Elastic Manufacturing Analytics: Improving aerospace assembly processes in an evolvable cell through adaptive data analytics and machine learning

弹性制造分析：通过自适应数据分析和机器学习改进可进化单元中的航空航天装配流程

• 批准号: 2775864
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2775864
• 关键词: Elastic; Manufacturing; Analytics; Improving; aerospace

Reconfigurable cells promise to deliver a new generation of manufacturing systems. These cells are designed to be adaptable in changing environments. Utilising data to make decisions, the cells will incrementally reconfigure to be better suited to production demands. However, it is not understood how these complex environments can be supported with current data analytical systems.As assets change, we may acquire new types of data as new sensors are added. Aggregating this with legacy data may be invalid as the datasets could vary in quality, quantity, or type. These datasets are used to train machine learning models. Unprocessed datasets may cause undesired outputs or overfitting. These outputs are essential to make appropriate decisions of reconfiguration.The goal of this research is twofold. Firstly, it is to identify the relevant type of data to obtain. Secondly, it is to develop data systems that make use of adaptive data analytics and machine learning methods for reconfigurable cells in complex environments.This work will deliver the foundations for future manufacturing systems ability to learn and improve reconfiguration and resilience over time. Particularly, it will become a pillar of the National Demonstrator Omnifactory, a unique reconfigurable assembly facility that aims to serve as a national test bed for future projects.

可重构单元有望提供新一代的制造系统。这些细胞被设计成能够适应不断变化的环境。利用数据做出决策，这些单元将逐步重新配置，以更好地适应生产需求。然而，目前还不清楚如何使用现有的数据分析系统来支持这些复杂的环境。随着资产的变化，我们可能会随着新传感器的添加而获得新类型的数据。将其与遗留数据汇总可能无效，因为数据集的质量、数量或类型可能会有所不同。这些数据集用于训练机器学习模型。未经处理的数据集可能会导致不期望的输出或过拟合。这些输出对于做出适当的重新配置决策至关重要。本研究的目标是双重的。首先，它是识别要获取的相关数据类型。第二，开发数据系统，利用自适应数据分析和机器学习方法，在复杂环境中可重构单元。这项工作将为未来制造系统的学习能力奠定基础，并随着时间的推移提高重构和弹性。特别是，它将成为国家演示器Omnifactory的支柱，这是一个独特的可重新配置的装配设施，旨在作为未来项目的国家试验台。