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与整个使用寿命期间的资产监控相结合，有助于从飞机、海船和可再生能源结构等使用的主要工程部件中获得最大的使用寿命（同时保持安全裕度）（破坏性测试）是核心。为了实现这种质量保证，NDT 现在通常被纳入工业 4.0 制造中采用的数字孪生生命周期工程方法中，我们与 TWI 的最初合作采用了固定机器人来提供 NDT 测量，通常按照预先编程的方式进行。路径，从而使它们不太适合处理和检查具有显着几何公差或变化的零件，然后我们转向基于计量和机器视觉的路径校正，为工业机器人采用一种新颖的实时方法来实现基于传感器的路径。除此之外，当我们考虑救生艇船体、飞机机翼或风力涡轮机叶片（10 米尺度）等大型部件时，这种方法非常成功。我们考虑在役不幸的是，尽管人们对自主系统产生了浓厚的兴趣（目前对自动驾驶车辆的兴趣驱动），但这种移动机器人技术仍处于起步阶段。将移动机器人平台基础单元与协作机器人手臂之间的运动学相结合，从而在制造以及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，面临着重大挑战，这些挑战涉及从可变几何表面进行动态路径规划。 ，碰撞和避障，控制NDT 测量过程以及自适应地响应不断变化的环境的能力，以便可以执行参考标准校准程序的高质量 NDT 测量。该项目的目标是：(1) 开发新型机器人基础机动性和控制，即以编程方式与行业标准协作机器人手臂的控制兼容(2)，以研究基于人工智能的场景识别处理的作用，从而在未知几何形状上实现高效的 NDT 路径创建，同时在杂乱的环境中保持无碰撞操作(3)基于在线机器学习的无损检测测量数据解释，从而允许进行过程补偿/重新测量。该方法将模拟和实际实验工作相结合，模拟将用于根据运动学来指导机器人路径规划。成功的学生将首先在位于 EEE 的价值 2900 万英镑的新 SEARCH 实验室中接受最新的自动化和 NDT 功能培训。并组建了团队搜索，学生将在第一年学习基本原理并完成文献综述和背景最新研究，然后学生将过渡到 TWI（塔尔伯特港）的定制设施继续学习并开发新的知识。在此期间，将进行一些来自航空航天、海军和可再生能源应用领域的工业案例研究检查。