Al-driven automotive material selection and structural design for manufacturing

汽车工业和道路运输对英国的净温室气体排放贡献很大，其中三分之一可归因于该部门。轻量化车辆设计在减少二氧化碳排放方面起着至关重要的作用，需要智能使用材料和汽车零部件制造技术的进步。零部件冲压约占整车总成本的11%，是创新的关键领域。板材冲压工业中冲压缺陷的精确模拟和预测依赖于材料的成形性数据。虽然模拟通过利用材料成形性数据节省了成本和时间，但它们的复杂性和对设计师的有限可访问性给设计师带来了挑战。集成人工智能（AI）可以通过捕获材料成型行为来提高准确性，但训练所需的高保真材料测试数据的缺乏仍然是一个障碍。Multi-X开发了领先的材料成形性测试解决方案，用于再现真实的制造条件，如热冲压。这些测试使得在各种制造工艺和条件下使用的结构材料的可成形性特性的高保真数据集得以管理。该项目旨在利用Multi-X的数据集来训练和增强由李博士和她在伦敦帝国理工学院的先进制造小组开发的尖端人工智能模型，以扩展其适用性，以便在实际条件下有效和准确地预测汽车零部件的可制造性。通过利用Multi-X测试的高保真数据，AI模型可以确保在金属的安全应变范围内实现轻量化部件的最佳设计，从而有效地减少不必要的故障。该项目将通过软件即服务（SaaS）交付模式引入一种改变游戏规则的、用户可访问的设计工具，从而彻底改变汽车制造业。该工具将优化复杂形状的部件设计，选择轻质材料，缩短开发时间，降低成本。通过基于人工智能的精确可制造性评估取代传统的模拟，该项目将通过创建高效、轻量化的组件设计来减少二氧化碳排放。

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.