meira-
实验设计Design Of Experiments, 在质量控制的整个过程中扮演了非常重要的角色，它是我们产品质量提高，工艺流程改善的重要保证。实验设计已广泛运用了从航天业到一般生产制造业的产品质量改善、工艺流程优化甚至已运用到医学界。籍此课程，您将通过对产品质量，工艺参数的量化分析，寻找关键因素，控制与其相关的因素。根据实际需求，学习判别与选择不同的实验设计种类，设计你的实验步骤，发现如何控制各种影响因素，以最少的投入，换取最大的收益，从而使产品质量得以提升，工艺流程最优化
DOE的实验步骤
(1)筛选主要显著的因子 (2)找出最佳之生产条件组合 (3)证明最佳生产条件组合有再现性
编辑本段如何判断第一阶段实验成功
(1)在ANOVA分析中出现了1~4个显著因子 (2)这些显著因子的累积贡献率在75%以上
编辑本段如何判断第二阶段实验成功
在ANOVA分析中没有出现显著因子
编辑本段DOE的方法
常见的试验设计方法，可分为二类，一类是正交试验设计法，另一类是析因法。 （1）正交试验设计法 ① 定义 正交试验设计法是研究与处理多因素试验的一种科学方法。它利用一种规格化的表格——正交表，挑选试验条件，安排试验计划和进行试验，并通过较少次数的试验，找出较好的生产条件，即最优或较优的试验方案。 ② 用途 正交试验设计主要用于调查复杂系统（产品、过程）的某些特性或多个因素对系统（产品、过程）某些特性的影响，识别系统中更有影响的因素、其影响的大小，以及因素间可能存在的相互关系，以促进产品的设计开发和过程的优化、控制或改进现有的产品（或系统）。 （2）析因法 ① 定义析 析因法又称析因试验设计、析因试验等。它是研究变动着的两个或多个因素效应的有效方法。许多试验要求考察两个或多个变动因素的效应。例如，若干因素：对产品质量的效应；对某种机器的效应；对某种材料的性能的效应；对某一过程燃烧消耗的效应等等。将所研究的因素按全部因素的所有水平（位级）的一切组合逐次进行试验，称为析因试验，或称完全析因试验，简称析因法。 ② 用途 用于新产品开发、产品或过程的改进、以及安装服务，通过较少次数的试验，找到优质、高产、低耗的因素组合，达到改进的目的。
编辑本段DOE的应用
Reducing Variability With DOE(1)Apply powerful design of experiments (DOE) tools to make your system more robust to variations in component levels and processing factors."Six Sigma" is the new rallying cry for quality improvement in the process industry. For example, Dow aims to generate an extra $1.5 billion per year in profits after training 50,000 of their employees on the methods of Six Sigma.3 Statistical tools play a key role in achieving savings of this magnitude. In fact, "sigma" is a Greek letter that statisticians use as a symbol for standard deviation - a measure of variability. If a manufacturer achieves a Six Sigma buffer from its nearest specification, they will experience only 3.4 off-grades per million lots. This translates to better than 99.99966% of product being in specification. To illustrate what this level of performance entails, imagine playing 100 rounds of golf a year with two putts per hole being the norm (par): At Six Sigma you"d make a three-putt (bogey) only every 163 years!4 Even Tiger Woods would be envious of this level of quality. Of all the statistical tools employed within Six Sigma, design of experiments (DOE) offers the most power for making breakthroughs. Via an inspirational case study, this article demonstrates how DOE can be applied to development of a formulation and its manufacture to achieve optimal performance with minimum variability, thus meeting the objectives of Six Sigma programs. Armed with knowledge gained from this article and the example as a template, chemists and engineers from any of the process industries (pharmaceutical, food, chemical, etc.) can apply these same methods to their systems and accomplish similar breakthrough improvements. Minimizing Propagation of Error (POE) from Varying Inputs After earning his PhD in chemistry and taking a job at a chemical company, a colleague of ours got assigned to an operator for an orientation to the real-world of production. As the operator watched with much amusement and disgust, the chemist carefully weighed out materials with a small scoop. The operator pushed the PhD chemist aside, grabbed a sack of chemicals and tossed it into the reactor. "You"re not in the laboratory anymore," he said, "This is how we do things in manufacturing." Hopefully the operators of your formulation process will be more exacting when adding ingredients. However, at the very least, you can expect some variation due to inherent limitations in equipment. How will these variations affect product quality and process efficiency? Can you do anything to make your system more robust to variations in component levels and process settings? The answers to these very important questions can be supplied via an advanced form of DOE called "response surface methods" (RSM). This statistical tool produces maps of product and product performance, similar to topographical displays of elevation, as a function of the input variables that you (or your clients) control. The objective of Six Sigma is to "find the flats" - the high plateaus of product quality and process efficiency that do not get affected much by variations in component levels or factor settings. You can find these desirable operating regions visually, by looking over the 3D renderings of response surfaces, or more precisely via a mathematical procedure called "propagation of error" (POE). To see how POE works, let"s look at a very simple response surface (Figure 1) generated by changing only one control factor X1. Assume that this factor exhibits a constant variation shown on the graph as a difference with magnitude delta (D). This variation, or error, will be transmitted to the measured response to differing degrees depending on the shape of the curve at any particular setting. In this example, because the curve flattens out as the control factor increases, a setting at the higher level causes less propagation of error (POE). Therefore, you see a narrower difference (DY) on the response axis as a result of setting the factor at the higher, rather than lower level. With the aid of some calculus, the POE itself can be graphed as a continuous function. In this case the original response surface can be described by the following quadratic equation: We will spare you the details, but after taking the partial derivative of this function with respect to the input (X1) and taking the square root, the following equation for standard deviation (s) is produced: Assume for now that the standard deviation of the control factor X1 equals one (sx = 1) and there are no other sources of variance (sResidual = 0). We"ve now obtained the information needed for graphing the standard deviation of the response (sy) transmitted from the variation in the input factor (X), in other words, the POE (see Figure 2). In this case the POE decreases in direct proportion to X1 as it increases.
编辑本段DOE实验设计
一、DOE简介 1、DOE的定义 2、DOE的历史与发展 3、DOE的用途 4、DOE的成功运用案例 二、DOE类型 1、全因子DOE 2、分部DOE 3、筛选DOE 4、中心复合DOE 5、Box-Behnken DOE 6、田口静态DOE 7、均匀DOE 三、设计一个DOE的步骤(案例模拟) 1、定义问题，定义项目 2、确定可能的因变量 关于选择因子与其水平的策略 输入因子的类型与应用 干扰因子 可控因子 常数项 3、选择设计类型 4、分析数据,标识主要影响因素 5、提出解决方案 6、重复实验以确认结果 7、过程能力评估 8、制定优化方案 四、DOE的有效性 1、内部有效性 2、外部有效性 3、统计结论的有效性 五、DOE结果分析 1、因素影响与交互影响 2、极差分析 3、ANOVA方差分析 单向方差分析 双向方差分析 4、回归分析 六、如何利用Minitab进行DOE分析 1、在Minitab中的图形分析 正态概率图 Pareto 主效果图 交互效果图 2、在Minitab中的统计分析 ANOVA 多元回归 简化模式 七、DOE在应用中的问题 1、因素影响与交互影响试验的阶段性 2、极差分析因子水平的选择 3、测量误差 4、重复与反复 5、随机化 6、分块 7、诊断与残差点 8、优化试验（EVOP） 八、设计DOE计划的成功关键 1、团队合作 2、知识技术的跨功能 3、定义问题 4、可量化的改善目标 九、DOE应用实例
蓓蓓-
这个不是一两句话就说得清楚，建议你买马逢时主编的《六西格玛管理统计指南-MINITAB使用指导》，同时安装一个MINITAB，很快你就会入门的。