Wire electrical discharge machining (WEDM) of oil hardening die steel materials is a complicated machining process. Hence, to determine the best set of process parameters is an important step in the wire EDM process. In the present work, multi-response optimization of machining parameters was done by using a technique called desirability function analysis coupled with the dimensional analysis(DA) approach. The experimentations were carried out as per Taguchi’ s L27 orthogonal array for (Oil Hardening Non-Shrinking Die Steel) the OHNS die steel material. Parameters of the WEDM process, such as pulse on time, pulse off time, input current, wire feed rate, and the servo voltage, were optimized by a multiresponse optimization technique to optimize the responses such as material removal rate and surface roughness. Based on desirability analysis, the set of most favorable levels of parameters has been identified. The significant contribution of parameters is determined by dimensional analysis. From the experimental results, it has been observed that the DA approach is in good agreement with the measured responses. The correlation up to 99% has been achieved between the developed model and the measured responses by using dimensional analysis approach. Thus, the presented methodology can be used in the future for the critical analysis of any engineering process.

This paper explains response surface methodology (RSM), a powerful collection of mathematical techniques, and discusses its role in optimization problems. Based on the optimization method, the existing studies are classified into three categories, namely, loss function, desirability function, and classical methods. Since the desirability function approach incorporates the decision maker's points of view in the analysis, it is considered as the primary method in this paper to determine the desirability function. An important aspect of the proposed method is its ability to incorporate the relationship among quality attributes and its flexibility to provide guidance to the decision maker (DM). The performance and efficiency of the proposed method is investigated via two numerical examples.

Robust design is a new and innovative approach for product quality improvement with the lowest cost which sustains the profitability and competitiveness of manufacturing and service organizations in today’s competitive markets. In the nearly 25 years of its existence, interesting innovative based solutions have been proposed to solve the problems in this area. Recent years have witnessed a growing interest of research in the study of the robustness on the time line. In this area, one often needs to consider multiple quality characteristics. However, little work has been done to address this critical issue. The purpose of this article is to solve this problem with the aid of mathematical modeling of quality characteristic via desirability function. It is expected that the obtained results can satisfy the designers’ requirements to a large extent.

In recent years much research has been conducted to study the variations in welding parameters and consumables on the mechanical properties of steels to optimize weld integrity. The quality of weld is a very important working aspect for the manufacturing and construction industries. In the present work, an attempt has been made to apply an efficient technique, fuzzy based desirability method to solve correlated multiple response optimization problems, in the field of flux cored arc welding. This approach converts the complex multiple objectives into a single fuzzy reasoning grade. Based on fuzzy reasoning grade, optimum levels of parameters (Welding current, arc voltage and electrode stickout) were identified. Experiments were performed based on Taguchi method. Weld bead hardness and material deposition rate are selected as quality targets. Significant contributions of parameters are estimated using Analysis of Variance (ANOVA). Confirmation test is conducted and reported. It is found that the electrode stickout is the most significant controlled factor for the process according to the weighted fuzzy reasoning grade of the maximum weld bead hardness and material deposition rate. The proposed technique allows manufacturers to develop intelligent manufacturing system to achieve the highest level of automation.

In this context, a rapid electrochemical decolorization of aqueous Acid Blue 25 solutions on a batch reactor using a rectifier was studied. The central composite design (CCD) has been chosen to elucidate the combined effect of four process variables; applied potential, pH, electrolyte concentration and temperature. The decolorization percentage (measure of remediation) and the energy efficiency (measure of energy saving) were considered as responses that were optimized simultaneously using desirability function. Under the optimum conditions of applied potential (5 V), pH (2), KCl concentration (0. 1 mol L-1) and temperature (20° C), 100% decolorization percent and 167. 1 mmolkW-1h-1 energy efficiency was predicted, that was close to obtained results (100% and 163. 2± 0. 5 mmolkWh-1).

Defects of the roll forming process are affected by the amount and situation of the strains distribution. The effect of the process parameters on the strain distribution in the round cross section roll forming process has been studied. Finite element and response surface method have been used for process modelling. Then, desirability functions approach and overlaid counter plots were employed for optimization of the process. Three factors including the roll diameter, distance between the stations and linear speed of the sheet were considered as the input parameters. The sum of longitudinal maximum strain and transverse strain distribution uniformity were taken as response functions. Response function model for each function was obtained using the RSM. Finally, optimization of the process was done using the desirability function approach and overlaid contour plots. The results show that both of the response surface models have good model adequacy. Optimization by desirability functions approach was presented as points which according to the type of the process and production requirements can only be used to start and preliminary design. But the overlaid contour plots have flexibility in output for manufacturing processes. Output overlaid contour plots provide optimum area that there is wide range of values for choices in different condition.

Background: Atorvastatin (AT), as a synthetic lipid-lowering agent, is a highly crystalline substance having poor solubility and low bioavailability. The objective of the present research was to improve the microprecipitation method of AT suspension preparation. Methods: Microprecipitation parameters were improved using Box-Behnken experimental design method. The suspension was formulated with Brij 35 (stabilizer agent) using methanol as solvent and water as non-solvent, respectively. DSC, XRD, FTIR studies were performed for characterization of the microcrystals. With the aim of evaluating the effect of independent variables, the amounts of organic solvent (X1), emulsifier concentration (X2), stirring rate (X3), and volume of aqueous solvent (X4) on dependent variables, drug content (DC, ) particle size (PS), drug released after 5 minutes (Q5), Gibbs free energy change (Δ G° tr), crystal yield (CY) and saturated solubility (Ss), a full factorial was used. Results: The results of DSC, XRD, and FTIR showed that there was not any interaction between AT and Brij 35. This research demonstrated a reduction in crystallinity in agglomerates. The microcrystals showed that micromeritics characteristics were significantly improved compared to pure AT. The content of drug and yield crystal was in the limit of 42. 58-110. 24% and 58. 33-98. 18% in all formulations, respectively. It was shown that the prepared microcrystals had a higher rate of release compared to the untreated AT powder (P< 0. 05). Size reduction of AT is needed for improving the solubility. Solubility and drug release rates of At was enhanced with the microprecipitation method. Conclusion: The results showed that microcrystals significantly increased AT dissolution rate.

The vortex tube is one of the widely used cooling systems in the industry. Investigating the effect of all input variables on the outlet cold temperature difference in laboratory state is timeconsuming and costly. To this purpose, in the current study, attempts were made to model and predict the effect of all input variables on the outlet cold temperature difference of air and inlet air using adaptive neuro-fuzzy inference system (ANFIS) method. The ANFIS method was designed with three structures of fuzzy inference systems called subtractive clustering (SC) algorithm, fuzzy c-means (FCM), and grid partition (GP) with four types of input membership functions including trimf, gaussmf, gbellmf, and pimf. For model training and testing, 326 laboratory data were used. The developed models were compared using statistical parameters of correlation coefficient, mean absolute relative deviation, standard deviation, and root mean square error (RMSD) together with general desirability function. The results showed that GP algorithm with input pimf membership function with the greatest value of correlation coefficient (0. 9975) and lowest value of RMSD for test data (0. 4199) and general desirability function value of 0. 71 is the best method to predict outlet cold temperature difference. Using the above-mentioned method, the most optimum state of vortex tube performance for industrial applications was found to be the use of 3 or 6 nozzels, at the pressure range of 0. 55 to 0. 6MPa and the nozzle angle of 20 to30 degrees, and for laboratory applications was obtained to be the use of 6 nozzles, at the pressure range of 0. 55 to 0. 6MPa, and the nozzle angle of 25 to 35 degrees.