On the Hardfacing Performance Optimization after Plasma Transfer Arc Experiments

PurposeThis paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.Design/methodology/approachThe experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.FindingsThe influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.Originality/value3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

Design of experiments using box behnken design in the development, characterization, mathematical modeling, and evaluation of lung targeted nebulized antiviral camostat mesylate loaded pegylated nanosuspension product

Optimization of the corrosion property of a high entropy alloy using response surface methodology

Response surface methodology for damage detection using frequency and mode shape

Rational and precise development of amorphous polymeric systems with dapsone by response surface methodology

The goal of this study is to present a methodology for the determination of the optimal cutting parameters (spindle speed, feed rate and tool point angle) during the drilling process of carbon fiber reinforced polymer composites (CFRP) to maximize the material removal rate by considering surface roughness, delamination and thrust force as the constraints through coupling Response Surface Method (RSM) and Genetic Algorithm (GA). In this regard, the advantages of statistical experimental design technique, experimental measurements, Response Surface Method (RSM) and the genetic optimization method are exploited in an integrated manner. To this end, the experiments on CFRP were conducted to obtain surface roughness, delamination factor and thrust force values based on the full factorial design of experiments, and then analysis of variance (ANOVA) is performed. The predictive models for outputs were created using Response Surface Method (RSM) taking advantage of the experimental data. Material removal rate constituted the main function for the genetic algorithm, and thrust force, delamination, and surface roughness were applied as the constraints of the GA function. The function was optimized by the GA code, and finally, the optimum variables were obtained, and the results of the GA were tested experimentally. It can be clearly observed that good agreement exists between the predicted values and the experimental measurements.

A new strain Streptomyces bingchenggensis BC-X-1, in which one of the impute was disappeared had been screened by N-methyl-N’-nitroso-N-nitroso-guanidine (NTG) mutation. In order to improve the yield of milbemycin by the new strain, response surface methodology was employed to optimize the composition of fermentation medium. The Plackett-Burman design indicated that yeast extract, soybean flour, KH 2 PO 4 , FeSO 4 and CaCO 3 had significant effects on milbemycin production. The concentrations of these five components were investigated using Box-Behnken design and a polynomial model related to medium components concentration effect on milbemycin yield had been established. Analysis of variance (ANOVA) showed a high coefficient of determination (R 2 ) value of 0.9701, which indicated that good agreement between the experimental and predicted values of milbemycin yield. Student’s T-test of each coefficient showed that all the linear and quadratic terms effect were significant. The optimum concentrations of components: yeast extract, soybean flour, CaCO 3 , FeSO 4 , KH 2 PO 4 were 2.58, 2.58, 0.40, 0.0058 and 0.088%, respectively. The yield of milbemycin reached 1110±98 μg/ml, which was 2.61- fold of the initial yield. Key words : Milbemycin, Streptomyces bingchenggensis, medium optimization, response surface methodology.

Hybrid approach based on response surface methodology and artificial neural networks coupled with genetic algorithm (RSM-GA-ANN) for the Prediction and optimization for the Photodegradation of dye using nano ZnO anchored glass fiber under solar light irradiation

In this study, optimization and statistical analysis of oxidative desulfurization (ODS) of an Iranian heavy crude oil (HCO) by applying ultrasonic waves were performed. The investigated HCO was from the West Paydar Field of Iran (sulfur: 38638 ppm, viscosity: 78.76 cSt (20 °C), API: 18.53). The process was performed in the presence of acetic acid (catalyst), hydrogen peroxide (oxidant), and copper sulfate (PTA, phase transfer agent). The studied parameters included power (10–15 W/ml), temperature (60–80 °C), PTA (0–1 wt%), and catalyst (4.8–19.2 wt%). The processing time was 10 min. Acetonitrile was an extraction solvent. The experimental design was performed based on Box Behnken design (BBD) and consisted of 27 experiments. RSM was applied to model the response and to investigate the effects of one factor, parameter interactions, and optimization. A quadratic polynomial equation was presented for the model of HCO sulfur reduction. Based on the results, power, PTA, and catalyst had significant effects on the response. The regression coefficient of 0.9273 indicated a good agreement between the experimental results and the model results. Optimization results showed that under optimal conditions including power: 14.63 w/ml, temperature: 72.47 °C, PTA: 0.79 wt%, and catalyst: 4.84 wt%, desulfurization was 45.10 wt%.

For all industrial processes, modelling, optimisation and control are the keys to enhance productivity and ensure product quality. In the current study, the optimization of process parameters for improving the conversion of isoeugenol to vanillin by Psychrobacter sp. CSW4 was investigated by means of Taguchi approach and Box-Behnken statistical design under resting cell conditions. Taguchi design was employed for screening the significant variables in the bioconversion medium. Sequentially, Box-Behnken design experiments under Response Surface Methodology (RSM) was used for further optimization. Four factors (isoeugenol, NaCl, biomass and tween 80 initial concentrations), which have significant effects on vanillin yield, were selected from ten variables by Taguchi experimental design. With the regression coefficient analysis in the Box-Behnken design, a relationship between vanillin production and four significant variables was obtained, and the optimum levels of the four variables were as follows: initial isoeugenol concentration 6.5 g/L, initial tween 80 concentration 0.89 g/L, initial NaCl concentration 113.2 g/L and initial biomass concentration 6.27 g/L. Under these optimized conditions, the maximum predicted concentration of vanillin was 2.25 g/L. These optimized values of the factors were validated in a triplicate shaking flask study and an average of 2.19 g/L for vanillin, which corresponded to a molar yield 36.3%, after a 24 h bioconversion was obtained. The present work is the first one reporting the application of Taguchi design and Response surface methodology for optimizing bioconversion of isoeugenol into vanillin under resting cell conditions.

Statistical experimental designs were carried out for enhancing the ethanol production from cashew apple juice after obtaining the biochemical analysis of cashew apple juice. The biochemical analysis of the cashew apple juice was as follows: 79.2 % moisture, 12.5 % total sugars, 4.9 % reducing sugars, 0.16 % protein, 0.2 % fat, 1.91 % tannin and 1.13 % ascorbic acid. Cashew apple juice can be preserved at -18oC for several months. The Plackett -Burman design was used initially to screen seven nutritional parameters which are critical and important for growth of Saccharomyces cerevisiae as well as enhancing ethanol production. Among them three parameters were found to have a significant effect on ethanol production and these three elements were further optimized using central composite design (CCD) by response surface methodology. The optimal values of three variables obtained for maximum production of ethanol was (g/l): ammonium chloride 0.45, magnesium sulfate 0.08 and dipotassium phosphate 0.21 and the predicted ethanol production was 61.34 g/l. With the predicted optimal values, experiments conducted in the laboratory yielded an ethanol production of 59.80 g/l equal to 93.62% of its theoretical yield. Statistical experimental designs such as Plackett-Burman design and CCD are critical and important for rapid screening and determining significant nutritional parameters and then optimizing them for enhancing the ethanol production.

Biosorption of copper(II) ions by flax meal: Empirical modeling and process optimization by response surface methodology (RSM) and artificial neural network (ANN) simulation

ABSTRACTIn this study the adsorption equilibria of acetic, butyric, and oxalic acids onto Amberlyst A21 were investigated experimentally at 25°C. The process was optimized using response surface methodology (RSM). The time to reach the equilibrium state, effects of adsorbent amount, and initial acid concentrations on adsorption efficiency were investigated. Freundlich, Langmuir, and Temkin isotherms were applied to experimental data. The Freundlich isotherm revealed better results than the others. In addition to the main aim of this research, a statistical/mathematical approach – RSM – was utilized to simulate and determine the optimum conditions of acetic, butyric, and oxalic acids removal by Amberlyst A21 using three selected parameters (adsorbent dose, initial dye concentration, and type of acids). The significance of independent variables and their interactions were tested by the analysis of variance (ANOVA). The optimum acid concentration, amount of adsorbent, type of acid, and removal of acid (%) were found by desirability function to be 0.199 mol/L, 1.999 g, butyric acid, and 84.537%, respectively.

In this paper, for the first time, the role of manufacturing parameters of fused deposition modeling (FDM) on the shape memory effect (SME) is investigated by design of experiments. PLA-TPU blend with a weight composition of 30:70% is processed by melt mixing and then extruded into 1.75 mm filaments for 3D printing via FDM. SEM images reveal that TPU droplets are distributed in the PLA matrix, and the immiscible matrix-droplet morphology is evident. Box-Behnken design (BBD), as an experimental design of the response surface method (RSM), is implemented to fit the model between variables and responses. The shell, infill density, and nozzle temperature are selected as variables, and their effects on loading stress, recovery stress, shape fixity, and shape recovery ratio are studied in detail. An analysis of variance (ANOVA) is applied to estimate the importance of each printing parameter on the output response and assess the fitness of the presented model. The ANOVA results reveal the high accuracy of the model and the importance of the parameters. Infill density and nozzle temperature had the greatest and least roles on shape memory properties, respectively. Also, the values of shape fixity and shape recovery were obtained in the ranges of 58–100% and 53–91%, respectively. Despite many researches on 4D printing of PLA, low ductility at room temperature and high stress relaxation rate are its weakness, which are covered by adding TPU in this research. Due to the lack of similar outcomes in the specialized literature, this paper is likely to fill the gap in the state-of-the-art problem and supply pertinent data that are instrumental for FDM 3D printing of functional shape memory polymers with less material consumption.

Metal Injection Moulding (MIM) is an effective near-net-shape process for producing small, intricate parts in high volumes. Many optimizations of the MIM process parameter have been published without using any of the statistical based Design of Experiment (DOE) methodology and ends with unsatisfactory results in a wide range of experimental settings. This paper focuses on analysing the optimization of injection moulding process parameters for titanium alloy Ti-6Al-4V green parts using response surface methodology (RSM) to determine significant parameters. The feedstock is prepared by mixing Ti-6Al-4V powder with palm stearin (PS) and polyethylene (PE) binders in three different powder loadings: 63 vol%, 65 vol%, and 67 vol%. RSM is then used to create a Design of Experiment (DOE) to optimize parameters, including A: injection pressure, B: injection temperature, C: powder loading, D: mould temperature, and E: holding pressure. Analysis of variance (ANOVA) was performed to identify the significant parameters influencing defects in Ti-6Al-4V green parts. The study successfully identified the optimal conditions for producing Ti-6Al-4V components using PS and PE binders: A: 443.139 bars, B: 142.11 ºC, C: 65.38 vol.%, D: 44.694 ºC, and E: 629.561 bar. Scanning electron microscopy (SEM) from optimised of green part shows that the primary backbone polymer PE holds the green part and generates wetting for the feedstock and will ease moulding and shaping while the secondary backbone PS will act as a soft polymer that can provide the moisture to the feedstock.