Desirability Function Approach Research Articles

Studying the influence of friction stir welding parameters on the mechanical properties of PP/EPDM/Clay nanocomposites by response surface methodology combined with desirability function approach

The main objective of the present study is to investigate the influence of friction stir welding (FSW) parameters using a novel tool on the mechanical properties of polypropylene-ethylene-propylene diene monomer/clay (PP/EPDM/Clay) nanocomposite. The response surface method of design of experiments in combination with the desirability function approach is applied to quantify the effects of clay content, tool rotational speed, welding speed, and tool shoulder temperature on tensile strength and elongation of welded joints. It was observed that the tool rotational speed had the maximum effect on the tensile strength, while the greatest influence on the elongation was due to the tool shoulder temperature. The addition of clay nanoparticles up to 6 wt% into the PP matrix reduced the tensile strength and elongation of the weld by about 38% and 20%, respectively. Moreover, an improvement of about 45% was observed in the tensile strength when the rotational speed increased from 800 to 1600 r/min, while the tensile strength deteriorated by about 29% when the linear speed increased from 10 to 20 mm/s. A rise in shoulder temperature from 50 to 100°C was also accompanied by an enhancement in the tensile strength and elongation by 20 and 50%, respectively. Nevertheless, the desired operating conditions were found to be 0.85 wt% for clay, 1390 r/min for the tool rotational speed, 10 mm/min for the welding speed, and 100°C for the tool shoulder temperature. The fracture morphologies obtained by scanning electron microscopy (SEM) were in good agreement with corresponding tensile strength and elongation results.

Multi-response optimization of IC wire bonding for large probe marks by the RSM and desirability function approach

Multi-response optimization of IC wire bonding for large probe marks by the RSM and desirability function approach

Efficient photodegradation of disulfine blue dye and Tetracycline over Robust and Green g-CN/Ag3VO4/PAN nanofibers: Experimental design, RSM, RBF-NN and ANFIS modeling

The g-CN/Ag3VO4/PAN nanofibers (NFs) is prominent from various nanophotocatalysts (NPCs) due to their higher surface to volume ratio, mechanical strength and recyclable characteristics. In this study, g-CN/Ag3VO4/PAN NFs were obtained through in situ method by immobilizing Ag3VO4 on g-CN/PAN NFs. Compared to g-CN/PAN and Ag3VO4/PAN, the g-CN/Ag3VO4/PAN NFs revealed excellent photocatalytic performance toward disulfine blue dye (DB). Also, the g-CN/Ag3VO4/PAN NFs showed highly activity over some interference of inorganic cations/anions and excellent cycling stability. Furthermore, the batch experiments designed by central composite design (CCD) were employed for remove of tetracycline (TC). Afterward, response surface methodology (RSM), Radial basis function artificial neural network (RBF ANN) and an adaptive neuro-fuzzy inference system (ANFIS) have been used for modeling of photocatalyst dose, concentration of TC and irradiation time. Due to strong influence of pH on the photocatalytic degradation, the pH was optimized as one at a time variable. For all three models, the values of the statistical parameters were calculated. The obtained results reveal that the ANFIS models is more accurate to predict the degradation of TC. Furthermore, the effective factors were optimized by employing the Desirability function (DF) and genetic algorithm (GA) approach and then used for real water samples. At this optimum condition, the removal percentage was obtained ∼97% by DF and GA approach, respectively. At the end, the LC-Mass method was employed to detect the intermediates of photodegradation of TC molecules.

Crabs Marine Waste-A Valuable Source of Chitosan: Tuning Chitosan Properties by Chitin Extraction Optimization.

Chitin extraction from crab shells was studied experimentally and optimized aiming to obtain chitosan with predefined deacetylation degree and molecular mass. To find out the optimum operating conditions that ensure the obtaining of a chitosan with highest deacetylation degree and specific molecular mass four parameters were varied: the concentration of NaOH and the temperature for deproteinization step, respectively HCl concentration and the number of acidic treatments for the demineralization stage. The experiment was carried on following Taguchi orthogonal array L9, and the best combination of factors was found using the desirability function approach. The optimization results showed that 5% NaOH concentration and low temperatures lead to a chitosan with high deacetylation degree. High molecular mass chitosan is obtained when a single step acidic treatment is used, while a chitosan with low molar mass is obtained for multiple acid contacts and higher HCl concentration.

Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy

Surface integrity has a very significant effect on surface roughness and surface microhardness. These are the main characteristics of surface integrity. The present study investigated the influence of the cutting depth (ap), the cutting speed ( v c ), and the feed rate (f) on the surface roughness (Ra) and surface microhardness (HV) in turning TC17 titanium alloy. Data obtained from the Box-Behnken design experiments were used to develop the response surface methodology (RSM) and artificial neural network (ANN) models. Through analysis of variance (ANOVA), the relative effects of each cutting parameter on the responses have been determined. To examine the interaction effects of cutting parameters, 3D surface plots were generated. The desirability function approach (DFA) was used to optimize cutting parameters to achieve the lowest surface roughness and highest surface microhardness. The results show that ANN response prediction models have higher prediction accuracy and lower error than RSM prediction models. The optimization parameters are 60 m/min cutting speed, 0.06 mm/r feed rate, and 0.2 mm cutting depth for the minimum surface roughness and maximum surface microhardness with a maximum error of 2.83%.

Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Magnesium (Mg) alloys are extensively used in the automotive and aircraft industries due to their prominent properties. The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement. This decision entails the selection of suitable process parameters concerning various conflicting factors, so it has to be addressed with the Multiple Criteria Decision Making (MCDM) method. Therefore, this work addresses the MCDM problem through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and COPRAS (COmplex PRoportional ASsessment) methods. The assessment carried out in the material Mg AZ91 with the Solid Carbide (SC) drill bit. The dependent parameters like drilling time, burr height, burr thickness, and roughness are considered with the independent parameters like spindle speed and feed rate. Drilling alternatives are ranked using the above said two methods and the results are evaluated. The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev. The identical sequencing order was observed in TOPSIS and COPRAS method. The empirical model was developed through Box-Behnken design for each response. Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation. The trends of various dependents based on the heterogeneity of various independents are not identical, these complex mechanisms are identified and reported. The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank. The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

Prediction and optimization of diesel engine characteristics for various fuel injection timing: Operated by third generation green fuel with alumina nano additive

Azolla pinnata is a macroalgae commonly known as mosquito weed that is widely utilized for biomass production and biodiesel because of its capacity to thrive in low nitrogen environments. In this study, the various concentration (25 ppm, 50 ppm and 75 ppm) of alumina nanoparticles dosed Azolla pinnata biodiesel blend performance and emission parameters were analyzed on a single-cylinder compression ignition engine for different load conditions and fuel injection timings. Using the experimental results, the artificial neural network and response surface methodology regression models were developed and compared its prediction capabilities of output parameters as brake thermal efficiency, brake specific fuel consumption, hydrocarbon, carbon monoxide, nitrogen oxide and smoke. The identified best model was applied to optimize the input parameters of nano additive, engine load and fuel injection timing. The attained R2 (0.9992) and root mean square error 2.437 values exposed that the developed response surface methodology model was more accurate than artificial neural network. The best possible responses provided through the desirability function approach were brake thermal efficiency (19.05 %), brake specific fuel consumption (785.66 g/kWh), hydrocarbon (25.18 ppm), carbon monoxide (0.0576 %), nitrogen oxide (443.79 ppm), smoke (7.48 %) respectively for optimized working factors of nano additive (44 ppm), engine load (1.368 kW) and fuel injection timing (26° before top dead centre). The overall error percentage calculated through the validation study was observed to be under 5 %. The developed response surface methodology model produced good results, which are helpful in predicting and optimizing engine emissions and performance parameters.

Optimization of ethanol based extraction of phenolics from Ocimum sanctum flowers by response surface methodology

Ocimum sanctum L. flowers have been reported to have various health benefits owing to its phenolic composition. However, the available phenolic compounds should be effectively extracted to efficiently utilize these flowers. This study was aimed to make use of response surface methodology (RSM), to simultaneously optimize the process parameters to obtain maximum yield of phenolics and anthocyanins along with maximum DPPH radical scavenging activity from O. sanctum flowers. Central composite design (CCD) was employed with four independent variables; solid:liquid ratio (XA), ethanol concentration (XB), temperature (XC) and time (XD) which were found to affect the response variables significantly and fit to the second order polynomial model. The results showed that the polynomial models for all responses were significant (p < 0.05) and did not show lack of fit and presented acceptable coefficients of determination. This indicates the suitability of the models for prediction purposes. The desirability function approach in the scale of 0–1 was used to determine the optimized conditions. The optimum process parameters obtained were XA; 1:35 (w/v), XB; 55%, XC; 44 °C and XD; 32 min. The experimental values obtained based on optimum extraction parameters agreed with the predicted values. The findings suggests the application of optimized parameters to acquire functionally active phenolics and anthocyanins from O. sanctum flowers using ethanol and make use of them in food and pharmaceutical industry.

MULTI-RESPONSE OPTIMIZATION AND INVESTIGATION OF DRY SLIDING WEAR BEHAVIOR OF Al7075 SURFACE HYBRID NANOCOMPOSITE USING RESPONSE SURFACE METHODOLOGY

Aluminum alloy-based (Al7075) surface hybrid nanocomposite (SHNC) was fabricated by incorporating a reinforcement mixture of nano-aluminum oxide, micro-boron carbide, and graphite by utilizing friction stir processing (FSP). The graphite particle ratio was varied in the reinforcement mixture and its influence on the tribological properties of Al7075 SHNC was studied. In the metal matrix surface composite, the scanning electron microscope (SEM) and field emission SEM (FESEM) depict a homogeneity in the distribution of reinforcements. The nanocomposite’s wear behavior under dry sliding environments was investigated by adopting a central composite design (CCD) at three levels by response surface methodology (RSM). The designed experiments were executed in pin-on-disc (POD) apparatus, with load, sliding distance, and graphite ratio as input variables. The influence of applied factors and their interaction with the response were determined using an analysis of variance. To predict the wear characteristics, a mathematical model is formulated. Load is discovered to be a significant factor influencing the wear rate and friction coefficient. In addition, an increase in graphite% results in a lower wear rate for the given quantity of load and sliding distance. SEM image shows a severe wear pattern for higher load and lower graphite content. The optimum combination of the parameter obtained from multi-response optimization was load 10[Formula: see text]N, sliding distance 503.86[Formula: see text]m, and 14.99% graphite for reducing the wear rate and friction coefficient by applying the desirability function approach.

Modeling and multi-response optimization of cutting parameters in turning of AISI 316L using RSM and desirability function approach

Modeling and multi-response optimization of cutting parameters in turning of AISI 316L using RSM and desirability function approach

Nanoparticle-induced control (MWCNTs–TiO2) on grain size and tensile strength response and multi-response optimization on TIG welded joints

This study discusses the effectual variable parameters of multi-walled carbon nanotubes and titanium oxide (MWCNT–TiO2) contents and welding current in obtaining a response on mechanical behavior. The experimental work involved three levels of both parameters, which included 1, 1.5, and 2 wt.% of MWCNTs–TiO2 and 160, 180, and 200 A of current by forming a full design that represented a 32 (3 k) factorial model. The results of an analysis of variance (ANOVA) were critically employed to assess the variation between the variables and within their levels for approaching the significant combination of parameters. The joints welded with MWCNT–TiO2-coated fillers tendered a significant reduction in grain size (GS) along with high values of tensile strength (TS). From the ANOVA, it was determined that both the investigated parameters and their combined effect created a significant response for GS reduction and increment in TS. The presented empirical regression quadratic model was validated for the adequacy of the projected results that were shown to be acceptable with the experimental investigated data. In addition, the desirability function approach was applied for a multiple response optimization in yielding the desired responses of GS and TS and simultaneously providing an optimized set of process parameters. Thus, the function declared 1.5 wt.% MWCNT–TiO2 and 180 A as the optimized process set for delivering the maximum TS at the designated range of GS for this system.

Study of machining parameters in reciprocating magnetorheological polishing process based on response surface methodology

PurposeThis paper aims to analyze the significance of machining parameters (workpiece’s rotational speed, magnet coil current and working gap) on final Ra (surface roughness) and material removal rate (MRR) of workpiece in reciprocating magnetorheological polishing (RMRP) process.Design/methodology/approachThe research is planned to analyze, model and predict the optimum machining parameters to anticipate final Ra and MRR by applying response surface methodology (RSM) and multiresponse optimization (desirability function approach). The experiments have been planned by design of experiments (DOE). Analysis of variance (ANOVA) is applied to determine the significances of machining parameters on RMRP performance characteristics.FindingsResponse surface plots for final Ra and MRR by RSM show that machining parameters are significant for the responses. The optimum machining parameters obtained are optimized by desirability function approach (DFA), and the optimum parametric combination has been validated by confirmatory experiments. The experimental results of the final Ra and MRR are deviated by 5.12% and 2.31% from the response results under the same optimization conditions, respectively.Originality/valueIn this study, the RMRP responses (final Ra and MRR) are improved at predicted input machining parameters condition obtained by RSM and DFA approach. Furthermore, the research results provide a reference for experimental design and optimization of MRP process.

Modeling of sugarcane bagasse conversion to levulinic acid using response surface methodology (RSM), artificial neural networks (ANN), and fuzzy inference system (FIS): A comparative evaluation

Levulinic acid is recognized as a prominent value-added chemical that can be obtained from bioresources and has versatile industrial applications. This work reports bioprocess modeling of levulinic acid synthesis from sugarcane bagasse employing response surface methodology and artificial intelligence techniques, including artificial neural network and fuzzy inference system approaches. The influence of process parameters, namely reaction temperature, concentration of levulinic acid and reaction time on the production of levulinic acid was investigated. The levulinic acid production values predicted by empirical models were compared with experimentally acquired data. The predictive capability of various models was appraised by computing statistical indices. The artificial neural network model was determined to be the best predictive model with the highest coefficient of determination value of 0.96 and the lowest error values (root mean square error = 0.272, mean absolute error = 0.072 and mean absolute percentage deviation = 2 %). The feedforward back propagation network with 3–10-1 architecture was employed to model the production of levulinic acid. The process optimization was performed using the desirability function approach. The bagasse, on treatment under optimum conditions, with 1 M sulphuric acid at 190 °C for 15 min yielded 5.40 mg/mL levulinic acid accounting for 77.1 % process efficiency. The utilization of waste biomass sugarcane bagasse would offer a sustainable approach for the production of platform chemical levulinic acid.

Application of a comfort index for evaluating tactile and thermo-physiological comfort properties of surgical gowns

The primary objective of this study is to develop a mathematical tool to calculate a global desirability index value (comfort index) for disposable surgical gowns which will enhance the market value for the product. The mathematical tool is based on the desirability function approach, by using its different modules such as maximization, minimization, and target as per the objectives chosen. Three different disposable nonwoven surgical gowns, such as Fabrics C (44.35 g/m 2 , 0.32 mm thickness &amp; 11 micron pore size), A (46.31 g/m 2 , 0.29 mm thickness &amp; 10 micron pore size) &amp; S (44.71 g/m 2 , 0.36 mm thickness &amp; 8.9 micron pore size), intended for hospital application have been used. The developed model focuses on predicting tactile and thermophysiological comfort, by considering the importance of certain physical properties, leading to the comfort of the wearer. Results reveal that out of the three gowns, Fabric C gown fetches the best comfort index value of 0.5689 followed by the Fabric S and A gowns with the values of 0.3009 and 0.1969 respectively.

Interaction analysis and multi-response optimization of transformer winding design parameters

This work conducted a multi-response optimization on the design parameter of a transformer winding based on the response surface methodology and desirability function approach to find a design with better combined-thermal-hydraulic performance. A numerical experiment was performed with two-dimensional axisymmetric numerical heat transfer models. Response surface models were established based on experimental results and checked through analyses of variance. Regression equations were fitted and then verified. Interactions among factors were investigated in detail. A multi-response optimization was carried out on the winding design parameters and optimum design was identified using the desirability function approach. The results showed that the response surface models built up in this work were all significant and all the responses could be predicted accurately based on the regression equations. The design with the block washer number of 1, axial duct size of 8 mm and radial duct size of 3 mm was found as the optimum winding design of this work. Compared with the conventional design, the optimum design increased the Colburn-j factor and Darcy friction factor by 212% and 9%, respectively, simultaneously decreased the maximum and mean temperature rises by 67.1% and 70.5%, respectively.

Implementation of factorial experimental design in chitosan - tripolyphosphate nanoparticles development by ionotropic gelation

Glimepiride (GM) is third generation sulfonylurea group and preferably used for the treatment of type -II diabetes mellitus. It belongs to BCS class II drugs according to biopharmaceutical classification system which shows low aqueous solubility. The purpose of this work was to develop and optimize glimepiride loaded chitosan- tripolyphosphate nanoparticles by ionotropic gelation method using 32 full factorial experimental designs. The developed chitosan nanoparticles were evaluated for particle size, zeta potential, entrapment efficiency, %yield and in vitro drug release etc. Factorial experimental design was implemented to optimize the independent variables like drug: polymer ratio (X1) and concentration of cross linking agent (X2) on dependent variables like particle size (Y1) and entrapment efficiency (Y2). A statistical analysis was performed using minitab 16 software with respect to ANOVA, regression analysis. The surface plots and contour plots represent the relationship between the experimental responses and the set of independent variables. The factorial experimental design was validated by checkpoint analysis and desirability function approach was applied for optimization of chitosan nanoparticle formulation. Drug entrapment efficiency and mean particle size of optimized formulation was found to be 61.25% and 222 nm respectively. Nanoparticles revealed initial burst release followed by slow drug release.

Modelling and statistical analysis of plastic biomass mixture co-gasification

In this work, an Aspen Plus model was developed to study the plastic/biomass co-gasification thermodynamically. Tar inclusion and temperature restricted equilibrium approach were applied for model modification. Using different gasifying agents, it was found that steam co-gasification is superior to air and oxygen-gasified processes in terms of gas composition and lower heating value (LHV). The use of an oxygen/steam mixture enhances H2 production if the steam to fuel ratio (S/F) is kept low. The modelling results were integrated with Response Surface Methodology to study the impacts of temperature (T), steam to fuel ratio (S/F), and plastic to biomass ratio (P/B) and their interactions within the steam co-gasification process. It was found that T and S/F, and their interaction, are the most influential parameters. Increasing the T, S/F, and P/B was favourable for enhancing H2 production, gas yield (GY), and carbon conversion efficiency (CCE), while CO content and LHV can be negatively affected. Using ANOVA results, empirical correlations were derived for estimating the responses (H2, CO, CO2, CH4, LHV, GY, and CCE). The optimum condition obtained using the desirability function (DF) approach was T = 921 °C, S/F = 1.186, and P/B = 74.99, at which the overall desirability function was found to be DF = 0.9.

Study on interactive effects of CRDi engine operating parameters through RSM based multi-objective optimization technique for biofuel application

The current research focuses on the optimization of D+LPO (20% waste-based lemon peel oil and 80% diesel) fueled Common Rail Direct Injection (CRDi) engine fuel injection parameters for the optimum performance and emission characteristics. The foremost influential fuel injection parameters such as Injection Pressure (IP), Dwell Time (DT), Pilot Mass (PM) and Pilot Timing (PT) are considered to enhance the performance and decrease the emissions. Engine output responses like Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), Hydrocarbons (HC), Carbon monoxide (CO), nitrogen oxides (NOx) and smoke emissions are modeled by using Response Surface Methodology (RSM). Analysis of Variance (ANOVA) disclosed that all the developed models are statistically significant with an R2 value of 0.9735 for BTE, 0.9719 for BSFC, 0.8517 for CO, 0.956 for HC, 0.9282 for NOx and 0.927 for smoke. Multi-objective optimization is performed using the desirability function approach to minimize the BSFC, HC, CO, NOx and smoke with maximum BTE. The D+LPO blend at IP 700 bar, PM 20%, PT 27°bTDC and 12° DT was predicted to be an optimum operating condition for this particular engine. Confirmatory tests were used to validate the predicted combination, and the prediction error was determined to be less than 5%.

Rapid and easy ICP OES determination of selected major, minor and trace elements in Pu-erh tea infusions using the response surface methodology along with the joint desirability function approach

A new analytical method was proposed for multielement (Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Sr and Zn) analysis of Pu-erh teas infusions by inductively coupled plasma optical emission spectrometry. The Box-Behnken response surface design together with individual desirability functions and the joint desirability function approach was applied to develop experimental conditions of this new procedure, being alternative to high-temperature wet digestion. The procedure involved the samples to be just 5-fold diluted with 1.7 mol L−1 HNO3. The proposed method was precise (relative standard deviations within 2–8%), true (relative errors from −8 to +6%) and guaranteed very good detectability (detection limits within 1–6 ng g−1, except for Ca and Fe). It was used for analysis of infusions of Pu-erh teas as well as to verify the effect of their preparation conditions (steeping water temperature, steeping time).

A computational technique for prediction and optimization of VCR engine performance and emission parameters fuelled with Trichosanthes cucumerina biodiesel using RSM with desirability function approach

Global concerns about pollution, rapidly diminishing fossil fuels, energy accessibility, and sustainability have prompted researchers to focus on alternative energy sources. Unlike fossil fuels, second-generation biofuels are considered a viable and promising substitute for the existing resources. This study utilizes waste-to-energy to produce biodiesel from trichosanthes cucumerina waste seeds. Biofuel characteristics were determined using gas chromatography-mass spectrometry and Fourier transform infrared spectroscopy analysis. The engine test blends are prepared with different trichosanthes cucumerina biodiesel (TCB) proportions (30%, 50%, 70% and 100%), and their thermophysical properties were assessed according to the ASTM standards. The performance and emission characteristics were analyzed using TCB blends in the variable compression ratio diesel engine. With the support of experimental results, the response surface methodology (RSM) was used for developing the multiple regression model. The desirability function approach minimizes the brake specific fuel consumption, exhaust gas temperature and engine exhaust emissions and maximizes brake thermal efficiency. The optimum input factors were found from the RSM modeling as 45% TCB blend, the compression ratio 17.5:1 and 1.5 kW engine load. Experimental results validate the engine's optimized response, and the observed error percentage is less than 6.5%. In comparison to the baseline diesel fuel, the 45% TCB blend delivered lower emissions of HC by 4.26%, CO by 3.74% and smoke opacity by 4.67% and produced identical engine performance. Based on the above results, the 45% TCB blend can be considered a feasible alternative source for diesel fuel.