce the embedded carbon emissions in vehicle components by up to 95%. Using advanced grain refinement techniques, the team will overcome the challenges associated with recycled aluminium, allowing it to be used in critical structural parts, such as vehicle chassis systems.
Mark Nunan, Managing Director of Sarginsons Industries, added “PIVOT represents a major leap forward for the casting industry. With the help of Altair’s cutting-edge technology, we are developing components that are not only lighter and stronger but also much greener.”
Funded in part by a £2.9 million grant from Innovate UK and APC, the PIVOT project will demonstrate how AI, recycled materials, and advanced casting methods can reshape the future of vehicle manufacturing. The collaboration is expected to deliver key advancements, helping Original Equipment Manufacturers (OEMs) and foundries transition to more sustainable and efficient production processes.
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