统计运用及品管实务工具.ppt

統計運用及品管實務工具,資料數據 基礎統計運用概念 生產製造環境 實用品質統計工具 製程能力分析與SPC統計製程控制,天马行空官方博客：/tmxk_docin ；QQ:1318241189；QQ群：175569632,資料及數據,你想瞭解什麽?,資訊源:,分組,離散型,名義型,順序型,間距型,“資料本身並不能提供資訊 必須對資料加以處理以後才能得到資訊, 而處理資料的工具就是統計學”.,衡量,連續型,比率型, 文字的 (A to Z) 圖示的 口頭的 數位的 (0-9),數據,天马行空官方博客：/tmxk_docin ；QQ:1318241189；QQ群：175569632,FAIL,PASS,數量 單價 說明 總價 1 $10.00 $10.00 3 $1.50 $4.50 10 $10.00 $10.00 2 $5.00 $10.00,裝貨單,離散型資料和連續型資料,電氣電路,溫度,溫度計,連續型,離散型,卡尺,錯誤,離散型資料 (通常) 分組 / 分類 是 /否, 合格 / 不合格 不能計算 離散型資料 分級 很少用 很難加以計算 連續型資料 最常見的尺規 計算時要很小心 連續型資料 比例關係 可應用演算法的多數公式,分類 標簽 第一、第二、第三 相對高度 字母順序 1234 溫度計 刻度盤 速度= 距離/時間 直尺,衡量工具分類,說明,例子,衡量工具分類,名義型:不相關類, 只代表符合條件或不符合條件個體數. 順序型:順序類,但沒有各類間隔的資訊. 間距型:順序類,兩類之間間隔相等,但沒有絕對零點. 比例型:順序類,兩 類之間間隔相等, 同時存在絕對零點. .,離 散 型,連 續 型,天马行空官方博客：/tmxk_docin ；QQ:1318241189；QQ群：175569632,$,$,連續資料的優勢,連續的,離散的,信息量少,信息量多,基礎統計運用概念,變異(Variation),當我們從一過程中收集數據,會發現數據不會永遠相同,因為變異(Variation)在過程中隨時存在,變異(Variation),我們觀察到的變異,是在過程中各種擾動累積起來的.,變異(Variation),參數,X,X,X,X,X,X,X,X,X,量測值,分佈,多數在此,少數在此,Center均值,Spread散佈,雖然變異是隨機的,但他們的隨機性通常有模式存在,這種模式可用統計上的分佈(Distribution)來形容.如此變異加以統計分析,便可有某種程度的預測性存在並易於被理解或控制.,變異(Variation),中心Center: 數據最集中在何處? 散佈Spread:數據變異程度及分散狀況如何? 形狀Shape:分佈是否對稱?扁平?凹凸? 是否有異常區,描述分佈(Distribution),變異(Variation),變異可以是穩定(Stable)或不穩定(Unstable)的. - 穩定變異:變化的分佈較具預測性及一致性,對時間而言具可預測性 - 不穩定變異:對時間而言不具可預測性,PROCESS #1 - Stable Variation穩定,Part,T h i c k n e s s,PROCESS #2 - Unstable Variation不穩定,Part,Distribution,Distribution,T h i c k n e s s,變異(Variation),在製造過程中,有變異都是不好.問題是我們能容忍到何種範圍.我們能容忍的變異是具有以下兩項特徵:,STABLE (i.e., consistent and predictable over time).,CAPABLE (i.e., small variation compared to the product specifications.),Product Specifications,Parameter Distribution,穩定,散佈小,控制變異(Variation),瞭解過程:,使制程更好:,保持穩定並維持高制程能力,過程由時間來看是否穩? 制程能力是否能滿足目標規格?,確認並除去不穩定原因 確認並降低變異程度使滿足規格,持續監視及控制過程的變異源,特徵化,改善,控制,因為用抽樣統計,其結果只是估計, 和真實可能有差異. 適當的抽樣可使統計分析更準確.,Statistics 分佈的數學描述與定義,中心Center: 數據最集中在何處? 散佈Spread:數據變異程度及分散狀況如何? 形狀Shape:分佈是否對稱?扁平?凹凸? 是否有異常區,樣本均值,=,X,样本,抽樣概念-母體參數和樣本統計量,母體: 包含所關心特性的已經製造或將要製造的物件 的全體 樣本: 在統計研究中實際測量的物件組。 樣本通常爲所關心母體的子集,“母體參數”,“樣本統計量”,m = 母體均值,s = 樣本標準偏差,母體,s = 母體標準偏差,抽樣方法,抽樣方法 上面介紹了幾種從母體中抽樣的方式 隨機性-從母體中抽取的樣本設計應使母體中每一個都有同等機會抽中. 代表性-作為同一母體中其他樣本的實例.,系統隨機抽樣,分組抽樣,每一小時在該點 抽3個樣本,隨機抽樣,每個均有被選上的相等机會,層別式抽樣,母体被“層別”成几個組,在每個組內隨机選擇.,行進中的過程,每隔n個柚樣,一般準則,計數數據:50-100 計量數據:每個分組最少是30,均值: 一組值的算術平均均值: - 反映所有值的影響 - 受極值影響嚴重 中位數: 反應 50% 的序一組數排序後居中的數 - 在計算中不必包含所有值 - 相對於極值具有 “可靠性” 眾數值: - 在一組資料中最常發生的值,Median,(Mean平均),(Median中數),眾數,Center(中心),50%,50%,全距: 在一組資料中，最高值和最低值 間的數值距離 變異 (s2): 每個資料點與均值的平均平方偏差 標準偏差 (s): 變異數的平方根. 量化變動最常用的量,全距最大值最小值,Spread(散佈),The “ Rule“ states how and can be used to describe the entire distribution: Roughly 60-75% of the data are within 1 of . Roughly 90-98% of the data are within 2 of . Roughly 99-100% of the data are within 3 of .,60-75%,90-98%,99-100%,m,m - s,m - 2 s,m + s,m + 2 s,m + 3 s,m - 3 s,Spread(散佈),The shape of a distribution can be described by skewness 歪斜 (denoted by 1) and by kurtosis凹凸平坦 (denoted by 2).,歪斜,凹凸平坦,Shape (形狀),母體均值,樣本均值,母體標準偏差,樣本標準偏差,常用計算公式,母體變異,樣本變異,The most important and useful distribution shape is called the Normal distribution, which is symmetric(對稱), uni-modal(單峰), and free of outliers (沒有特異點):,Normal Distribution常態分佈,“常態” 分佈是具有某些一致屬性的資料的分佈 這些屬性對理解基礎過程（資料從該過程中收集）的特徵非常有用. 大多數自然現象和人爲過程都符合常態分配，可以用常態分配表示, 故大部份統計都假設是常態分佈。 即使在資料不完全符合常態分配時，分析結果也很接近。 特別不正常的分佈若假設為常態而去分析則有可能得到誤導結果。 有數學技術可將其轉變成常態分佈來作分析。,A Normal probability plot is a cumulative distribution plot where the vertical scale is changed in such a way that data from a Normal distribution will form a straight line:,Histogram,Cumulative Distribution,Normal Probability Plot,常態概率圖,Normal Distribution常態分佈,第一個屬性: 只要知道下面兩項就可以完全描述常態分配: 均值 標準差,常態分配的好處 -簡化,第一個分佈,第二個分佈,第三個分佈,這三個分佈有什麽不同?,常態曲線和其概率,4,3,2,1,0,-,1,-,2,-,3,-,4,40%,30%,20%,10%,0%,99.73%,第二個屬性: 曲線下方的面積可以用於估計某“事件”發生的累積概率,95%,68%,樣本值的概率,距離均值的標準偏差數,得到兩值之間的值的累積概率,常態概率圖,我們可以用常態概率圖檢驗一組給定的資料是否可以描述爲“常態” 如果一個分佈接近常態分配，則常態概率圖將爲一條直線。,資料收集時的重點,How the data are collected affects the statistical appropriateness and analysis of a data set(資料如何收集可影響統計的適切性). Conclusions from properly collected data can be applied more generally to the process and output. Inappropriately collected data CANNOT be used to draw valid conclusions about a process. Some aspects of proper data collection that must be accounted for are: The manufacturing environment(製程環境)from which the data are collected. When products are manufactured in batches or lots, the data must be collected from several batches or lots. Randomization(隨機). When the data collection is not randomized, statistical analysis may lead to faulty conclusions.,Continuous Manufacturing (連續)occurs when an operation is performed on one unit of product at a time. An assembly line is typical of a continuous manufacturing environment, where each unit of product is worked on individually and a continuous stream of finished products roll off the line. The automotive industry is one example of Continuous Manufacturing. Other examples of continuously manufactured product are: television sets, fast food hamburgers, computers.,Lot/Batch Manufacturing (批次) occurs occurs when operations are performed on products in batches, groups, or lots. The final product comes off the line in lots, instead of a stream of individual parts. Product within the same lot are processed together, and receive the same treatment while in-process. Lot/Batch Manufacturing is typical of the semiconductor industry and many of its suppliers. Other examples of lot/batch manufactured product include: chemicals, semiconductor packages, cookies.,生產製造環境,In Continuous Manufacturing the most important variation is between parts In Lot/Batch Manufacturing, the variation can occur between the parts in a lot and between the lots: Product within the same lot is manufactured together. Product from different lots are manufactured separately. Because of this, each lot has a different distribution. This is important because Continuous Manufacturing is a basic assumption for many of the standard statistical methods found in most textbooks or QC handbooks. These methods are not appropriate for Lot/Batch Manufacturing. Different statistical methods need to be used to take into account the several sources of variation in Lot/Batch Manufacturing. 要注意: 連續和批量生產所用的統計方法有些不同,With Lot/Batch Manufacturing, each lot has a different mean. Due to random processing fluctuations, these lots will vary even though the process may be stable. This results in several “levels” of distributions, each level with its own variance and mean: A distribution of units of product within the same lot. A distribution of the means of different lots. The total distribution of all units of product across all lots.,2,2,2,2,2,2,2,X,1,2,X,2,2,1,2,1,2,1,;,X,;,X,;,X,X,X,X,+,=,+,=,=,=,=,總,總,總,6原則,變異數可相加, 標準差則不能相加 輸入變數變異數相加計算輸出中的總變異數,所以,那麽,引起的變異數,輸入變數,引起的變異數,輸入變數,過程輸出的變異數,如果,process has small within-lot variation and large lot-to-lot variation (which is very common), data values from the same lot will be highly correlated, while data from different lots will be independent:,實用品質統計工具,直方圖(Histograms) 柏拉圖(Pareto Diagrams) 散佈圖(Scatterplots) 趨勢圖(Trend Charts),品質統計圖表 -直方圖(Histograms),Histograms provide a visual description of the distribution of a set of data. A histogram should be used in conjunction with summary statistics such as and s. A histogram can be used to: Display the distribution of the data(現示數據的分佈). Provide a graphical indication of the center, spread, and shape of the data distribution (較定性地顯示數據的均值,散佈及形狀). Clarify any numerical summary statistics (which sometimes obscure information). (顯示較模糊的統計結果). Look for outliers - data points that do not fit the distribution of the rest of the data. (顯示異常點),: : . . . : . . : : :.: : . : . : .:.:.:.:.: : . -+-+-+-+-+-加侖/分鐘 49.00 49.50 50.00 50.50 51.00,點圖分佈,設想有一個泵流量爲50加侖/分鐘的計量泵。 按照節拍對泵的實際流量進行了100次獨立測量。 畫出各個點，每點代表一個給定值的輸出“事件”。當點聚集起來時，泵的實際性能狀況可以看作泵流量的“分佈”。,5,1,.,3,5,0,.,8,5,0,.,3,4,9,.,8,4,9,.,3,4,8,.,8,4,0,3,0,2,0,1,0,0,直方圖分佈,還是這些資料，現在設想將其分組後歸入“區間”。泵流量點落入指定區間的次數決定區間條的高度。,頻率,加侖/分鐘,品質統計圖表 -直方圖(Histograms),品質統計圖表 -直方圖(Histograms), Multi-Modal Shape(雙峰):, Skewed Shape(偏一邊): Data can be right-skewed or left-skewed. This data is right-skewed the right tail is longer than the left tail.,Outliers:特異點,品質統計圖表 -柏拉圖(Pareto Diagrams),While histograms are used to display the distribution of a set of continuous (measured) data, Pareto diagrams are used to display the distribution of discrete (counted) data, such as different types of defects. Pareto diagrams can also be used with continuous (measured) data, particularly in displaying variance components analysis results, as we will see later in this course. Pareto diagrams are a useful tool for determining which problems or types of problems are most severe or occur most frequently, hence should be given high priority for process improvement efforts. Pareto diagrams separate the significant “vital few“ problems from the “trivial many“ to help determine which problems to address first (and which to address later). 重點中找重點!,Pareto圖分析,Pareto 圖根據 frequency 欄的內容判斷各個缺陷影響的大小，並按從大到小的次序排列。 最後一組總是標有 “其他” ，並以默認方式包括所有缺陷的分類計算，這幾類缺陷非常少， 它們占總缺陷的 5% 以下。 該圖右側 Y 軸表示占總缺陷的百分比，左側 Y 軸表示缺陷數。 紅線 (在螢幕上可以看到) 表示累積百分比，而直方圖表示每類缺陷的頻率 (占總量的百分比) 。在圖的下方列出所有的值,百分比,缺陷的Pareto圖,計數,缺陷 計數 274 59 43 19 10 18 百分比 64.8 13.9 10.2 4.5 2.4 4.3 累積百分比 64.8 78.7 88.9 93.4 93.4 100.0,螺釘丟失,夹子丢失,襯墊泄漏,外殼有缺陷,零件不完整,其他,400 300 200 100 0,100 80 60 40 20 0,百分比（%）,品質統計圖表 -柏拉圖(Pareto Diagrams),層別Pareto圖: 解釋分組資料,上圖使用了一個 By Variable（從屬變數），所有的圖都在一頁上。 下圖使用同樣的命令，沒有從屬變數。 當選擇每頁一張圖時，所有的圖的計數(左軸)刻度相同。 右側的百分比只反映該圖占總體的百分比。 這些圖表明， 70%的記錄缺陷是刮傷和剝落的 (下部)，約有一半的缺陷是夜班人員記錄的 (上右圖)。 此外，記錄缺陷是刮傷和剝落的比例，對白班和夜班的 來說似乎也差不多。然而，晚班和周末班出現的缺陷樣式是不同的。,裂紋Pareto圖,白班,晚班,夜班,周末班,刮傷 剝落 其他 污點,15 10 5 0,15 10 5 0,15 10 5 0,15 10 5 0,裂紋Pareto圖,40 30 20 10 0,100 80 60 40 20 0,缺陷 計數 15 13 6 6 百分比 37.5 32.5 15.0 15.0 累積百分比 35.5 70.0 85.0 100.0,刮伤,拨落,其他,污点,計數,計數,計數,計數,計數,百分比（%）,品質統計圖表 -柏拉圖(Pareto Diagrams),品質統計圖表 -散佈圖(Scatterplots),Until now, all the graphical tools weve discussed have been for examining the distribution of a single process characteristic. The scatterplot is a graphical tool for examining the relationship between two process characteristics. A scatterplot is an X-Y plot of one variable versus another. Each unit of product usually has many characteristics, process input variables, etc. One objective might be to see whether two variables or characteristics are related to each other (i.e., to see what happens to one of the variables when the other variable changes). This relationship between two variables is called correlation. Scatterplots can help us answer this type of question.,品質統計圖表 -散佈圖(Scatterplots),品質統計圖表 -散佈圖(Scatterplots),In addition to telling us whether or not two variables are related, scatterplots can tell us how they are related, and the strength of the relationship:,Strong Positive Correlation 強正相關,No Correlation無關,Weak Negative Correlation 弱負相關,Weak Positive Correlation 弱正相關,Strong Negative Correlation 強負相關,品質統計圖表 -散佈圖(Scatterplots),In addition, scatterplots are an excellent tool for determining the type of relationship between the two variables, as well as looking for outliers:,Linear Relationship 線性相關,Outliers 特異,Non-Linear Relationship 非線性相關,品質統計圖表 -散佈圖(Scatterplots),Correlation and Causation We must always take care not to confuse correlation with causation. The fact that two characteristics are correlated does not prove that one causes the other. Both may be related to some other factor which is the true root cause.,But is there a cause-effect relationship between the two? Did the increase in TVs cause the number of accidents to go up? (Not likely.) Did the increase in traffic accidents cause people to buy more TVs? (Not likely, either.),品質統計圖表 -趨勢圖(Trend Charts),Trend Charts Stability: A process is stable if its mean and standard deviation are constant and predictable over time. A disadvantage of histograms and normal probability plots is that they cannot be used to determine whether the process is stable over time. A plot of the data in time order will allow us to do that. These time-ordered plots, called Trend charts and Control charts are essential when examining the stability of a distribution over time. A trend chart or a control chart can detect instability if it exists. Control charts, which are a special kind of trend chart, are discussed in detail separately in a later course module. 可看出穩定性及預測性,品質統計圖表 -趨勢圖(Trend Charts),The table below contains average plating thickness measurements taken from 21 lots of product. Below that is a trend chart of the data.,品質統計圖表 - Noisy,The results of a statistical analysis can be seriously affected by the failure of the data to meet certain required assumptions. One of the most common assumptions is that the data values are independent and that they come from a Normal distribution. This assumption can be violated in several ways: Outliers (points that do not fit the rest of the distribution) in the data, Non-Normal-shaped distributions (multi-modal or skewed distributions), Data that exhibit these characteristics can be thought of as noisy data. The procedures in this section provide techniques for effective detection and analysis of noisy data.,雜訊,品質統計圖表 - Noisy,Boxplots,Trend Chart,Histogram,Scatterplot,Normal Prob. Plot,品質統計圖表 - Noisy,Recommended strategy for handling outliers: 1. Identify the outliers using the methods described in the following pages. If possible, find the causes of the outliers. Remove the outliers with identified causes from the data set(找原因). 2. If all the outliers can be explained, then analyze the data as usual. 3. However, if there are any outliers that do not have explanations, analyze the data twice: including the outliers, excluding the outliers. See if and how the analysis results differ.,製程能力分析與SPC統計製程控制,當製程開始產生變異時,其統計分佈圖的形狀也開始變化。通常變化不外下面三種基本狀況的組合:,若將每日之統計分佈串起來一起看,則又可看到更多變異現象,一般可分為兩種如下:,時間,時間,1.突發變異:製程中有特殊或突發原因而產生變異, 造成不穩定。例:每日生產參數設定漂移。,2.共同變異:製程中只有共同原因的變異 此種現象是穩定的”不良”。例:模具尺寸超差。,瞭解以上基本觀念後便開始加入管制的觀念。作管制時加入規格上下線, 超出規格則視為不良如下圖:,製程能力不好,中心值不在目標,分佈雖集中但超出規格外,製程能力最差,中心值不在目標,分佈不集中且超出規格外,計算Ca,Cp,Cpk公式,規格中心m,LSL,+ 3 ,- 3 ,製程寬度6 ,規格寬度T,USL,Su,SL,Ca: Capability of Accuracy準確度:,實際中心,Ca只對雙邊規格適用. 分級標準如下:,主值,計算Ca,Cp,Cpk公式,規格中心m,LSL,+ 3 ,- 3 ,製程寬度6 ,規格寬度T,USL,Su,SL,Cp: Capability of Precision精確度:,實際中心,當僅有下限時:Cp = ( -SL)/(3),對雙邊規格: Cp = T/(6),當僅有上限時: Cp = (Su- )/(3),分級標準如下:,計算Ca,Cp,Cpk公式,Cpk: 指制程能力參數, 是Cp和Ca的綜合. 對雙邊規格: Cpk=(1-Ca)*Cp= Min(Su- )/(3), ( -SL)/(3) 對單邊規格, 可以認為T為, 則 Ca= ( -)/ (T/2)= 0 Cpk= (1-Ca)*Cp= Cp,分級標準如下:,SPC介紹,SPC是用於研究變動的一種基本工具,它使用統計信號監測並改善過程績效。該工具可用於任何領域：製造業、商業，銷售業等等 SPC是統計程式控制（ Statistical Process Control）的縮寫。大多數公司是將 SPC用於最終産品 (Y)上, 而不是用於過程特徵 (X)。 第一步是使用統計方法控制公司的輸出。然而，只有我們將重點放在控制輸入 (X),而不是控制輸出 (Y)時, 我們才能認識到我們在提高質量、生産率及降低成本上的努力收效有多大。,什麽是統計製程控制（SPC）,所有過程都有固有變動（由於一般原因）和非固有變動（由於特殊原因）， 我們使用SPC來監測並改善過程。 SPC的使用使我們能夠通過失控信號發現特殊原因。這些失控信號無法說明過程失控的原因，只能表明過程處於失控狀態。 控制圖表是在統計上從時間上跟蹤過程和産品參數的方法。控制圖表中包括反映過程隨機變動固有限值的上下控制限值。 這些限值不應與 顧客規定限值相比較 。,什麽是統計製程控制（續）,基本統計原理，控制圖表能夠用於識別過程變數中的非固有（非隨機）型式。當控制圖表出現非隨機型式信號時，我們就可以知道特殊原因引起的變動改變了過程。我們採用措施修正控制圖表中非隨機型式，這是成功使用 SPC的關鍵。 控制限值是以爲衡量的Y或X建立 3限值爲基礎。,過程改善及控制圖,過程,衡量系統,輸入,輸出,1. 發現可指定的原因,4. 驗證結果,3.實施修正措施,2. 確定根本原因,控制圖的益處,用於提高生産率的已證實的技術 有效防範缺陷 防止不必要的過程調整 提供診斷資訊 提供關於過程能力的資訊,控制圖類型,控制圖有許多類型，但是它們的根本原理是相同的 利用 SPC和過程目標方面的知識選擇正確的類型 根據以下幾方面選擇控制圖類型： 資料類型: 屬性還是變數? 採樣容易：樣本同質性 資料分佈: 正常或非正常? 分組大小: 不變的或變化的? 其他考慮,控制圖的組成,KVOP的X均值圖,2,0,1,0,0,6,1,5,6,0,5,5,9,5,5,8,5,樣本數,X,=,5,9,9,.,1,U,C,L,=,6,1,3,.,6,L,C,L,=,5,8,4,.,6,控制下限,UCL = m +ks 中線 = m LCL = m - k s,其中 m = 樣本均值 s = 樣本標準偏差 k = 控制限制距中線的差值 (通常爲 3),記住: 控制限值與顧客規定限值無關,控制上限,中線,樣本均值,常用控制圖類型(X-S),常用控制圖類型(X-R),短期N 30,For control charts with N 30 lots, rather than the usual UCL (upper control limit) and LCL (lower control limit), there are dual sets of control limits: Outer Control Limits(3s). Inner Control Limits (1s).,短期N 30,Any point outside either of the outer control limits indicates an unstable process.,All points falling between both inner control limits indicates a stable process.,If any points fall inside either “uncertainty zone” (but none are outside the outer control limits), we cannot say whether or not the process is stable, because we do not yet have enough lots to be sure at this time.,With few lots, the control chart has wide “uncertaint