Sustainable digital frameworks to support deployment of autonomous analytics in knowledge intensive manufacturing environments" and its in partnership

支持在知识密集型制造环境中部署自主分析的可持续数字框架”及其合作伙伴关系

• 批准号: 2280697
• 金额: --
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2280697
• 关键词: Sustainable; digital; frameworks; support; deployment

The key competitive driver for the PhD is to study Industry 4.0 total connectivity concepts within a specific, knowledge intense industrial background. Industry 4.0 is the fourth industrial revolution that strives for smart manufacturing and autonomy. As with all new innovations, there are a lot of underlying unanswered questions to this shift in paradigm which this research aims to examine and address. On the technological side this will include Cyber Physical Systems, Industrial Internet of Things, Big Data & Monitoring, Machine Learning and others. This research will provide scientific underpinnings for Industry 4.0 approaches to knowledge intense industrial applications. This PhD is in partnership with Tata Steel. The underlying research will focus on developing a system model for the Cold Rolling Mills in their Port Talbot plant to support the Industry 4.0 standard. The Cold Rolling Mills is the process of condensing steel into thin, ductile coils. Currently, there is heavy automation, but decisions are made from human interaction instead of any form of autonomy. The key objective will be researching into the creation of a digital framework to model the total connectivity concepts that Industry 4.0 requires to enable such autonomy. To achieve our goals, we will use a model-driven approach to create such a digital framework. Model-driven approaches are known to be essential in achieving a level of abstraction that allows to deal with the complexity of the tasks ahead in an efficient way. Our work will include exploring the use of digital twins and digital threads to create realistic simulations that will use real time data to accurately replicate the Cold Rolling Mill system digitally. This will allow us to commit changes to the digital form without any impact on the physical form. For example, we may simulate the increase pressure levels on the rolls to see the impact it has without any risks of the actual physical assets being damaged. Additionally, the rolls in the Cold Rolling Mills suffer massively from wear and must be refurbished often. Using the data collected from Tata Steel and our modelling techniques, this research also aims to optimize and increase efficiency of Tata's roll stock.

对于博士来说，关键的竞争驱动力是在一个特定的、知识密集型的工业背景下研究工业4.0的整体连接概念。工业4.0是追求智能制造和自主的第四次工业革命。与所有新的创新一样，对于这种范式的转变，有许多潜在的未解决的问题，本研究旨在研究和解决这些问题。在技术方面，这将包括网络物理系统、工业物联网、大数据和监控、机器学习等。这项研究将为工业4.0方法提供科学基础，以实现知识密集型工业应用。这位博士是与塔塔钢铁公司合作的。基础研究将侧重于为塔尔伯特港工厂的冷轧机开发系统模型，以支持工业4.0标准。冷轧机是把钢压缩成薄而有韧性的卷的过程。目前，自动化程度很高，但决策是通过人类互动做出的，而不是任何形式的自主。关键目标将是研究创建一个数字框架，以模拟工业4.0所需的整体连接概念，以实现这种自主性。为了实现我们的目标，我们将使用模型驱动的方法来创建这样一个数字框架。众所周知，模型驱动的方法对于实现抽象级别至关重要，抽象级别允许以有效的方式处理前面任务的复杂性。我们的工作将包括探索使用数字双胞胎和数字线程来创建逼真的模拟，将使用实时数据来精确地复制冷轧机系统的数字。这将使我们能够在不影响物理形式的情况下对数字形式进行更改。例如，我们可以模拟轧辊上压力水平的增加，以查看它所产生的影响，而不存在任何实际实物资产损坏的风险。此外，冷轧机中的轧辊磨损严重，必须经常翻新。利用从塔塔钢铁公司收集的数据和我们的建模技术，本研究还旨在优化和提高塔塔机车车辆的效率。