Abstract: Optimization of a microwave-assisted extraction of cherry laurel (Prunus laurocerasus) fruit using methanol was studied. The influence of process parameters (microwave power, plant material-to-solvent ratio and the extraction time) on the extraction efficiency were optimized by using response surface methodology. The predicted maximum yield of extractive substances (41.85 g/100 g fresh plant material) was obtained at microwave power of 600 W and plant material to solvent ratio of 0.2 g/cm3 after 26 minutes of extraction, while a mean value of 40.80±0.41 g/100 g fresh plant material was obtained from laboratory experiments. This proves applicability of the model in predicting optimal extraction conditions with minimal laborious and time consuming. The results indicated that all process parameters were effective on the extraction efficiency, while the most important factor was extraction time. In order to rationalize production the optimal economical condition which gave a large total extract yield with minimal energy and solvent consumption was found.
Abstract: Abrasive waterjet is a novel machining process capable of processing wide range of hard-to-machine materials. This research addresses modeling and optimization of the process parameters for this machining technique. To model the process a set of experimental data has been used to evaluate the effects of various parameter settings in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. Depth of cut, as one of the most important output characteristics, has been evaluated based on different parameter settings. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. The pairwise effects of process parameters settings on process response outputs are also shown graphically. The proposed model is then embedded into a Simulated Annealing algorithm to optimize the process parameters. The optimization is carried out for any desired values of depth of cut. The objective is to determine proper levels of process parameters in order to obtain a certain level of depth of cut. Computational results demonstrate that the proposed solution procedure is quite effective in solving such multi-variable problems.
Abstract: In this paper, a set of experimental data has been used to assess the influence of abrasive water jet (AWJ) process parameters in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. The effects of these input parameters are studied on depth of cut (h); one of most important characteristics of AWJ. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the AWJ process parameters. The objective is to determine a suitable set of process parameters that can produce a desired depth of cut, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.
Abstract: This paper addresses modeling and optimization of process parameters in powder mixed electrical discharge machining (PMEDM). The process output characteristics include metal removal rate (MRR) and electrode wear rate (EWR). Grain size of Aluminum powder (S), concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control variables to study the process performance. The experimental results are used to develop the regression models based on second order polynomial equations for the different process characteristics. Then, a genetic algorithm (GA) has been employed to determine optimal process parameters for any desired output values of machining characteristics.
Abstract: The approach of subset selection in polynomial
regression model building assumes that the chosen fixed full set of
predefined basis functions contains a subset that is sufficient to
describe the target relation sufficiently well. However, in most cases
the necessary set of basis functions is not known and needs to be
guessed – a potentially non-trivial (and long) trial and error process.
In our research we consider a potentially more efficient approach –
Adaptive Basis Function Construction (ABFC). It lets the model
building method itself construct the basis functions necessary for
creating a model of arbitrary complexity with adequate predictive
performance. However, there are two issues that to some extent
plague the methods of both the subset selection and the ABFC,
especially when working with relatively small data samples: the
selection bias and the selection instability. We try to correct these
issues by model post-evaluation using Cross-Validation and model
ensembling. To evaluate the proposed method, we empirically
compare it to ABFC methods without ensembling, to a widely used
method of subset selection, as well as to some other well-known
regression modeling methods, using publicly available data sets.