Introduction Design of experiments is the useful techniques for controlling statistical quality control and also it’s a much complicated techniques.it has a vital role in the field of optimization techniques.
DOE: The Jewel of Quality Engineering: These methods (DOE) all involve the activities of experimental planning, conducting experiments, and fitting models to the outputs.
Designof Experiments Methods: Five classes of experimentation and analysis methods are:
|
Method of DOE |
Advantage |
Disadvantage |
|
Two-sample t-tests |
Provide a relatively high level of evidence that a single level of a single factor causes a higher average response |
Methods only address one factor-ate- time (OFAT). Compared with screening using fractional factorials, for comparable total costs the Type I and Type II errors are more likely. |
|
Screening using Fractional Factorials (FF) |
Provides an inexpensive way to determine which factors from a long list significantly affect system performance. Sometimes, users apply results to support final engineering design decisions |
Compared with Response Surface Methods, the methods generate a relatively inaccurate prediction model. Compared with two-sample ttests, the level of evidence associated with significance claims is subjectively lower. |
|
One-shot Response Surface Methods (RSM) |
Create a relatively accurate prediction model and significance information, permitting identifying of interaction effects |
Compared with factor screening methods, these methods require Substantially larger numbers of experimental runs for a given number of factors. |
|
Sequential Response Surface Methods (RSM) |
Generate a relatively accurate Prediction model and may require fewer runs than one shot response surface methods. |
The derived prediction model will, in general, be less accurate than the one from one-shot response surface Methods if the method terminates without using all the runs. |
|
Robust Design based on Profit Maximization (RDPM) |
Builds on RSM to directly maximize the sigma level in a cost-effective manner addressing production noise |
Complicated; may require substantial experimental cost |
|
Analysis of Variance (ANOVA) followed by multiple tests |
Offers a standard approach for analyzing significance of factors and/or model terms that addresses the multiplicity of the tests |
Compared with Lenth’s method and Normal probability plots, the Type II errors are generally higher. This is Only an analysis method that does not explain which data to collect. |
