Global research project update

ISMR outlines a selection of the latest global research projects for metal forming and fabrication.

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Lightweight future vehicle chassis architecture project 

Gordon Murray Group (GMG) has announced a new consortium project that is working to create a new generation of ultra-lightweight, environmentally friendly vehicle structures. In just three years, the project aspires to create a new vehicle monocoque architecture that is lighter and stronger, alongside 50% less carbon intensive. 

‘Project M-LightEn’ (Monocoque architecture – Lightweight and Low Energy) has funding support from Innovate UK and the Advanced Propulsion Centre (APC). The venture is projected to create as many as 160 new jobs across the Gordon Murray Group, and partners Carbon ThreeSixty, Constellium and Brunel University (London, UK).

Gordon Murray Group will lead the project in its mission to research, design, build and validate a series of digital and physical monocoque prototypes. The target result is the validation of several new solutions paving the way for the development and industrialisation of innovative monocoque structures for a portfolio of new vehicles. 

“With a goal of achieving even greater performance through further weight reduction, the process could enable future Gordon Murray Automotive (GMA) vehicles to achieve the lowest lifecycle carbon footprint of any supercar,” GMG explained.

GMG’s Strategy and Business Director, Jean-Phillipe Launberg, said: “Alongside Gordon Murray Automotive’s niche supercar application, Project M-LightEn will enable decarbonisation across the wider automotive industry by shortening and de-risking the path to market for innovative new materials and processes.” 

Targeting a reduction in vehicle lifecycle CO2 by a third or more, the consortium will use AI to optimise designs, while also developing new materials and advanced manufacturing processes. Constellium and Brunel aim to provide STEP-enhanced, ultra-high-strength extrusions made from 80% recycled UK consumer scrap aluminium within the monocoque structure. While production of lightweight carbon fibre composite components by Carbon ThreeSixty will achieve near-zero-waste levels in manufacturing and low weight through the highly precise ‘tailored-fibre-placement’ production process. 

Prof Geoff Scamans, Professor of Metallurgy at Brunel University of London, said: “This project represents an excellent opportunity to exploit the high-strength extrusion aluminium alloy technology developed in the EPSRC strain-enhanced precipitation in aluminium (STEP Al) programme, funded as an EPSRC Prosperity Partnership between Constellium and Brunel. The M-LightEn project will use the highest performing aluminium extrusion alloys formulated from recycled end-of-life aluminium using novel thermomechanical processing techniques developed in this five-year programme.” 

The first phase of the project is already underway, exploring new materials and ‘joining’ techniques. From late 2027, developments from M-LightEn are projected to be available for low-volume commercial use, with larger, mainstream applications rolled out thereafter.

Thermal spraying research

Thermal spraying is an advanced manufacturing method in which material in powder or wire form is melted and sprayed onto a surface to create a coating with specific properties. Alleima, a global manufacturer of advanced stainless steel and specialty alloys, industrial heaters, is now investing in this technology to develop new products in sustainable energy. For example, the technology can be used in the production of components for green hydrogen through to electrolysers. The first prototype of the coated material, it confirmed, has already been sent to a customer.

Thermal spraying enables coatings with specific properties that are crucial for improving products (such as electrolyses for the production of green hydrogen). Green hydrogen, produced by the electrolysis of water using renewable energy, plays a central role in the transition to a carbon-free economy. Thermal spraying enables the development and industrial production of key components in an electrolysis stack.

 “This investment is part of the company's long-term strategy to drive innovation and create sustainable solutions. It is a pilot that will primarily be used for research purposes but will also be used for small-scale production when possible. By using this technology in our manufacturing processes, we can offer advanced materials and components that meet the high demands of hydrogen production. This initially includes the development of components for electrolyser cells, but also other applications that require robust and durable coatings,” said Tom Eriksson, Head of Strategic Research at Alleima.

 The technology will eventually make it possible to develop products that replace expensive material solutions. This will help to reduce the cost of electrolyser stacks, which in turn can promote the increased use of green hydrogen and thus reduce carbon emissions. 

AI for 3D metal printing 

Researchers at the University of Toronto in Canada are leveraging machine learning to improve additive manufacturing (or 3D printing). Led by Yu Zou, a professor of materials and engineering in the Faculty of Applied Science & Engineering, the research team has developed a new framework dubbed the Accurate Inverse process optimisation framework in laser Directed Energy Deposition (AIDED). 

By predicting how the metal will melt and solidify to find optimal printing conditions, the new AIDED framework, detailed in a paper published in ‘Additive Manufacturing’, “enhances the accuracy and robustness of the finished product.” The researchers say that the approach can be used to produce higher quality metal parts for industries that include aerospace, automotive, nuclear and healthcare.

“The wider adoption of directed energy deposition, a major metal 3D printing technology, is currently hindered by the high cost of finding optimal process parameters through trial and error,” said PhD candidate Xiao Shang, first author of the new study. “Our framework quickly identifies the optimal process parameters for various applications based on industry needs.” 

Metal additive manufacturing uses a high-powered laser to selectively fuse fine metallic powder, building parts layer by layer from a precise 3D digital model. Metal additive manufacturing creates complex, highly customised components with minimal material waste. 

AIDED operates in a closed-loop system where a genetic algorithm (a method that mimics natural selection to find optimal solutions) first suggests combinations of process parameters, which machine-learning models then evaluate for printing quality. The genetic algorithm checks these predictions to make sure they are optimal, repeating the process until the best parameters are found. 

“We have demonstrated that our framework can identify optimal process parameters from customisable objectives in as little as one hour, and it accurately predicts geometries from process parameters,” confirmed Shang. “It is also versatile and can be used with various materials.” 

To develop the framework, the researchers conducted numerous experiments to collect their vast datasets. The team is now working to develop an enhanced autonomous, or ‘self-driving’, additive manufacturing system that operates with minimal human intervention.

“By combining cutting-edge additive manufacturing methods with artificial intelligence, we aim to create a novel closed loop controlled self-driving laser system.  This system will be capable of sensing potential defects in real-time, predicting issues before they occur and automatically adjusting processing parameters to ensure high-quality production. It will be versatile enough to work with different materials and part geometries, making it a game-changer for manufacturing industries,” said Yu Zou.

In the meantime, the researchers hope AIDED will transform process optimisation in industries that currently use metal 3D printing. 

Decreasing use of raw materials in production

As part of a Fraunhofer flagship project, researchers are developing a digital ecosystem that collects data along the entire value chain for raw materials with the goal of ensuring a sustainable and resilient supply. This makes it possible to reuse and recycle materials energy-efficiently and with as little loss as possible, the German research specialists told ISMR. At the HANNOVER MESSE 2025, the research team presented a demonstrator that showcases the different options offered by this ecosystem.

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