Li Xiongbing, Fu Yingdong, Zhang Feng, Rao Yanni
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China; Joint International Research Laboratory of Key Technology for Rail Traffic Safety, Changsha 410075, China; National & Local Joint Engineering Research Center of Safety Technology for Rail Vehicle, Changsha 410075, China.
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China; Joint International Research Laboratory of Key Technology for Rail Traffic Safety, Changsha 410075, China.
Ultrasonics. 2020 Aug;106:106128. doi: 10.1016/j.ultras.2020.106128. Epub 2020 Mar 10.
Surface roughness degrades the performance of ultrasonic methods when detecting sub-wavelength flaws. In this work, a roughness-modified doubly-scattered response (DSR) model is developed to enhance the flaw detection method for two-phase Ti-6Al-4V with rough surfaces. Extreme value statistics are used to calculate the confidence bounds of grain noise, then the bounds are treated as a time-dependent threshold for segmenting flaws in C-scan images of two-phase Ti-6Al-4V with rough surfaces. Three Ti-6Al-4V samples with different surface roughness are designed and manufactured for validating the present method; ultrasonic C-scan results show that it can distinguish sub-wavelength flaws (about 1/3 wavelength) in two-phase Ti-6Al-4V with rough surfaces, which can be extended to industry applications.
在检测亚波长缺陷时，表面粗糙度会降低超声检测方法的性能。在这项工作中，开发了一种粗糙度修正的双散射响应（DSR）模型，以增强对具有粗糙表面的两相Ti-6Al-4V的缺陷检测方法。使用极值统计来计算晶粒噪声的置信界限，然后将这些界限作为随时间变化的阈值，用于分割具有粗糙表面的两相Ti-6Al-4V的C扫描图像中的缺陷。设计并制造了三个具有不同表面粗糙度的Ti-6Al-4V样品来验证本方法；超声C扫描结果表明，该方法能够区分具有粗糙表面的两相Ti-6Al-4V中的亚波长缺陷（约为1/3波长），这可扩展到工业应用中。
