Optimum Values Of Process Parameters Research Articles

Investigation into Generation of Micro Features by Localised Electrochemical Deposition

With the fast advancement of technology, localised electrochemical deposition (LECD) is becoming very advantageous in generating high aspect ratio micro features to meet the steep demand in modern precision industries of the present world. Except many other advantages, this technology is highly uncomplicated and economical for fabricating metal micro-parts with in micron ranges. In the present study, copper micro-columns have been fabricated utilizing LECD process. Different process parameters such as voltage, frequency, duty ratio and electrolyte concentration, which affect the deposition performance have been identified and their effects on deposition performances such as deposition rate, height and diameter of the micro-columns have been experimentally investigated. Taguchi’s methodology has been used to study the effects as well as to obtain the optimum values of process parameters so that localised deposition with best performance can be achieved. Moreover, the generated micro-columns were carefully observed under optical and scanning electron microscope from where the surface quality of the deposited micro-columns has been studied qualitatively. Also, an array of copper micro-columns has been fabricated on stainless steel (SS-304) substrate for further exploration of LECD process capability.

Tumbling fluidized-bed process parameters affecting quality of biopolymer coating on surface of pristine urea particles

When pristine urea particles are subjected to spray coating in a fluidized bed, the frequency of substrate particles to come across the spray zone changes due to various hydrodynamic factors of the fluidized bed giving rise to preferential coating. The heterogeneity of coating film on the surface of urea particles results in poor coating quality which ultimately affects the controlled-release properties of the coated product. In this study, the effect of tumbling fluidized bed process parameters is studied on coating quality and the Response Surface Methodology is employed for the optimization of process parameters for better results. In case of coating uniformity w.r.t. coefficient of variance (CV) of coating thickness, atomizing air pressure appears as the most influential parameter. The best coating uniformity in this case is achieved at 2.25bar of atomizing pressure. The lowest and highest values of atomizing pressure resulted in higher CV of coating thickness. In case of coating uniformity w.r.t. change in coating mass, coating time appears the most influential parameter. Coating mass increases linearly with coating time. For coating uniformity in terms of CV of size distribution, atomizing pressure and fluidizing gas temperature are the most influential parameters. The best coating uniformity is witnessed at 85°C and 1.90bar. The optimum values of process parameters and response objectives can be used to scale-up the unit used.

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm.

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon’s entropy.

Modifications of the adsorption-catalytic system for organic impurities removal

The study was dedicated to optimization of the adsorption-catalytic process for abatement of volatile organic compounds (VOC), combining the adsorption of VOC at ambient temperature in the bed of the oxidation catalyst with the periodical regeneration of the catalyst by oxidation of adsorbed species at elevated temperature. The detailed mathematical model, accounting for occurrence of unsteady-state adsorption and oxidation reactions, heat and mass transfer process both in the catalyst bed and the catalyst pellets, was constructed. The good quality of the model was verified by bench experiments. The process simulation has confirmed the high efficiency of the new versions of the adsorption-catalytic processes: the process with the startup heater located directly inside the catalyst bed and the process with the system of parallel beds with non-simultaneous regeneration; these approaches give the way to significantly decrease the specific energy consumption and to level off the peaks of VOC outlet content and outlet gas temperature. The optimal values of process parameters were defined in the study. The developed process may be applied for VOC abatement in different waste gases, being especially advantageous in terms of energy consumption and operation flexibility in processing of lean vent gases with high flow rate.

Defect minimization and feature control in electrospinning through design of experiments

ABSTRACTElectrospinning is affected by high variability, and an accurate setting of process parameters is fundamental for producing high quality nanofibers. This work aims at determining the optimal values of the main process parameters (polymer concentration, polymer feed rate, and voltage) as a function of the environmental factors (temperature and humidity), in order to obtain nanofibrous materials within a specific range of fiber diameter and porosity, and at the same time to minimize production defects. The response surfaces of diameter, porosity, and defects are first determined with a central composite design. These surfaces are then employed as an input for the optimization problem: diameter and porosity surfaces are used to constrain an admissible region, where the minimum of the defect surface is searched. The approach is tested on a prototype electrospinning machine. The estimated response surfaces capture the variability of the process with respect to both production parameters and environmental factors, and are capable of getting the optimal values of the process parameters. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44740.

Fiber laser cutting of CFRP composites and process optimization through response surface methodology

ABSTRACTIn this experimental study, the effects of major laser process control parameters, such as the laser power, beam scanning speed and assisting gas flow rate, on cut surface integrity defined by the kerf width, taper percentage, and the extent of heat affected zone (HAZ) were investigated. Response surface methodology (RSM) along with central composite design (CCD) of the experiment was used to optimize the process parameters to get better-cut surface quality. The optimum values of process parameters corresponding to cut surface with minimum defects are laser power 260 W, cutting speed 4500 mm per min, and assistance gas flow rate 14.23 l/min and the corresponding kerf width, taper percentage, and the width of HAZ are found to be 163.7 µm, 5.75%, and 573.28 µm. The confirmation experiments have been conducted that provide favorable results with an error of 2.70%, 1.87%, and 0.36%, for kerf width, taper percentage, and width of HAZ, respectively.

Optimisation of cutting parameters in CNC turning of EN-19 using tungsten carbide

Efficient turning of high performance EN series material can be achieved through proper selection of turning process parameters to minimise surface roughness and maximise the material removal rate. This present paper outlines an experimental study to optimise and study the effects of process parameters in CNC turning on surface roughness of EN19/AISI4140 (medium carbon steel) work material in dry environment conditions. The orthogonal array, signal to noise ratio and regression technique were employed to study the performance characteristics in CNC turning operation. Four machining parameters were chosen as process parameters. They are cutting speed, feed rate, tool nose radius and depth of cut. The experimentation plan was designed using Taguchi's L9 orthogonal array (OA) and Minitab-16 statistical software. Optimal values of process parameters for desired performance characteristics were obtained by Taguchi design of experiment. Moreover prediction models had been developed with the help of regression analysis to find the effect of cutting parameters.

Response surface modeling and optimization of nitrate removal from aqueous solutions using magnetic multi-walled carbon nanotubes

In this paper, the application of magnetic multi-walled carbon nanotubes (MMWCNTs) was studied for the removal of nitrate from aqueous solutions. MMWCNTs and Multi-walled carbon nanotubes (MWCNTs) are characterized by XRD, TEM, FT-IR, and VSM. The XRD results confirm that MWCNTs/Fe3O4 nanocomposites were successfully synthesized by a facile hydrothermal method. The MMWCNTs can be easily manipulated in a magnetic field for the desired separation, leading to the removal of nitrate from aqueous solution. In this study, the application of response surface methodology (RSM) is presented for optimizing the removal of nitrate from aqueous solutions using MMWCNTs. The effects of D/C (adsorbent dosage per initial concentration of pollutant ((mg) adsorbent/(mg/l) initial)) and initial pH on nitrate removal (%) were evaluated by RSM. Using RSM methodology, a quadratic polynomial equation was obtained for removal of nitrate by multiple regression analysis. The optimum conditions for maximum removal of nitrate from aqueous solution were as follows: D/C (40 (mg)/(mg/l)) and initial pH=4. Under optimal value of process parameters, maximum removal (97.15%) was obtained. The empirical Freundlich isotherm was found to describe well the equilibrium adsorption data. The adsorption capacity amount of nitrate could reach up to 183.11mg/g at the high range concentration (50–100mg/l). It was found out that the MMWCNTs can be considered as a promising adsorbent for removal of nitrate from aqueous solution.

Effect of thermal cycling process parameters on recrystallization kinetics for processing of fine-grained pure copper

ABSTRACTThe present work developed a two-step thermal cycling technique for processing of fine-grained pure copper for improved properties. This included initial annealing of specimens followed by heavy cold rolling and a series of heating and cooling cycles. The study investigated the important microstructural changes occurring in the cold deformed grains in the absence of phase transformations. A major interest of the present research was to closely describe the effect of thermal cycling parameters on the recrystallization and grain growth kinetics for processing of fine-grained structure. The study determined the optimum values of process parameters for the developed thermal cycling route including the extent of cold deformation, annealing temperatures, holding periods, and the number of thermal cycles. The thermal cycling process produced closely distributed fine grains with bi-modal microstructure leading to increased hardness and strength without any significant loss in electrical conductivity.

Development of a new staking process for an automotive part

This paper systematically investigates the infrared staking (IS) process via process modeling, numerical simulations, and experimental validation. The objective of this work is to optimize process parameters for improving the joint strength of polypropylene car door trim. A holistic approach based upon numerical simulation was proposed considering the manufacturing history of sequential processing steps, including heating, forming, and cooling. The process parameters evaluated were heating time, cooling time, and airflow rate, while the structural testing force was considered as the objective. Firstly, numerical simulations were applied in conjunction with the Box-Behnken design (BBD) experimental method and a response surface methodology (RSM) to create the quadratic mathematical model of the testing force. An analysis of variance (ANOVA) was then conducted to investigate the adequacy of the model and to identify significant factors. Finally, a multi-island genetic algorithm (MIGA) was applied to determine optimal values of process parameters and the resulting response. The testing force was maximized at the optimal parameters of 14 s, 14 s, and 60 ft3/h for heating time, cooling time, and airflow rate, respectively. Correlation between simulated and experimental results was conducted to illustrate the effectiveness of the proposed approach. This work is expected to contribute toward improving the manufacturing efficiency of the infrared staking process.

Multi-objective optimization of electroless ternary Nickel–Cobalt–Phosphorous coating using non-dominant sorting genetic algorithm-II

Study of electroless Nickel–Cobalt–Phosphorous coating was conducted on pure Copper substrate. The weight percentages of Nickel and Cobalt in the deposited mass, evaluated through energy dispersive X-ray analysis, have been selected as the response variables for statistical analysis. A response surface model with central composite design is developed to explain the effect of curvature in the predicted responses. Student’s t test has identified that concentration of Cobalt Sulfate is the significant process parameter for both the responses. The response surface plots and p-values have predicted the same. Fisher’s F test confirms that the response surface equation has excellent fitting to the observed values. A multi-objective optimization method has been employed in a quest to obtain the optimal values of process parameters, to maximize the responses. The Pareto optimal front of solutions, obtained from NSGA-II, provides more flexibility to the decision maker with the values of the optimal process parameters.

Optimization of process parameters of chrome plating for providing quality indicators of reciprocating pumps parts

The analysis of methods of surface hardening to improve wear resistance and corrosion resistance of replacement parts of double-acting reciprocating pump hydraulics is performed. Application of electrochemical chrome plating of parts in the spilling solution, which provides wear-resistant coatings with high surface quality is justified. The influence of process parameters of chrome plating of steel parts: mass ratio of the solution component concentrations (C), current density (i), solution flow rate (v) and solution temperature (T) on the microhardness (Y h ), wear (Y w ), roughness (Y r ) and taper (Y t ) using mathematical experimental design is investigated. The optimum values of process parameters which provide maximum microhardness, minimum wear, minimum roughness and taper of the chromium coating are determined. It is found that the maximum microhardness of the chromium coating provides minimum wear. Optimum process parameters are within the factor space. To achieve minimum roughness and taper, process parameters are outside the space factor. Based on the results of studies, it is recommended to take the optimum process parameters of electrochemical chrome plating in the spilling solution as those that provide minimum wear of the coating: Y w =0.095 g: C=79.5; i=133.5 A/dm 2 ; v=114.7 cm/s; T=59.3 о С, and the necessary surface roughness and taper of the part is advisable to obtain in further machining operations.

Application of response surface methodology (RSM) for the removal of methylene blue dye from water by nano zero-valent iron (NZVI).

In this study, iron zero-valent nanoparticles were synthesized, characterized and studied for removal of methylene blue dye in water solution. The reactions were mathematically described as the function of parameters such as nano zero-valent iron (NZVI) dose, pH, contact time and initial dye concentration, and were modeled by the use of response surface methodology. These experiments were carried out as a central composite design consisting of 30 experiments determined by the 2(4) full factorial designs with eight axial points and six center points. The results revealed that the optimal conditions for dye removal were NZVI dose 0.1-0.9 g/L, pH 3-11, contact time 20-100 s, and initial dye concentration 10-50 mg/L, respectively. Under these optimal values of process parameters, the dye removal efficiency of 92.87% was observed, which very close to the experimental value (92.21%) in batch experiment. In the optimization, R(2) and R(2)adj correlation coefficients for the model were evaluated as 0.96 and 0.93, respectively.

Employing artificial neural networks for minimizing surface roughness and power consumption in abrasive machining of wood

One of the biggest challenges in machining processes of wood is to detect the optimum values of process parameters for reducing the final production cost. In the present study, the effects of various process parameters on surface roughness and power consumption in abrasive machining process of wood using experimental data collected from the literature were modeled by artificial neural networks (ANNs). The results have indicated that accurate prediction of the experimental data by neural network models was achieved with the mean absolute percentage error (MAPE) less than 2.51 % for power consumption and 2.65 % for surface roughness in the testing phase. Besides, the values of determination coefficient (R2) were found as 0.994 and 0.985 in the prediction of surface roughness and power consumption by the ANN modeling, respectively. Based on the results, it can be said that by means of the proposed models the surface roughness and power consumption can easily be predicted with very high degrees of accuracy in abrasive machining process of wood. Consequently, the present study can effectively be applied to the wood industry to reduce the time, energy consumption and high experimental costs because it eliminates the need for a large number of experiments.

Possibilities for preservation of coarse particles in pelleting process to improve feed quality characteristics

Poultry diets are mainly used in pelleted form because pellets have many advantages compared to mash feed. On the other hand, pelleting causes reduction of feed particle size. The aim of this research was to investigate the possibility of increasing the content of coarse particles in pellets, and, at the same time, to produce pellets with satisfactory quality. In this research, the three grinding treatments of corn were applied using hammer mill with three sieve openings diameter: 3mm (HM-3), 6mm (HM-6) and 9mm (HM-9). These grinding treatments were combined in pelleting process with three gaps between rollers and the die of pellet press (roller-die gap, RDG) (0.30, 1.15 and 2.00mm) and three moisture contents of the pelleted material (14.5, 16.0 and 17.5%). The increased coarseness of grinding by the hammer mill resulted in the increased amount of coarse particles in pellets, especially when the smallest RDG was applied (0.30mm), but pellet quality was greatly reduced. Increasing of RDG improved the quality of pellets produced from coarsely ground corn, but reduced the content of coarse particles in pellets and increased specific energy consumption of the pellet press. Increasing the moisture content of material to be pelleted (MC) significantly reduced energy consumption of the pellet press, but there was no significant influence of MC on particle size after pelleting and on the pellet quality. The optimal values of the pelleting process parameters were determined using desirability function method. The results of optimization process showed that to achieve the highest possible quantity of coarse particles in the pellets, and to produce pellets of satisfactory quality, with the lowest possible energy consumption of the pellet press, the coarsest grinding on hammer mill (HM-9), the largest RDG (2mm) and the highest MC (17.5%) should be applied.

Optimization of reactive black 5 degradation using hydrothermally synthesized NiO/TiO2 nanocomposite under natural sunlight irradiation

In this study, the photocatalytic degradation of reactive black 5 using NiO/TiO2 nanocomposite as catalyst was optimized based on response surface methodology (RSM). NiO/TiO2 nanocomposite was fabricated through hydrothermal technique (P = autogenous, T = 150°C). The nanocomposite fabricated was characterized using powder X-ray diffraction, scanning electron microscopy, UV–vis spectrophotometer, and Fourier transmission infrared spectroscopy. The central composite design (CCD) was applied for the experimental design and process optimization. The optimization studies were carried out by changing initial pH, catalyst dosage, dye concentration, and contact time. The high regression coefficient (R2 = 0.9460, Radj = 0.9040) between the variables and the response indicated excellent estimation of experimental data by quadratic model. The optimum catalyst dose, pH, dye concentration, and contact time were found to be 2 g/l, 5, 40 mg/l, and 150 min, respectively, and under the optimal values of process parameters, the dye degradation performance of 86.1% was achieved. Based on the data of present study, it is concluded that CCD and RSM could be employed to model dye degradation parameters using NiO/TiO2 nanocomposite while optimizing and minimizing the number of experiments required.

Study of 1-Chloro-4-Nitrobenzene adsorption on Carbon nanofibers by experimental design

ABS TRACT: In this study, the adsorption of 1-chloro-4-nitrobenzene (1C4NB) on carbon nanofibers (CNFs), was investigated in a batch system. The combined effects of operating parameters such as contact time, pH, initial 1C4NB concentration, and CNFs dosage on the adsorption of 1C4NB by CNFs were analyzed using response surface methodology (RSM). The analysis of variance results confirmed that there was significant agreement between the model and experimental data. In addition, it was indicated that the residuals followed a normal distribution. The screening experiments showed that significant factors in 1C4NB removal were CNFs dosage, interaction between initial 1C4NB concentration-CNFs dosage and CNFs dosage-contact time. High efficiency removal (>90%) was obtained under optimal value of process parameters in the first 6 min of the removal process. The results indicate that RSM is a suitable method for modeling and optimizing the process, so that experimental design by RSM leads to time and cost saving. Non-linear form of Langmuir, Freundlich and Temkin models were fitted to adsorption equilibrium data. The results showed that the isotherm data can be well described by Freundlich isotherm equation.

Optimisation of electrochemical micromachining and electrochemical discharge machining process parameters using firefly algorithm

The process parameter in the non-traditional machining process is an important aspect of enhancing the performance parameter. Precision machining and machining of complex part is a challenging task which is time consuming and expensive. Therefore, the optimal values of process parameters should be selected so that performance parameter, like, material removal rate (MRR), radial overcut (ROC) and heat affected zone (HAZ), etc., can be optimised. In this paper, a recently developed optimisation technique firefly algorithm (FA) is considered for finding out the optimal solution. The FA technique is attempted to two non-traditional machining processes, electrochemical micromachining (EMM) and electrochemical discharge machining (ECDM). The variations of process parameters w.r.t performance parameter are represented graphically for better and effective study of the machining processes. The results of EMM and ECDM obtained by using FA are compared and it is found that the results obtained by the application of FA are better compared to the previous researcher's result.

Photo-catalyzed and phyto-mediated rapid green synthesis of silver nanoparticles using herbal extract of Salvinia molesta and its antimicrobial efficacy

Current study presents an economic, ecofriendly and simple photo-catalytic green route for the swift biosynthesis of silver nanoparticles (AgNPs) within 20s, devoid of any instrumental support or chemical reductant. Aqueous leaf-extract of an aquatic fern, Salvinia molesta (AES), was used as a bioreductant as well as a stabilizing agent. Rapid change in color of reaction mixture from yellowish green to reddish brown within 20s in direct sun light exposure was considered as the primary visual indication of AgNPs biosynthesis. The biosynthesis of AgNPs was confirmed by UV–visible spectroscopy through the presence of a characteristic surface plasmon resonance (SPR) band for AgNPs at λmax of 425nm. The process parameters were optimized through one factor at a time approach. Optimal values of different process parameters for the current biosynthetic system were found as; 35min of reaction time under sun light, 8.0mM AgNO3 concentration and 5.0% (v/v) AES inoculum dose. Field emission scanning electron microscopy (FESEM), energy dispersive X-Ray spectroscopy (EDX), high resolution transmission electron microscopy (HRTEM), atomic force microscopy (AFM) and X-ray diffraction (XRD) analysis showed that most of AgNPs were spherical in shape with average size distribution of 12.46nm having face centered cubic (fcc) crystal lattice. IR analysis of AES and synthesized AgNPs indicated the involvement of both hydroxyl and amino groups in the biosynthesis and stabilization of AgNPs. The synthesized AgNPs were found to be an effective antibacterial agent against both Gram positive and Gram negative bacteria. On the basis of results and facts, a probable mechanism has also been proposed to explore the possible route of biosynthesis of AgNPs through AES.

Parameter Optimization for Friction Stir Welding AA1100

Friction stir welding (FSW) has become a potential solid state joining technique with considerable advantages over conventional joining process. Defect-free friction stir welded joints with high joint strength are obtained when optimum process parameters are used. Although a large number of parameters govern the FSW process, the tool rotation speed, Welding speed and tool geometry are key parameters that influence the joint strength. In this work, a statistical model relating process parameters and the tensile strength (TS) of friction stir welded AA1100 joints is build using response surface methodology. The four independent variables are tool rotational speed (TRS), welding speed (WS), shoulder diameter (SD) and pin diameter (PD). Central Composite design is used and Analysis of Variance at 95% confidence level was applied to assess the adequacy of the developed model. Genetic algorithm is used for optimizing the parameters. The optimum process parameter values predicted using the genetic algorithm are as follows. Tool rotation speed: 1001.9 rpm; welding speed: 62 mm/min; shoulder diameter: 17.8 mm and pin diameter: 6.5 mm. The corresponding tensile strength of the joints is 73.1556 MPa