【正文】 storage plan (who, what, when, where,etc.) The supervisor collected the data and entered it in a worksheet6. Describe the procedure and settings used to run the process Standard, constant process settings.7. Assemble and train the team. Define responsibilities. For a small project, the supervisor did all the work8. Collect the data. The data are in Minitab worksheet 9. Analyze the data Analysis is on the following slides the Camp。運(yùn)用Minitab分析數(shù)據(jù)并評估量具能力StatQuality ToolsGage Ramp。量具研究偏差并不一定代表真實(shí)的工藝偏差P/TV（%Ramp。第一步：確認(rèn)標(biāo)準(zhǔn)這一階段常被忽視。定性型量具 Ramp。消費(fèi)者偏見員工傾向把合格產(chǎn)品判為廢品有效篩選分?jǐn)?shù)（%）在定性型Ramp。用 直線連到箭頭線上。觀察導(dǎo)致行為改變確認(rèn)實(shí)際工藝設(shè)置與記錄的設(shè)置相同跨班跨機(jī)器觀察工藝如何繪制工藝流程細(xì)圖:工藝流程細(xì)圖：6 Sigma 工藝流程圖要素：工藝或產(chǎn)品是輸出指標(biāo)Y和輸入指標(biāo)X標(biāo)準(zhǔn)上下限和標(biāo)準(zhǔn)控制文件所用設(shè)備/工具繪制工藝流程細(xì)圖工藝流程細(xì)圖必須依工藝流程圖而畫。5問題的嚴(yán)重程度是什么？1問題是什么？a目標(biāo)b質(zhì)量/缺陷水平a1宏觀圖a3魚骨圖？？4，繪制宏觀圖。更改其一應(yīng)在另一個(gè)中反映出來。，集思廣益并列出所有可能引起問題發(fā)生的因子。R檢驗(yàn)過程中，所有員工本身前后一致且相互之間也一致的比例。R 結(jié)論:我們?nèi)绾卧O(shè)定標(biāo)準(zhǔn)？設(shè)計(jì)部門設(shè)計(jì)藍(lán)圖設(shè)計(jì)部門如何得到各項(xiàng)要求？工藝部門標(biāo)準(zhǔn)由工藝以前能夠做到的或開始使用時(shí)的能力定這想法有錯(cuò)嗎？客戶我們總是對客戶說可以嗎？對上例而言：設(shè)備室目標(biāo)溫度是1250C177。R）6 Sigma 首選測量量具與工藝偏差相比其性能如何使用時(shí)應(yīng)小心。R Study(Crossed)...Minitab 量具Ramp。E matrix
Before or after the control plan, depending on the maturityof the processWhy?Warm up exercise:
You have 60 seconds to document: What would you want to know about a “defect”?
For the process:
FMEA improves the reliability of the process
An FMEA identifies problems before they occur
FMEA serves as a record of improvement amp。 中心限理論:Central Limit TheoremQ: Why Are So Many Distributions Normal? Why is something thisplicated somon?Science has shown us that variables thatvary randomly are distributed normally. Soa normal distribution is actually a randomdistribution.Another reason why some distributionsare normally distributed is becausemeasurements are actually averages overtime of many submeasurements. Thesingle measurement that we think we aremaking is actually the average (or sum) ofmany measurements. The Central LimitTheorem, discussed in the following slides,provides an explanation of why averages ofnonnormal data appear normal.Dice Demonstration (Integer Distribution)
What does a probability distributionfrom a single die look like?
What is the mean?
What is the standard deviation?
Construct a dataset in Minitab
Select Calc Random Data Integer… from the mainmenu
Generate 1,000 rows of data in C1: Min = 1, Max = 6
Use Minitab’s Graphical Summary routine for analysis
Stat Basic Statistics Display Descriptive Statistics… Minitab Output (Typical)The probability distribution of thepossible outes of the roll of a single dieis obviously nonnormal.A perfect distribution would have hadall six bars exactly equal, but even with10,000 data points, there is still somedifferences in the histogram. If a betterestimate is required, a different data setcould be constructed with exactly equalcounts of each possible oute. Try itand see if the numbers are any different. Sampling a Nonnormal Distribution – Exercise
Each person in the class is to toss a single die sixteentimes and record the data. Calculate the mean and standard deviation of eachsample of sixteen Record the means and standard deviations from eachperson in the class in a Minitab worksheet
Use Minitab’s Graphical Summary routine for analysis Stat Basic Statistics Display Descriptive Statistics…Alternately, a sample of sixteen throwsof the dice can be simulated in Minitab asfollows:Select: Calc Random Data Integer… fromthe main menuGenerate 16 rows of data in C1: Min = 1, Max= 6Analyze the Sample Data
What is the mean of the sample averages? Mean ≈
What is the standard deviation of the sample averages? Sigma ≈
Is the distribution normal? What is the pvalue?
What is the relationship between the average of thesample means and the population average?
What is the relationship between the sigma of theaverages and the sigma of the individuals?The Central Limit Theorem
Formal Definition:
If random samples of n measurements are repeatedlydrawn from a population with a finite mean μμμμand a standarddeviation σ σσ σ , then, when n is large, the relative frequencyhistogram for the sample means (calculated from therepeated samples) will be approximately normal with amean μμμμand a standard deviation equal to the populationstandard deviation, σ σσ σ , divided by the square root of n.(Note: The approximation bees more precise as nincreases.)Central Limit Theorem – Exercise
From a Minitab analysis of the uniformly distributeddata:
For an exercise, verify that the Central Limit Theorem isvalid for this uniform dataVariable N Mean StDevn=1 (Individuals) 10000 n=2 (Means) 10000 n=5 (Means) 10000 n=30 (Means) 10000 相關(guān)性及簡單線性回歸:Regression amp。 storage plan (who, what, when, etc.)6. Describe the procedure and settings used to run the process7. Assemble and train the team. Define responsibilities8. Collect the data9. Analyze the data10. Verify the results11. Draw conclusions. Report results. Make remendationsInjection Molding Example1. Clearly state the objective Determine the process capability of the injection molding process Determine the major sources of noise variation2. List the X’s and Y’s to be studied Output: Thickness Inputs: Cavity (slot), cycle, sample3. Ensure measurement system capability An MSA was conducted and the system was found capable4. Describe the sampling plan One sample from each slot, five consecutive runs, four times aday for five days.5. Describe the data collection amp。E matrix team.
May need to add a rep from quality, a supplier, reliability
When should the FMEA be constructed?
After the process map amp。 中。R）6 Sigma 首選測量量具與量具研究偏差相比其性能如何最適合進(jìn)行工藝改進(jìn)的評估使用時(shí)應(yīng)小心。設(shè)備室溫度和在最小飽和蒸汽濃度的周期時(shí)間決定殺菌程度在整個(gè)設(shè)備室維持前后一致的溫度范圍很重要。R試驗(yàn)，核實(shí)調(diào)整后