Design of Experiments for Robust Optimization and Tolerance Design
This course is designed for individuals working directly on product and process develelopment to characterize, optimize, and control product and process performance.
Learn how to
- explain the fundamental principles of designed experiments
- create and analyze full factorial and screening designs
- create and analyze response surface designs
- augment existing experiments to address new questions about higher order effects
- understand mixture designs, EVOP, and Taguchi arrays
Who should attend
Engineers, scientists, and Six Sigma professionals who actively work on any aspect of product and process development where the goal is to improve product and process performance
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Prerequisites
Before attending this course, you should complete an ANOVA and regression course. The recommended prerequisites are JMP Software: Statistical Data Exploration and JMP Software: ANOVA and Regression or Engineering Statistics and Data Analysis for Performance Excellence
Course Contents
Introduction to DOE and Robust Design
Experimental Preparation
Full Factorial Design
Screening Design
Custom Designs
- generating custom designs
- evaluating custom designs
- analysis of custom designs
- strategies to minimize experimental size
- adding covariate and uncontrolled factors
- supersaturated designs
- split plot designs
- strip plot designs
- blocking designs
- mixtures in custom designs
- setting constraints in the design
Response Surface Design
Special Topics in DOE (optional)
- mixture designs
- evolutionary operations (EVOP)
- Taguchi arrays
Software
This course addresses JMP.
Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.
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Not currently scheduled.
Available for
on-site training or can be scheduled at any SAS training facility
if demand warrants.
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