Advantages of Response Surface Methodology:
1. It allows for the estimation of optimal response at a specific level of independent variables.
2. It efficiently identifies interactions between variables.
3. It requires a smaller number of experiments than a full factorial design, saving time and resources.
4. It provides a mathematical model that can be used to predict the response for any combination of independent variables.
Disadvantages of Response Surface Methodology:
1. The mathematical models used in RSM may not adequately capture the complexity of real-world systems.
2. The accuracy of the response surface model is dependent on the quality of the data collected, and outliers may affect the results.
3. The design of experiments can be complex, and the choice of factors and levels can affect the accuracy and reliability of the results.
4. RSM is less effective when the relationship between independent and dependent variables is nonlinear or complex.
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