What is Robust Design or Taguchis method An

What is Robust Design or Taguchi’s method? • An experimental method to achieve product and process quality through designing in an insensitivity to noise based on statistical principles.

History of the method • Dr. Taguchi in Japan: 1949 -NTT – develops “Quality Engineering” – 4 time winner of Demming Award • Ford Supplier Institute, early 1980 s • American Supplier Institute, ASI – Engineering Hall of Fame • Statistics Community – DOE – S/N Ratio

Who uses Taguchi’s Methods • • Lucent Ford Kodak Xerox Whirlpool JPL ITT • • Toyota TRW Chrysler GTE John Deere Honeywell Black & Decker

Documented Results from Use • 96% improvement of Ni. CAD battery on satellites (JPL/ NASA) • 10% size reduction, 80% development time reduction and 20% cost reduction in design of a choke for a microwave oven (L. G. Electronics) • $50, 000 annual cost savings in design of heat staking process (Ann Arbor Assembly Corp) • 60% reduction in mean response time for computer system (Lucent) • $900, 000 annual savings in the production of sheet-molded compound parts (Chrysler) • $1. 2 M annual savings due to reduction in vacuum line connector failures (Flex Technologies) • 66% reduction in variability in arrival time and paper orientation (Xerox) • 90% reduction in encapsulation variation (LSI Corp)

Insensitivity to Noise • Noise = Factors which the engineer can not or chooses not to control – Unit-to-unit • Manufacturing variations – Aging • Corrosion • UV degradation • wear – Environmental • human interface • temperature • humidity

How Noise Affects a System

Step 1: Define the Project Scope 1/2 • A gyrocopter design is to be published in a Sunday Comics section as a do-it-yourself project for 6 -12 year old kids • The customers (kids) want a product they can easily build and have a long flight time. | WW | --WL ----1/4” --- BL ----

Step 1: Define the Project Scope 2/2 • This is a difficult problem from an engineering standpoint because: – hard to get intuitive feel for effect of control variables – cant control materials, manufacturing or assembly – noise factors are numerous and have strong effect on flight.

Step 2: Identify Ideal Function Time of Flight • Ideally want the most flight time (the quality characteristic or useful energy) for any input height (signal or input energy) • Minimize Noise Effect • Maximize Slope Drop Height

Step 3: Develop Noise Strategy 1/2 • Goal is to excite worst possible noise conditions • Noise factors – unit-to-unit – aging – environment

Step 3: Develop Noise Strategy 2/2 • Noise factors – unit-to-unit Construction accuracy Paper weight and type angle of wings – aging damage from handling – environment angle of release humidity content of air wind + many, many others

Step 4: Establish Control Factors and Levels 1/4 • Want them independent to minimize interactions – Dimensionless variable methods help – Design of experiments help – Confirm effect of interactions in Step 7 • Want to cover design space – may have to guess initially and perform more than one set of experiments. Method will help determine where to go next.

Step 4: Establish Control Factors and Levels 2/4 • Methods to explore the design space – shot-gun – one-factor-at-a-time – full factorial – orthogonal array (a type of fractional factorial)

Step 4: Establish Control Factors and Levels 3/4

Step 4: Establish Control Factors and Levels 4/4

Step 5: Conduct Experiment and Collect Data

Data for Runs 5 and 15

Step 6: Conduct Data Analysis 1/7 • Calculate signal-to-noise-ratio (S/N) and Mean • Complete and interpret response tables • Perform two step optimization – Reduce Variability (minimize the S/N ratio) – Adjust the mean • Make predictions about most robust configuration

Step 6: Conduct Data Analysis 2/7 • Calculate signal to noise ratio, S/N, a metric variability in decibels S/N gain reduction S/N = Useful output Harmful output 3 6 12 27% 50% 75% Effect of Mean = Variability around mean 2 y = 10 log 2 s Note: This is one of many forms of S/N ratios.

Step 6: Conduct Data Analysis 3/7

Step 6: Conduct Data Analysis 4/7 Response Table

Step 6: Conduct Data Analysis 5/7 Response plot

Step 6: Conduct Data Analysis 6/7 Two Step Optimization • Reduce Variability (minimize the S/N ratio) – look for control factor effects on S/N – Don’t worry about mean • Adjust the mean – To get desired response – Use “adjusting factors”, those control factors which have minimal effect on S/N

Step 6: Conduct Data Analysis 7/7 • For gyrocopter – – – wing width =. 75 in wing length = 2. 00/0. 75 = 2. 67 in body length = 2. 00 x 2. 67 = 5. 33 in size = 50% Predicted Performance no body folds no gussets S/N = 9. 44 d. B Slope =. 31 sec/ft

Step 7: Conduct Conformation Run • To check validity of results • To check for unforeseen interaction effects between control factors • To check for unaccounted for noise factors • To check for experimental error Predicted Confirmed S/N 9. 44 d. B Slope. 31 sec/ft 9. 86. 32 sec/ft

How Taguchi’s Method Differs from an Ad-hoc Design Process • Organized Design Space Search • Clear Critical Parameter Identification • Focus on Parameter Variation (Noise) • Clear Stopping Criteria • Robustness centered not Failure Centered • Reusable Method • Concurrently Addresses Manufacturing Variation • Concurrent Design-Test Not Design-Test-Fix • Minimize Development Time (Stops Fire Fighting) • Corporate Memory Through Documentation • Encourages Technology Development Through System Understanding

How Taguchi’s Method Differs from Traditional Design of Experiments • Focused on reducing the impact of variability rather than reducing variability • Focused on noise effects rather than control factor effects • Clearly focused cost function - maximizing the useful energy • Tries to reduce interaction between control factors rather than study them Requires little skill in statistics • Usually lower cost

How Taguchi’s Method Differs from Shainin’s Method • Focused on both Product and Process Design rather than Primarily on Process • Oriented to developing a robust system not finding a problem (Red X). Taguchi tells what parameter values to set to make system insensitive to parameter Shainin identifies as needing control. • Widely Used Internationally • Fire prevention rather than fire fighting • Accessible • Many Case Studies Available

Plan for Application at Tektronix • • • Select a parameter design problem Design the experiment Perform the experiment Reduce data Report results to Company Assuming success – design more experiments – train more engineers – Plan for student-run experiments