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Considering these two responses variables, Analysis of Variance has been performed in order to identify the effect of each input process parameters on the MRR and surface roughness.
Regression models describing the variations of the responses variables showed high coefficient of determination (R2) values ranging from 0.866 to 0.960.
This method has been designed to obtain significant effects on two responses variables, delay time and equilibrium temperature of THF hydrate formation, and 8 experimental runs have been carried out with two replications and four central points.
RSM is one of the best ways of evaluating the relationships between responses, variables and interactions that exist [16].
The regression model of significant factors such as pulse-on time, pulse-off time, peak current, and wire feed is considered for optimising the responses variables material removal rate (MRR), surface roughness and Kerf width.
Furthermore, the calculated adjusted R 2 values for studied responses variables were higher than 0.80, hence there is a close agreement between the experimental results and the theoretical values predicted by the proposed models.
Similar(53)
Response variables were the frequencies of control and infested individuals at each stage in each colony.
Similar ANCOVA analyses were done with both expression noise and expression consistency as response variables.
Response variables were the number of morphospecies and families collected in each room, with house designated as a random effect.
Prior to regression, response variables with near zero variation were removed using the NearZero function of the caret R package.
Ranks were not consistent across all response variables.
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