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DOE - Design Of Experiments Training

DOE - Design Of Experiments Training - Understand and use simple methods to establish quality loss. Design simple experiments and analyze data through the 2-Step Optimization to improve process.

DOE Advance - Trainings for Six Sigma, APQP and TQM projects

DOE Advance Introduction

  • Do you know you can optimize your processes and products by computer simulation?
  • Do you have processes or products with known functions?
  • Do you know you can use computer simulation to reduce unit cost?
  • Do you conduct your own R&D?

Design of Experiments Advance (DOE Advance) employs computer simulation and data analysis. A process or product with a known functional characteristic can be optimized purely by computer simulation. Advanced methods of data transformation are used based on the lambda and beta techniques. Tolerancing of process and product parameters can also be performed through this method. On completion, delegates will have a highly sophisticated method of computer aided parameter and tolerance designs.

Benefits Of Attending The DOE Advance Training

The DOE Advance Training enables the delegates to:

  • Understand and use computer simulation.
  • Use computer aided parameter design simulation for known functions.
  • Use data transformation to identify target and noise performance measures.
  • Use computer aided tolerance design simulation for known functions.

Who Should Attend The DOE Advance Training ?

DOE Advance is particularly useful for those involved in controlling process or product parameters. It will be most appropriate for those involved in Design, Quality, R&D, Reliability, Maintenance, Engineering, Manufacturing and Production. Teams are encouraged to attend for maximum benefit.

Brief DOE Advance Training Outline

Day 1 (AM)
Computer Simulation
  • Generating noise
  • Grid search
  • Parameter Design
  • Comparison
Day 1 (PM)

Grid Search

  • Wheatstone Bridge
  • Partial Derivatives
  • Simulation
  • Data analysis
Day 2 (AM)
CAPD
  • Choice of OA
  • TPM and NPM
  • Analysis of variance
  • Dynamic CAPD
Day 2 (PM)
Data Transformation
  • Stabilizing Transforms
  • Lambda technique
  • Beta technique
  • Recommended technique
Day 3 (AM)
CATD Simulation
  • Analysis of variance
  • Successive approach
  • Tolerancing
Day 3 (PM)
Course conclusion
  • Discussion
  • Summary
  • Close
 
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