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

这个跨行业的合作研发示范项目进一步推进了i3D Robotics，Lucideon和Glass Technology Services行业合作伙伴关系的先前工业研究，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。主要目的是提高效率，通过提高能源和资源效率来提高生产力。这将通过使用先进的机器人技术来实现，并与3D机器视觉系统集成，该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。当前的制造方法不灵活，需要时间密集型编程和人工干预生产任务，以应对意外事件或生产错误。一些英国基础工业无法满足未来环境目标的要求，同时在激烈的全球市场中保持竞争力，因为他们无法在生产过程中进行改进，直到生产方法得到更新。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展示，在以前的研发基础上，以数字化方式检测材料和组件的缺陷。通过对收集到的数据进行机器学习，人工智能可以开始增强这些传统的基础工业生产流程和工艺，以提高工业生产率，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和人工智能算法，以监控和改善金属，玻璃和陶瓷生产工艺。为了保证测量的可重复性和准确性，将展示通过现代多轴机器人系统提供的灵活性实现的自动化。该系统的最终产出将为玻璃、陶瓷和金属生产带来基础行业的整体效益。该项目将通过提供经过数字化检测和学习的增强现有制造工艺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制造相关的能源成本降低和产量提高将受到显著和积极的影响，钢铁行业就是如此。

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