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The relationships between the process parameters and the various characteristics have been established by developing second order mathematical models using response surface methodology (RS M.
Design of experiment (DOE) methodology is used for a screening of the design space and for the generation of approximation models using response surface techniques.
Statistically-based predictive models using response surface regression for relative density, meltpool geometry, peak temperature, and time above melting point are developed and multi-objective optimization studies are conducted by using genetic algorithm and swarm intelligence.
Rao and Pawar (2009) highlighted the development of mathematical models using response surface modeling for correlating the relationships of various WEDM parameters such as pulse on-time, pulse off-time, peak current, and servo feed setting on the machining speed and surface roughness.
In the letter by Hsieh et al., 'Immortal time bias in retrospective analysis', the group questions the use of log-rank tests (or Cox regression models using response as a time-independent covariate), and remarks that this could have resulted in 'immortal time bias'.
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These parameters were modeled using response surface methodology.
The process was then modeled using response surface methodology (RS M based on central composite design (CCD).
Then, in order to develop CFB function, we design experiments in developed simulation model using response surface methodology (RS M.
In this study, based on the updated model using response surface methodology, a practical model validation methodology via uncertainty propagation is presented.
The experimental data on cumulative hydrogen production and COD reduction were fitted into a quadratic polynomial model using response surface regression analysis.
The interactions between distillery effluent concentration, initial pH, moisture content and inoculum size were investigated and modeled using response surface methodology (RS M involving Box Behnken design (BBD).
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