Optimum Level Of Input Parameters Research Articles

Application of Taguchi design in optimization of performance and emissions characteristics of n-butanol/diesel/biogas under dual fuel mode

Combustion experts are in search of some alternative fuel from last few decades owing to diminishing petroleum products and unexpected variations in habitat, which are result of venomous emissions from the CI engines. The present investigation intended to assess the performance and emission parameters of a diesel engine by fueling it with pilot fuel (blends of diesel and n-butanol) and primary fuel (Biogas). Results revealed that BTE, HC and CO increases whilst NOx and smoke emissions were reduced by using the pilot and primary fuel together in relation with natural diesel. Experimentation was done using Taguchi L9 orthogonal array design. The engine load, flow rate of biogas and butanol in fuel blend percentage were selected as input parameters whereas brake thermal efficiency (BTE) and emission characteristics i.e., HC, CO, NOx and smoke were chosen as response variables. ANOVA was carried out for the responses by utilizing MINITAB software. The higher value of raw data and S/N ratio for BTE was noted with high engine load, low flow rate of biogas and butanol blend percent. For the emission characteristics i.e., HC, CO and smoke, lower raw data and high S/N ratio values were attained in the order of rank engine load > butanol blend percent > biogas flow rate while the similar values for NOx were attained in the rank engine load > biogas flow rate > butanol blend percent. Taguchi design was noted to be an effective tool for the optimization of various response parameters and the optimum levels of input parameters were calculated after analysis. Full engine load for BTE and HC, Biogas flow rate of 15 lpm for BTE, HC and CO, and 20 % of butanol blend for HC, CO and smoke were found to be the optimum conditions for the conducted experimentation.

Modeling and optimisation of magnetic field assisted electrochemical spark drilling using hybrid technique

ABSTRACT The present paper focuses on the application of a hybrid methodology for multi-objective optimisation (MOO) of an in-house deigned and fabricated magnetic field-assisted electrochemical spark drilling (MF-ECSD) process where an electromagnetic unit is added in the setup to create magnetic field of different intensities. The process combines Taguchi methodology (TM) with response surface methodology (RSM) for modelling and grey relational analysis (GRA) with principal component analysis (PCA) methodology for MOO. TM is utilised as the core values in RSM to create the second-order response model, and it is used to find the optimum level of input parameters, namely, voltage (V), electrolyte concentration (EC), tool rotational speed (TRS) and magnetic field intensity (MFI). Material removal rate (MRR), machining depth (MD) and overcut (OC) are the responses. PCA is used to calculate the weight associated with each quality feature.

WITHDRAWN: Design & optimization of machining parameters of minimum quantity lubrication in milling of Ti-6Al-4V

Abstract Milling process uses minimum quantity lubrication (MQL) method to give a tiny amount of restrained liquid particles sandwiched between the space of work-piece and tool. In this research work, to examine the different types of lubricating cutting fluids such as water, castor oil and ester oil applied through MQL for nano-finishing of titanium alloy namely Ti64. The L16 orthogonal array by Grey–Taguchi method was considered to examine and optimize the process parameters of MQL viz. different cutting conditions like dry, MQL–W (water), MQL–V (vegetable oil – castor oil), MQL–S (synthetic oil, ester), cutting speed and feed rate on the responses of surface roughness, cutting forces, flank wear and coefficient of friction. All the four different response values were changed into a mono grey relational grade and the optimal machining parameters were determined. The hybrid Taguchi-Gray relational analysis (TGRA) method was used to obtain the optimum level of input parameters as well as to find the judgment on the impact of the input parameters considering all of the four responses simultaneously.

Optimization of dry sliding wear behavior of flyash & zirconia reinforced Al-Mg-1Si-Cu HMMC using Taguchi technique

This research represents an efficient conceptualization to improve the tribo characteristics of hybrid aluminium matrix composites (6061-Flyash-Zirconia) fictitious by stir casting method, with multiple performances supported by Taguchi Technique. During this study flyash was unbroken constant at 100% (6061, 6061–10% Flyash-2% Zirconia and 6061–10% Flyash-4% Zirconia) and Zirconia was varied from 0 to 4%. Dry sliding wear checks were conducted employing a well-planned experimental schedule supported by Taguchi’s orthogonal arrays. The input parameters like Load (L), sliding Speed (S), sliding Distance (D) and Reinforcement (R) are optimized with issues of multiple performance characteristics: Specific Wear Rate (SWR) and Co-efficient of Friction (COF). The optimum levels of input parameters were selected from response table &graph. SEM was done to look at the worn surface of composite specimen. The regression equation was developed and valid through confirmative experiment.

A study of material removal and surface characteristics in micro‐electrical discharge machining of carbon fiber‐reinforced plastics

This article presents micro electrical discharge machining (μEDM) of carbon fiber‐reinforced plastics (CFRP) with rotating tool electrode and assisting electrode (AE). The experimental investigation majorly focused on establishing the mechanisms of material removal and surface integrity of the machined blind holes. Taguchi and regression analysis were carried out to assess the material removal rate (MRR). Analysis of variance (ANOVA) was applied to understand the significance of input parameters and their contribution toward achieving maximum MRR. The surface integrity of the machined hole and damage modes of composite constituents were also examined through micrographic images. The experiments were carried out varying the voltage, pulse duration, and tool speed at three levels. The investigations showed that μEDM process can be applied for fabricating micro holes in CFRP if assisting electrode (AE) is used to initiate the spark. From the analysis of variance (ANOVA), it was observed that the percentage contribution of voltage, pulse duration, and tool rotational speed on MRR are 75.09, 5.20, and 16.09%, respectively. The optimal condition for the highest MRR was found to be V3/T1/S3 (voltage of 170 V, pulse duration of 10 μs, and tool speed of 800 rpm). The calculated value of MRR (341,264.94 μm3/s) at the optimum level of input parameters was found to be close to the experimental MRR (340,727 μm3/s). The percentage error between the experimental and calculated value of MRR at the optimum level is very low as 0.15%. This indicates that the developed statistical model is quite significant in predicting MRR during μEDM of CFRP. POLYM. COMPOS., 40:4033–4041, 2019. © 2019 Society of Plastics Engineers

Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel

The purpose of this study is to investigate solid lubricant-assisted cutting fluids applied through minimum quantity lubrication technique (MQL) for nano-finishing of AISI 4340 steel using TiCN/Al2O3/TiN chemical vapor deposition coated tungsten carbide inserts during flat end milling process. Four different types of cutting fluids were examined, namely pure emulsion, boric acid dissolved in the emulsion, graphite suspended in the emulsion and hybrid solid-liquid minimum quantity lubrication. Optimization of the flat end milling process was done through single performance Taguchi analysis as well as through a multi-performance Taguchi-Gray relational analysis (TGRA). The hybrid TGRA technique was utilized to get the optimum level of input parameters and to get the knowledge about the significance of the input parameters considering both of the responses simultaneously. The results of the hybrid analysis were confirmed by multi-objective genetic algorithm. Novel and physically based analytical interpretation of the results was provided. Low viscosity fluids were found to provide better results during MQL application. The results lead to cleaner and sustainable production aiming at reducing/omitting waste and making the process environment-friendly.

Application of the Taguchi based entropy weighted TOPSIS method for optimisation of diesel engine performance and emission parameters

Diesel engines convert chemical energy into mechanical energy generally with low efficiency. However, if these engines are operated at optimum levels of input parameters, they can produce power more efficiently with minimum harmful exhaust emissions. The current research work is carried out with an aim to determine these optimum levels of the input parameters. Four input parameters were selected and their optimum combinations were determined using Taguchi based technique for order of preference by similarity to ideal solution (TOPSIS) coupled with Entropy Weight method. Taguchi's L25 orthogonal array was adopted to perform the experiments. All the test runs were carried out in random manner with three replicates of each to improve the accuracy of the experiments. The preference values (relative closeness of the distinct alternative to the positive ideal solution) predicted by Taguchi method and the values obtained by regression analysis, at optimum input parameter settings were compared and a good relationship was found between them.

Experimental investigations on natural fiber embedded additive manufacturing-based biodegradable structures for biomedical applications

PurposeThe purpose of this paper is to explore and investigate the mechanical as well as bacterial characteristics of chemically treated waste natural fiber inserted three-dimensional structures (NFi3DS) produced with fused filament deposition (FFD) for biomedical applications.Design/methodology/approachIn this work, a novel approach has been used for developing the customized porous structures particularly for scaffold applications. Initially, raw animal fibers were collected, and thereafter, the chemical treatment has been performed for making their wise utility in biomedical structures. For this purpose, silk fiber and sheep wool fibers were used as laminations, whereas polylactic acid was used as matrix material. A low-cost desktop time additive manufacturing setup was used for making the customized and porous parts by considering type of fiber, number of laminates, infill density and raster angle as input parameters.FindingsThe results obtained after using design of experimental technique highlighted that output characteristics (such as dimensional accuracy, hardness, three-point bending strength and bacterial test) are influenced by input parameters, as reported in the obtained signal/noise plots and analysis of variance. Optimum level of input parameters has also been found through Taguchi L9 orthogonal array, for single parametric optimization, and teaching learning-based algorithm and particle swarm optimization, for multiple parametric optimization. Overall, the results of the studies supported the use of embedded structures for scaffold-based biomedical applications.Research limitations/implicationsPresently, NFi3DS were produced by using the hand-lay-based manual approach that affected the uniform insert’s distribution and thickness. It is advised to use the automatic fiber placement system, synced with a three-dimensional printer, to achieve greater geometrical precision.Practical implicationsAs both natural fibers and polymer matrix used in this work are well established for their biological properties, hence the methodology explored in this work will help the practitioners/academicians in developing highly compatible scaffold structures.Social implicationsThe present work defines a new practice where the researchers can use natural fibers to reduce the cost associated with fabrication of customized scaffold prints.Originality/valueThe development of natural fiber embedded FFD-based structures is not yet explored for their feasibility in biomedical applications.

Improving Impact Strength in FSW of Polymeric Nanocomposites Using Stepwise Tool Design

ABSTRACTIn recent years, many studies have been conducted on process parameters of polymers friction stir welding, while material parameters are still facing serious problems especially in polymeric nanocomposites. In the present study, the impact behavior of friction stir–welded polycarbonate (PC) nanocomposites under different material and process conditions has been investigated using Taguchi approach. A stepwise tool design procedure has been carried out to enhance the welding process. The samples containing various weight percentages of alumina nanoparticles have been welded under different welding process parameters. The analysis of variance results illustrated that nanoalumina content is the most effective parameter on impact strength followed by rotational and transverse speeds. Impact strength of welded samples was conspicuously improved up to 15% by adding 2 wt% of nanoalumina compared with pure PC samples. Also increasing rotational speed and decreasing transverse speed leads to increase of impact strength. In order to optimize the process, signal-to-noise ratio analysis was performed. The results indicated that the optimum levels of input parameters that give the maximum impact strength are as following: 2 wt% of nanoalumina, 2500 rpm of rotational speed, and 8 mm/min of transverse speed which causes 26.14% improvement in impact strength of samples.

Modelling the Cast Component Weight in Hot Chamber Die Casting using Combined Taguchi and Buckingham’s π Approach

Hot chamber (HC) die casting process is one of the most widely used commercial processes for the casting of low temperature metals and alloys. This process gives near-net shape product with high dimensional accuracy. However in actual field environment the best settings of input parameters is often conflicting as the shape and size of the casting changes and one have to trade off among various output parameters like hardness, dimensional accuracy, casting defects, microstructure etc. So for online inspection of the cast components properties (without affecting the production line) the weight measurement has been established as one of the cost effective method (as the difference in weight of sound and unsound casting reflects the possible casting defects) in field environment. In the present work at first stage the effect of three input process parameters (namely: pressure at 2nd phase in HC die casting; metal pouring temperature and die opening time) has been studied for optimizing the cast component weight ‘W’ as output parameter in form of macro model based upon Taguchi L9 OA. After this Buckingham’s π approach has been applied on Taguchi based macro model for the development of micro model. This study highlights the Taguchi-Buckingham based combined approach as a case study (for conversion of macro model into micro model) by identification of optimum levels of input parameters (based on Taguchi approach) and development of mathematical model (based on Buckingham’s π approach). Finally developed mathematical model can be used for predicting W in HC die casting process with more flexibility. The results of study highlights second degree polynomial equation for predicting cast component weight in HC die casting and suggest that pressure at 2nd stage is one of the most contributing factors for controlling the casting defect/weight of casting.

Application of the Taguchi-based entropy-weighted TOPSIS method for optimisation of diesel engine performance and emission parameters

Diesel engines convert chemical energy into mechanical energy generally with low efficiency. However, if these engines are operated at optimum levels of input parameters, they can produce power more efficiently with minimum harmful exhaust emissions. The current research work is carried out with an aim to determine these optimum levels of the input parameters. Four input parameters were selected and their optimum combinations were determined using Taguchi based technique for order of preference by similarity to ideal solution (TOPSIS) coupled with Entropy Weight method. Taguchi's L25 orthogonal array was adopted to perform the experiments. All the test runs were carried out in random manner with three replicates of each to improve the accuracy of the experiments. The preference values (relative closeness of the distinct alternative to the positive ideal solution) predicted by Taguchi method and the values obtained by regression analysis, at optimum input parameter settings were compared and a good relationship was found between them.

Multi-objective optimisation in the microturning of cobalt chromium with coated and uncoated tools using the grey relational analysis

In this work, the microturning of cobalt chromium has been carried out using coated and uncoated tungsten carbide inserts. The experiments were conducted as per the design of experiments (DoEs) approach using the L9 orthogonal array by varying the spindle speed (S), feed (F) and depth of cut (Dcut) at three levels. The output parameters considered are the material removal rate (MRR), surface roughness (Ra) and tool wear (TW). The significant parameters and their levels were identified using analysis of variance (ANOVA) and response graphs. It is observed that the significant microturning process parameters are found to be different for different tool/workpiece combinations, for achieving a higher MRR, lower Ra and low TW individually. Therefore, a multi-objective optimisation using the grey relational analysis was performed considering MRR, Ra and TW together and the optimum level of input parameters were identified to achieve high MRR, low Ra and low TW together.

Fuzzy Logic Modeling and Multiple Performance Optimization in Turning GFRP Composites Using Desirability Function Analysis

Glass fibre-reinforced polymer (GFRP) composites are extensively used now-a-days in manufacturing various components in aerospace, oil, gas and process industries. It replaces conventional materials due to their excellent properties such as light weight, corrosive resistance and superior properties. Development of predictive modeling and optimization of machining process in producing components is important for machining industries. In this work, fuzzy logic based multi response predictive model development and multi objective optimization of processes parameters using Desirability Function Analysis (DFA) in turning GFRP composite has been attempted. The input variables are cutting speed (v), feed rate (f) and depth of cut (d). The responses are surface roughness (Ra), metal removal rate (MRR) and tool wear (VB). The average percentage error in fuzzy logic prediction obtained as 2.74%, 12.67% and 3.06% for Ra, MMR and VB respectively. The optimum level of input parameters for composite desirability was found v2 f1 d3. The corresponding optimum parameter is v=100 m/min, f=0.10mm/rev and d= 1.5mm for obtaining combine optimization of Ra, MRR and VB having equal weightage. The analysis of variance of composite desirability at 95% confidence level showed that depth of cut is the most significant parameter with 39.38% contribution followed by feed and cutting speed.

Modelling and optimisation of travelling wire electro-chemical spark machining process

This paper presents modelling and optimisation of cut quality during a self developed travelling wire electro-chemical spark machining (TW-ECSM) process. In the present study, four input process parameters such as applied voltage, electrolyte concentration, wire feed velocity and workpiece thickness and two output parameters such as surface roughness (Ra) and material removal rate (MRR) are considered. The hybrid approach comprising of Taguchi methodology (TM) and response surface methodology (RSM) is used for modelling whereas multi-objective optimisation is performed using hybrid approach of TM and grey relational analysis (GRA) coupled with entropy measurement methodology. The approach first uses the Taguchi quality loss function to find the optimum level of input parameters. This optimum input parameter values are further used as the central values in the RSM to develop the second-order response model. The entropy measurement methodology is employed for the calculation of weight corresponding to each quality characteristic. The results have been observed that in comparison to TM, the hybrid approach enhances MRR by 117% and reduces the Ra by 10%.

Experimental modeling and multi-objective optimization of traveling wire electrochemical spark machining (TW-ECSM) process

The present paper attempts to focus an application of a hybrid methodology comprising of Taguchi methodology (TM) coupled with response surface methodology (RSM) for modeling and TM coupled with weighted principal component (WPC) methodology for multiobjective optimization of a self developed traveling wire electro-chemical spark machining (TW-ECSM) process. First optimum level of input parameters is found using TM which is used as the central values in RSM to develop the second-order response model. For multiobjective optimization two quality characteristics surface roughness (Ra) and material removal rate (MRR), which are of opposite nature (Ra is the lower-the-better type, while MRR is the higher-the-better type), have been selected. The WPC is employed for the calculation of weight corresponding to each quality characteristic. The results indicate that the hybrid approaches applied for modeling and optimization of the TW-ECSM process are reasonable.

Optimization of Conversion of High Free Fatty Acid Jatropha curcas Oil to Biodiesel Using Response Surface Methodology

A five-level-four-factor central composite design (CCD) with 54 assays was employed to study the effect of catalyst concentration (NaOH), reaction temperature, reaction time, and methanol/oil molar ratio on the methyl esters yield from Jatropha curcas oil (JCO) during its transesterification. Using response surface methodology (RSM), a quadratic polynomial equation was obtained for Jatropha curcas biodiesel (JCB) yield by regression analysis. Verification experiments confirmed the validity of the predicted model. The high free fatty acids (FFAs) (14.6%) of JCO could be reduced to 0.34% by acid-catalyzed esterification and a JCB yield of 98.3% was obtained with methanol/oil ratio (11 : 1) using NaOH as catalyst (1% w/w) in 110 min time at 55°C temperature. The predicted value of JCB yield is found to be in good agreement with the experimental value at the optimum level of input parameters. The properties of the biodiesel, thus, produced conform to the ASTM and IS specifications, making it an ideal alternative fuel for diesel engines. The model can be effectively used in oil industry to maximize the biodiesel yield from given oil.