1.1 本规程描述了将纱线装运划分为测试批次的程序，以及对此类批次进行测试的抽样。 1.1.1 本规程可用于织物生产前和织物生产后的纱线批量样品测试。 1.2 本规程适用于由任何纤维或纤维混合物制成并支撑在任何形式的包装（包括梁）上的单股、合股或电缆纱线和绳索。 1.3 本规程还描述了从机织物或针织物上取下的纱线的取样程序；然而，当这样取样时，纱线通常不能代表整个货物，如中所述 1.1 . 因此，最终取样只能用于确定纱线的特性，通常不用于验收测试。此外，应认识到，在进入织物制造过程之前，织物纱线的特性可能不同于相同纱线的特性。 1.4 以国际单位制或英寸-磅单位表示的数值应单独视为标准值。每个系统中规定的值不一定是精确的等价物；因此，为确保符合本标准，每个系统应独立使用，且两个系统的值不得组合。 1.5 本标准并非旨在解决与其使用相关的所有安全问题（如有）。本标准的用户有责任在使用前制定适当的安全、健康和环境实践，并确定监管限制的适用性。 1.6 本国际标准是根据世界贸易组织技术性贸易壁垒（TBT）委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认标准化原则制定的。 ====意义和用途====== 5.1 为容器或批次、托运或交付中材料的任何属性赋值涉及一个测量过程，该过程包括采样和测试程序。分配值的正确性取决于测试和采样计划引起的可变性。即使通过精心制定的程序将测试引起的可变性降至最低，只有在采样程序避免系统偏差、将采样引起的变化降至最低并提供足够大小的实验室样品时，才可能正确且一致地估计特性的真值。 5.2 实践 D2258 可能不会给出在特殊情况下可能设计的最有效的采样计划，但确实提供了一个通用程序，以经济的采样量提供令人满意的精度。包括分层抽样在内的许多计划可以在教科书中找到，也可以通过使用统计软件工具和计算器找到。 5.2.1 如果买方未指定，制造商将根据以下一个或多个条件定义合适的生产批次:供应批次、生产班次/时间段、生产设备或生产线、操作员、指定装运、生产运行或本文的组合。 5.2.2 如果买方未规定，制造商将根据以下内容定义采样和测试频率:过程能力或能力分析、历史趋势、所需检测率水平、置信水平要求、已知变化或特殊原因，或两者兼而有之。制造商应尽一切努力确保生产合格产品，识别潜在的不合格产品或进行适当的隔离和识别。 5.2.3 采样计数和样本数量将基于文件中列出的标准实践。计数或频率的增加或减少可能基于典型标准偏差、精度和置信水平、测量系统分析（MSA）、量规R&R研究或量规线性和偏差研究。 较高的变化将表明计数增加的原因，而较低的变化将表明计数减少的原因，而较低的变化将表明计数减少的原因。 5.2.4 选择合适的样本量时，需要考虑以下几点:( 1. )抽样批次的大小( 2. )历史趋势( 3. )数据的分发( 4. )准确度和置信度( 5. )成本，以及( 6. )实用性。 5.2.5 最小化和减少测量误差将提高产品测试的可靠性，减少测试数据准确性的总体变化，并提高报告值的置信水平。 5.3 平均结果中给定可变性所需的最小样本数通常通过以下方式获得:( 1. )最大限度地增加批次样本中的集装箱数量( 2. )在实验室样品中，每个运输集装箱取一个单包装端，以及( 3. )每个包装只取一个样本。 不幸的是，这很少是测试产品最经济的方法，因为通常情况下，将运输容器作为批次样本的一部分成本最高，从运输容器中取出包装作为实验室样本的一部分成本居中，从包装或纱线中取取取和测试样本的成本最低。 5.4 为了将大量材料的抽样成本降到最低，有必要就大量材料的报告平均值的所需方差达成一致: 5.4.1 估计因批次样本引起的方差、因实验室样本引起的方差和因测试样本引起的方差。 5.4.2 计算批次样本数量、每个批次样本的实验室样本数量和每个实验室样本的样本数量的几种组合的平均测试结果的总方差。 5.4.3 计算执行中考虑的每个采样方案的成本 5.4.2 . 5.4.4 选择以下采样方案:( 1. )具有所需的精度和( 2. )是最经济的表演。

1.1 This practice describes a procedure for the division of shipments of yarn into test lots and the sampling of such lots for testing. 1.1.1 This practice can be used for lot sample testing of yarns for both pre-fabric production and post-fabric production. 1.2 This practice is applicable to single, plied, or cabled yarns, and cords, made of any fiber or mixture of fibers, and supported on any form of package, including beams. 1.3 This practice also describes procedures for the sampling of yarn(s) removed from woven or knitted fabrics; however, when thus sampled, the yarns are usually not representative of entire shipments, as referred to in 1.1 . Consequently, the resultant sampling can only be used to determine the characteristics of the yarn and is usually not used for acceptance testing. Moreover, it should be recognized that the characteristics of yarns from fabrics may be different than the characteristics of the same yarn(s), prior to being entered into the fabric manufacturing process. 1.4 The values stated in either SI units or inch-pound units are to be regarded separately as standard. The values stated in each system are not necessarily exact equivalents; therefore, to ensure conformance with the standard, each system shall be used independently of the other, and values from the two systems shall not be combined. 1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use. 1.6 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. ====== Significance And Use ====== 5.1 Assigning a value to any property of the material in a container or in a lot, consignment, or delivery involves a measurement process that includes both sampling and testing procedures. The correctness of the value assigned depends upon the variability due to testing and sampling plan. Even when the variability due to testing is minimized by carefully developed procedures, correct and consistent estimates of the true value of the property are possible only when the sampling procedure avoids systematic bias, minimizes variations due to sampling, and provides a laboratory sample of adequate size. 5.2 Practice D2258 may not give the most efficient sampling plan that might be devised in special situations but does present a general procedure that gives satisfactory precision with an economical amount of sampling. Many plans that include stratified sampling can be found in textbooks and through the use of statistical software tools and calculators. 5.2.1 If not specified by the purchaser, the manufacturer will define suitable production lots based on one or more of the following: supply lot, production shift/time segment, production equipment or production line, operator, designated shipment, production run, or a combination herein. 5.2.2 If not specified by the purchaser, the manufacturer will define sampling and testing frequency based on the following: process capability or capability analysis, historical trends, level of detection rate required, confidence level requirements, known variations or special causes, or both. Every attempt to ensure conforming product is being produced, identify potential nonconforming product or proper isolation and identification, will be carried out by the manufacturer. 5.2.3 Sampling count and number of specimens will be based on standard practice listed within the document. Increase or decrease in count or frequency might be applied based on typical standard deviation, precision and confidence level, Measurement System Analysis (MSA), gauge R&R study, or gauge linearity and bias study. Higher variations would indicate reason for an increase in count, while lower variations would indicate reason for decrease in count, while lower variations would indicate reason for decrease in count. 5.2.4 When selecting a suitable sample size, several considerations need to be made: ( 1 ) size of the lot being sampled, ( 2 ) historical trends, ( 3 ) distribution of the data, ( 4 ) level of accuracy and confidence, ( 5 ) cost, and ( 6 ) practicality. 5.2.5 Minimizing and reducing measurement error will improve product testing reliability, reduce overall variation of test data accuracy, and improve confidence level of the reported values. 5.3 The smallest number of specimens required for a given variability in the average result will usually be obtained by ( 1 ) maximizing the number of shipping containers in the lot sample, ( 2 ) taking a single package end per shipping container in the laboratory sample, and ( 3 ) taking only one specimen per package. Unfortunately, this is rarely the most economical way to test a product because it normally costs most to take a shipping container as part of the lot sample, costs an intermediate amount to take a package from a shipping container as part of a laboratory sample, and costs least to take and test a specimen from a package or yarn. 5.4 To minimize the cost of sampling a lot of material, it is necessary to agree on the required variance for the reported average for a lot of material: 5.4.1 Estimate the variance due to lot samples, the variance due to laboratory samples, and the variance due to testing specimens. 5.4.2 Calculate the total variance for average test results for several combinations of the number of lot samples, the number of laboratory samples per lot sample, and the number of specimens per laboratory sample. 5.4.3 Calculate the cost of performing each of the sampling schemes considered in 5.4.2 . 5.4.4 Select the sampling scheme that ( 1 ) has the required precision and ( 2 ) is most economical to perform.