Response surface

This report reviews the analysis used to extract the complex conductivity of a compound from a microwave cavity perturbation measurement. We intend to present a generalized treatment valid for any spheroidally shaped sample of arbitrary conductivity which is placed at either the electric or magnetic field antinode of the cavity. To begin with, we establish the relationship between the measured parameters and the conductivity for a spherical sample. Next, we extend these results to the case of spheroids; and for the first time, we cover all different configurations that one can possibly use to study an arbitrary conducting sample inside a cavity: in particular, all possible orientations of the sample with respect to the applied field are solved.

Photo-Fenton degradation of a reactive dyestuff, Reactive Black 5, was investigated in this study. The effects of UV light on Fenton Reagent process and the influence of reagents dosage, UV light intensity and initial dissolved oxygen concentration on photo-Fenton were explored. The experiments were conducted in a 3-L batch-mode reactor and were designed using Response Surface Method. The performance of

Statistical mixture designs are used to systematically study the densification properties of silicon carbide (SiC) ceramics sintered with SiO2, Dy2O3, and Al2O3. Mixture models for percentage theoretical density and SiC weight loss as a function of the SiO2, Dy2O3, and Al2O3 oxide proportions have been determined and validated by analysis of variance. The results indicate a region confined by about 0–20 mol% silica, 50–65 mol% dysprosia, and 40–65 mol% alumina, with all samples containing 10% by volume of additives, and simultaneously maximization of density values and minimization of weight loss during SiC-based ceramic sintering.

Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a 'sensitivity analysis'. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.

Focused ultrasonic-assisted extraction (FUSE) is a new and particular technique based on the cavitation effect. In this work, the focused ultrasound assisted extraction was studied and developed for the extraction of polycyclic aromatic hydrocarbons from marine sediments and mussel tissues. The variables influencing the extraction (amplitude of the ultrasound pulse, the extraction time and the solvent) were studied by a full factorial design and a central composite design. As a result, flat response surfaces were obtained and the most convenient conditions were 45% of ultrasound amplitude, 120 s of extraction time and 5 mL of acetone. Both accuracy and precision of the method were evaluated by means of two certified reference materials (marine sediment and mussel tissue) and the results were also compared to those obtained by microwave assisted extraction.

Response surface methodology was used to analyze the effect of screw speed (220–340 rpm), feed moisture (11.0–15.0%, wet basis) and feed rate (22.0–26.0 kg/h, wet basis) on the physical properties (i.e., bulk density, expansion, porosity) of a nutritionally balanced extruded snack food. Regression equations describing the effect of each variable on the responses were obtained. Responses were most affected by changes in screw speed followed by feed moisture and feed rate (P<0.05). Expansion and porosity increased with screw speed and feed moisture whereas the opposite was observed for bulk density. Radial expansion was found to be a better index to measure the extent of expansion than the axial and overall expansions, indicated by a higher correlation coefficient.

Drilling thrust force calculations require a large amount of experimental work, which can be greatly reduced, since an extensively validated CAD-based approach, using the DRILL3D software application, has become available. DRILL3D calculates the thrust force of both the cutting areas of the tool (main edges and chisel edge) simultaneously, which means that every simulation can substitute two separate lab experiments. Nevertheless, as the number of parameters involved is increasing, the amount of the necessary simulations becomes substantial. This is the reason that led to the combined use of the DRILL3D and the design of experiments methodology, which reduces the amount of the necessary digital experiments to an impressive degree. The main factors affecting the current analysis are the tool diameter, the web to diameter ratio, the feed rate and the cutting speed used. Using an L16 Taguchi table, a function of the developed thrust force can be calculated using the response surface methodology. This statistical modeling tool employs the regression analysis to establish the relationship between various process parameters and response.► A general purpose CAD system is used to calculate the thrust force during drilling. ► DRILL3D uses 3D solid modeling for tools, work pieces and undeformed chips. ► Increased parameter levels led to the combined use of DRILL3D and design of experiments. ► The proposed combination limits the costs and promotes the results’ accuracy. ► This approach can be of a great assistance for the design and manufacturing industry.

In order to improve the production of human-like collagen III (HLC III) by fed-batch culture of recombinant Escherichia coli BL21, the Plackett-Burman and Box-Behnken design were applied to optimize the fermentation process parameters. Three variables (induction time, inoculum age and pH), which have significant effects on HLC III production, were selected from eight variables by Plackett-Burman design. With the regression coefficient analysis in the Box-Behnken design, a relationship between HLC III production and three significant factors was obtained, and the optimum levels of the three variables were as follows: induction time 3.2h, inoculum age 12.6 h and pH 6.7. The 3D response surface plots and 2D contour plots created by the Box-Behnken design showed that the interaction between induction time and pH and that between innoculum age and pH were significant. An average 9.68 g·L−1HLC III production was attained in the validation experiment under optimized condition, which was 80% higher than the yield of 5.36 g·L−1 before optimization.

Nowadays numerical and combined Artificial Neural Networks (ANN) and genetic algorithm methods are widely used for both modeling and optimizing the performance of the manufacturing technologies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Therefore the present research is aimed at finding the optimal process parameters for hard turning of AISIM2Steel by using CBN Inserts. The hard turning process is an extensively used machining process in tool making industries to tool, punch and die shops, because of its versatility and efficiency. The reason for being widely used is that it may be used for the rough and finish machining. The present work involves the estimation of optimal values of the process variables like, cutting speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear were taken as the output response .Full factorial Response Surface Methodology (RSM) with Central Composite Design (CCD) was used to carry out the experimental design. Optimum machining parameters for hard turning process were found out using ANN& GA and compared to the experimental results. The obtained results proved the ability of ANN and genetic algorithm method for hard turning process modeling and optimization.