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.

在航空航天和核能等高价值制造领域，安全至关重要，因此安全关键部件的设计和检验鉴定是整个开发周期的关键部分。然而，目前的实践广泛使用实验。对物理组件和模型进行试验，必须在其中引入仅限于少量特定测试用例的人为缺陷，以证明它们可以被检测到并进行表征，因此检验资格极其耗时且成本高昂（有些是完整的）。包含缺陷的组件成本计算的模型数百万英镑），这与敏捷、小批量、定制、数字化制造的普遍趋势相悖，我们建议用数字化替代方案取代这些昂贵、浪费的物理测试样本，以提高制造效率。将需要开发高速、具有代表性、真实的数值模拟能力，并结合在参数空间的高维度上进行可靠采样和插值的解决方案。这种虚拟测试能力将能够设计高价值组件的检测。 ,在制造单个零件之前进行优化和合格，它将为操作员培训提供模拟工具的基础，并能够生成培训未来超声波阵列检测机器学习解决方案所需的规模和保真度的数据。这是演示案例，因为这是评估安全关键组件的内部完整性（无论是在制造还是在使用中）最广泛使用的方法，为了实现这一目标，需要经过验证的工具来大规模综合真实的检测数据，并需要一种稳健探索的方法。可能缺陷的巨大参数空间我们实现这一雄心勃勃的愿景的想法是从两个互补的方向解决这个问题。自下而上：我们将在申请人之前世界领先的研究的基础上提高原始数据的直接数值模拟。将展示如何更好地利用数值模拟工具将计算负担减少至少一个数量级：我们将利用我们的技术对多维参数空间进行定量表征，以更有效地验证检测性能。领域知识并与工业合作者进行广泛讨论（Rolls-Royce、EDF、Jacobs、Airbus 和 KANDE），我们将开发合适的代理建模、采样和集成策略，通过少量高保真数值模拟准确表征参数空间。在解决这个问题时，我们将开发一套工具和技术，确保检验资格的成本和复杂性降低几个数量级，使其适合数字制造的未来。