Article(id=1236369226450129503, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236369220812984708, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202404085, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1713974400000, receivedDateStr=2024-04-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772702994215, onlineDateStr=2026-03-05, pubDate=1732464000000, pubDateStr=2024-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772702994215, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772702994215, creator=13701087609, updateTime=1772702994215, updator=13701087609, issue=Issue{id=1236369220812984708, tenantId=1146029695717560320, journalId=1210938733613449225, year='2024', volume='53', issue='11', pageStart='1', pageEnd='168', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772702992871, creator=13701087609, updateTime=1772703093306, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236369642126627337, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236369220812984708, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236369642126627338, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236369220812984708, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=155, endPage=161, ext={EN=ArticleExt(id=1236369226856977033, articleId=1236369226450129503, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Magnetic memory detection in welding joint of 20 steel plate, columnId=1211002409397129992, journalTitle=Thermal Power Generation, columnName=Power generation technology forum, runingTitle=null, highlight=null, articleAbstract=

Compared with magnetic particle detection and ray detection, magnetic memory detection is more sensitive. The magnetic memory technology is employed to carry out nondestructive testing and evaluation for weld defects of 20 steel plate. The results show that, the magnetic memory detection method can effectively characterize the weld defects of steel plates. Compared with the magnetic field intensity parameter, the magnetic field gradient value is more sensitive to the weld defects of the four tested steel plates, which manifested as its normal component increases significantly and presents a convex peak, and the abnormal magnetic memory signals also appear in some places of the weld with local stress concentration. In practical application, the rapid characteristics of the magnetic memory detection technology can be used to extract the abnormal signals of the weld seam of the tested components at first, and then compare it with other non-destructive testing methods, to effectively improve the accuracy and reliability of defect detection.

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与磁粉检测和射线检测相比,磁记忆检测灵敏度更高,利用金属磁记忆技术对20钢板的焊缝缺陷进行无损检测评价。研究表明:磁记忆检测法能够有效表征钢板的焊缝缺陷;与磁场强度参数相比,磁场梯度对焊缝缺陷更为敏感,表现为在被测4块钢板的焊缝缺陷处,其磁场梯度的法向分量均出现了明显增加、并呈凸峰状,且在焊缝一些局部应力集中处,也出现了异常磁记忆信号。在实际应用时,可以利用磁记忆检测快速的特点,先对被测构件焊缝的异常信号进行提取,同时与其他无损检测进行比较,有效实现提高缺陷检测准确性与可靠性的目的。

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王啸(1989),男,博士,校聘教授,主要研究方向为无损检测技术,
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谢云辰(1988),男,工程师,主要研究方向为无损检测技术,

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Magnetic memory test results of weld seams of the above 4 steel plates

, figureFileSmall=null, figureFileBig=null, tableContent=
钢板序号焊接接头中SFI的位置/mmHSF,i的极值(最小/最大值)/(A·m–1)检测参数备注
SFI的KSF,ij/(A·m–2)磁场梯度极值Kmax,ij/(A·m–2)Kmed,ij/(A·m–2)mij
170~110–28.0/35.46 8346 8341 6764.078j=x/i=z
180~220–109.0/63.95 9875 9871 1965.007
290~125–53.9/42.56 1296 1292 4492.502
225~240–0.8/3.54 6624 6621 8342.520
370~82–30.8/60.017 73817 7386 7892.613
121~131–83.8/82.915 11715 1177 0842.134
172~201–51.6/16.811 95711 9574 3692.737
435~90–74.3/76.917 83917 8396 4522.765
170~230–56.3/553 6853 6851 4432.554
), ArticleFig(id=1236369232397652026, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236369226450129503, language=CN, label=表1, caption=

4块钢板的焊缝磁记忆检测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
钢板序号焊接接头中SFI的位置/mmHSF,i的极值(最小/最大值)/(A·m–1)检测参数备注
SFI的KSF,ij/(A·m–2)磁场梯度极值Kmax,ij/(A·m–2)Kmed,ij/(A·m–2)mij
170~110–28.0/35.46 8346 8341 6764.078j=x/i=z
180~220–109.0/63.95 9875 9871 1965.007
290~125–53.9/42.56 1296 1292 4492.502
225~240–0.8/3.54 6624 6621 8342.520
370~82–30.8/60.017 73817 7386 7892.613
121~131–83.8/82.915 11715 1177 0842.134
172~201–51.6/16.811 95711 9574 3692.737
435~90–74.3/76.917 83917 8396 4522.765
170~230–56.3/553 6853 6851 4432.554
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20钢板焊缝缺陷的磁记忆检测
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谢云辰 1 , 纳日苏 1 , 王啸 2
热力发电 | 发电技术论坛 2024,53(11): 155-161
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热力发电 | 发电技术论坛 2024, 53(11): 155-161
20钢板焊缝缺陷的磁记忆检测
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谢云辰1 , 纳日苏1, 王啸2
作者信息
  • 1.国能锅炉压力容器检验有限公司,北京 100070
  • 2.南通大学机械工程学院,江苏 南通 226004
  • 谢云辰(1988),男,工程师,主要研究方向为无损检测技术,

通讯作者:

王啸(1989),男,博士,校聘教授,主要研究方向为无损检测技术,
Magnetic memory detection in welding joint of 20 steel plate
Yunchen XIE1 , Risu NA1, Xiao WANG2
Affiliations
  • 1.CHN Energy Boiler and Pressure Vessel Inspection Co., Ltd., Beijing 100070, China
  • 2.School of Mechanical Engineering, Nantong University, Nantong 226004, China
出版时间: 2024-11-25 doi: 10.19666/j.rlfd.202404085
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与磁粉检测和射线检测相比,磁记忆检测灵敏度更高,利用金属磁记忆技术对20钢板的焊缝缺陷进行无损检测评价。研究表明:磁记忆检测法能够有效表征钢板的焊缝缺陷;与磁场强度参数相比,磁场梯度对焊缝缺陷更为敏感,表现为在被测4块钢板的焊缝缺陷处,其磁场梯度的法向分量均出现了明显增加、并呈凸峰状,且在焊缝一些局部应力集中处,也出现了异常磁记忆信号。在实际应用时,可以利用磁记忆检测快速的特点,先对被测构件焊缝的异常信号进行提取,同时与其他无损检测进行比较,有效实现提高缺陷检测准确性与可靠性的目的。

磁记忆检测  /  焊缝缺陷  /  裂纹  /  无损检测

Compared with magnetic particle detection and ray detection, magnetic memory detection is more sensitive. The magnetic memory technology is employed to carry out nondestructive testing and evaluation for weld defects of 20 steel plate. The results show that, the magnetic memory detection method can effectively characterize the weld defects of steel plates. Compared with the magnetic field intensity parameter, the magnetic field gradient value is more sensitive to the weld defects of the four tested steel plates, which manifested as its normal component increases significantly and presents a convex peak, and the abnormal magnetic memory signals also appear in some places of the weld with local stress concentration. In practical application, the rapid characteristics of the magnetic memory detection technology can be used to extract the abnormal signals of the weld seam of the tested components at first, and then compare it with other non-destructive testing methods, to effectively improve the accuracy and reliability of defect detection.

metal magnetic memory detection  /  weld defects  /  crack  /  nondestructive testing
谢云辰, 纳日苏, 王啸. 20钢板焊缝缺陷的磁记忆检测. 热力发电, 2024 , 53 (11) : 155 -161 . DOI: 10.19666/j.rlfd.202404085
Yunchen XIE, Risu NA, Xiao WANG. Magnetic memory detection in welding joint of 20 steel plate[J]. Thermal Power Generation, 2024 , 53 (11) : 155 -161 . DOI: 10.19666/j.rlfd.202404085
20钢被广泛应用在汽车制造业、重中型机械制造业与锅炉制造业内[1-4]。在上述工业领域中,焊接技术作为一种普遍使用的基础工艺,是大型金属构件制备、安装或改造的重要一环[5-10]。由于焊接质量与焊缝缺陷往往是影响金属构件服役安全的重要因素,因此有必要对焊接构件采取有效的损伤评定和监督措施,防范接头的泄漏或破裂,确保大型焊接构件的服役安全。
金属磁记忆(MMM)是一种新型的弱磁检测技术[11-16],该技术适用于在役、安装或修复后铁磁体、亚稳态奥氏体、合金钢以及铸铁材质任何尺寸与结构类型(对接、三通、圆角、搭接、边缘、间断等)的焊接接头的检测,可以检测出的微观和宏观缺陷包括气孔、夹渣、不连续、裂缝与断裂,且能够有效表征上述缺陷可能发生的位置区域。金属磁记忆法的优势包括:对被检工件的表面粗糙度要求低;不需要专门的磁化设备就能进行检测;设备轻便、检测快速、无需打磨、操作简单、灵敏度高等。本研究首先对焊接接头进行磁记忆检测,再对存在异常信号的区域进行其他类别的无损检测,通过对比与分析试验结果,验证金属磁记忆技术的有效性,掌握利用该技术对钢板焊缝缺陷及其分布状况的检测与评价方法。
金属磁记忆检测是指对工件的磁化状态进行测量,判断其应力集中区域的一种无损检测方法[17-22]
在微观层面上,金属材料内部可视为由许多磁畴与磁畴壁组成。当无外部应力场作用时,材料中的磁畴与磁畴壁呈随机自由状态,在宏观上表现为无磁性;当存在外部应力场时,金属材料在地磁场和应力场的共同作用下,其磁畴与磁畴壁在磁-机械耦合效应下发生旋转与定向移动,且在宏观上显示出磁性。同时,材料的磁化状态也会发生改变,表现为材料磁场强度H的切向分量Hx会在应力集中区域达到最大值,且其法向分量Hy通过原点,符号发生反方向变化,具体如图1所示。这种能够保留构件应力集中区域与磁变化不可逆的物理特征便称为磁记忆[23-26]
基于上述原理,可以利用试样在应力状态变化时出现的磁记忆效应,对其进行无损检测。通过测量材料表面磁场法向分量和切向分量的磁信号,可以实现构件的应力状态和损伤程度评价,这是由于磁信号的分布与铁磁性金属的应力状态和损伤程度密切相关。
试验材料为20钢,将钢板加工为300 mm× 150 mm×15 mm(长×宽×厚)的试板,通过手工电弧焊方法进行焊接,坡口型式为V型,焊缝宽度为20~22 mm,焊缝余高为0~2 mm。
共设计了4块焊接模拟试板(分别为1号钢板、2号钢板、3号钢板、4号钢板),并在试板上预制典型纵向裂纹缺陷。遵循《无损检测 磁记忆检测 总体要求》(GB/T 26641—2021)、《焊缝无损检测 金属材料应用通则》(GB/T 34628—2017)、与《无损检测仪器 金属磁记忆检测仪技术条件》(JB/T 11605—2013),利用爱德森TSC-5M-32型智能化磁记忆金属诊断仪开展检测试验,该仪器具有检测参数显示、数据数字采集、探头位置编码运动、多测量通道等功能,能够用于被检对象的磁场测量与外部磁场影响补偿。采用具有较高灵敏度的EMS-PL4型探头,该探头能够检测工件近表面区域的磁场。探头磁场测量值的相对误差为2.5%,灵敏度范围为1nT-90uT√(HZ)。探头的测量范围为>1 000 A/m,实测值为3 000 A/m,分辨率为1 A/m。
试验过程如图2a)所示,将磁性探头垂直放置在检测表面,由操作人员进行手动检测。检测时朝着焊缝长度方向,即沿焊缝材料和两侧热影响区进行扫查,并记录表面磁场指示的表面磁场(SF)正负振幅(HSF,ii=x, y, z),A/m),探头提离为1 mm。
扫描探头的结构如图2b)所示,将传感器和位置编码器安装在小车外壳内,用于测量磁性分量的大小与检测区长度。图中1、2、3、4为4个磁通门传感器,5为具有位置编码器的车轮,其可随着磁场强度Hx的变化完成被检测区段长度的测量。在检测过程中,传感器1和传感器3从焊缝两侧对热影响区进行检测,传感器2位于其中心,传感器4则用于外部磁场补偿。
被检区域与扫描方向如图2c)所示,其中6、7、8为检测区,9为母材,10为接头热影响区,11为焊缝。在开始检测前,应根据GB/T 26641—2021设置采样间距,注意沿测量线的采样间距应小于焊缝厚度与图2b)中相邻传感器1、传感器2和传感器3的间距(Δy)。
根据磁记忆检测结果确定下列参数:
KSF,ix=|ΔHSF,i|Δx  KSF,iy=|ΔHSF,i|Δy
式中:下标SF为表面磁场,下标i为磁场相对于检测对象的笛卡尔分量(i=x, y, zz是表面法线);KSF,ix为每一测量通道沿x方向上的磁场梯度;KSF,iy为每一测量通道沿y方向上的磁场梯度;|ΔHSF,i|为相邻2个扫描点的HSF,i之差;Δx为测量线相邻点的采样距离,本研究中Δx为1 mm;Δy为测量线间距。
根据式(2)计算测量线中值:
Kmed,ij=median(KSF,ij)
式中:j为在同一笛卡尔空间中梯度计算方向(j=x,y,z);median为平均值。
根据式(3)计算磁指数mij,该参数取决于焊接接头的质量,能够描述钢板表面磁场分布的不均匀程度:
mij=KSF,ijKmed,ij
如果mij大于预先定义的阈值mlim,ij(如2.0),则表示存在由高机械应力或应变梯度引起的表面磁场偏离,其可能是由于裂纹、点蚀等缺陷集中所引起的。
图3示出了1号钢板的磁记忆与其他无损检测结果。其中横坐标为检测位置与距离;左侧纵坐标对应图中上部分曲线,为表面磁场强度;右侧纵坐标对应图中下部分曲线,为磁场梯度。图中,Hx1Hx2Hx3分别为磁场强度在通道1、通道2、通道3上的切向分量;Hy1Hy2Hy3分别为磁场强度在通道1、通道2、通道3上的法向分量;KSF,x1KSF,x2KSF,x3分别为磁场梯度在通道1、通道2、通道3上的切向分量;KSF,y1KSF,y2KSF,y3分别为磁场梯度在通道1、通道2、通道3上的法向分量。
图3可以看出,1号钢板的磁记忆检测发现焊缝有2处位置存在信号异常,表面磁场指示(SFI)的位置约在70~110 mm与180~220 mm处。与其他位置相比,异常信号表现为SFI位置处的磁场强度H发生明显起伏、总体变化较大。进一步观察发现,在70~110 mm处,与通道1和通道3相比,只有通道2上存在磁场法向分量“过零点”现象,这可能是由于通道2正对焊缝中心部位,因此该通道上的磁信号变化最为敏感。此外,在70~110 mm与180~220 mm区域内,通道2法向分量上的磁场梯度变化存在明显凸峰,远高于其他位置处的磁场梯度变化水平。
图3还示出了1号钢板的其他无损检测验证结果。在2个SFI位置处,射线检测并没有观测到缺陷痕迹,而磁粉检测也没有发现明显的黑线。这表明1号钢板磁记忆检测中在70~110 mm与180~220 mm处的曲线变化并不是由体积型缺陷引起的,可能是焊缝在这2个位置存在局部应力集中,从而致使磁记忆检测信号异常。
图4示出了2号钢板的磁记忆与其他无损检测结果。由图4可以看出,3个通道上的磁场强度曲线在85~130 mm与220~280 mm位置上变化最为明显。与通道1和通道3相比,通道上2显示的磁场强度曲线虽然没有反复“过零点”的现象,但是其突变次数更多、反复振荡的频率更大。同时,通道2法向分量上的磁场梯度变化在上述区域内存在几处凸峰,其位置约在90~100 mm、115~125 mm、180~220 mm、220~255 mm与255~280 mm处。相反,与通道2相比,其他通道上显示出的磁场梯度变化则明显较小。
图4还示出了2号钢板的磁粉检测与射线检测验证结果。可以看出,磁粉检测在110~125 mm处呈现出了清晰可辨的磁痕,且在215~230 mm处也显示出了不连续的黑线。射线检测验证了上述结果,表现为在以上2处焊缝出现了亮白色的影像细节。结合磁记忆检测结果能够说明,这2处缺陷是引起磁场强度与磁场梯度变化的主要原因。但值得指出的是,在220~280 mm位置上,通道2上的磁场强度与磁场梯度在220~255 mm区域(前)内变化明显小于255~280 mm区域(后),而射线检测却显示只在220~255 mm区域(前)内才存在缺陷影像,这表明磁记忆检测对缺陷的定位可能存在一定偏差,同时无缺陷部位由于应力集中等原因,也可能会引起异常磁记忆信号的出现。
图5示出了3号钢板的磁记忆与其他无损检测结果。研究发现在60~95 mm、110~155 mm、165~220 mm 3个区域中出现异常磁记忆信号。可以看出,虽然通道1和通道3上所测磁场强度法向分量的变化幅度最大,但是通道2上测得的磁场强度曲线振荡次数更多,包括磁场强度切向分量与法向分量,且有明显的“过零点”现象。与此同时,通道2法向分量上的磁场梯度变化在上述三个区域内明显较大,表现为出现凸峰,远高于其他各类曲线的磁场梯度变化水平。
3号钢板的磁粉检测结果显示,约在焊缝70~90 mm、130~160 mm、200~220 mm 3处出现了可见的黑线,其中第1处和第3处的磁痕方向平行于焊缝纵向(前者较为连续、后者相对分散),而第2处的磁痕方向则垂直于焊接方向,对应为焊缝横向缺陷。另一方面,3号钢板的射线检测结果显示,在上述区域中只有第3处检测到微弱的影像细节,其他位置则没有检测出缺陷信息。从以上结果可以看出,与射线检测相比,磁记忆检测与磁粉检测灵敏度更高。
图6示出了4号钢板的磁记忆与其他无损检测结果。
与前3块钢板相比,4号钢板3个通道上的磁记忆信号在焊缝全区域振荡明显。其中通道1所测磁场强度切向分量的变化幅度最大,而通道2测得的磁场强度法向分量与切向分量曲线的突变次数则更多,且存在反复“过零点”的现象。另一方面,通道2中的磁场梯度法向分量在2个区域内相对较高,分别为35~90 mm和170~230 mm。此外,通道3中磁场梯度法向分量也出现了一些凸峰,相比其他各类曲线的磁场梯度,其变化水平明显较大。
图6还示出了4号钢板的磁粉检测与射线检测结果。能够发现,磁粉检测在70~90 mm处显示出了连续的黑线,且在190~210 mm处也呈现出了间断可辨的黑色磁痕。射线检测证实了上述结果,特别是在第1处位置,表现出了较为明显的亮白色细节。结合磁记忆结果可以认识到,3个通道所测的磁场强度曲线虽然在70~90 mm与190~210 mm处变化较大,然而在焊缝其他位置也同样变化明显,因此对于4号钢板而言,只通过磁场强度曲线无法准确辨识缺陷位置。与磁场强度不同的是,所测磁场梯度值只在35~90 mm和170~230 mm处变化较大,因此更能够有效定位焊缝的潜在缺陷位置。
表1示出了上述4块钢板不同SFI位置的磁记忆检测数据,包括磁场强度极值、区域内的磁场梯度极值与中值以及磁指数。由表1可以看出,所有磁场指示SFI处计算出的磁指数m均大于2.0,表示存在表面磁场偏离。然而研究发现,该磁指数与钢板焊缝缺陷的大小并不呈单一线性对应关系。例如,磁粉与射线检测发现裂纹较为明显的2号与4号钢板第1处缺陷的磁指数分别为2.502和2.765。而无明显缺陷的1号钢板,其2个SFI位置的磁指数分别高达为4.078和5.007。此外,缺陷较小的3号钢板的第2处SFI位置,其磁指数为2.134。综合以上结果可以发现,磁指数大于2.0的SFI位置处虽然值得注意,但是该数值并不能简单用于缺陷尺寸的定量评价。
本文研究了20钢板焊缝缺陷的磁记忆检测方法,并同时开展了磁粉检测与射线检测进行印证,得出了以下结论。
1)钢板焊缝缺陷部位普遍存在磁记忆异常信号,例如在裂纹较为明显的2号与4号钢板上,测得的磁场强度切向分量与法向分量曲线都存在明显的突变现象,表明可以通过磁场强度曲线变化对焊缝缺陷进行初步表征。然而,上述曲线在缺陷部位处并不都呈现出反复“过零点”的现象。例如对于2号钢板第2处缺陷,其磁场强度法向分量曲线都在零点以下。
2)在钢板焊缝没有明显缺陷的部位,磁记忆检测也会呈现出异常信号。例如1号钢板的磁记忆检测发现焊缝有2处位置存在信号异常,表现为磁场强度发生明显起伏与变化较大。然而,在这2个信号异常位置,射线检测并没有观测到缺陷痕迹,而磁粉检测也没有发现明显的磁痕。这表明局部应力集中等其他原因也会致使磁记忆检测信号异常。
3)磁场梯度曲线是表征钢板焊缝缺陷的有效参数。在4块钢板焊缝的所有缺陷位置处,其法向分量上的磁场梯度均出现了明显的增加,表现为凸峰的出现。该结果表明能够通过磁场梯度值的变化判定钢板焊缝缺陷情况。
4)在钢板磁记忆信号异常的位置,磁指数m均大于2.0。然而磁指数与钢板焊缝缺陷的大小并不存在单一线性对应关系。这表明磁指数大于2.0的磁场指示SFI位置虽然值得注意,但该数值并不能简单用于缺陷尺寸的定量评价。
  • 国家能源集团科学技术研究院资助项目(GJ2022Y01)
  • 国家自然科学基金青年基金项目(52305156)
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2024年第53卷第11期
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doi: 10.19666/j.rlfd.202404085
  • 接收时间:2024-04-25
  • 首发时间:2026-03-05
  • 出版时间:2024-11-25
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  • 收稿日期:2024-04-25
基金
Project of National Energy Group Science and Technology Research Institute(GJ2022Y01)
国家能源集团科学技术研究院资助项目(GJ2022Y01)
Youth Fund of National Natural Science Foundation(52305156)
国家自然科学基金青年基金项目(52305156)
作者信息
    1.国能锅炉压力容器检验有限公司,北京 100070
    2.南通大学机械工程学院,江苏 南通 226004

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王啸(1989),男,博士,校聘教授,主要研究方向为无损检测技术,
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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