6160sigma160bb黑带培训资料-160160trw_booster_wk2_11_dmaicreview(编辑修改稿)

6160sigma160bb黑带培训资料-160160trw_booster_wk2_11_dmaicreview(编辑修改稿)内容摘要：

= Copier paper, paper clip, ” wing (This corner represents all the High levels: +, +, +) Yes Copier Paper clip Paper type Wing Length No Recycled 27 Introduction to MSD 23 Experiment We will do an experiment that will take you through each of the steps needed to plan, carry out, and analyze a designed experiment Experiment Design Experiment Analysis 1. Identify responses 2. Identify factors 3. Select design 4. Choose factor levels 5. Randomize runs 6. Conduct experiment and collect data 7. Analyze data 8. Draw conclusions 9. Verify results 28 Focused question: What are reasons why MSDs would fail (be nondurable)? Shaded areas indicate potential causes the team thought were most likely to contribute to the problem Environment Procedures Materials People Nondurable MSDs Causes Effect Raw Material Vendor Abel Vendor Noesting Sizes Vendor Quality Small Large Bad Good Purchasing Agent Management Total Cost Warehouse Requester Specs Detail Storage Time Inventory cost High quality Low bid Personality Knowledge Out of date Lacking Packaging Storage Inspection Processing Method Date Type Batch Date Temperature Time Type Quality vendor Consistent Temperature Time Attributes Type Heat Treatment Raw Material Chemicals Inspection Cleaning Purchasing Cheapest No Specs. Analyze MSD Example 29 Main Effects Plot Here is a typical Main Effects plot (this is not the MSD data).  The Main Effects Plot is an efficient way to see the change in the average response (Y) for each factor  Use Pvalues from the output to discern which effects are significant (distinguishable from mon cause variation) C B A 90 85 80 75 70 Response (Y) Main Effects Plot (data means) for Response (Y) Low A High A Low B High B Low C High C Positive effect of A Negative effect of B Nonsignificant effect of C Dotted line indicates overall average 30 Interaction Plot 60 80 100 60 80 100 A B C Low A High A Low B High B Interaction Plot (data means) for Response (Y) Low B High B Low C High C Nonsignificant interaction of AB Nonsignificant interaction of AC Significant interaction of BC Average response (Y) Average response (Y) 31 Cube Plots  Each corner represents a particular experimental condition  The average response is labeled at each condition  Compare responses on the faces of the cube for factor effects: • Left to right = Effect of A • Bottom to top = Effect of B • Front to back = Effect of C 81 91 107 93 45 67 83 73 C B A High Low High Low High Low Cube Plot (data means) for Response (Y) 32 Confounding From observing the result, it is impossible to tell if it was caused by A alone, or B alone, or a bination of both. 33 Available Factorial Designs Minitab mands Stat DOE Factorials Create Factorial Design Display Available Designs Rows = number of experimental runs (before replication) Columns = number of factors being investigated 34 16 18 20 22 24 26 42 52 62 7 8 9 Soak Time (min.) Concentration (%) Contour Plot of Plating What is Response Surface Methodology? Response Surface Methodology refers to the design and analysis of experiments that can model curved relationships. For Response Surface Analysis, all X’s must be continuous variables. 40 15 20 Plating (mm) Soak Time (min.) 50 60 25 Concentration (%) x2 x1 y x2 x1 y = Plating (mm) Wire Diagram 35 Central Composite Designs Since we have previously covered the design of a full factorial with more than two levels, we will now focus on the Central Composite Design.  is used to represent the distance the axial point is from the center point in scaled units.  is chosen to assure rotatability and orthogonal blocking Factorial points Axial points Center points  This design has desirable properties, including: • It can be run in sequential orthogonal blocks, first cube points, then axial points •  can be chosen for rotatability 36 Optimizing for Multiple Responses The results of this mand are shown below: An overall desirability score less than 1 shows a trade off was necessary between the responses The Cur values are the factor level settings that achieve the optimum desirability. The y values listed give the response at the Cur settings The vertical lines slide. As you move them, you can see the effect on the desirability score 37 Recap of DOE Module A. Awareness B. Full Factorial Designs Topic Purpose Subtopics Get people thinking about what’s involved with running an experiment Demonstrate benefits of structured multifactor experiments Learn ways to reduce the number of trials needed for a designed experiment and still get all the information you need Understand the practical aspects of experimental design How we currently run an experiment • Change one thing at a time • Change everything we think matters all at once Do by hand Using Minitab to design and analyze experiments Random ization Replica tion , Residuals Main effects Inter actions Effects amp。 factorial plots C. Half Fractions D. Other Fractions E. Screening Designs F. Planning an experiment Which runs we’d pick intuitively Nice properties (balance, collapsing) Cost of fraction ation is confound ing。