Dynamic temperature measurement and real-time monitoring for characterising material formability during straining

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The global metal stamping market is projected to reach a size of $278Bn by 2027, with the automotive sector accounting for 35.5% of the total. Hot formed car components are increasingly utilised in vehicles to achieve lightweight car body/chassis structures and reduce CO2 emissions. In sheet metal forming, the accurate prediction and prevention of forming-induced defects are crucial to ensure successful forming of high-quality, lightweight structural components. However, the accuracy and efficiency of process simulation for hot forming are hindered by the lack of material formability data at elevated temperatures to quantify complex straining states, resulting in high energy consumption and costs in prototyping and production.Multi-X Solutions Limited, a spin-out of Imperial College and a lifetime member of the Enterprise Hub at the Royal Academy of Engineering, provides cost-effective material testing equipment and services using invented instrumentation, methods, and developed AI (Artificial Intelligence) models for automotive applications, enabling novel quantitative measurement and evaluation of material formability properties and performance under real-life manufacturing conditions. Multi-X aims to further develop and train the Al-based deep learning platform/software for car component stamping processes using our established high-fidelity datasets.To address the challenges related to temperature distribution mapping and monitoring during the straining process, Multi-X will implement contactless temperature measurement using imaging phosphor thermometer technology from the National Physical Laboratory (NPL). This implementation will significantly enhance the value propositions of Multi-X's flagship testing service and AI tools for hot forming lightweight car components.A successful project outcome will further improve the quality assurance of Multi-X's leading material formability testing technology and generate high-fidelity datasets used for training Al tools across a wide range of sectors, including automotive, aircraft, and other public transportation industries.The ultimately goal is to standardise the developed testing method for the advancement of the data-driven manufacturing industry. The success of this project will enable accurate formability measurement for hot forming processes by providing real-life data for computer-based simulation and Al models, thereby advancing product designs and optimising manufacture processes. It will also lead to a significant reduction in development time, trial and error, and costs from product design to prototyping, while increasing productivity in hot forming. Moreover, the project aims to fully exploit material formability properties to maximise component complexity, reduce vehicle weight, and directly contribute to CO2 emissions reduction.