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首页> 外文期刊>Journal of Materials Engineering and Performance >Generation of Hot Dip Galvanized Coated Sheet Certified Reference Materials Using X-Ray Fluorescence and Gravimetry
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Generation of Hot Dip Galvanized Coated Sheet Certified Reference Materials Using X-Ray Fluorescence and Gravimetry

机译：使用X射线荧光和重量分析法生成热浸镀锌薄板认证参考材料

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摘要

A method for the generation of hot dip galvanized sheet steel Certified Reference Materials (CRMs) certified for coating mass is described. The method produces high quality coating mass CRMs with combined estimated uncertainties (95 percent confidence) of about 3 percent relative standard deviation (rsd). Initially, coating mass x-ray fluorescence (XRF) data are collected on multiple test specimens and used to sort the test specimens by observed coating mass. Several specimens near the mean of the sorted data are retained and the remaining specimens are analyzed for coating mass by a National Institute for Standards and Testing (NIST) traceable weigh-strip-weigh analytical method. The weigh-strip-weigh data are correlated to the initial XRF data using statistical techniques. The retained specimens' coating masses are calculated using this relationship and are used as the certified coating masses for the retained specimens. This correlation method is a more accurate and efficient technique relative to the traditional "perimeter" certification methods. The present method does not suffer from an increased estimated uncertainty due to the inherent hot dip Zn coating variability. Also, this method provides a simple means to generate NIST traceable high quality CRMs that can be tailored to meet individual users' requirements.
机译：描述了一种用于产生镀层质量的热浸镀锌钢板认证参考材料（CRM）的方法。该方法可产生高质量的涂层质量CRM，其组合不确定性（95％置信度）约为3％相对标准偏差（rsd）。最初，在多个测试样品上收集涂层质量X射线荧光（XRF）数据，并根据观察到的涂层质量对测试样品进行分类。保留几份接近已分类数据平均值的样本，并通过美国国家标准与测试协会（NIST）可追溯的称重条带称量分析方法对其余样本的涂层质量进行分析。使用统计技术，将称重条带称重数据与初始XRF数据相关。使用此关系计算保留样品的涂层质量，并将其用作保留样品的认证涂层质量。相对于传统的“边界”认证方法，此关联方法是一种更准确和有效的技术。由于固有的热浸锌涂层的可变性，本方法不会遭受增加的估计不确定性。而且，此方法提供了一种简单的方法来生成NIST可追溯的高质量CRM，可以对其进行定制以满足各个用户的需求。

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