Improving Ultrasonic Imaging using Machine Learning

使用机器学习改进超声成像

• 批准号: 2889657
• 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: 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=studentship-2889657
• 关键词: Improving; Ultrasonic; Imaging; using; Machine

Ultrasonic non-destructive evaluation (NDE) describes the practice of transmitting sound waves through solid objects and analysing the resulting scattered waves to construct images of the object's interior, facilitating defect detection and materials characterisation. The ultimate aim of this project is to investigate enhanced ultrasonic NDE of metal components directly at the point of manufacture, to deliver high-quality components right, first time. Importantly, this in-process approach requires the development of real-time data processing algorithms. Machine learning algorithms used in conjunction with models and simulations of wave propagation will be explored to facilitate this. Specifically, this project will examine how to compensate for the effects that thermal gradients, complex build geometries and heterogeneous microstructures have on the probing ultrasonic waves. Three research questions will be addressed: i) Can complex and dynamic build geometries be accurately mapped out in near real-time using in-process ultrasonic inspection data, where extreme thermal gradients (generated by the manufacturing process) cause distortion of the expected wave paths? ii) Can this knowledge of the component geometry, coupled with models of the thermal gradient be used to better constrain and drive the microstructure characterisation problem? iii) Can the knowledge from steps i) an ii) be used to reliably image components as they are built? The final deliverable of the project will be a fully automated in loop capability, to be demonstrated on test structures manufactured for aerospace and energy applications (with other industrial members applications supported as appropriate).

超声波非破坏性评估（NDE）描述了通过固体物体传输声波的实践，并分析所得的散射波以构建对象内部的图像，从而促进缺陷检测和材料表征。该项目的最终目的是直接在制造时直接研究金属组件的超声nde，以便第一次提供高质量的组件。重要的是，这种过程中的方法需要开发实时数据处理算法。将探索与模型和波传播的模型结合使用的机器学习算法，以促进这一点。具体而言，该项目将研究如何补偿热梯度，复杂的几何形状和异质微观结构对探测超声波的影响。将解决三个研究问题：i）可以使用过程中的超声检查数据在近实时地实时准确地绘制复杂的构建几何形状，其中极端的热梯度（由制造过程产生）会导致预期波动路径的变形？ ii）对组件几何形状的这种知识，再加上热梯度模型来更好地约束和驱动微观结构表征问题吗？ iii）可以从步骤i）ii）使用的知识来可靠地构建图像组件？该项目的最终交付将是循环能力完全自动化的，可以在为航空航天和能源应用制造的测试结构上证明（在其他工业成员的应用程序中适当支持）。