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以及通过使用寿命通过资产监控的资产监控，可以帮助提取最大的有用寿命（同时维持安全利润率），例如，在飞机，海上船只和可再生能源结构中，使用了主要的工程组件。 NDT（非破坏性测试）的作用对于这种质量保证至关重要。现在，NDT常规地内置在工业4.0制造业中使用的数字双线方法中。我们与TWI使用的固定机器人的最初合作可提供NDT测量值，通常是按照预编程的路径进行的，因此使它们不适合处理和检查具有显着的几何耐受性或可变性的零件。然后，我们采用了一种用于基于传感器的路径校正的工业机器人的新型实时方法来研究计量和基于机器视觉的路径校正。这种方法非常成功地在制造规模多达几米的组件的制造检查中非常成功。除此之外，当我们考虑大型组件，例如救生艇HUL，飞机机翼或风力涡轮机叶片（比例为10米），并且当我们考虑使用服务内检查时，使用移动机器人平台进行了移动机器人平台来提供NDT测量。不幸的是，这种移动机器人技术仍处于婴儿目的地对自动驾驶系统的强烈兴趣（在对自动驾驶汽车的兴趣下）。目前，将移动机器人平台基础单元与协作机器人武器之间的运动学相结合的挑战是巨大的挑战，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。挑战涉及可变的几何表面，碰撞和避免障碍，控制NDT测量过程以及对不断变化的环境的响应能力的能力，可以执行高质量的NDT测量值，从而可以执行该项目的目标。协作机器人武器（2）调查基于AI的处理对场景识别的作用，以在未知的几何形状上实现有效的NDT路径创建，同时保持混乱环境中无冲突的操作（3）以使用在线机器学习的数据解释来进行NDT测量值，从而可以进行赔偿/赔偿的实验。仿真将用于从硬件运动学的机器人路径计划中告知既有机器人路径计划，又要了解取决于样本，材料属性和本地几何形状的最佳NDT策略。最初，成功的学生将接受最新的自动化和NDT功能的培训。学生将与广泛且既定的团队合作，学生将花费1年的基本原理，并完成文献综述和背景最先进的研究。然后，学生将过渡到TWI（塔尔伯特港）的定制设施，以继续学习并开发新的硬件。在此期间，将进行几项工业案例研究检查，从航空航天，海军和可再生能源应用领域进行。