Polyhedrika

  A wooden catapult has been widely used in Six Sigma education to illustrate multiple factor process variation and optimization. We will use a catapult simulator with 4 factors to practice: Process variation Linear regression Response surface analysis Process optimization This will be done using Microsoft Excel Analysis and Solver Download this Excel simulator Catapult.xlsx from One Drive Working Plan 1. What factors affect distance? 2. What values of these factors to achieve maximum distance? 3. Run a full factorial set of experiments with 4 central points 4. Can you detect curvature? Is this linear model valid? 5. Run response surface experiments to find a better model 6. What is the resulting formula to estimate distance as a function of factors? 7. What values give us the maximum distance? 8. Make 10 replicates to estimate the confidence iterval of the distance 9. Calculate the angle a to hit a target of 20 with maximum or minimum values of f, m and l Possible Facto

Design of Experiments (DOE) is a very useful process improvement methodology.  Microsoft Excel has some powerful data analysis tools, which I have, successfully, used for DOE. When it comes to analyzing cause and effect, we want to know what process factors affect our outputs.   Correlation and Regression could give us an indication of the main factors, and the extent they affect the outputs. In some cases, there may be interactions among the different factors, or the mathematical relation between factors and outputs may not be linear. In these cases, it is useful to run a systematic set of experiments, to test all possible combinations of factors, to relate them to the outputs. Factorial Experiment Example We want to minimize process loss, and after some brainstorming among the process specialists, we concluded that 5 factors may affect loss. Based on current factor levels, we have selected the following levels to experiment with:    Download this Excel file DOE_with_Excel.xlsm fro