Automated Digital Inspection for Asset Lifecycle Certification

资产生命周期认证的自动化数字检查

• 批准号: 2907093
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2907093
• 关键词: Automated; Digital; Inspection; Asset; Lifecycle

Quality assurance during manufacture, combined with asset monitoring through service life, can help extract maximum useful life (whilst maintaining safety margins) from major engineering components used, for example, in aircraft, seagoing vessels and renewable energy structures. The role of NDT (non-destructive testing) is central to such quality assurance. NDT is now routinely built into the digital-twin approaches to lifecycle engineering employed in industry 4.0 manufacturing. Our initial collaboration with TWI employed fixed robots to deliver NDT measurements, typically following pre-programmed paths, thus making them less suitable to handle and inspect parts with a significant geometrical tolerance or variability. We then moved to look at metrology and machine vision-based path correction, employing a novel real-time approach for industrial robots for sensor-based path correction. This approach has been very successful for manufacturing inspection of components up to several metres in scale. Beyond this, when we consider large components such as lifeboat hulls, aircraft wings, or wind turbine blades (scale of 10 of metres), and when we consider in-service inspection, there is a shift to using mobile robotic platforms to deliver the NDT measurements. Unfortunately, such mobile robotics technology is still in its infancy despite strong interest in autonomous systems (driven by interest in self-driving vehicles). At present, there are significant challenges for combining the kinematics between mobile robot platform base units with collaborative robotic arms allowing for safe working in crowded and often dynamic environments encountered in manufacturing and periodic inspection and repair operations. The challenges involve on-the-fly path planning from variable geometry surfaces, collision and obstacle avoidance, control of the NDT measurement process and the ability to adaptively respond to changing circumstances such that high-quality NDT measurements referenced to standard calibration procedures can be performed.The aims of this project will be:(1) to develop novel robot base manoeuvrability and control that is programmatically compatible with the control of industry-standard collaborative robot arms(2) to investigate the role of AI-based processing for scene recognition to enable efficient NDT path creation on unknown geometries whilst maintaining collision-free operation in cluttered environments(3) to use on-line machine learning-based data interpretation for the NDT measurements, thus allowing for in-process compensation/ remeasurement The methodology will be a combination of simulation and practical experimental working. Simulation will be used to inform both robot path planning from the kinematics of the hardware, and also to understand the optimal NDT strategy dependent on sample, material properties and local geometry. The successful student will initially be trained in the latest automation and NDT capabilities in the new £29M SEARCH laboratory based in EEE. Working with the extensive and established team in SEARCH, the student will spend year 1 grounding in fundamental principles and completing a literature review and background state-of-the-art study. The students will then transition to bespoke facilities at TWI (Port Talbot) to continue their studies and develop the new hardware. Several industrial case study inspections will be conducted during this period, drawing from aerospace, naval and renewable energy application sectors.

制造过程中的质量保证，结合使用寿命期间的资产监控，可以帮助从飞机、海船和可再生能源结构等主要工程部件中获得最大的使用寿命（同时保持安全裕度）。NDT（无损检测）的作用是这种质量保证的核心。NDT现在已常规内置于工业4.0制造中采用的生命周期工程数字孪生方法中。我们与TWI的最初合作采用固定机器人进行NDT测量，通常遵循预先编程的路径，因此不太适合处理和检查具有显著几何公差或可变性的零件。然后，我们开始研究计量学和基于机器视觉的路径校正，采用一种新的实时方法对工业机器人进行基于传感器的路径校正。这种方法对于高达几米规模的组件的制造检测非常成功。除此之外，当我们考虑大型部件，如救生艇船体，飞机机翼或风力涡轮机叶片（10米的尺度）时，当我们考虑在役检查时，会转向使用移动的机器人平台来提供NDT测量。不幸的是，这种移动的机器人技术仍处于起步阶段，尽管人们对自动驾驶系统有浓厚的兴趣（由对自动驾驶车辆的兴趣驱动）。目前，将移动的机器人平台基本单元与协作机器人臂之间的运动学结合起来，以允许在制造和定期检查和维修操作中遇到的拥挤且经常动态的环境中安全工作，存在重大挑战。这些挑战涉及可变几何表面的飞行路径规划、碰撞和障碍物避免、无损检测测量过程的控制以及对不断变化的环境做出自适应响应的能力，以便能够执行参考标准校准程序的高质量无损检测测量。（1）开发新的机器人基础机动性和控制，其与工业标准协作机器人臂的控制程序兼容（2）研究基于AI的场景识别处理的作用，以实现在未知几何形状上有效的NDT路径创建，同时在混乱的环境中保持无碰撞操作（3）使用基于在线机器学习的NDT测量数据解释，从而允许过程中的补偿/重新测量。该方法将是模拟和实际实验工作的组合。模拟将用于通知机器人路径规划从硬件的运动学，也了解取决于样品，材料属性和局部几何形状的最佳NDT策略。成功的学生最初将在位于伦敦的新的2900万英镑的实验室中接受最新的自动化和无损检测能力的培训。与SEARCH广泛而成熟的团队合作，学生将在第一年学习基本原则并完成文献综述和最先进的背景研究。然后，学生将过渡到TWI（塔尔博特港）的定制设施，继续他们的学习和开发新的硬件。在此期间，将进行几次工业案例研究考察，涉及航空航天、海军和可再生能源应用领域。