Overview:
This course will provide you with the framework for creating experimental designs that lay out a detailed experimental plan in advance of doing the experiment. In industrial settings, the primary goal is usually to extract the maximum amount of unbiased information regarding the factors affecting a production process from as few costly observations as possible. In general, every machine used in a production process allows its operators to adjust various settings, affecting the resultant quality of the product manufactured by the machine. Experimentation allows the engineer to adjust the settings of the machine in a systematic manner and to learn which factors have the greatest impact on the resultant quality. Using this information, the settings can be constantly improved until optimal quality is obtained.
What You Will Learn:
- Analyze and interpret data that is generated in the experiment
- Design experiments to identify the optimum combination to minimize cost and increase quality and productivity
- Determine operating levels for key process factors
- Identify critical process characteristics
Course Content
- Single-factor design
- Completely randomized design
- Two-factor design or randomized block design
- Factorial with interaction design
- Balanced and unbalanced
- Two-factor ANOVA
- Calculations for two-factor tables
- Interaction
- Computer applications
- Latin Square designs
- Replications
- Full-factorial design
- Fractional-factorial design