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Purpose

Purpose. To provide direction in understanding the process and application of Parameter Design in the development of robust products and processes. Parameter Design Course Objectives. Describe how Parameter Design relates to quality engineering.

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Purpose

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  1. Purpose To provide direction in understanding the process and application of Parameter Design in the development of robust products and processes ENGN8101 Modelling and Optimization

  2. Parameter Design Course Objectives • Describe how Parameter Design relates to quality engineering. • Apply the Engineered System model to the product/process of study. • Identify the ideal function of the system. • Identify control and noise factors affecting the system. ENGN8101 Modelling and Optimization

  3. Parameter Design Course Objectives (continued) • Develop an experimental plan for both static and dynamic Parameter Design experiments. • Use statistical concepts and tools from Experimental Design to analyze data from Parameter Design experiments. • Make predictions and confirm improved robustness. • Apply Parameter Design principles to teamwork to optimize project outcomes. ENGN8101 Modelling and Optimization

  4. Introduction to Robustness Introduction to Robustness

  5. Traditional Philosophy of Quality No Good Good No Good Loss No Loss Loss y Response Lower Limit Target Value Upper Limit ENGN8101 Modelling and Optimization

  6. Either Side ofthe Specification Limit Just“out of Spec” Just “in Spec” Lower Limit Target Value Upper Limit ENGN8101 Modelling and Optimization

  7. The Difference Between Targetand Specification Limit Lower Limit Target Value Upper Limit ENGN8101 Modelling and Optimization

  8. Meeting the Target The further away from target value, the lower the quality and the less satisfied the customer Meet the Target Lower Limit Target Value Upper Limit The goal is to meet the target, not the specification ENGN8101 Modelling and Optimization

  9. Loss Function Philosophy Poor Poor $ Target Fair Fair Good Good Loss Best y Response m ENGN8101 Modelling and Optimization

  10. How the Distribution Affects Quality Loss Loss Function Producing this distribution, we would experience some loss Lower Limit Target Value Upper Limit ENGN8101 Modelling and Optimization

  11. How the Distribution Affects Quality Loss Loss Function Lower Limit Target Value Upper Limit Producing this distribution, that has less variance from target, the loss would be lower ENGN8101 Modelling and Optimization

  12. Definition of Noise NOISE FACTORS:Parameters which affect system response variability and are difficult, impossible or expensive to control. ENGN8101 Modelling and Optimization

  13. Robustness ROBUSTNESS: Low system response variability in the presence of noise. ENGN8101 Modelling and Optimization

  14. For Example... As consumers, we expect our automobiles to start every time, on the first turn of the key, regardless of: • Ambient temperature • Altitude • Age of the car • Hot or cold start, etc. ENGN8101 Modelling and Optimization

  15. The Robust Product/Process The robust product/process can operate within a wide range of conditions and still perform its intended function at a high quality level as perceived by the customer. ENGN8101 Modelling and Optimization

  16. The Non-Robust Product/Process The non-robust product/process will operate at a high quality level onlywithin a limited range of conditions. ENGN8101 Modelling and Optimization

  17. Parameter Design Introduction Parameter Design Intro

  18. Dealing With Noise Traditional • Eliminate or reduce • Compensate • Overlook Parameter Design • Minimize the effect of noise ENGN8101 Modelling and Optimization

  19. Parameter Designat the INA Tile Company Initial Distribution TileDimension Target Acceptable Deviation Burners Tiles Kiln Wall ENGN8101 Modelling and Optimization

  20. The Traditional Approach • Upgrade technology • Build a new kiln • Add more burners • Feedback control • Control the temperature variation of the oven ENGN8101 Modelling and Optimization

  21. Alternative Solutions 1. Redesign and build a new kiln • Cost = $1 million 2. Adjust the parameters that could be changed easily and inexpensively • Cost = Unknown ENGN8101 Modelling and Optimization

  22. INA Tile Company Alternatives Cost Comparisons $ $ $ Inspection and Scrap Redesigned Kiln Increased Lime Content Actions ENGN8101 Modelling and Optimization

  23. Improved Distribution Initial Distribution Parameter Design at the INA Tile Company Summary Tile Dimension Target Acceptable Deviation ENGN8101 Modelling and Optimization

  24. What is the intent and how do we maximize it? Dr. Genichi Taguchi’s Approach • Shift from: What is wrong and how do we fix it? to: ENGN8101 Modelling and Optimization

  25. Making the system insensitive to usage variations Dr. Genichi Taguchi’s Approach • Shift from: Controlling, compensating or eliminating usage variations to: ENGN8101 Modelling and Optimization

  26. Taguchi’s 3-Step Robust Engineering Process 1. Concept Design 2. Parameter Design 3. Tolerance Design ENGN8101 Modelling and Optimization

  27. The Impact ofIgnoring Parameter Design Concept Design • Often requires longer lead times • May introduce unnecessarily high levels of technology • Tolerance Design • Makes products more expensive to manufacture ENGN8101 Modelling and Optimization

  28. Parameter Design 2. Selecting optimum values for parameters that will reduce the variability of the response to noise The best design is determined at the least cost by: 1. Running a designed experiment withlow cost components and processsettings ENGN8101 Modelling and Optimization

  29. Parameter Design The best design is determined at the least cost by: 3. Selecting optimum values for controlfactors that will shift the mean of theresponse toward target 4. Selecting the lowest cost setting for factors that have minimal effect onresponse ENGN8101 Modelling and Optimization

  30. 1. Identify Project and Team 2. Formulate Engineered System: Ideal Function 3. Formulate Engineered System: Parameters 4. Assign Control Factors to Inner Array 5. Assign Noise Factors to Outer Array 6. Conduct Experiment and Collect Data 7. Analyze Data and Select Optimal Design 8. Predict and Confirm Steps in Parameter Design ENGN8101 Modelling and Optimization

  31. Parameter Design: Static Parameter Design: Static

  32. 7. Analyze Data and Select Optimal Design Steps in Parameter Design: Static 1. Identify Project and Team 2. Formulate Engineered System: Ideal Function 3. Formulate Engineered System: Parameters 4. Assign Control Factors to Inner Array 5. Assign Noise Factors to Outer Array 6. Conduct Experiment and Collect Data 8. Predict and Confirm ENGN8101 Modelling and Optimization

  33. Project Selection • Project selection should be based on the potential to: • Increase customer satisfaction • Increase reliability • Incorporate new technology • Reduce cost • Reduce warranty • Achieve Best-in-Class ENGN8101 Modelling and Optimization

  34. Successful Project Requirements • A clear objective • Cross-functional team • Thorough planning • Management support ENGN8101 Modelling and Optimization

  35. 7. Analyze Data and Select Optimal Design Steps in Parameter Design: Static 1. Identify Project and Team 2. Formulate Engineered System: Ideal Function 3. Formulate Engineered System: Parameters 4. Assign Control Factors to Inner Array 5. Assign Noise Factors to Outer Array 6. Conduct Experiment and Collect Data 8. Predict and Confirm ENGN8101 Modelling and Optimization

  36. Customer’s World “Voice of the Customer” INTENT: What the customer expects PERCEIVED RESULT: What the customer gets Customer’s World ENGN8101 Modelling and Optimization

  37. Engineer’s World SYSTEM: Transforms intent into perceived result SIGNAL: Causes the system to fulfill the intent RESPONSE (y): System output that determines the perceived result Engineer’s World ENGN8101 Modelling and Optimization

  38. Intended Result Unintended Result Energy Transformations Signal Response (y) Energy Transfer System: Engineer’s World Maximizing energy which produces the intended result will minimize the unintended results (error states) ENGN8101 Modelling and Optimization

  39. Intended Result Signal Ideal Function The ideal function is the transfer of energy to the intended result System Response (y) Engineer’s World ENGN8101 Modelling and Optimization

  40. Measuring the transformation of energy Dr. Genichi Taguchi’s Approach • Shift from: Measuring symptoms of poor quality to: ENGN8101 Modelling and Optimization

  41. Response A response should: • Be related to the Perceived Result • Be an engineering metric • Be related to the Ideal Function of the system • Quantify the energy transfer ENGN8101 Modelling and Optimization

  42. Customer Intent Perceived Result Composite View “Voice of the Customer” System Signal Response (y) ENGN8101 Modelling and Optimization

  43. 7. Analyze Data and Select Optimal Design Steps in Parameter Design: Static 1. Identify Project and Team 2. Formulate Engineered System: Ideal Function 3. Formulate Engineered System: Parameters 4. Assign Control Factors to Inner Array 5. Assign Noise Factors to Outer Array 6. Conduct Experiment and Collect Data 8. Predict and Confirm ENGN8101 Modelling and Optimization

  44. CONTROL FACTORS: parameters that are controllable by the engineer NOISE FACTORS: parameters that affect the system and are difficult, impossible or expensive to control Completing the Engineered System Response Signal System ENGN8101 Modelling and Optimization

  45. Where to Look for Noise environment customer usage manufacturing deterioration neighboring systems ENGN8101 Modelling and Optimization

  46. Noise Control Noise or Control? Is material hardness (Rockwell) a noise or control factor? ENGN8101 Modelling and Optimization

  47. 7. Analyze Data and Select Optimal Design Steps in Parameter Design: Static 1. Identify Project and Team 2. Formulate Engineered System: Ideal Function 3. Formulate Engineered System: Parameters 4. Assign Control Factors to Inner Array 5. Assign Noise Factors to Outer Array 6. Conduct Experiment and Collect Data 8. Predict and Confirm ENGN8101 Modelling and Optimization

  48. Experimental PlanSelect Control Factors • Filter control factor list based on: • Improvement potential • Change feasibility • Cost • Testing resources • Prioritize list Control Factors A B C … ENGN8101 Modelling and Optimization

  49. Experimental PlanIdentify Levels • Keep in mind: • 2 or 3 levels is most common • Level range should be as wide as possible • One level may represent baseline • More levels require more testing Control Factors A B C … A1 A2 Levels … ENGN8101 Modelling and Optimization

  50. Experimental PlanSelect Orthogonal Array Control Factors • Select based on: • Number of factors • Number of levels • Testing resources A B C … Levels Orthogonal array L? This array is called the Inner Array ENGN8101 Modelling and Optimization

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