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Doe usimg taguchi

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Doe usimg taguchi

  1. 1. Shalaka S Kulkarni Department of Mechanical engineering Design of Experiments (DOE) Using The Taguchi Approach
  2. 2. Dr. TAGUCHI IN QUALITY ENGINEERING • Measuring cost of quality. • Loss Function • Consistency of performance • Reduced variation around the target.
  3. 3. TAGUCHI’S METHOD • Focus on PARAMETER DESIGN • Off-line Quality Control • Quality Loss Function • Signal To Noise Ratio(s/n) For Analysis • Reduced Variability, a Measure Of Quality
  4. 4. TAGUCHI’S NEW PROCESS PLAN FOR IMPROVEMENT IN PRODUCT QUALITY
  5. 5. WHAT IS AN EXPERIMENT? Systematic procedure carried out under controlled condition in order to • Discover an unknown effect • To illustrate a known effect • Test or establish a hypothesis
  6. 6. DOE • It all began with R. A. Fisher in England back in 1920’s. • Fisher wanted to find out how much rain, sunshine, fertilizer, and water produce the best crop.
  7. 7. • Many factors/inputs/variables must be taken into consideration when making a product especially a brand new one – Ex. Baking a new cake without a recipe • The Taguchi method is a structured approach for determining the “best” combination of inputs to produce a product or service – Based on a Design of Experiments (DOE) methodology for determining parameter levels • DOE is an important tool for designing processes and products – A method for quantitatively identifying the right inputs and parameter levels for making a high quality product or service • Taguchi approaches design from a robust design perspective
  8. 8. 3 Aspects which are to be analyzed by designed experiment 1. Factor/Input 1. Controllable – Ingredients of cake and oven 2. Uncontrollable- Noise factors 2. Level- Include the oven temperature setting and the amounts of Butter, Sugar, Milk, flour, and eggs for making cake. 3. Responses- Taste, Consistency, moisture and appearance of the cake.
  9. 9. Control factors with their levels Factors Level-1 Level-2 A: Egg B: Butter C: Milk E: Sugar D: Flour A2A1 B1 B2 C1 C2 E2E1 D1 D2
  10. 10. A1 B1 C1 D1 E1 Condition #1 A1B1C1D1E1 E2 A1 B1 C1 D1 Condition #2 A1B1C1D1E2 Experimental Conditions
  11. 11. A1 B1 C1 E1 Condition #3 A1B1C1D2E1 D2 E2 A1 B1 C1 Condition #4 A1B1C1D2E2 D2 Experimental Conditions
  12. 12. Condition # 5 through 30 . . . . . E1 Condition #31 A2B2C2D2E1 D2 E2 Condition #32 A2B2C2DE2 D2C2 C2 A2 B2 A2 B2
  13. 13. Experiment Design Using L-8 Orthogonal Array
  14. 14. Analysis Using S/N ratio • Used to analyze the experimental data. • Determines the optimal parametric combinations • Each response variable is analysed using s/n ratio and a combination of process parameter is found. • This gives the optimum value of respective response variable – Smaller the better. – Larger the better. – Nominal the better
  15. 15. Exp. No EGG BUTTER MILK FLOUR SUGER TASTE MOISTURE SNR1 SNR2 1 3 1 2 1 1 4 3 12.0412 9.5425 2 3 1 2 3 2 5 4 13.9795 12.0412 3 3 2 4 1 1 3 4 9.5425 12.0412 4 3 2 4 3 2 2 2 6.0206 6.0206 5 6 1 4 1 2 5 3 13.9795 9.5425 6 6 1 4 3 1 4 3 12.0412 9.5425 7 6 2 2 1 2 2 4 6.0206 12.0412 8 6 2 2 3 1 4 5 12.0412 13.9795
  16. 16. Purpose • Significance of inputs – What are the significant factors beyond flour, eggs, sugar and baking?“ • Comparing alternatives – Makes a decision which evaluate both quality and cost. • Optimal process output. – What are the necessary factors, and what are the levels of those factors, to achieve the exact taste and consistency of Mom's chocolate cake?
  17. 17. • Reducing variability – Can the recipe be changed so it is more likely to always come out the same?" • Improve robustness – Can the factors and their levels (recipe) be modified so the cake will come out nearly the same no matter what type of oven is used?"
  18. 18. TAHNK YOU!!!!

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