Intelligent Robotic Inspection for Foundation Industry Optimisation demonstration - IRIFIO:D2

这一合作的跨行业研发示范项目进一步推进了以前的行业研究，以促进和展示为支持基础行业生产过程优化转型而开发的新技术。主要目标是提高效率，通过提高能源和资源效率来实现更高的生产率。这将通过使用先进的机器人技术与3D机器视觉系统相集成来实现，这些系统增加了定制的传感器，创造了一个数据丰富的环境。机器人、视觉和传感技术将被应用并展示，在以前研发的基础上建立工业生产流程，以数字方式检测金属、玻璃和陶瓷中的缺陷。随着机器学习(ML)对收集的数据的额外利用，开发的先进人工智能(AI)可以开始增强这些传统的基础工业生产过程，以实现更高的工业生产率，同时显著降低玻璃、金属和陶瓷制造中的能源消耗和二氧化碳排放。目前的制造方法缺乏灵活性，通常需要对生产任务进行时间密集型的预编程或人工干预，以响应意外事件或生产错误。这意味着，基础工业无法响应未来环境目标的要求，在生产方法更新之前，无法在制造过程中进行进一步改进。解决这一问题至关重要；成功将使英国制造业在面临日益激烈的全球竞争时保持竞争力，因为在全球竞争中，劳动率和排放法规明显较低。该项目旨在使用先进的3D视觉传感器数据来产生ML和AI算法，以监控和改进金属、玻璃和陶瓷生产过程。为了保证测量的重复性和准确性，将演示通过现代多轴机器人系统提供的灵活性实现自动化。该系统的最终输出将在玻璃、陶瓷和金属生产方面为整个基础行业带来好处。该项目将通过提供数字化检测和智能机器学习带来的增强的现有制造过程来满足这些基础行业的特定需求。预计与制造钢化玻璃和窑烧陶瓷材料相关的能源成本的降低和产量的提高将产生重大和积极的影响，就像铸造铸件行业的情况一样。

This collaborative, cross sector R&D demonstration project furthers previous industrial research to advance & showcase novel technology developed to support transformation of Foundation Industry production process optimisation. The primary aim is to increase efficiency to achieve greater productivity by increased energy and resource efficiency. This will be achieved by using advanced robotics integrated with 3D machine vision systems which are augmented with bespoke sensors creating a data rich environment.The robotic, vision and sensory technology will be applied and demonstrated with foundation industry production processes building on previous R&D to digitally inspect defects in metals, glass and ceramics. With additional utilisation of machine learning (ML) on data collected, the advacned artificial intelligence (AI) developed can begin to enhance these traditional Foundation Industry production processes to enabling greater industrial productivity whilst significantly reducing energy consumption and CO2 emissions in both glass, metals, and ceramic manufacturing.Current manufacturing methods are inflexible, often requiring the time-intensive pre-programming or manual intervention of production tasks responding to unexpected occurrences or production errors. This means that foundation industries are unable to respond to the demands of future environmental targets and cannot make further improvements within the manufacturing process until the production methods are updated. This is critical to address; success will allow UK manufacturing to remain competitive when facing increasing global competition where labour rates and emissions regulations are significantly lower.This project aims to use advanced 3D vision sensor data to produce ML and AI algorithms to monitor and improve the metals, glass and ceramic production process. To guarantee the repeatability and accuracy of measurement, automation through the flexibility offered by modern multi-axis robotic systems will be demonstrated. The ultimate output of the system will result in foundation industry-wide benefits in glass, ceramics, and metals production.This project will address specific needs in these foundation industries by offering an augmented, existing manufacturing process brought about by digitised inspection & intelligent machine learning. It is anticipated that a reduction in energy costs and improved production yields associated with the manufacture of tempered glass & kiln fired ceramic materials will be significantly and positively impacted, as is the case in the foundry castings industries.