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

这个合作的、跨部门的研发示范项目进一步推进了以前的工业研究，以推进和展示为支持基础工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大的生产力。这将通过使用与3D机器视觉系统集成的先进机器人来实现，该系统通过定制传感器进行增强，从而创建一个数据丰富的环境。机器人、视觉和感官技术将应用于基础工业生产过程，并在之前的研发基础上进行演示，以数字检测金属、玻璃和陶瓷的缺陷。通过对收集数据的机器学习（ML）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流程，从而提高工业生产率，同时显着降低玻璃，金属和陶瓷制造中的能耗和二氧化碳排放。当前的制造方法不灵活，通常需要花费大量时间进行预编程或人工干预生产任务，以响应意外事件或生产错误。这意味着，在生产方式更新之前，基础行业无法应对未来环境目标的要求，也无法在制造过程中进一步改进。解决这个问题至关重要；成功将使英国制造业在面对日益激烈的全球竞争时保持竞争力，因为劳动力价格和排放法规都要低得多。该项目旨在使用先进的3D视觉传感器数据来生成机器学习和人工智能算法，以监控和改进金属、玻璃和陶瓷的生产过程。为了保证测量的重复性和准确性，将展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，陶瓷和金属生产的基础行业范围内产生效益。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流程来满足这些基础行业的特定需求。预计与钢化玻璃和窑烧陶瓷材料的生产相关的能源成本的降低和产量的提高将受到显著和积极的影响，正如铸造铸造行业的情况一样。

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.