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

到2027年，全球金属冲压市场预计将达到2780亿美元的规模，其中汽车行业占总数的35.5%。热成型汽车部件越来越多地用于车辆中，以实现车身/底盘结构的轻量化并减少二氧化碳排放。在板料成形中，准确预测和预防成形缺陷对于确保高质量、轻质结构件的成功成形至关重要。然而，由于缺乏高温下材料的可成形性数据来量化复杂的应变状态，阻碍了热成形工艺模拟的准确性和效率，从而导致原型和生产中的高能耗和成本。Multi-X Solutions Limited是帝国理工学院的一个分支，也是皇家工程学院企业中心的终身成员，提供具有成本效益的材料测试设备和服务，使用发明的仪器，方法和开发的AI（人工智能）模型用于汽车应用，实现在现实生产条件下对材料成形性能和性能的新型定量测量和评估。Multi-X旨在利用我们已建立的高保真数据集，进一步开发和训练基于铝的深度学习平台/软件，用于汽车零部件冲压工艺。为了解决应变过程中与温度分布映射和监控相关的挑战，Multi-X将利用国家物理实验室（NPL）的成像荧光温度计技术实现非接触式温度测量。该项目的成功实施将进一步提高Multi-X领先的材料成型性测试技术的质量保证，并生成高保真数据集，用于培训汽车、飞机、汽车、汽车、和其他公共交通行业。最终目标是推广开发的测试方法，以促进数据驱动的制造业的发展。该项目的成功将通过为基于计算机的模拟和铝模型提供真实数据，实现热成形工艺的精确可成形性测量，从而推进产品设计并优化制造工艺。它还将大大减少从产品设计到原型制作的开发时间、试错和成本，同时提高热成型的生产率。此外，该项目旨在充分利用材料的可成形性，以最大限度地提高部件的复杂性，减轻车辆重量，并直接促进二氧化碳减排。

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.