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

在航空航天和核能等高价值制造部门，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，目前的做法是广泛使用的物理组件和模型，其中人工缺陷，有限的特定测试案例的数量很少，必须引入实验性试验，以证明他们可以检测和表征。因此，检验鉴定非常耗时且成本高昂（一些含缺陷组件的完整模型成本高达数百万英镑），并且与敏捷，小批量，定制，数字化制造的总体趋势不一致。我们建议用数字替代品取代这些昂贵、浪费的物理测试样本，以提高制造效率。要实现这一点，需要开发高速、有代表性、逼真的数值模拟能力，并结合解决方案，在参数空间的高维空间中进行可靠的采样和插值。这种虚拟测试能力将使高价值部件的检测能够在单个部件制造之前进行设计、优化和鉴定。它将为操作员培训提供模拟工具的基础，并能够以培训未来机器学习解决方案所需的规模和保真度生成数据。超声波阵列检测将是示范案例，因为这是在制造和使用中评估安全关键部件内部完整性的最广泛使用的方法。为了实现这一目标，需要经过验证的工具来大规模合成真实的检测数据，并需要一种方法来稳健地探索可能缺陷的巨大参数空间，以确定检测性能。为了实现这一雄心勃勃的愿景，我们从两个互补的方向来解决这个问题：自下而上：我们将使原始数据的直接数值模拟更有效。在申请人之前世界领先的研究的基础上，我们将展示如何更好地利用数值模拟工具来减少至少一个数量级的计算负担。自上而下：我们将使多维参数空间的定量特征化，以更有效地鉴定检测性能。利用我们的领域知识并与行业合作者进行广泛讨论（劳斯莱斯，法国电力公司，雅各布斯，空中客车公司和康德），我们将开发合适的替代模型，抽样，和积分策略，用于精确地表征具有少量高-保真度数值模拟。在解决这个问题，我们将产生一套工具和技术，以确保减少检查资格，成本和复杂性的数量级，使其适合未来的数字制造。

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.