Application of Decision trees for the identification of weld central line in austenitic stainless steel weld joints

P. Madhumitha, S. Ramkishore, K. Srikanth, P. Palanichamy
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引用次数: 2

Abstract

Austenitic stainless steels (ASS) are preferred in chemical and nuclear industries mainly due to their high corrosion resistance and unique high temperature creep properties. Austenitic stainless steel welding is an integral part of the Indian nuclear components and ultrasonic non-destructive testing technique (NDT) plays a major role in testing the integrity of the weld joints. The concept of manual ultrasonic testing (UT) of defects/flaws/discontinuities has now been replaced by computerization, automation and mechanization concepts. Remote ultrasonic NDT inspection assumes great dimension in the industrial system and in particular testing of pressure vessels made of several weld joints. Identification of weld centre line is very important is very important in flaw evaluation in the weld joints particularly while carrying out remote ultrasonic testing of pressure vessels. Recently, successful attempts are being made in applying machine learning techniques for accurate flaw detection, sizing and location of weld joints. In this work, a 42 mm thick single “V” butt weld joint was fabricated and A-scan ultrasonic signals (time domain signals) were acquired at the weld centre and across the weld joint at the 5 mm distance interval and stored for further analysis using Decision tree algorithm. Critically refracted longitudinal (Lcr) wave probe at 2 MHz was used for this purpose. Decision tree algorithm which is an artificial Intelligence technique, classified under supervised machine learning algorithms, was used for training the acquired A-scan data and to reliably identify the centre line in the weld region for the purpose finding flaw location during remote ultrasonic testing. The developed procedure/ technique is first of its kind, simple to use and straight forward and useful for identifying the weld centre line and for accurate flaw location in the weld regions during ultrasonic testing of ASS weld joints.
决策树在奥氏体不锈钢焊缝中心线识别中的应用
奥氏体不锈钢(ASS)主要由于其高耐腐蚀性和独特的高温蠕变性能而在化学和核工业中得到首选。奥氏体不锈钢焊接是印度核部件的重要组成部分,超声波无损检测技术在检测焊接接头的完整性方面起着重要作用。人工超声检测缺陷/缺陷/不连续性的概念现已被计算机化、自动化和机械化概念所取代。远程超声无损检测在工业系统中,特别是在由多个焊接接头组成的压力容器的检测中,具有很大的规模。焊缝中心线的识别在焊缝缺陷评定中非常重要,尤其是在对压力容器进行远程超声检测时。最近,人们成功地尝试将机器学习技术应用于准确的缺陷检测、焊接接头的尺寸和位置。在这项工作中,制作了一个42 mm厚的单“V”型对接焊缝,并在焊缝中心和整个焊缝处以5 mm的距离间隔获取a扫描超声信号(时域信号),并使用决策树算法存储以供进一步分析。在2兆赫的临界折射纵向(Lcr)波探头用于此目的。决策树算法是一种人工智能技术,属于有监督机器学习算法,用于训练获取的a扫描数据,并可靠地识别焊缝区域的中心线,以便在远程超声检测中找到缺陷位置。所开发的程序/技术是同类中第一个,使用简单,直接,可用于识别焊缝中心线,并在超声波检测ASS焊接接头时准确定位焊缝区域的缺陷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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