Densification and volumetric change during supersolidus liquid phase sintering of prealloyed brass Cu28Zn powder: Modeling and optimization

Abstract

An investigation has been made to use response surface methodology and central composite rotatable design for modeling and optimizing the effect of sintering variables on densification of prealloyed Cu28Zn brass powder during supersolidus liquid phase sintering. The mathematical equations were derived to predict sintered density, densification parameter, porosity percentage and volumetric change of samples using second order regression analysis. As well as the adequacy of models was evaluated by analysis of variance technique at 95% confidence level. Finally, the influence and interaction of sintering variables, on achieving any desired properties was demonstrated graphically in contour and three dimensional plots. In order to better analyze the samples, microstructure evaluation was carried out. It was concluded that response surface methodology based on central composite rotatable design, is an economical way to obtain arbitrary information with performing the fewest number of experiments in a short period of time.

Experimental investigation of CO2–brine–rock interactions in relation with CO2 sequestration in an Iranian oil reservoir

“…The porosity of the slices was also measured at the end of the experiments using the Archimedes principle. 26…”

“…Solids were washed with deionized water, dried in an oven at 70°C for 24 hours, and analyzed using SEM/EDS and XRD. The porosity of the slices was also measured at the end of the experiments using the Archimedes principle 26 …”

“…The density of porous rock was measured by the Archimedes principle. Therefore, the porosity of the rock can be determined according to the following equations 26 :…”

Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed

“…The applied RSM methodology is based on the assumption of the model structure and subsequent identification of significant inputs using analysis of variance. The mechanism of the FSW process is complex and involves heat mechanisms [20,21]; therefore, the assumption of the model structure induces ambiguity in the extrapolation ability of the model. An alternative route is to apply advanced optimization tools [22], such as artificial neural networks and its variants [23][24][25], genetic programming (GP) and its variants [26,27], hybrid optimization methods of GP [28,29], and molecular dynamics [30,31].…”

The influence of pre-oxidation and leaching parameters on Iranian ilmenite concentrate leaching efficiency: Optimization and measurement

“…However, present day industrial application demands comprehensive theoretical simulation before actual design [24][25][26]. There are very few studies about using factorial design in Fe and Ti leaching [27].…”