Forming Ahead with Deep Learning for Composites Manufacturing

非常需要允许在快速周转时间内试验形成航空航天几何形状的速率使能解决方案。这与最近的未来复合材料制造研究中心的活动是一致的，该中心探索了干纤维织物成形技术，旨在提高生产率，产量和零件质量。数字双胞胎的引入在智能优化这些复合材料设计和制造过程方面显示出了希望。为了利用这些技术，需要解决一个重要的分析挑战：处理捕获的车间数据质量的变化。例如，可变的照明条件可能使基于计算机视觉的分析复杂化。为了提高鲁棒性，需要获得具有代表性的工厂条件的实验数据，并使用计算机视觉模型进行训练。为此，该项目的目的是开发数字线程要求：（a）允许快速执行仪表化测试台试验，（B）跟踪和更新数字孪生中的变化，以及（c）提供针对规范的自动缺陷分析。这些贡献将通过减少验证形成响应所需的物理试验的数量来提供益处-从而节省成本并降低材料报废率。目标：1.研究传感解决方案，以在代表工厂环境的条件下捕获预成型缺陷。2.探索深度学习技术以分析成形试验数据。3.制定数字线程要求并理解数据处理流程。方法：本研究的新奇在于自动分析捕获的数据，以跟踪成型过程中预制件的变化。这需要复杂飞机结构的成型和灌注试验的车间数据，如照片。为了说明缺陷捕获时工厂环境的变化，引入了不同的代表性照明配置。还进行了控制参数和材料响应的获取。累积的传感器数据在被馈送到数字孪生之前用于构建训练集。宏观缺陷，如面内波纹和面外波纹，使用基于计算机视觉的方法自动表征。这使得特许权数据库能够自动填充，并根据工程规范对预制件进行验证。

Rate-enabling solutions that allow the trialling of forming aerospace geometries in a rapid turnaround time are highly desirable. This is consistent with recent Future Composites Manufacturing Research Hub activities which explored dry-fibre fabric forming technologies, with the aim of improving production rates, volumes, and part quality. The introduction of digital twins has shown promise in intelligently optimising these composite design and manufacturing processes. To leverage these technologies, a significant analysis challenge needs to be addressed: handling the variability in quality of captured shopfloor data. For instance, variable lighting conditions might complicate a computer-vision based analysis. To improve the robustness, experimental data with representative factory conditions needs to be obtained and trained with computer vision models. To this end, the aim of this project is to develop the digital thread requirements that: (a) allows instrumented testbed trials to be quickly performed, (b) tracks and updates changes in a digital twin, and (c) provides automatic analysis of defects against specification. These contributions would provide benefit by lessening the number of physical trials needed to characterise forming responses - resulting in cost-savings and reduced material scrap rates.Objectives: 1. Investigate sensing solutions to capture preforming defects in conditions representative of a factory environment. 2. Explore deep learning techniques to analyse forming trial data. 3. Develop the digital thread requirements and generate understanding of the data process flow. Method: The novelty of this research is in the automation of analyses of captured data to track changes of preforms during forming processes. This requires shopfloor data, such as photographs, from forming and infusion trials of complex aerostructures. To account for variation in factory environment on defect capture, different representative lighting configurations are introduced. Acquisition of the control parameters and material response is also carried out. The accumulated sensor data is used to construct training sets before being fed into the digital twin. Macroscale defects, such as in-plane waviness and out-of-plane wrinkling, are automatically characterised using computer-vision based methods. This enables the concessions database to be automatically populated and the preform to be verified against the engineering specifications.