Inspection of Semi-Elliptical Defects in a Steel Pipe Using the Metal Magnetic Memory Method

材料科学 磁存储器 冶金 机械工程 工程类 复合材料 图层(电子)
作者
J. Jesús Villegas-Saucillo,Javier Díaz-Carmona,Juan Prado-Olivarez,Monserrat Sofía López-Cornejo,Ernesto A. Elvira-Hernández,C. A. Cerón-Álvarez,Agustín L. Herrera‐May
出处
期刊:Applied sciences [MDPI AG]
卷期号:14 (12): 5308-5308
标识
DOI:10.3390/app14125308
摘要

Ferromagnetic pipes are widely used for fluid transportation in various industries. The failure of these ferromagnetic pipes due to surface defects can generate industrial accidents, economic losses, and environmental pollution. Non-destructive testing techniques are required to detect these surface defects. An alternative is the metal magnetic memory (MMM) method, which can be employed to detect surface flaws in ferromagnetic structures. Based on this method, we present an analysis of experimental results of the magnetic field variations around five different surface semi-elliptical defects of an ASTM A36 steel pipe. A measurement system of MMM signals is implemented with a rotatory mechanism, a magnetoresistive sensor, a data processing unit, and a control digital unit. The MMM method does not require expensive equipment or special treatment of the ferromagnetic structures. In order to research a potential relationship between the defect sample size and the measured MMM signals, variable defect dimensions are experimentally considered. According to these results, the shape and magnitude of the normal and tangential MMM signals are altered by the superficial semi-elliptical defects. In particular, the maximum and mean tangential components and the maximum and minimum normal components are related to the defect dimensions. The proposed measurement system can be used to study the behavior of magnetic field variations around surface defects of ferromagnetic pipes. This system can be adapted to measure the position and damage level of small defects on the surface of ferromagnetic pipes.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
An_mie完成签到,获得积分10
刚刚
刚刚
刚刚
Arabella完成签到,获得积分10
1秒前
HEIKU应助追梦人采纳,获得10
1秒前
1秒前
小T儿发布了新的文献求助10
1秒前
852应助woxiangbiye采纳,获得10
1秒前
飞羽完成签到,获得积分10
2秒前
Owen应助cherry采纳,获得10
2秒前
坚定的老六完成签到,获得积分10
2秒前
协和_子鱼完成签到,获得积分0
2秒前
3秒前
Hyde完成签到,获得积分10
4秒前
小南孩完成签到,获得积分10
4秒前
4秒前
5秒前
研友_VZG7GZ应助keyancui采纳,获得10
5秒前
康康完成签到 ,获得积分10
6秒前
英姑应助毕业就好采纳,获得10
6秒前
虚心的迎荷完成签到,获得积分10
6秒前
脑洞疼应助少侠不是菜鸟采纳,获得10
6秒前
6秒前
祝雲完成签到,获得积分10
6秒前
新的心跳发布了新的文献求助10
6秒前
壹拾柒完成签到,获得积分10
7秒前
7秒前
7秒前
mimi发布了新的文献求助10
7秒前
呆呆完成签到,获得积分10
8秒前
blebui应助姜茶采纳,获得10
8秒前
幼稚园小新完成签到,获得积分10
8秒前
123完成签到,获得积分10
8秒前
9秒前
snowball完成签到,获得积分10
9秒前
10秒前
duoduozs发布了新的文献求助10
10秒前
velpro完成签到,获得积分10
10秒前
qqqq完成签到,获得积分10
10秒前
11秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527304
求助须知:如何正确求助?哪些是违规求助? 3107454
关于积分的说明 9285518
捐赠科研通 2805269
什么是DOI,文献DOI怎么找? 1539827
邀请新用户注册赠送积分活动 716708
科研通“疑难数据库(出版商)”最低求助积分说明 709672