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【文章內(nèi)容簡(jiǎn)介】 ，因?yàn)槲覀兛床坏絻蓚€(gè)或更多因子同時(shí)改變時(shí)的效果。這是說(shuō)因子 A在因子 B處于高水平時(shí)和處于低水平時(shí)對(duì)輸出的影響可能是不一樣的。 OFAT Six Sigma Green Belt Training Design of Experiments (DoE) Read the case material provided and prepare the following as teams: 閱讀所提供的案例材料并分組回答下面問(wèn)題： ? What was different about the designed experiment that Alex ran as pared to the OFAT experiment? Alex運(yùn)行的經(jīng)過(guò)設(shè)計(jì)的實(shí)驗(yàn)與 OFAT實(shí)驗(yàn)有什么不同？ ? Is there now a pointer to an area where yields may be higher? 現(xiàn)在是否有指示出更高產(chǎn)出率的方向？ ? What new inference space should Alex explore? Alex應(yīng)該探求的新推論空間是什么？ Case Study – Yield Improvement Sections 4 amp。 5 案例分析 – 改善產(chǎn)出率之第四、五節(jié) Six Sigma Green Belt Training Design of Experiments (DoE) ? Full Factorial Designs (Factorial designs) test every possible bination of factors over the inference （因子實(shí)驗(yàn)）檢驗(yàn)推論空間范圍內(nèi)的所有可能的組合 ? These designs are of particular interest because they provide a great deal of information. 這些設(shè)計(jì)極其重要因?yàn)樗鼈?提供了大量的信息 。 ? The amount of resources necessary to run full factorial designs can be exorbitant if the number of factors is large. Full factorials are generally run when the experimenter has a high degree of confidence that the factors in the study have an influence over the ，運(yùn)行全因子實(shí)驗(yàn)所必需的資源數(shù)量是非常大的。一般實(shí)驗(yàn)者在有高度的信心認(rèn)為所研究的因子對(duì)輸出有影響時(shí)才運(yùn)行全因子實(shí)驗(yàn)。 Full Factorials 全因子實(shí)驗(yàn) 因子數(shù)量 (2水平 ) 運(yùn)行次數(shù) 2 22 = 4 3 23 = 8 4 24 = 16 5 25 = 32 6 26 = 64 2n Factor columns (2n): 因子列 (2n): Exponent。 n = of factors to be tested 指數(shù) 。 n = 因子數(shù) Base。 2 = the of levels to be tested for each factor 基數(shù) 。 2 = 每個(gè)因子的水平數(shù) Six Sigma Green Belt Training Design of Experiments (DoE) DOE Design and Results: DOE設(shè)計(jì)和結(jié)果： Which Factors appear to be important? 哪一個(gè)參數(shù)看起來(lái)重要些 ? How should the important factors be set? 這些參數(shù)應(yīng)該如何設(shè)定 ? Is there an Interaction between the variables? 因子之間是否存在交互作用 ? Full Factorial – Yield Improvement DOE 案例分析 – 改善產(chǎn)出率之 DOE Time Temp 時(shí)間 溫度 Yield A B AB 產(chǎn)出率 70 () 145 () + 56 130 (+) 145 () 69 70 () 165 (+) 82 130 (+) 165 (+) + 58 Six Sigma Green Belt Training Design of Experiments (DoE) ? The first consideration before applying any statistical analysis technique is whether the results of the DOE are of any practical 統(tǒng)計(jì)工具之前要考慮其結(jié)果是否有實(shí)用價(jià)值。 ? Did the response variable change? Did it change the desired amount?響應(yīng)變量會(huì)隨之變化嗎 ? 變化量是否足夠大 ? ? If the response variable did not change substantially across the factor treatment binations, it may be that: 如果相應(yīng)變量變化不夠明顯，可能是因?yàn)?: 1. Factor levels were not set far enough apart (didn’t go bold enough).因子水平設(shè)置不夠大 (不夠大膽 ) 2. The selected factors do not affect the response 影響 3. The measurement system is not ? Practical 實(shí)用 ? Graphical 圖示 ? Analytical 解析 Six Sigma Rules of Analysis: 六西格瑪分析的規(guī)則 ? Practical實(shí)用 Six Sigma Green Belt Training Design of Experiments (DoE) ? Another
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