intelligent Weld inspection (iWeld)

智能焊缝检测（iWeld）

• 批准号: 10040812
• 金额: $ 9.27万
• 依托单位: IMPERIAL COLLEGE LONDON
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10040812
• 关键词: intelligent; Weld; inspection; iWeld

Periodic inspection of the integrity of welds is essential for maintaining safe and efficient operation of critical assets. Methods based on ultrasound have numerous advantages – they are fast, accessible and non-disruptive – but they meet serious obstacles when used on thick welds with a complex microstructure. The H2020 ADVISE project developed and demonstrated techniques to address these challenges in the nuclear energy context. For most of these, the knowledge of the internal material structure of the weld is essential. Building up foundations laid by ADVISE, iWeld aims to progress towards implementing the imaging and characterisation methods in situ and broaden their application scope beyond the nuclear energy sector. The project will combine insights from a wide spectrum of industries to construct a comprehensive weld library which will be used to train an AI model predicting the internal material structure of the weld. These predictions will be used in further refined inversion and imaging algorithms to deliver a comprehensive inspection planning workflow. The ambition of iWeld is to transform the way to perform ultrasonic inspection on complex welds across several branches of industry and to deliver open tools to support their development.

定期检查焊缝的完整性对于维护关键资产的安全和高效运行至关重要。基于超声波的方法有许多优点——它们快速、易于使用和无破坏性——但当用于具有复杂微观结构的厚焊缝时，它们会遇到严重的障碍。H2020 ADVISE项目开发并展示了在核能背景下应对这些挑战的技术。对于其中的大多数，焊缝内部材料结构的知识是必不可少的。在ADVISE奠定的基础上，iWeld的目标是在现场实施成像和表征方法，并将其应用范围扩大到核能领域以外。该项目将结合来自广泛行业的见解，构建一个全面的焊缝库，用于训练预测焊缝内部材料结构的人工智能模型。这些预测将用于进一步完善的反演和成像算法，以提供全面的检查计划工作流程。iWeld的目标是改变对工业多个分支的复杂焊缝进行超声波检测的方式，并提供开放式工具来支持其发展。