Optimum Values Of Process Parameters Research Articles

Studies Regarding the Influence on the Surface Roughness Material of the Cutting Regime during the Milling Processing

The aim of the present paper is to establish the optimal parameter values of the cutting regime of a milling process. The paper presents a study regarding the influence of the cutting parameters on the surface roughness of the material and also on the vibration generated by their combinations, during a processing by milling. The studies are made on samples made from S355 JR steel with a metal milling machine FUS 25, which is used also for the experiments. The samples dimensions are 210x150x16mm. For the experiments there was used a cylindrical - frontal milling tool, with 32mm diameter and 10 tooth. Basic parameters of milling processing of materials we have considered in this paper are: feed rate [mm/min]; cutting speed RPM [rot/min]; depth of cut [mm]. For each of this parameters three levels were envisaged. For a 100% accurate experiment results at least 27 experiments must be done. Using an L9orthogonal array, the number of experiments is reduced to nine and the accurate of the method is around 99.96%. The optimal process parameters values are obtained using Taguchi method considering three situations. In the first case the goal is to get only a fine roughness for the sample. The second studied case is focused on finding a low level for the vibration generated during the milling process. The aim of the last study is to find a fine roughness and also a low level of vibration for the process. The analysis of variance (ANOVA) is applied, in all cases, in order to estimate the error variance and to rank the process parameters according to their importance.

Application of Response Surface Method as an Experimental Design to Optimize Clarification Process Parameters for Sugarcane Juice

In this study, the response surface method and experimental design were applied for optimization of independent variables for clarification of sugarcane juice. A central composite design, with 6 axial points, 8 factorial points and 6 replicates at the centre point were used to build a model for predicting and optimizing clarification process parameters. The independent parameters viz Temperature (73.59, 77, 82, 87 and 90.41°C), Activated charcoal thickness (0.83, 1.0, and 1.25, 1.5 and 1.67 mm) and deola (0.063, 0.2, 0.4, 0.6 and 0.74 g/l) were selected and analyzed. Statistical checks (ANOVA table, R2 value, model lack of fit test and F-value) indicating that the model was adequate for representing the experimental data. The dependent parameters measured were viscosity, °Brix, and total solids values. Optimum values of process parameters obtained by using simultaneous optimization of dependent parameters were: 77.55°C temperature, 1.5 mm activated charcoal thickness and 0.48 g/lit deola.

Optimisation and Evaluation of Machining Parameters for Turning Operation of Inconel-625

In this research paper a new approach for the optimization and evaluation of machining parameters for turning operation on Inconel-625 on CNC machining with the help of Taguchi Methods is presented. Taguchi Parameter Design is a powerful and efficient method for optimizing quality and performance output of manufacturing processes, thus a powerful tool for meeting this challenge. The main objective of this investigation is to obtain an optimal setting of process parameters in turning for maximizing the material removal rate of the manufactured component, the MRR has been investigated by the analysis while machining practically used component. As per Taguchi ‘DOE’, the number of experiments to be conducted in this analysis very systematic and well calculated, the turning operations are performed as per the machining conditions shown in the orthogonal array. The data for calculating material removal rate in all the test conditions are observed and recorded. The result of this analysis identifies the optimal values of process parameters for effective and efficient machining. The results from confirmation runs indicated that the determined optimal combination of machining parameters improved the performance of the machining process.

Optimization and Kinetic Studies on Biodiesel Production from Kusum (Schleichera triguga) Oil Using Response Surface Methodology.

In the present study, the low-cost non-edible kusum (Schleichera triguga) oil with a substantial amount of free fatty acid (FFA) was utilized for biodiesel synthesis. In pretreatment step, FFA was reduced by the acid catalyzed esterification method. Then, response surface method (RSM) in conjunction with centre composite design (CCD) containing 30 experimental runs were statistically employed for process optimization and kinetic study for the base catalyzed transesterification process. A statistical model predicted highest fatty acid methyl ester (FAME) yield of 97.37% at the optimal values of process parameters as follows: sodium methoxide concentration 0.9 wt% of oil, Methanol to oil molar ratio 9:1, temperature 58.9 ℃ and reaction time 58.5 min. Using these optimal parameters under experimental conditions in three independent replicates an actual FAME content of 98.14% was obtained which was in reasonable agreement with predicted one. The developed kinetic model suggested a 1.8(th) order reaction with activation energy of 31.42 kcal mol(-1) and frequency factor of 5.53×10(19) L mol(-1)min(-1). Furthermore, Important fuel properties of kusum oil biodiesel (KOB) was compared with ASTM 6751 and DIN EN 14214. The viscosity was found to be 5.34 Cst at 40 °C and the flash point was 152°C.

Optimization of Feed and Radial Force in Turning Process by using Taguchi Design Approach

Efficient turning of high performance Mild Steel material can be achieved through proper selection of turning process parameters to minimize surface roughness, feed and radial forces. In this present paper outlines an experimental study to optimize Feed and Radial forces and study the effects of process parameters in Lathe turning on Mild Steel work material in dry environment conditions using HSS tool. The orthogonal array, signal to noise ratio and analysis of variance are employed to study the performance characteristics in Lathe machine turning operation. Three machining parameters are chosen as process parameters: Cutting Speed, Feed rate and Depth of cut. The experimentation plan is designed using Taguchi's L9 Orthogonal Array (OA) and Minitab-16 statistical software is used. Optimal values of process parameters for desired performance characteristics are obtained by Taguchi design of experiment. Prediction models are developed with the help of regression analysis method using Minitab-16 software and finally the optimal and calculated results are also verified with the help of confirmation experiments. Surface roughness is measured on each run according to Taguchi design of experiment and finally average roughness (Ra) is measures on confirmation experiment to compare with previous given machining parameters according to the Taguchi design of experiment.

Experimental Design for Removal of Fe(II) and Zn(II) Ions by Different Lactic Acid Bacteria Biomasses

This paper investigates the removal of iron and zinc from aqueous solution with dairy wastewaterin three different biomasses such as Lactobacillus delbrueckii ssp bulgaricus (Lb-12), Streptococcus thermophillus(STM-7) and a combination of both this bacteria culture (YC-380) and the optimization of removal efficiencyusing MINITAB program. A full 23 factorial design of experiments was applied to determine the optimumconditions of removal of Fe(II) and Zn(II) from aqueous solution. The three tested factors were biomassconcentration (5-15 g/L), pH (3-9), and temperature (20-40 °C). The optimum biomass concentration, pH andtemperature were found to be 15 g/L, 9 and 20 °C, respectively. Under optimal value of process parameters, 90-100% Fe(II) and 70-90% Zn(II) removal were obtained with all tested biomasses. Removal results indicated thata combination of both this bacteria might be a promising biomass alternative to ST-M7 and Lb.12 in removingZn(II) and Fe(II) ions from waters.

A Study of Silica Separation in the Production of Activated Carbon from Rice Husk in Viet Nam

Environmental pollution due to mismanagement of the waste from rice production is a serious problem in agricultural countries where rice cultivation occupies the largest proportion of the crops produced, including in Viet Nam. At the same time the use of activated carbon as an adsorption agent for water or gas purification is rapidly increasing due to the development of industry and technology in the developed countries. In this paper we consider the process of separating silica from charcoal, which is an important step in the production of activated carbon from rice husks in Viet Nam. The efficiency of the process rises with increasing temperature up to 133°C, the ratio of alkali/charcoal up to 0.6 and sodium hydroxide concentration up to 6M as well. A regression equation has been obtained, which allows describing the influence of the parameters on the degree of silica separation from the carbon. Under optimal values of process parameters, the efficiency reaches up to 95.6%

A Study on the Optimization Performance of Fireworks and Cuckoo Search Algorithms in Laser Machining Processes

Laser machining is a promising non-contact process for effective machining of difficult-to-process advanced engineering materials. Increasing interest in the use of lasers for various machining operations can be attributed to its several unique advantages, like high productivity, non-contact processing, elimination of finishing operations, adaptability to automation, reduced processing cost, improved product quality, greater material utilization, minimum heat-affected zone and green manufacturing. To achieve the best desired machining performance and high quality characteristics of the machined components, it is extremely important to determine the optimal values of the laser machining process parameters. In this paper, fireworks algorithm and cuckoo search (CS) algorithm are applied for single as well as multi-response optimization of two laser machining processes. It is observed that although almost similar solutions are obtained for both these algorithms, CS algorithm outperforms fireworks algorithm with respect to average computation time, convergence rate and performance consistency.

Multi Response Optimization for Processing Al–SiCp Composites: An Approach Towards Enhancement of Mechanical Properties

The present study illustrates the manufacturing aspects of composite with different wt% of SiC particulates as reinforcement in the aluminum alloy matrix developed through investment casting route. The composites were prepared by varying certain process parameters like preheating temperature of particulates, percentage reinforcement and stirring speed. Detailed micro-structural characterization and estimation of mechanical properties (viz., strength, hardness and porosity measurement) are necessary supplements to this investigation. A mathematical model is developed using regression analysis technique for prediction of optimum process parameters viz. percentage reinforcement, preheating temperature of particulates and stirring speed for producing MMCs with enhanced mechanical properties and adequacy of the model has been validated using analysis of variance techniques. Finally the optimization of parameters has also been done using design expert software. The authors have achieved 81.56 % desirability level using multi-response optimization; the optimum value of process parameters would lead to enhanced mechanical properties of cast component.

Optimization of lead (II) sorption potential using developed activated carbon from tamarind wood with chemical activation by zinc chloride

An adsorbent prepared from tamarind wood with chemical activation by zinc chloride was used to study its sorption potential on removing lead (II). An efficient response surface methodology (RSM) is used for optimization of removal of lead (II) from aqua solutions. While the goal of adsorption of lead (II) optimization was to improve adsorption conditions in batch process, i.e. to minimize the adsorbent doses and to increase the initial concentrations of lead (II). A 24 full factorial central composite design experimental design was employed. Analysis of variance showed a high coefficient of determination value (R2 = 0.996) and satisfactory prediction second-order regression model was derived. Maximum lead removal efficiency was predicted and experimentally validated. The optimum adsorbent dose, temperature, initial concentration of lead (II), and initial pH of the lead (II) solution were found to be 1.44g L−1, 50°C, 49.23 mg L−1, and 4.07, respectively. Under optimal value of process parameters, high removal (>99%) was obtained for lead (II). The study clearly showed that RSM was one of the suitable methods to optimize the operating conditions and maximize the lead removal. Graphical response surface and contour plots were used to locate the optimum point.

Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact

Injection molding is the most widely used process in manufacturing polymer products. The warpage induced during injection molding process has an important influence on the mechanical performance of injection molded products. Therefore, how to optimize process parameters becomes the key issue in improving the mechanical performance of the product towards the expected service conditions. In this paper, a combined artificial neural network and particle swarm optimization (PSO) algorithm method is proposed to optimize the injection molding process. An integrated finite element analysis of the injection molding process, the warpage-induced residual stresses during assembly, and mechanical performance of serviced product is firstly proposed. A back propagation neural network model is then developed to map the complex nonlinear relationship between process parameters and mechanical performance of the product. The PSO algorithm is interfaced with this predictive model to optimize process parameters and thereby significantly improve the mechanical performance. A case study of vehicle window made of polycarbonate (PC) is presented. Optimum values of process parameters are determined to minimize the maximum von Mises stress within the PC vehicle window under impact loading.

Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology

A study on the radial-mode abrasive waterjet turning (AWJT) of 96 % alumina ceramic is presented and discussed. An experimental investigation is carried out to explore the influence of process parameters (including water pressure, jet feed speed, abrasive mass flow rate, surface speed, and nozzle tilted angle) on the material removal rate (MRR) when turning 96 % alumina ceramic. The experiments are conducted on the basis of response surface methodology (RSM) and sequential approach using face-centered central composite design. The quadratic model of RSM associated with the sequential approximation optimization (SAO) method is used to find optimum values of process parameters in terms of surface roughness and MRR. The results show that the MRR is influenced principally by the water pressure P and the next is abrasive mass flow rate ma. The optimization results show that the MRR can be improved without increasing the surface roughness when machining 96 % alumina ceramic in the radial-mode abrasive waterjet turning process.

Study on the Forming Process of the Crossmember in Car Intermediate Floor

Flanging and bending forming processes of the crossmember in car intermediate floor are investigated respectively by using numerical simulation technology. The numerical model of the crossmember was established and its press forming effect was simulated to determine the feasible process parameters affecting its manufacturability. Forming limit diagram and thickness distribution diagram are used to evaluate simulation results of different process schemes. And then optimum values of process parameters for flanging and bending are found, which can reduce the tendencies of wrinkling, springback and crackling during the stamping of the product.

Optimization of mechanical properties of non-woven short sisal fibre-reinforced vinyl ester composite using factorial design and GA method

This work presents a systematic approach to evaluate and study the effect of process parameters on tensile, flexural and impact strength of untreated short sisal fibre-reinforced vinyl ester polymer-based composites and predicts the optimum properties of random natural fibre-reinforced composites. The natural fibre of sisal at lengths of 10, 30 and 50 mm and vinyl ester resin at loadings of 15, 30 and 45 (wt%) were prepared. The composite panel was then fabricated using hand lay method in cold process of size 180×160 mm2. Samples were then cut from the panel and subjected to mechanical properties testing such as tensile, flexural and impact strengths. The average tensile strength ranges between 27·1 and 43·9 MPa. The flexural strength ranged between 26·9 and 49·5 MPa and the impact strength ranged between 16 and 93 J/m. The strength values were optimized using factorial design and genetic algorithm (GA) method. The predicted optimum process parameter values are in good agreement with the experimental results.

A Numerical Model for the Combustion Synthesis of Titanium Aluminide in the Self-Propagating Mode

A numerical model was developed for the self-propagating high-temperature synthesis (SHS) of titanium aluminide from starting elemental powders. The energy equation coupled with the reaction kinetic equation was numerically solved. The calculated temperature-time results were in good agreement with available experimental data. The effect of such parameters as preheating temperature, green density of powder compact, titanium particle size, and effective duration of applying the ignition source on the process behavior was estimated by the model. The optimum value of process parameters to gain a self-sustaining reaction was determined under certain circumstances of the present study. Once properly modified, the model is applicable to predict the conditions suitable to utilize the SHS process for the synthesizing of other types of compounds with similar mechanism of formation.

Production of Activated Carbon from Cellulose Wastes

Cellulose wastes of a wood and paper factory were used to produce activated carbon. Several chemical agents under various conditions were used for production of activated carbon and hence their adsorption properties have been evaluated. In addition the effect of process parameters such as raw material, chemical agent concentration, impregnation ratio, carbonization temperature, carbonization time, activation temperature, activation time, particle size and soaking time on adsorption properties and product yield have been studied. Based on experimental results, the optimum values of process parameters were determined. As an achievement, samples of activated carbon with specific surface area up to 1100 m2 /g and iodine adsorption number up to 1080 mg/g were prepared.

A study on fiber laser lap welding of thin stainless steel

This study analyzed variations of shear strength that depend on the fiber laser process during lap welding of AISI 304 stainless thin sheets. The experimental plan was based on a Taguchi orthogonal array table. A hybrid method that includes the response surface methodology (RSM) and back-propagation neural network (BPNN) integrated genetic algorithm (GA) are proposed to search for an optimal parameter setting of the lap welding process. The results show that the RSM and BPNN/GA methods are both effective tools for the optimization of lap welding process parameters. At the same time, an analysis of variance (ANOVA) was implemented to identify significant factors influencing the lap welding process parameters. In addition, the micrographs also show that the appropriate setting of the laser power, befitting the penetration depth and welding line length of the fusion zone sections were produced. A confirmation experiment was also conducted in order to validate the optimal welding process parameter values.

Performance analysis for D-3 material using response surface methodology on WEDM

The present paper identifies process parameters that affect metal removal rate and surface roughness in machining of cryogenic treated D-3 material on wire-cut electric discharge machine tool. Central composite design is used to plan and conduct the experiments. The regression is employed to develop empirical relationships between different process parameters and output responses, and subsequently to analyse and yield the optimal values of process parameters. As the effect of process parameters on metal removal rate and surface roughness is contrary, the problem is formulated as a multi objective optimisation problem and solved using weight method. Effort is made to find the machine settings to obtain maximum metal removal rate and minimum surface roughness. Confirmation experiments are also carried out which reflect that the developed mathematical models are appropriate for the effective machining of D-3 material. The optimal combination of WEDM process parameters obtained from the study satisfies the real requirement of quality machining of D-3 material in practice. In this research paper, cryogenic treatment of work piece is carried out before machining for improving the wear resistance of the material which is necessary for die manufacturing.

Process Optimization and Remanufacturability Analysis of Fuel Cell— Membrane Electrode Assembly With Process Simulation

One of the most pressing environmental problems faced globally is waste management and landfill space. Remanufacturing is one of the green manufacturing techniques in which the geometrical form of the product is retained and the product is reused for the same purpose as during its original life cycle. This work analyzes the remanufacturability of membrane electrode assembly (MEA) which is the heart of the polymer-exchange membrane fuel cell (PEMFC). MEA was obtained by sandwiching the membrane (proton conducting membrane) between the anode and cathode of the fuel cell by hot pressing the anode and cathode onto the membrane at a desired temperature, pressure for a period of time. It is observed that 10% of MEAs are getting wasted while manufacturing it in the laboratory level. In order to utilize these waste MEAs, remanufacurability analysis is done. Wastages created in manufacturing (hot pressing) of MEA can be reduced by optimizing the manufacturing process parameters, such as temperature of the press, pressure applied, pressing time, and thickness of membrane. Using design of experiment and ANOVA contributing factors which influence the quality of MEA are identified with the help of DESIGN EXPERT software. Optimal values of process parameters are found out using desirability function in the software. The process parameter optimization will lead to reduction of wastage of MEA in hot pressing operation but these wastes cannot be avoided completely due to the presence of uncontrollable factors. So remanufacturability analysis will be useful for investigating the wastes. As a part of remanufacurability analysis design consideration for remanufacturing and recycling, the procedure for recovering the valuable materials from the retired membrane electrode assembly, reusing of electrodes are discussed. Two simulation models (current manufacturing system and manufacturing system with remanufacturing) have been created in WITNESS software in order to find the benefits of remanufacturing. The benefits are increase in MEA production and recovery of scrapped anode and cathode. Increase in MEA production due to remanufacturing has been found as 11.11%. Because of recovery process in remanufacturing, 10% of scrapped anode and cathode are utilized which leads to zero scrap of anode and cathode.

A study on fiber laser micro-spot welding of thin stainless steel using response surface methodology and simulated annealing approach

This study analyzed variations of shear strength that depend on the fiber laser process during micro-spot welding of AISI 304 stainless thin sheets. A preliminary study used ANSYS results to obtain initial process conditions. The experimental plan was based on a Taguchi orthogonal array table. A hybrid method that includes the response surface methodology (RSM)- and back propagation neural network (BPNN)- integrated simulated annealing algorithm (SAA) is proposed to search for an optimal parameter setting of the micro-spot welding process. In addition, an analysis of variance was implemented to identify significant factors influencing the micro-spot welding process parameters, which was also used to compare the results of BPNN-integrated SAA with the RSM approach. The results show that the RSM and BPNN/SAA methods are both effective tools for the optimization of micro-spot welding process parameters. A confirmation experiment was also conducted in order to validate the optimal welding process parameter values.