Experiments/Metrology Flashcards
For continuous improvement in product/process quality it is fundamental to
Understand the PROCESS BEHAVIOUR; the AMOUNT OF VARIABILITY and its IMPACT ON PROCESS.
the objective of a carefully planned experiment is to: (2)
- understand which set of variables in a process affect the performance most
- then determine the best levels for these variables to obtain satisfactory output functional performance.
In a engineering environment, experiments are conducted to
explore, estimate or confirm
Can also set the levels of unimportant variables to their most
Economic setting
For the successful application of an industrial designed experiment, the following skills are required:
- Planning skills
- Statistical skills
- Teamwork skills
- Engineering skills
why do an experiment:
- When analysing a process, experiments are used to evaluate which process inputs have a significant impact on the process output, and what the target level of those inputs should be to achieve a desired result
- Experiments can be designed in many different ways to collect this information. This process is called Design of Experiments (DOE) – or:
Designed Experiments or Experimental Design
A designed experiment aims at (6)
1) Comparing alternatives
2) Identifying those significant inputs (factors) affecting an output (response) - separating the vital few from the trivial many
3) Achieving an optimal process output (response)
4) Reducing variability
5) Minimizing, maximizing, or targeting an output
6) Achieve product & process robustness
Experiment Design: Strategy (3)
1) The factors to be tested
2) The levels of those factors
3) The structure and layout of experimental runs, or conditions
Error refers to
all unexplained variation that is either within an experiment run or between experiment runs and associated with level settings changing.
Uncontrollable factors that induce variation under normal operating conditions are referred to as
“Noise Factors”
Two factors that vary together may be highly correlated without one causing the other and they may both be caused by a
third factor
Randomization and stratification, three simple rules:
- If you can (and want to), fix a variable
- If you don’t fix a variable, stratify it
- If you can neither fix nor stratify a variable, randomize it
In a factorial experiment several independent factors are controlled and their effects are investigated at each of
2 or more levels
The factorial design may be appropriate to use when
When two or more variables being studied have an interaction effect