Machine learning to locate defects in ultrasonic inspection images

重要的是，对制造的部件进行检查，以识别可能导致早期故障的缺陷，特别是在安全关键系统中。经常使用非破坏性技术，如超声波，以便能够看到表面以下，以识别隐藏的缺陷。该项目旨在开发自动技术来帮助识别缺陷。学生将联合收割机结合图像分析和机器学习方法，构建一个系统，可以可靠地区分正常部位和异常区域。该学生将成为非破坏性评估研究中心（RCNDE）（www.rcnde.ac.uk）的一部分-这是六所大学和许多工业合作伙伴之间的合作。该项目将研究新的分析技术在超声无损检测中的应用，旨在支持相控阵或飞行时间衍射图像的分析。该项目将得到BAE系统海事公司的积极支持，该公司将这些技术应用于大规模制造环境。强大的自动化分析过程的好处将是非常显著的，并且可以潜在地降低许多工业部门中大规模焊接结构的检测成本和持续时间。

It is important that manufactured components are inspected to identify defects which may cause early failure, particularly in safety critical systems. Non-destructive techniques, such as ultra-sound, are used regularly to be able to see below the surface to identify hidden defects. This project aims to develop automatic techniques to help identify the defects. The student will combine image analysis and machine learning methods to build a system that can reliably distinguish between normal parts and regions with abnormalities. The student will be part of the Research Centre for Non-Destructive Evaluate (RCNDE) (www.rcnde.ac.uk) - a collaboration between six universities and many industrial partners. This project will investigate the application of novel analysis techniques to ultrasonic NDE inspection, aiming to support the analysis of phased-array or Time-of-Flight-Diffraction images.The project will be actively supported by BAE Systems Maritime who deploy these techniques in a large scale manufacturing environment. The benefits of a robust automated analysis process would be very significant and could potentially reduce the inspection cost and duration for large scale welded structures across many industrial sectors.