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Responsive Manufacturing of High Value Thin to Thick Films.

高价值薄膜到厚膜的响应制造。

基本信息

批准号：
EP/V051261/1
负责人：
Jonathan Howse
金额：
$ 258.15万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV051261%2F1
关键词：
Responsive Manufacturing Value Thin Thick

项目摘要

Thin films with a high technical specification are used in many everyday devices, including displays, solar cells, electronic devices, batteries, and sensors. Printing of the high-value flexible electronic films with insulating, dielectric, semiconducting and conducting materials used in these devices makes a major and rapidly growing contribution to UK industry.The thickness of the films required, the starting materials used and the final high-value functions desired in the finished product vary significantly. However, the scientific principles that govern the behaviour of the printing processes for these diverse applications have many similarities, because they are all formed by selectively spreading a wet film of suspended solid particles and drying it. At present the optimisation of the printing parameters for these films is commonly achieved through a trial and error process rather than systematic intelligent control. Individual processes are being optimised in isolation without cross-fertilization of knowledge. In a fast changing world, where disruption to supply chains or development of improved materials can change the process input materials, the need to reconfigure the formulations/printing parameters used increases. Furthermore, desired outputs can also change rapidly as the manufacturers and customers seek to meet changing demands of their market for example requiring more precise control of film parameters such as thickness and electrical properties. Adjusting to such continually moving goal posts by relying on trial and error testing is time-consuming, wasteful and costly. The responsive manufacturing technology we propose to develop will have sufficient flexibility to overcome such problems by utilizing intelligent machine learning to control the printing parameters in real-time and therefore maintain an optimized printing process robustly in the face of variations in feedstock materials and/or the required output. It is surprising that there has been no major attempt to implement this approach to process control and optimisation for solution printed materials. This is despite process monitoring and feedback-based optimisation being proven enabling methods in other sectors such as additive manufacturing.This will be achieved by developing control algorithms for the printing process that take into account our theoretical understanding of the processes occurring and utilizing high-speed (minimized and proxy) in situ data acquisition to respond autonomously and continuously to perturbations in the feedstock materials or required film properties. We will make use of the wide range of laboratory scale processing systems our project team regularly use for the production of model colloidal films, ceramic dielectrics, photovoltaics and battery electrodes to provide the datasets required to educate the machine learning algorithms, test our theoretical understanding, develop models of the printing processes and to ultimately test the autonomous control system that we develop. Having proven the system works at a laboratory scale we plan to perform a series of demonstration runs at industrial scale in collaboration with project partners CPI who are world leading experts in production of printed electronics. This will provide the evidence needed to prove that this approach can work at an industrial scale in a highly demanding production environment (printed electronics require a high degree of control of the surface chemistry between subsequent layers to perform correctly and are typically made in cleanroom/glove-boxes within strict environmental tolerances). We envisage a future where a deep theoretical understanding of the processes that are taking place is utilised by artificial intelligence to continuously control and optimise the manufacture of 21st century high-value printed films autonomously using the minimum number of high-speed measurements to achieve the desired results.

具有高技术规格的薄膜用于许多日常设备，包括显示器，太阳能电池，电子设备，电池和传感器。在这些设备中使用绝缘、介电、半导体和导电材料的高价值柔性电子薄膜的印刷对英国工业做出了重要且快速增长的贡献。所需薄膜的厚度、所用的起始材料和成品中所需的最终高价值功能差异很大。然而，控制这些不同应用的印刷工艺行为的科学原理有许多相似之处，因为它们都是通过选择性地铺展悬浮固体颗粒的湿膜并干燥而形成的。目前，这些膜的印刷参数的优化通常通过试验和错误过程而不是系统的智能控制来实现。个别过程正在孤立地进行优化，而没有知识的交叉施肥。在快速变化的世界中，供应链的中断或改进材料的开发可能会改变工艺输入材料，因此需要重新配置所使用的配方/印刷参数。此外，随着制造商和客户寻求满足其市场的变化需求，例如需要更精确地控制膜参数（例如厚度和电性能），期望的输出也会迅速变化。依靠试错测试来适应这种不断变化的目标是耗时、浪费和昂贵的。我们建议开发的响应式制造技术将具有足够的灵活性，通过利用智能机器学习实时控制打印参数来克服这些问题，从而在原料材料和/或所需输出变化的情况下稳健地保持优化的打印过程。令人惊讶的是，一直没有重大尝试实施这种方法来控制和优化溶液印刷材料。尽管过程监控和基于反馈的优化已被证明是增材制造等其他领域的可行方法，但这将通过开发印刷过程的控制算法来实现，该算法考虑到我们对发生过程的理论理解，并利用高速（最小化和代理）原位数据采集，以自主地和连续地响应原料材料或所需膜性质的扰动。我们将利用我们的项目团队经常用于生产模型胶体膜、陶瓷膜、光化学和电池电极的各种实验室规模的处理系统，以提供教育机器学习算法所需的数据集，测试我们的理论理解，开发打印过程模型，并最终测试我们开发的自主控制系统。在实验室规模上证明了该系统的可行性后，我们计划与项目合作伙伴CPI（印刷电子生产领域的世界领先专家）合作，在工业规模上进行一系列演示运行。这将提供所需的证据来证明这种方法可以在高要求的生产环境中以工业规模工作（印刷电子产品需要对后续层之间的表面化学进行高度控制以正确执行，并且通常在严格的环境公差内在洁净室/手套箱中制造）。我们设想的未来是，人工智能利用对正在发生的过程的深刻理论理解，使用最少数量的高速测量来自动持续控制和优化21世纪世纪高价值印刷薄膜的制造，以达到预期的结果。