Al-driven automotive material selection and structural design for manufacturing

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The automotive industry and road transport contribute significantly to the UK's net greenhouse emissions, with one-third attributable to this sector. Lightweight vehicle design plays a crucial role in reducing CO2 emissions, necessitating the intelligent use of materials and advancements in vehicle component manufacturing technologies. Component stamping, representing approximately 11% of total vehicle cost, is a critical area for innovation.Accurate simulation and prediction of stamping-induced defects in the sheet metal stamping industry rely on material formability data. While simulations offer cost and time savings by utilising material formability data, their complexity and limited accessibility to designers create challenges. Integrating Artificial Intelligence (AI) can enhance accuracy by capturing material forming behaviours, but the scarcity of high-fidelity material testing data needed for training remains an obstacle.Multi-X has developed leading material formability testing solutions for reproducing real manufacturing conditions, such as hot stamping. These tests have enabled the curation of high-fidelity datasets of formability properties for structural materials used in various manufacturing processes and conditions. This project aims to leverage Multi-X's datasets to train and enhance the cutting-edge AI models developed by Dr Li and her Advanced Manufacturing Group at Imperial College London to extend their applicability to efficiently and accurately predicting vehicle component manufacturability under real conditions. By harnessing the high-fidelity data from Multi-X's testing, the AI models can ensure the optimal design of lightweight components within safe strain limits of metals thereby mitigating undesirable failures efficiently.This project will revolutionise vehicle manufacturing by introducing a game-changing, user-accessible design tool, offered through the Software-as-a-Service (SaaS) delivery mode. This tool will optimise complex-shaped component design, select lightweight materials, reduce development time, and lower costs. With accurate AI-based manufacturability evaluations replacing traditional simulations, the project will contribute to reducing CO2 emissions through the creation of efficient and lightweight component designs.