Model of Magnetic Barkhausen Noise for Characterization of Steel Materials

用于表征钢材的巴克豪森磁噪声模型

• 批准号: RGPIN-2017-06365
• 负责人: Krause, Thomas
• 金额: $ 2.26万
• 依托单位: Royal Military College of Canada
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683974
• 关键词: Model; Magnetic; Barkhausen; Noise; Characterization

The goal of this scientific investigation is to develop portable surface magnetic Barkhausen noise (MBN) capability for non-destructive characterization of ferromagnetic steel properties. The goal includes combined measurement of residual stress, degree of plastic deformation, grain size, carbon content, crystallographic texture, magnetic anisotropy and, in the case of electrical steels, core loss. Motivation for this work is the requirement for rapid identification of steel type and evaluation of stress state within structures such as submarine and ship hulls, oil and gas pipelines, motor or transformer laminates, landing gear in aircraft and cases where structures may have undergone localized damage and/or repair. Development of tools, analysis methods and modeling, along with identification of factors that affect the measurement outcome will facilitate application of MBN analysis and advance its potential as a diagnostic tool for identification and characterization of steel materials.*******The scope of work will include characterization of a wide range of steel materials, in order to develop models of MBN generation that will allow the extraction of key material parameters for non-destructive characterization of materials. The five year project will examine Barkhausen noise emissions from a number of steel types with the goal of developing general models describing micro-magnetic Barkhausen emissions in order to enhance MBN inspection capabilities for future applications. The specific ferromagnetic steel properties to be evaluated will include characterization of magnetic properties and optimization of core loss and permeability in non-oriented electrical steels (NOES), the detection and measurement of residual elastic and/or plastic stress in structural steels, including naval vessels, with a focus on monitoring of fatigue and carbide precipitate migration, as may occur during tempering embrittlement in HY80, used for submarines, and identification/assessment of specific steel grades, such as would be useful in the oil and gas industry, where installed pipelines may be made of unknown steel material. Barkhausen noise will be examined for specific NOES steels obtained under controlled manufacturing processes, as part of a collaboration with CanmetMATERIALS, Natural Resources Canada (see attached letter indicating in-kind support of $150,000-$250,000 over 5 years). Determination of naval steel condition including presence of elastic residual stress, plastic deformation and potential for detection and characterization of tempering embrittlement will be performed in collaboration with Defence Research Development Canada (DRDC), Atlantic Research Centre (see attached letter of support). Identification of signal parameters that could be used to identify steels will be performed in collaboration with Lynann Clapham at Queen's University and CanmetMATERIALS.***

这项科学研究的目标是开发便携式表面磁巴克豪森噪声 (MBN) 功能，用于铁磁钢性能的无损表征。目标包括残余应力、塑性变形程度、晶粒尺寸、碳含量、晶体结构、磁各向异性以及电工钢铁损的综合测量。这项工作的动机是快速识别钢材类型并评估结构内的应力状态，例如潜艇和船体、石油和天然气管道、电机或变压器层压板、飞机起落架以及结构可能遭受局部损坏的情况和/或修理。工具、分析方法和建模的开发，以及影响测量结果的因素的识别，将促进 MBN 分析的应用，并提升其作为钢铁材料识别和表征诊断工具的潜力。********范围工作内容将包括对各种钢铁材料进行表征，以开发 MBN 生成模型，从而提取关键材料参数以对材料进行无损表征。这个为期五年的项目将检查多种钢种的巴克豪森噪声排放，目标是开发描述微磁巴克豪森排放的通用模型，以增强未来应用的 MBN 检测能力。待评估的具体铁磁钢性能将包括无取向电工钢 (NOES) 的磁性能表征和磁芯损耗和磁导率的优化，结构钢（包括海军舰艇）中残余弹性和/或塑性应力的检测和测量，重点是监测疲劳和碳化物沉淀迁移，如用于潜艇的 HY80 回火脆化期间可能发生的情况，以及特定钢种的识别/评估，例如在安装的石油和天然气工业中有用的钢种管道可能由未知的钢材制成。作为与加拿大自然资源部 CanmetMATERIALS 合作的一部分，将对巴克豪森噪声进行受控制造工艺下获得的特定 NOES 钢的检查（参见所附信件，表明 5 年内提供 150,000 美元至 250,000 美元的实物支持）。将与加拿大国防研究发展部 (DRDC)、大西洋研究中心合作确定海军钢的状况，包括是否存在弹性残余应力、塑性变形以及回火脆化的检测和表征潜力（请参阅随附的支持信）。将与女王大学的 Lynann Clapham 和 CanmetMATERIALS 合作，对可用于识别钢材的信号参数进行识别。***