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

这个合作的跨行业研发示范项目进一步推进了i3D Robotics、Lucideon和Glass Technology Services的行业合作伙伴关系以前的行业研究，以推进和展示为支持基础行业生产流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来演示，并与3D机器视觉系统相集成，3D机器视觉系统可以通过定制的传感器数据来增强，从而创建一个数据丰富的智能生产环境。目前的制造方法缺乏灵活性，需要对生产任务进行时间密集型编程和人工干预，以响应意外事件或生产错误。一些英国基础工业无法响应未来环境目标对他们的要求，同时在激烈的全球市场中保持竞争力，因为在生产方法更新之前，他们无法在制造过程中进行改进。机器人、视觉和传感技术将在英国玻璃、陶瓷和金属基础工业的生产过程中应用和演示，以之前的研发为基础，以数字方式检测材料和部件中的缺陷。随着对收集到的数据进行更多的机器学习，人工智能可以开始改进这些传统的基础工业生产过程和工艺，以帮助提高工业生产率，显著降低玻璃和陶瓷制造中的能源和资源消耗。该项目旨在使用先进的3D视觉传感器数据来产生机器学习，并使用人工智能算法来监控和改进金属、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。该系统的最终产出将在玻璃、陶瓷和金属生产方面为整个行业带来效益。该项目将通过提供全面数字化检查和学习的增强的、现有的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炉烧制陶瓷材料的制造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，就像钢铁行业的情况一样。

This collaborative, cross sector R&D demonstration project furthers previous industrial research from the industry partnership of i3D Robotics, Lucideon & Glass Technology Services, to advance & showcase novel technology developed to support the 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 deomonstrated by using advanced robotics, integrated with 3D machine vision systems which can be augmented with bespoke sensor data creating a data rich, intelligent producution environment.Current manufacturing methods are inflexible, requiring time-intensive programming and manual intervention of production tasks responding to unexpected occurrences or production errors. Some UK Foundation Industries are unable to respond to the demands placed on them by future environmental targets whilst remaining competative in a fierce global marketplace as they are unable to indroduce improvements within the manufacturing process until the production methods are updated.The robotic, vision and sensory technology will be applied and demonstrated within the UK Glass, Ceramics and Metal Foundation Industry production processes, building on previous R&D to digitally inspect defects in materials and components. With additional utilisation of machine learning on the data collected, artificial intelligence can begin to enhance these traditional Foundation Industry production processes and crafts to aid industrial productivity, significantly reduce energy and resource consumption in both glass and ceramic manufacturing.This project aims to uses advanced 3D vision sensor data to produce machine learning 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 thorough digitised inspection & 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 steel industry