Assumption made while conducting an experiment in six sigma
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A designed experiment can help you choose between alternatives and select key factors affecting a response even despite uncontrollable noise factors. You can also use Response Surface modeling to hit a certain target, reduce variability in a process, maximize or minimize a response, make a process more robust despite uncontrollable noise and even pursue multiple goals.
Basic steps of DOE
Define objective(s)
Gather knowledge about the process
Develop a list and select your variables
Assign levels to variables
Conduct experiments
Data analysis and conclusions
Items to avoid when conducting the experiment:
Unwarranted assumptions of the process.
Undesirable combinations of the factors.
Violation of known laws of physics.
Too large or small design sizes.
Inappropriate confounding.
Imprecise measurement.
Unacceptable prediction error (Type 1 & type 2 errors).
Undesirable run order.
Introduction to Design of Experiments Video
Planning and Organizing Designed Experiments
Define interactions
Understand how to analyze interactions
Balanced experiment: each factor appears the same number of times.
Full factorial experiment: At least one trial for all possible combinations of factors and levels
One Factor at a Time (OFAT)
Explain the one factor at a time (OFAT) approach
OFAT- One Factor at a Time
Randomized block plans
Latin square designs
See other design types
Experimental Notation
Experiments can be abbreviated numerically.
Design Ex.
2^5 means that there are 5 factors at 2 levels.
Fractional Design Ex.
2^5-2 means that there are 5 factors at 2 levels and 2 generators. The generator determines what effects are confounded or combined with one another.
We would call this example an 1/L^g fractional factorial.
Thus, 2^5-2 is a 2 level, 5-factor, 1/4th fractional design.
Basic steps of DOE
Define objective(s)
Gather knowledge about the process
Develop a list and select your variables
Assign levels to variables
Conduct experiments
Data analysis and conclusions
Items to avoid when conducting the experiment:
Unwarranted assumptions of the process.
Undesirable combinations of the factors.
Violation of known laws of physics.
Too large or small design sizes.
Inappropriate confounding.
Imprecise measurement.
Unacceptable prediction error (Type 1 & type 2 errors).
Undesirable run order.
Introduction to Design of Experiments Video
Planning and Organizing Designed Experiments
Define interactions
Understand how to analyze interactions
Balanced experiment: each factor appears the same number of times.
Full factorial experiment: At least one trial for all possible combinations of factors and levels
One Factor at a Time (OFAT)
Explain the one factor at a time (OFAT) approach
OFAT- One Factor at a Time
Randomized block plans
Latin square designs
See other design types
Experimental Notation
Experiments can be abbreviated numerically.
Design Ex.
2^5 means that there are 5 factors at 2 levels.
Fractional Design Ex.
2^5-2 means that there are 5 factors at 2 levels and 2 generators. The generator determines what effects are confounded or combined with one another.
We would call this example an 1/L^g fractional factorial.
Thus, 2^5-2 is a 2 level, 5-factor, 1/4th fractional design.
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