Fast, efficient and reliable: digital qualification of ultrasonic inspection for safety-critical components

快速、高效、可靠：安全关键部件超声波检测的数字化鉴定

• 批准号: EP/X02427X/1
• 负责人: Peter Huthwaite
• 金额: $ 128.55万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX02427X%2F1
• 关键词: Fast; efficient; reliable; digital; qualification

In high-value manufacturing sectors such as aerospace and nuclear, safety is paramount. For this reason, the design and qualification of inspection for safety-critical components is a crucial part of the overall development cycle. However, current practice makes extensive use of experimental trials on physical components and mock-ups, into which artificial defects, limited to small numbers of specific test cases, must be introduced to demonstrate that they can be detected and characterised. Inspection qualification is therefore extremely time-consuming and costly (with some full mock-ups of defect-containing components costing £millions), and at odds with the general move toward agile, small-batch, bespoke, digitally-enabled manufacturing. We propose replacing the use of these expensive, wasteful, physical test specimens with digital alternatives, to improve manufacturing efficiency. Delivering this will require high-speed, representative, realistic numerical simulation capabilities to be developed, in combination with solutions to reliably sample and interpolate across the high dimensionality of the parametric space. This virtual testing capability will enable the inspection of a high value component to be designed, optimised, and qualified before a single part has been manufactured. It will provide the basis of a simulation tool for operator training and be able to generate data at the scale and fidelity needed to train future machine learning solutions for inspection automation. Ultrasonic array inspection will be the demonstrator case as this is the most widely used method for assessing the internal integrity of safety-critical components, both at manufacture and in service. To achieve the goal requires validated tools to synthesise authentic inspection data at scale and a methodology to robustly explore the vast parameter space of possible defects to determine inspection performance. Our idea to achieve this ambitious vision is to approach the problem from two complimentary directions.Bottom-up: we will make the direct numerical simulation of raw data more efficient. Building on previous world-leading research by the applicants, we will show how numerical simulation tools can be better exploited to reduce the computational burden by at least one order of magnitude. Top-down: we will make the quantitative characterisation of the multi-dimensional parameter space to qualify inspection performance more efficient. Drawing on our domain knowledge and in extensive discussion with industrial collaborators (Rolls-Royce, EDF, Jacobs, Airbus, and KANDE), we will develop suitable surrogate modelling, sampling, and integration strategies for accurately characterising the parameter space with a small number of high-fidelity numerical simulations.In addressing this problem we will produce a set of tools and techniques that ensure that inspection qualification is reduced in cost and complexity by orders of magnitude, leaving it fit for the future of digital manufacturing.

在高价值制造业（例如航空航天和核能）中，安全至关重要。因此，对安全 - 关键组件的检查的设计和资格是整体开发周期的关键部分。但是，当前的实践广泛使用了对物理组件和模型的实验试验，必须引入少量特定测试用例的人造缺陷，以证明可以检测和表征它们。因此，检查资格非常耗时且昂贵（拥有数百万英镑的缺陷组件的一些完整模型），并且与朝着敏捷，小批量，定制，以数字化的制造业的一般转变相反。我们建议用数字替代品代替这些昂贵，浪费，物理测试标本的使用，以提高制造效率。提供此功能将需要开发高速，代表性的，现实的数值模拟能力，并结合解决方案的可靠样品和跨参数空间高维度插值的解决方案。这种虚拟测试能力将使在制造单个零件之前设计，优化和合格的高价值组件能够检查。它将为操作员培训提供模拟工具的基础，并能够以训练未来的机器学习解决方案进行检查自动化所需的规模和忠诚度。超声波阵列检查将是示范案例，因为这是评估制造和服务中安全关键组件内部完整性的最广泛使用的方法。为了实现该目标，需要经过验证的工具来大规模合成真实的检查数据，并进行一种鲁棒性探索可能缺陷的广泛参数空间以确定检查性能的方法。我们实现这一雄心勃勃的愿景的想法是从两个免费方向解决问题。在申请人以前的世界领先研究的基础上，我们将展示如何更好地探索数值模拟工具，以将计算刻录减少至少一个数量级。自上而下：我们将对多维参数空间进行定量表征，以提高检查性能的效率。借鉴我们的领域知识并与工业合作者（Rolls-Royce，EDF，Jacobs，Airbus和Kande）进行广泛讨论，我们将开发合适的代孕建模，抽样和集成策略，以准确地表征参数空间，以确保少量的数字模拟，以确保综述的综述，我们将在少量的质量上进行构图，我们的工具构成了一组工具，我们的工具构成了工具，这些工具是在工具中构成的。数量级，使其适合数字制造的未来。