Optimization Of Performance Characteristics Research Articles

A Taguchi-based hybrid multi-criteria decision-making approach for optimization of performance characteristics of diesel engine fuelled with blends of biodiesel-diesel and cerium oxide nano-additive

Given the pressing demand and ongoing necessity for fossil fuels, there is an imperative to actively seek alternative resources to replace petroleum-based fuels. The presents study considers a problem of experimentally investigating the effect of varying levels of important input parameters of a diesel engine fuelled with a novel blend of biodiesel-diesel and cerium oxide nano-additive on the sustainable performance characteristics of a diesel engine. Four input parameters, i.e., blend percentage (B in %), nanoparticle concentration (NPC in ppm), engine load (LD in %) and ignition pressure (IP in bar) each at four levels are considered. Experiments are conducted as per the Taguchi’s L16 standard orthogonal array and for each experiment, performance parameters (such as Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)), emission measures (Carbon monoxide (CO), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC) and Vibration level (VL)) of the diesel engine are collected. A hybrid multi-criteria decision-making (MCDM) approach, i.e., integrated MEREC-MARCOS method along with signal-to-noise (S/N) ratio and analysis of mean (ANOM) is employed to determine optimal setting of the input parameters that yield optimal multiple performance characteristics. The results reveal that B at 40%, NPC at 80 ppm, LD at 50% and IP at 200 bar is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. Further, results of the analysis of variance (ANOVA) show that Nanoparticle concentration percentage contribution is the maximum (79.63%) followed by engine load (8.40%), ignition pressure (6.28%), and blend percentage (2.11%). The optimization results are: BTE is 32.87%, BSEC is 0.285, CO is 0.018%, NOx is 559.6 ppm, UBHC is 28.1 ppm and VL= 19.57m2/sec which were validated with a confirmation test. Henceforth, such hybrid fuels provide sustainable energy solutions and environmental conservation simultaneously addressing the current and future demands.

Experimental study and optimization of performance characteristics of compression ignition hydrogen engine with diesel pilot injection

World's energy demand is majorly met by fossil fuels. An increase in the demand for energy and the depletion of fossil fuels leads to look for the alternate source of energy. Hydrogen is one of the most viable alternate sources of alternate energy. This paper presents the performance study of a Compression Ignition engine using a fuel combination of diesel and hydrogen-diesel. The performance parameters have been analyzed with two input factors, load and Compression Ratio and three output responses, Brake Thermal efficiency (BTE), Brake Specific Fuel Consumption (BSFC) and Volumetric efficiency. The experimental results indicate the improvement in BTE, reduction in BSFC and volumetric efficiency. The BTE was improved by 6.38%, BSFC was reduced by 4.87% and Volumetric efficiency got reduced by 1.89% when hydrogen-diesel fuel is used in place of neat diesel. The experiments have been designed with the help of Design Expert® 13.0.5 using Response Surface Method. The experiments are conducted using Optimal (Custom) design. Mathematical relations are developed between the process parameters and the input factors using regression analysis. The optimal solution has been obtained using the desirability approach. The confirmation experiments have been performed on the optimal solution to verify with the experiment results. Results of the confirmation test shows that the developed mathematical are appropriate for the use in CI engine.

Optimization of performance characteristics in diesel engine utilizing Chlorella vulgaris fuel-a green approach towards sustainable development.

Sustainable renewable energy fuel is used to reduce fossil fuel consumption and mitigate global warming pollution. The effect of diesel and biodiesel blends on engine combustion, performance, and emissions were studied at various engine loads, compression ratios, and engine speed. Chlorella vulgaris biodiesel is derived through a transesterification process and diesel and biodiesel blends are prepared at 20% incremental volume up to CVB100. The performance such as brake thermal efficiency reduced by 1.49%, specific fuel consumption increased by 2.78%, and exhaust gas temperature increased by 0.43% for CVB20 as compared to diesel. Similarly, emissions were reduced such as smoke, particulate matters. CVB20 shows close performance and lower emission than diesel at a 15.5 compression ratio and 1500 rpm engine speed. The increasing compression ratio has a positive impact on engine performance and emission except for NOx. Similarly, increasing engine speed has a positive impact on engine performance and emission except for exhaust gas temperature. The performance of a diesel engine fueled with a blend of diesel and Chlorella vulgaris biodiesel is optimized by varying compression ratio, engine speed, load, and blend. It was found that at 8 compression ratio, 1835 rpm speed, 88% engine load, and 20% biodiesel blend the maximum BTE obtained 34% while minimum SFC 0.158 kg/kWh is obtained employing research surface methodology tool.

Optimization of Performance Characteristics of Magnetofluidic Seals for Wind Power Plants

The use of magnetofluidic seals are a promising direction in sealing rotating shafts of wind power plants. Magnetofluidic seals are characterized by high tightness, simplicity of the design, low losses by friction. Magnetic fluid seal of the rotating shaft consists of a ring magnet and two concentrators of magnetic field, forming with the shaft a narrow ring gap in which the magnetic fluid retained by magnetic field is a hermetic seal. Magnetic forces provide balance of volume of magnetic fluid under the impact of pressure drop and centrifugal forces. With an increase in the speed of rotation of the shaft up to 10 m/s, deformation of the free surface of the magnetic fluid at the surface of the shaft in the form of a funnel is visually observed, which causes a decrease in the retained pressure drop. As the rotation speed increases, the funnel increases, part of the magnetic fluid is ejected from the working area, the retained pressure drop decreases, and at 50 m/s there is a complete release of the magnetic fluid and depressurization of the seal. In order to increase the stability of the free surface of the magnetic fluid in the field of centrifugal forces, multilayer carbon nanotubes were introduced into it. They are characterized by a high specific surface area and, accordingly, a strong Van der Waals attraction. In a magnetic fluid, multilayer carbon nanotubes form structures which are oriented along the magnetic field lines. The following increase in the static load retained by a layer of magnetic fluid has been experimentally determined with the introduction of nanocarbon structures: when coinciding with the axis of the structures the increase was by 100 %, for the normal direction of the load to the axis of the structure – by 50 %. In the seal, with an increase in the shaft rotation speed, deformation of the free surface of a magnetic fluid with nanocarbon structures was observed at 18 m/s at a distance of 3 mm from the shaft surface. The pressure drop retained by the seal increased in the speed range of 10–40 m/s, the maximum effect of 50 % was obtained at a speed of 40 m/s. Thus, the introduction of nanocarbon structures into the magnetic fluid made it possible to reduce the influence of centrifugal forces on the retained pressure drop and increase the efficiency of the magnetofluidic seal at high speeds of rotation of the shaft of wind power plants.

Performance characteristics optimization of CRDI engine fuelled with a blend of sesame oil methyl ester and diesel fuel using response surface methodology approach

The primary aim of this experiment was to use response surface methodology (RSM) to optimize engine operating parameters for optimal performance and emission characteristics of a common rail direct injection (CRDI) diesel engine fuelled with sesame oil methyl ester (SOME)/diesel blends. The experiments were carried out on a water-cooled common rail direct injection engine with a 4-stroke, single-cylinder connected to an eddy current dynamometer. As input variables, the SOME% (0%–20%), fuel injection pressure (FIP) (500–600 bar), EGR rates (0%–14%), and engine load (0–12 kg) were used. The optimization method is utilized to maximize brake thermal efficiency (BTE) while minimizing BSFC, CO, HC, and NOx emissions. Experimental research data were used to create the RSM model through DoE (Design of experiments). The most relevant factors impacting the responses were identified using an ANOVA analysis. According to the optimization findings, the engine’s optimum working parameters were found to be a 20% SOME ratio, 577.5 bar FIP, 5.26% EGR rates, and 5.12 kg engine load. Under these operating circumstances, the optimal responses were determined to be 18.92% BTE, 0.3705 kg/kWh BSFC, 0.03190% vol. CO, 13 ppm HC, and 447.5 ppm NOx emission. At the same time, R2 values were 96.35%, 87.54%, 91.57%, 95.87%, and 93.73% for BTE, BSFC, CO, HC, and NOx respectively.

Optimisation of performance characteristics in laser welding of Nitinol wires using Taguchi and grey relation analysis

ABSTRACT NiTi Shape Memory Alloy (SMA) finds wide applications in high-performance engineering industries such as medical devices, micro-electrical and electronic components, aerospace, aircraft structure, and so on, to name a few. This is owing to its superior properties like shape memory effect (SME), superelasticity, and biocompatibility. However, getting a proper joining of NiTi alloys is hard considering its unique characteristics. Therefore, joining this alloy has been a domain of great attention for scientists. The current research work investigates the weldability of NiTi wires using Laser spot welding. The microhardness and the tensile strength of the weld joint are acquired according to Taguchi’s design of experiment. The significance of the control factors (laser power, scan speed, and focal length) is identified through ANOVA, and an ideal factor setting for optimum individual responses is established. Moreover, simultaneous optimisation of multi-performance characteristics is also attempted through Grey Relational Analysis (GRA) method. From the GRA, 300 W laser power, 1 mm/sec scan speed, and 0.5 mm focal position are found to be giving optimum results for both the responses. The result from the confirmation runs showed that the predicted optimal welding parameters improve the individual as well as the multiple performance characteristics.

Optimization of Performance Characteristics of Homogeneous Charge Compression Ignition Engine with Biodiesel using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)

Optimization of Performance Characteristics of Homogeneous Charge Compression Ignition Engine with Biodiesel using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)

Multi performance characteristics optimization of end milling parameters on surface quality and Micro-hardness of SS-304

Taguchi and Grey Relational Analysis approaches are used in the present study for optimizing the end milling process parameters of stainless steel 304 (SS-304). The four key input machining parameters i.e., rate of feed presence/absence of coolant, speed is investigated experimentally. An orthogonal Taguchi L18 array was used to perform the trials, which consisted of 18 trials with two coolant levels, three levels for the remaining three parameters. Three performance features for improvement: surface roughness (SR), surface strain (SS), and microhardness (MH) are taken into consideration. Multi performance characteristics were improved using Taguchi with grey relational analysis (GRA), and the data was then analysed using mean response tables and variance analysis i.e., ANOVA. The optimal combination is obtained as A1B3C3D1 i.e., coolant on, feed rate 2500 mm/min, depth of cut 0.4 mm, and cutting speed 1500 rpm. Further, it is also observed that highest contributing parameter is speed with the percentage of 25.75. This approach detects several performances optimum improvements of 3.9615 percent through a confirmation test.

Performance characteristics optimization in dissimilar friction stir welding using Grey relational analysis

In the present work, Friction stir welding of dissimilar materials AA5083 & AA6061 is carried out and by using Taguchi-based Grey Relational Analysis. The output characteristics like tensile strength, elongation, and hardness values are investigated using response surface methodology and grey relational analysis. The quality of the welded joint is measured based on the experimental values of tensile strength, elongation, and hardness of the joint the experiments were conducted in accordance with the L9 orthogonal array assigning different levels values to the process parameters. Tool pin profile, rotational speed, welding speed, and tool tilt angle are selected as the significant input parameters. Analysis of variance (ANOVA) is used to analyze the effect of each input welding parameter on the output responses. Statistical results show that the results of the confirmatory experiments are within 95% confidence optimal conditions with smaller deviations.

Effect of machining parameters on cutting forces during turning of EN 08, EN 36 & mild steel on high speed lathe by using Taguchi orthogonal array

The selection of the optimal cutting parameter in turning operation is very important to achieve high cutting performance. This project deals with the optimization of performance characteristics of turning EN-08, EN-36, mild steel using tungsten carbide inserts by taguchi’s approach. The experiment was performed on the basis of an L-09 orthogonal array given by taguchi’s parameter design approach. To recognize the essential factor, the design of experiment (DOE) is employed using MINITAB-19 software. The cutting parameters are varied to observe performance characteristics and find the optimal outcomes.

Analytical Calculation and Optimization of Performance Characteristics of the Ultralight Helicopter with Piston Engine

Analytical Calculation and Optimization of Performance Characteristics of the Ultralight Helicopter with Piston Engine

Multiobjective optimization of performance characteristics in turning of AZ91 Mg alloy using grey relational analysis

In recent decades, magnesium and its alloys have attracted more attention because of their exceptional mechanical properties. These Mg alloys are known for their high strength to weight ratio and found enormous applications in automobile and aeronautical industries. Machining of these alloys is always a challenge because of the issues related to the work surface integrity. In the present work, an experimental study on turning of AZ91 Mg alloy using the uncoated carbide tools in dry condition has been presented. This work comprises input factors such as cutting tool edges (45° and 90°), cutting speed, and feed rate. The full factorial experimentation has been performed for the analysis. And the optimization of the performance characteristics (surface finish and main cutting forces) is done by using grey relational analysis. Finally, the GRA method provided the optimal conditions for achieving the minimum surface roughness and minimum cutting forces are at cutting edge angle 90° (A2), cutting speed 270 m/min (V2), and feed rate 0.04 mm/rev (F1). The next optimal conditions close to desired outputs are at cutting edge angle 45° (A1), cutting speed 200 m/min (V1), and feed rate 0.04 mm/rev (F1). Chip morphology (size and shape) were also studied and found that the continuous type chip formation at a lower feed rate (0.04 mm/rev) and at the machining speed of 270 m/min, using both the cutting tool edges (45° and 90°).

Precision Multi-Response Optimization of Performance Characteristics In Wire-Cut Electric Discharge Machining (Wedm) Process

In this paper, an effective two stage multi-response optimization technique (i.e. grey relational analysis coupled with Taguchi technique) has been applied to achieve a better performance characteristic in wire-cut electrical discharge machining (WEDM) process. A zinc coated brass wire of 0.25 mm diameter was used as tool electrode for machining a D2 tool steel specimen. Experiments were planned according to Taguchi’s L9 orthogonal array under different cutting parameters such as: pulse on time (TON), pulse off time (TOFF), peak current (IP) and wire feed rate (WF). The three quality characteristics (i.e. performance characteristics), namely cutting rate, kerf width and surface roughness have been simultaneously optimized in two different stages. It wasobserved that the cutting speed is increased by 24.60% compared to first stage/primary optimization process. From the analysis of variance (ANOVA), pulse-on time is found to be the most influencing cutting parameter having 74.91% contribution towards overall performance of the WEDM process. Finally,a confirmatory experiment has been carried out at optimum set of cutting parameters obtained from the precision optimization stage to identify the effectiveness of this proposed method. It was observed that the predicted values of the responses obtained from regression models were in good agreement with the experimental findings.

Multi-objective optimization of performance characteristics in drilling of Mg AZ61 using twist end mill drill tool

Magnesium alloy has proved its role in various applications because of its lightest structure and strength. Hence study of machinability of Mg alloys highly contributing in various fields. In this present study, Drilling of Mg AZ61 alloy using end mill tool and its performance characteristics were studied. Taguchi L9 orthogonal array is used for optimization with drill diameter; spindle speed and feed rate were the process parameters. Taguchi based GRA and GRG analysis were carried out to optimize the process parameters and to identify the better machining condition. Based on the experimental study, 1200 rpm spindle speed, 1.50 mm/m feed rate and 7 mm drill diameter were found as optimal parameters and drill diameter is the significant parameter to minimize the overcut and improve the surface roughness. Results were ensured with the help of SEM analysis and confirmation experiment.

Multiple performance characteristics optimization in end milling of thin-walled parts using desirability function

With the development of high-performance CNC machine tools, milling has been established as one of the main means of machining thin-walled parts. Thus, the selection of process parameters for milling operations is an important issue in end milling of thin-walled parts to assure product quality and increase productivity. The current study explores three machining parameters, namely wall thickness, feed, and machining strategies, that influence dimensional and form errors, surface roughness, and machining time milling of 7075-T6 aluminum alloy thin-walled parts. The effects of machining parameters on each of the response variables were analyzed using graphs of the main effects and three-dimensional surface plots. Analysis of the results show that the most influential factor for wall thickness deviation, dimensions deviation, perpendicularity deviation, flatness deviation, surface roughness of inner walls, surface roughness of outer walls, and surface roughness of reference plane was machining strategy, while feed is the most influential parameter affecting the machined time, followed by the machining strategy. The desirability concept has been used for simultaneous optimization in terms of machining parameters of the thin-walled parts machining process. Finally, a confirmation test with the optimal parameter settings was carried out to validate the results.

Multiple performance characteristics Optimization of WEDM by Grey Grade for EN-8

Multiple performance characteristics Optimization of WEDM by Grey Grade for EN-8

Experimental investigations, empirical modeling and multi objective optimization of performance characteristics for ECDD with pressurized feeding method

Electrochemical discharge drilling (ECDD) is a hybrid micromachining process that can be used to drill micro holes on all kind of materials, irrespective of their mechanical and electrical properties. In ECDD, the continual penetration of tool electrode having electrochemical discharge (ECD) energy along the work material generates the holes. The relative movements between tool electrode and work material controls the energy penetration rate, and subsequently effects the performance characteristics. Therefore, the effect of process parameters associated with feeding motion and ECD energy are important to investigate simultaneously with respect to the performance characteristics. In the current study, a Buckingham’s dimensional method was used to determine the relation between input process parameters (that includes simultaneous interaction of ECD energy based parameters and feeding motion based parameters) and performance characteristics. The material removal rate (MRR) and hole overcut (HOC) were considered as the performance characteristics. MRR and HOC symbolize the productivity and accuracy of the process, respectively. During predictive modeling, the constant terms and coefficients were estimated from the non-linear experimental data. The outcomes manifested by the predictive models are in good agreement with the experimental results. The underlying process mechanism for ECDD with the pressurized feeding system has been explored by considering the simulation results as an evidences. Microscopic images of machined holes and discharge characteristics were used to develop the fundamental background of ECDD process mechanism with pressurized feeding method. Additionally, genetic algorithm (GA) was used to perform the multi objective optimization of ECDD process.

Multi performance characteristics optimization in cryogenic turning of 17-4 PH stainless steel using Taguchi coupled grey relational analysis

Cryogenic machining is one of the environmental friendly machining techniques offers high productivity in machining of difficult to cut materials. The present article investigates the multi response optimization in turning of 17-4 Precipitation Hardened Stainless Steel (PH SS) under the jetting of cryogenic coolant (liquid nitrogen) on the rake face of the cutting tool using Taguchi coupled grey relational analysis (TGRA). Cutting velocity, feed rate and depth of cut were considered as the controllable process parameters. Cutting force, surface roughness, tool flank wear and material removal rate (MRR) were considered as the performance characteristics. Experiments were performed based on Taguchi L9 orthogonal array. From the TGRA result, it was found that at cutting velocity of 120.89 m/min, feed rate at 0.048 mm/rev and depth of cut 0.4 mm obtained the improved turning process performance characteristics. Scanning electron microscope (SEM) has been used to obtain the tool flank wear and machined surface images at the optimum machining conditions. Further analysis of variance (ANOVA) has been carried out and it was identified that feed rate was most influencing process parameter contributing 53.76% on the multi performance characteristics.

Measurement and optimization of performance characteristics in turning of Mg alloy under dry and MQL conditions

Recent research in the field of magnesium has brought out the enormous potential in terms of their applications and machining process. Lightness and high strength-to-weight ratio of such materials have led to an increased interest in their use in automobile and aerospace industries. In this paper investigation on turning of magnesium alloy using uncoated tungsten carbide cutting insert in dry and minimum quantity lubrication (MQL) cutting conditions have been presented. This work includes input parameters like cutting conditions, cutting speed, feed and depth of cut as cutting factors and the responses of tool flank wear, surface roughness, cutting force and cutting temperature have been optimized by grey relational analysis. The optimization has been performed firstly as mono-objective optimization by using Taguchi method; secondly as multi-objective optimization through Taguchi based grey relational analysis (GRA). The mono-objective optimization results gave the combination of 40 m/min cutting speed, 0.10 mm/rev feed rate and 0.5 depth of cut when run under MQL for optimizing the tool wear and cutting temperature and also the same combination optimized cutting force when operated under dry condition. The optimal condition for surface roughness was at 140 m/min cutting speed, 0.10 mm/rev feed rate, 0.5 depth of cut at MQL. GRA technique provided optimum condition i.e. 90 m/min cutting speed, 0.1 mm/rev feed rate and 0.5 mm depth of cut at MQL that minimized the output responses.

Multi Performance Characteristics Optimization in Wire Electric Discharge Machining of Nitinol Superelastic Alloy

The optimization of wire electric discharge machining (Wire EDM) process parameters for simultaneous maximization of material removal rate and minimization of surface roughness during machining of Nitinol (Nickel-Titanium) superelastic alloy has been carried out. The process variables selected in this study are wire feed, pulse on time, pulse off time and spark gap set voltage. Multi performance characteristics optimization was performed using Taguchi’s utility and Quality loss function. Analysis of means and analysis of variance were performed on signal to noise ratio to determine the optimal parameters. The optimal results reveal that the pulse on time of 115µsec, wire feed of 6m/min and spark gap set voltage of 40V with pulse off time of 25µsec are beneficial for maximizing the material removal rate and minimizing the surface roughness. The results indicate that the wire feed is the most significant parameter that affects the surface roughness and material removal rate.