1.1 该实践为在统计过程控制程序中使用控制图提供了指导，该程序通过识别和消除变异的特殊原因的影响来减少变异，从而提高过程质量。 1.2 控制图用于持续监控产品或过程特性，以确定过程是否处于统计控制状态。当达到该状态时，工艺特性将至少近似地以给定概率在一定限度内变化。 1.3 这种做法适用于变量数据（在连续数值尺度上测量的特征）和属性数据（在定义的时间或空间间隔内以百分比、分数或出现次数测量的特征）。 1.4 这种做法的单位制没有规定。实践中的尺寸量仅作为计算方法的说明。这些实施例对处理的产品或测试方法没有约束力。 1.5 本标准并不旨在解决与其使用相关的所有安全性问题（如果有）。本标准的使用者有责任在使用前建立适当的安全、健康和环境实践并确定法规限制的适用性。 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》中确立的国际公认的标准化原则制定的。 ======意义和用途====== 4.1 本实践描述了控制图作为统计过程控制(SPC)工具的使用。控制图由Shewhart开发 ( 2 ) 3 在20世纪20年代，至今仍在广泛使用。SPC是统计质量控制的一个分支 ( 3 , 4 ) 其中还包括工艺能力分析和验收抽样检查。过程能力分析，如实践中所述 E2281 ，要求在其某些程序中使用SPC。验收抽样检查，见实施规程 E1994 , E2234 ，和 E2762 ，要求使用SPC以最大限度地减少产品的拒收。 4.2 SPC的原理- 过程可以被定义为将输入转换为输出的一组相互关联的活动。SPC使用各种统计方法来通过减少一个或多个输出（例如产品或服务的质量特征）的可变性来提高过程的质量。4.2.1 无论流程设计或维护得如何，所有流程输出都会存在一定程度的可变性。仅具有这种固有可变性的过程被称为处于统计控制状态，其输出可变性仅受偶然或共同原因的影响。 4.2.2 过程扰动，据说是由于可分配的或特殊的原因，表现为输出水平的变化，如尖峰、偏移、趋势，或输出可变性的变化。控制图是SPC中的基本分析工具，用于检测作用于过程的特殊原因的发生。 4.2.3 当控制图表示存在特殊原因时，各种参考文献中描述的其他SPC工具，如流程图、头脑风暴、因果图或帕累托分析 ( 4- 8 ) ，用于识别特殊原因。特殊原因一旦被发现，要么被消除，要么被控制。当特殊原因的变化被消除时，过程的变化被降低到其固有的变化，然后控制图起到过程监视器的作用。进一步减少变化将需要修改工艺本身。 4.3 不建议使用控制图来调整一个或多个过程输入，尽管控制图可能表明需要这样做。工艺调整方案超出了本实践的范围，由Box和Luceño讨论 ( 9 ) . 4.4 控制图的作用随着SPC程序的发展而变化。SPC程序可以分为三个阶段 ( 10 ) . 4.4.1 阶段A，工艺评价- 将来自该过程的历史数据绘制在控制图上以评估该过程的当前状态，并计算来自该数据的控制限度以供进一步使用。参见参考文献。 ( 1 ) 关于使用控制图进行数据分析的更完整的讨论。理想情况下，建议为该阶段收集100个或更多的数字数据点。对于每个亚组的单个观察，应至少收集30个数据点 ( 6 , 7 ) .对于属性，建议总共20到25个子组数据。在这一阶段，很难找到特殊的原因，但汇编一份可能的来源清单供下一阶段使用将是有用的。 4.4.2 阶段B，工艺改进- 实时收集过程数据，并使用阶段A中计算的限值的控制图来检测特殊原因，以便识别和解决。团队方法对于寻找特殊原因变异的来源至关重要，并且将增加对过程的理解。当控制图的进一步使用表明存在统计控制状态时，该阶段完成。 4.4.3 阶段C，过程监控- 控制图用于监控过程，以不断确认统计控制的状态，并对进入系统的新的特殊原因或先前特殊原因的再次出现做出反应。在后一种情况下，可以制定失控行动计划(OCAP)来处理这种情况 ( 7 , 11 ) .定期更新控制限度或如果发生工艺变更。 附注1: 一些从业者将阶段A和B合并为阶段I，并将阶段C表示为阶段II ( 10 ) .

1.1 This practice provides guidance for the use of control charts in statistical process control programs, which improve process quality through reducing variation by identifying and eliminating the effect of special causes of variation. 1.2 Control charts are used to continually monitor product or process characteristics to determine whether or not a process is in a state of statistical control. When this state is attained, the process characteristic will, at least approximately, vary within certain limits at a given probability. 1.3 This practice applies to variables data (characteristics measured on a continuous numerical scale) and to attributes data (characteristics measured as percentages, fractions, or counts of occurrences in a defined interval of time or space). 1.4 The system of units for this practice is not specified. Dimensional quantities in the practice are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated. 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 ====== 4.1 This practice describes the use of control charts as a tool for use in statistical process control (SPC). Control charts were developed by Shewhart ( 2 ) 3 in the 1920s and are still in wide use today. SPC is a branch of statistical quality control ( 3 , 4 ) , which also encompasses process capability analysis and acceptance sampling inspection. Process capability analysis, as described in Practice E2281 , requires the use of SPC in some of its procedures. Acceptance sampling inspection, described in Practices E1994 , E2234 , and E2762 , requires the use of SPC to minimize rejection of product. 4.2 Principles of SPC— A process may be defined as a set of interrelated activities that convert inputs into outputs. SPC uses various statistical methodologies to improve the quality of a process by reducing the variability of one or more of its outputs, for example, a quality characteristic of a product or service. 4.2.1 A certain amount of variability will exist in all process outputs regardless of how well the process is designed or maintained. A process operating with only this inherent variability is said to be in a state of statistical control, with its output variability subject only to chance, or common, causes. 4.2.2 Process upsets, said to be due to assignable, or special causes, are manifested by changes in the output level, such as a spike, shift, trend, or by changes in the variability of an output. The control chart is the basic analytical tool in SPC and is used to detect the occurrence of special causes operating on the process. 4.2.3 When the control chart signals the presence of a special cause, other SPC tools, such as flow charts, brainstorming, cause-and-effect diagrams, or Pareto analysis, described in various references ( 4- 8 ) , are used to identify the special cause. Special causes, when identified, are either eliminated or controlled. When special cause variation is eliminated, process variability is reduced to its inherent variability, and control charts then function as a process monitor. Further reduction in variation would require modification of the process itself. 4.3 The use of control charts to adjust one or more process inputs is not recommended, although a control chart may signal the need to do so. Process adjustment schemes are outside the scope of this practice and are discussed by Box and Luceño ( 9 ) . 4.4 The role of a control chart changes as the SPC program evolves. An SPC program can be organized into three stages ( 10 ) . 4.4.1 Stage A, Process Evaluation— Historical data from the process are plotted on control charts to assess the current state of the process, and control limits from this data are calculated for further use. See Ref. ( 1 ) for a more complete discussion on the use of control charts for data analysis. Ideally, it is recommended that 100 or more numeric data points be collected for this stage. For single observations per subgroup at least 30 data points should be collected ( 6 , 7 ) . For attributes, a total of 20 to 25 subgroups of data are recommended. At this stage, it will be difficult to find special causes, but it would be useful to compile a list of possible sources for these for use in the next stage. 4.4.2 Stage B, Process Improvement— Process data are collected in real time and control charts, using limits calculated in Stage A, are used to detect special causes for identification and resolution. A team approach is vital for finding the sources of special cause variation, and process understanding will be increased. This stage is completed when further use of the control chart indicates that a state of statistical control exists. 4.4.3 Stage C, Process Monitoring— The control chart is used to monitor the process to confirm continually the state of statistical control and to react to new special causes entering the system or the reoccurrence of previous special causes. In the latter case, an out-of-control action plan (OCAP) can be developed to deal with this situation ( 7 , 11 ) . Update the control limits periodically or if process changes have occurred. Note 1: Some practitioners combine Stages A and B into a Phase I and denote Stage C as Phase II ( 10 ) .