Taguchi Grey Relational Analysis Research Articles

Optimization of binary and ternary concrete composed of fly ash and ultra-fine slag using GRA

The paper presents the study of concrete made with supplementary cementitious materials such as Fly Ash (FA) and Ultra Fine Slag (UFS). Ordinary Portland Cement (OPC) is partially replaced with 20%, 30%, and 40% of FA by weight of cementitious content in three FA binary combinations. OPC is further replaced with 5%, 10%, 15%, and 20% of UFS to all three binary combinations forming twelve ternary combinations. The paper assesses workability, strength behaviour, chloride migration, water permeability, and cost aspect of all mixes. The results indicate that the use of FA binary combinations improves the workability but mechanical and durability properties are compromised. In case of FA-UFS ternary combinations, the compressive, flexural, and split tensile strengths have been observed to improve up to 36%, 13%, and 22%, respectively. Chloride migration and water permeability of ternary mix improve by 92% and 94% compared to reference concrete. An optimization technique using Taguchi-grey relational analysis (GRA) suggested that a ternary combination with 55% OPC, 30% FA, and 15% UFS qualify as an optimum mix combination.

Grey relational analysis of friction stir welding parameters for the development of dissimilar joints between AA6082 and AA7075

ABSTRACT In this research, the dissimilar metals of AA6082 and AA7075 were joined using friction stir welding process with H13 steel as tool material. The parameters such as tool profile, rotational and traverse speeds were selected at three different levels. Taguchi L-9 array was selected to design the experiments. The response factors namely, ultimate tensile strength, % of elongation, hardness and impact strength were considered for optimising FSW parameters. The mechanical properties were evaluated by conducting tension test, hardness test and impact test according to ASTM standards. Taguchi Grey Relational Analysis was used to find the rank of each experiment. From GRA, an optimum parameters were observed at 2nd experimentation i.e. tool profile as circular geometry, tool speed at 900 rpm and feed rate at 40 mm/min. Further, analysis of variance was performed to determine the percentage of contribution of the welding parameters on the responses viz., tensile strength, % of elongation, hardness and impact strength. From the ANOVA grey relational analysis, order of effecting parameters was tool profile (33%) followed by feed rate (32%) and tool speed (26%) on output factors.

Multiresponse Optimization of Wear Parameters of Steel-Embedded Glass-Epoxy Hybrid Composites Using Taguchi-Grey Method Optimization

Abstract In this work, Taguchi-grey relational analysis (GRA) has been used to optimize the process parameters during the tribological performance of steel-embedded glass-epoxy hybrid composites. The specific wear rate (SWR) and the average coefficient of friction (CoF) were investigated. The tribological test was planned by using Taguchi L27 orthogonal array. The tests were carried using different factors like steel volume % (0–10 %), applied load (80–100 N), and sliding distance (1,000–2,000 m) for a constant time (20 min). The optimal process parameters were obtained with Taguchi-GRA, and the influencing factor was determined with the help of analysis of variance. The results show that the most influencing factor is steel volume %, followed by applied load and sliding distance. The dry sliding wear performance was optimized to achieving minimal SWR and average CoF. Steel volume 10 %, sliding distance 1,000 m, and applied load 80 N were observed as optimum parameters to achieving minimal SWR and average CoF. A confirmation test was carried out to verify the tests. The results show that the multiresponses such as SWR and average CoF were significantly enhanced through GRA. Finally, the worn surfaces of the hybrid composites were studied through scanning electron microscope (SEM). The SEM shows that the pits on the surface of the image were covered with voids and other defects. The SEM also shows that the specimen’s porosity decreases with the increasing fractional volume of steel in the composite.

Process optimization of polyphenol oxidase immobilization: Isotherm, kinetic, thermodynamic and removal of phenolic compounds

Chitosan/montmorillonite (CTS/MMT) and chitosan‑gold nanoparticles/montmorillonite (CTS-Au/MMT) composites were prepared, characterized through Fourier transformed infrared (FT-IR), X-ray powder diffraction (XRD), and scanning electron microscopy (SEM), and utilized as support for immobilization of polyphenol oxidase (PPO). PPO was immobilized on CTS/MMT (IPPO) and CTS-Au/MMT (IPPO-Au) by physical adsorption, respectively. In order to achieve simultaneous maximization of immobilization efficiency and enzyme activity, the immobilization process parameters were optimized by Taguchi-Grey relational analysis (TGRA) approach. Under the optimal immobilization condition, the immobilization efficiency and enzyme activity reached at 50.16% and 1.46 × 104 U/mg for IPPO, and 63.35% and 3.01 × 104 U/mg for IPPO-Au, respectively. The isotherm, kinetic and thermodynamics of PPO adsorption were investigated in detail. The adsorption process was better explained by Toth isotherm and Fractal-like pseudo second order model, respectively. Intra-particle diffusion and film diffusion were involved in the adsorption process and intra-particle diffusion was not the only rate-controlling step. The adsorption of PPO was exothermic, physical and spontaneous at the investigated temperature range. The immobilized PPO were used to oxidize phenolic compounds. All investigated phenolic compounds showed the higher conversion as catalyzed by IPPO-Au. For both IPPO and IPPO-Au, the conversion of substituted phenols was higher than that of phenol.

Optimization of the tribological properties of hybrid reinforced aluminium matrix composites using Taguchi and Grey's relational analysis

The tribological properties of synthesized hybrid reinforced aluminium matrix composites (AMCs) have been optimized in this study using Taguchi and grey relational analysis (GRA), methods where a L16 orthogonal array was used for the experimental design. Hybrid palm kernel shell ash (0–6 wt.%) and SiC (2 wt.%) formed the reinforcements of interest, which were combined in ratios ranging between 2 and 8 wt.%. Different loads (250, 500, 750, and 1000 g) and speeds (250, 500, 750, and 1000 rpm) were used as control factors. The wear samples were produced using the double-stir casting method, while a Taber type abrasion machine was used for the wear experiments. The evaluated wear index and volume loss showed that the speed and load were better influential factors on the performance characteristics of the composites than wt.% of reinforcements. The Taguchi-Grey's relational analysis gave the optimal combination of the process parameters for both the wear index and the volume loss as A3B1C1 (Reinforcement = 6 wt.%; Load = 250 g; Speed = 250 rpm) and A1B1C1 (Reinforcement = 2 wt.%; Load = 250 g; Speed = 250 rpm), respectively. The predicted and experimental values at the optimum conditions were confirmed to be within the range based on the performance of the confirmation test. The utilization of Taguchi and GRA methods have significantly confirmed that the influence of speed as a factor of performance was higher than load, which in turn was a better influencing factor than wt.% of reinforcements.

Multi response parameters optimization of ZA-27 nanocomposites

In this study, Taguchi-Grey relational analysis was used to investigate and optimize wear parameters such as sliding speed, reinforcement of Gr and reinforcement of Al2O3, and their effect on dry sliding wear performance of ZA-27 nanocomposites. Nanocomposites were synthesized via hot pressing process with pre-processing mechanical milling. Sixteen experimental tests were performed based on design of experiments which was created with the help of Taguchi L16 orthogonal array. Grey relational analysis (GRA) was applied for determination of optimal combination of parameters in order to improve tribological characteristics. Optimal combination of factors, obtained with Taguchi Grey relational analysis was sliding speed of 100 rpm, reinforcement content of 1 vol.% Gr and reinforcement content of 4 vol.% Al2O3. Validation of results was done by using Artificial Neural Network (ANN). Developed model had overall regression coefficient 0.99836, and output values showed good correlation with experimental results. Based on this research, it can be observed that nanocomposites with reinforcement of Gr and Al2O3 can be potentially employed in many industries as a good substitute for the base alloy. In addition, as a result of the analysis of the worn surfaces, it was determined that with the increase of the Al2O3 ratio, the hard Al2O3 nanoparticles turned the dominant wear mechanism into abrasive. Also, it was determined that the Gr nanoparticles appeared on the abrasive wear lines.

Multi-response Taguchi grey relational analysis of mechanical properties and weld bead dimensions of dissimilar joint of AA6082 and AA7075

ABSTRACT Selection of suitable parameter range is essential in joining dissimilar combinations joints when more than one number of influencing parameters are present in giving desired output. This paper discusses on combination joints of AA6082 and AA7075 that were joined with ER4043 filler wire using suitable input parameters obtained from Taguchi’s Method. The input parameters weld current, filler wire diameter and root gap were considered to design the experiments and output responses such as mechanical properties and bead geometry sizes were considered. For optimising the bead sizes and mechanical properties of weldments, an L9 array was used. Grey relational analysis was performed for optimising input parameters with multi-objective responses. ANOVA was performed to find the significance in effect of the welding parameters on dissimilar weldments. The analysis results of 8th trail was obtained as best performance set of values with main current at 210 A, filler wire diameter of 2.4 mm with a root gap at 2.0 mm. Results from ANOVA has shown that the welding current (36.75%) is affecting more on output responses followed by filler wire (35%) diameter and root gap (22%). Further, confirmation test was conducted for the optimum parameters and test results were validated.

Utility of Taguchi Grey Relation Analysis for Influencing the System Input Parameters and Impacts of Tool Geometry and Wear Response of the Cutting Tool on Machinability and Environmental Impact of Machining for Automobile Industries

Amid the delivering of an item, any material or abundance vitality created moreover to a definitive item will be named as waste. The waste produced in light of machining could be a noteworthy ecological worry for creators. The shape and condition of waste streams created and their transportation components contrast with the strategy utilized and also shift among the technique. The effect in view of each waste stream differs as well. This examination reports a machining strategy includes the procedure of material to give a completed or a semicompleted item. This is frequently done to abuse tools, frills, machines and distinctive data sources that are appropriate to the strategy. The procedures for this work include machining of material abuse devices to give parts and items. The yield of the technique incorporates the item and thusly the waste streams. The waste streams convey with the material inside the kind of chips, energy usage, worn cutting tools and operating time. Grey relational analysis and ANOVA was acclimated with the chief essential cutting speed, feed rate, depth of cut and tool nose radius conditions that influence the minimum response. The cooperative effects of the informational factors on the normal reactions are researched. The normal and estimated esteems are genuinely close. The given model may be acclimated to pick the measure of machining process parameters.

Taguchi grey relational analysis on the mechanical properties of natural hydroxyapatite: effect of sintering parameters

Previous studies have reported the synthesis and mechanical properties of natural hydroxyapatite (HA), but optimization of the measured hardness and compressive strength has not been examined. This paper presents optimization of HAp mechanical characteristics (hardness and compressive strength), using Taguchi grey relational analysis design. In the design, three factors with mixed levels (2 and 3) were employed with the consideration of sintering parameters (0 and 500 Pa compaction pressure, and 900, 1000, and 1100°C sintering temperature), reported in the previous study. The orthogonal array L18 having 18 rows corresponding to the number of tests and the required columns was selected. Results obtained show that HA with better hardness and compressive strength is feasible with little or no compaction pressure. An optimum grey relational grade (GRG) of the synthesized HA is 0.7171 and has an experimental value within 95% confidence interval. The optimum sintering parameters are gotten to be 500 Pa compaction pressure and 1100°C sintering temperature. The result shows that sintering temperature having 99.90 percentage of contribution is the most significant factor, while compaction pressure and residual error are insignificant on the overall hardness and compressive strength of the synthesized HA.

Application of hybrid Taguchi-Grey relational analysis (HTGRA) multi-optimization technique to minimize surface roughness and tool wear in turning AISI4340 steel

In this paper, an attempt is made to optimize the turning process by minimizing Surface Roughness and Tool Wear. The independent l factors used are Environmental Condition, Feed Rate, Depth of Cut, Nose Radius and Tool Types. The dependant factors are Surface Roughness and Tool Wear. Experimentations are conducted on CNC Spinner Lathe machine. AISI 4340 steel is selected as workpiece material. Three different types of Cutting tool are considered for the study. Grey Relational Analysis and Taguchi Philosophy together are used to optimize the process. As per the Taguchi method L27, Orthogonal Array (OA) is finalized for the experimentation. For the computation of the response table and ANOVA table, the Taguchi based data Analysis is used. The Variance Analysis (ANOVA) and S/N ratio (SRN) are employed to find the contribution and ranking of contribution parameters to optimize multiple output parameters.

Investigating a novel Ag/ZnO based hybrid nanofluid for sustainable machining of inconel 718 under nanofluid based minimum quantity lubrication

Sustainable machining, with implementation of eco-friendly dry and minimum quantity lubrication (MQL) methods, has gained attraction of researchers and engineers to solve the environmental and health allied issues caused by bulk usage of the conventional cutting fluids. Nanofluids have recently became more obvious choice as a cutting fluid in MQL applications owing to their superior thermo-physical properties. In this work, a novel hybrid nanofluid, consisting of Ag coated ZnO (Ag/ZnO) nanoparticles in ethylene glycol base, has been proposed as cutting fluid for MQL application. Initially, the Ag/ZnO hybrid nanoparticles were synthesized by a chemical precursor method. Then, synthesized nanoparticles were blended in ethylene glycol at several volume concentrations (Φ = 0.05 % to 0.2 %). The nanofluids were characterized for their thermo-physical properties and stability criterion, and then a nanofluid with the best performance was selected for nanofluid based MQL (NFMQL) experiments. The performance of NFMQL was compared with dry, MQL environments during milling of difficult-to-cut Inconel 718 superalloy with PVD coated carbide inserts. Taguchi L9 orthogonal array was incorporated for experimental design to investigate the effect of cutting speed, feed rate and machining environment on the machining performance in terms of the average surface roughness and cutting temperature. Analysis of variance shown that the cutting environment contributed to the average surface roughness and cutting temperature by 24.52 % and 44.74 %, respectively. As compared to dry and MQL condition, the NFMQL improved the surface finish by 23.5 % and 13.07 %, respectively, and reduced the cutting temperature by 15.38 % and 8.56 %, respectively, owing to proposed hybrid nanofluids enhanced lubrication and heat dissipation properties. Furthermore, the field emission scanning electron microscopic (FESEM) images of used cutting tool inserts reveal that the NFMQL condition induces the minimum tool wear as compared with MQL and dry environments. Finally, the multi-response optimization was achieved through the implementation of Taguchi grey relational analysis (TGRA) with the optimum combination of cutting speed of 30 mm/min, feed rate of 0.036 mm/tooth, and NFMQL cutting environment.

Multi-objective Soft Computing Approaches to Evaluate the Performance of Abrasive Water Jet drilling Parameters on Die Steel

In the current manufacturing methods, improvement in process performance will be resulting in huge benefits to the industries. For the improvement in part or product quality, the machining process must be operating with the optimal parameters, and thus, quality attributes can be produced. It applies to one of the modern machining techniques such as abrasive water jet (AWJ) as the use of this technique is growing rapidly in manufacturing industries for needful applications. Therefore, the present work is proposed to find the optimal level of AWJ parameters such as water jet pressure, stand-off distance, abrasive mass flow rate for drilling die steel with the simultaneous minimization of drilled hole features such as surface roughness, circularity, and cylindricity by using an unusual metaheuristic technique, namely harmony search algorithm (HSA). In addition, taguchi grey relational analysis (TGRA) was performed and their results were compared with the HSA technique. The multiple linear regression models were developed for each response, and the same were used in HSA to determine the optimum parameters for the minimization of responses where the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to convert the multi-objectives into single objective. In this study, the efficacy of HSA technique was demonstrated with the hole features of die steel. The results proved that the HSA technique had outperformed the TGRA based on the overall improvement in AWJ drilling performance on die steel, and the predicted process attributes such as surface roughness of 1.53625 μm, circularity of 0.04370 mm, and cylindricity of 0.02459 mm were obtained at water jet pressure of 275.4 MPa, stand-off distance of 3.96 mm, and abrasive flow rate as 0.25 kg/min. Also, the percentage deviation error (< 6%) of the predicted hole features of HSA is acceptable and analogous to the experimental results. Hence, it is confirmed that a new metaheuristic algorithm, namely HSA was found suitable for the AWJ drilling process.

WITHDRAWN: Optimization of process parameters by Hybrid Taguchi-Grey Relational Analysis for thermal behaviours of lubricant oil of worm gearbox

In this research work, the major focus is given on thermal behaviours of lubricant oil of worm gearbox. In worm gearbox due to sliding motion between teeth's of worm and worm wheel cause friction energy loss. The friction energy loss is converted into heat energy and that heat energy is dissipated into lubricant oil of worm gearbox. The performance of lubricant oil is decreasing due to the rise in temperature which causes decreases in the overall performance of gearbox. To observe thermal behaviour of lubricant oil of worm gearbox Lubricant Heating Time (time required to increase the temperature of the oil from 35 °C to 50 °C) and the Viscosity of oil (at 50 °C) is taken as response parameters while Load on the worm wheel (L), Volume of oil (V) and Type of oil (T) are taken as process parameters. Every process parameter is varied at three dissimilar levels. Taguchi optimization technique (L9) is used for the optimization of experiments for this Minitab19 software is used. Grey Relational Analysis (GRA) is used to determine the optimum levels of process parameters. The optimum levels of process parameters for maximum Lubricant Heating Time and maximum Viscosity are L1V2T2 i.e. the lowest level of Load on the worm wheel, intermediate level of Volume of oil and Type of oil B. ANOVA is performed to determine the relative contribution of process parameters on Lubricant Heating Time. ANOVA results show that Type of oil is the maximum influence parameter (98.26%) followed by Load on the worm wheel (0.99%) and Volume of oil (0.72%) on Lubricant Heating Time. There exist an inverse relationship between oil viscosity and lubricant heating time.

Optimization of self-compacting composite composition using Taguchi-Grey relational analysis

This paper presents the results from the multi-performance optimization self-compacting concrete (SCC) mixtures containing fly ash and manufactured sand using Taguchi-Grey relational analysis which translates the performances of all the parametric variables into a comparability sequence. An orthogonal array of trial runs of experiments was developed using the Taguchi method and the results analyzed using grey relational analysis. For the design of experiments, five parameters at different levels were identified and used in the array development. The parameters considered are ordinary Portland cement content (OPC), the content of fly ash used as the replacement of OPC, the content of fine aggregate, the content of manufactured sand used as the replacement of fine aggregate and the content of superplasticizer. Based on the analysis from the trial runs, the optimum mixture was identified, and additional experiments were carried out to validate the composition of the optimized mixture. The findings from this study showed that the content of fly ash used as the replacement of OPC has the highest effect on the properties of the SCC mixtures. A good correlation between the predicted properties of the optimized mixtures and experimental results used as validation indicated that Taguchi-grey relational analysis can be used successfully to optimize the composition of SCC mixtures. The properties of the optimized mixtures also conform with the recommended limits by EFNARC. Thus, SCC mixtures exhibiting acceptable properties and made with fly ash as replacement of OPC and manufactured sand as replacement of the natural fine aggregate can be produced for various construction applications.

Multi-response optimization of the Thermal properties of Bio fluids using Taguchi-Grey Analysis

In order to study and investigate the overall performance of bio fluids for solar thermal applications, this work consists of integration of Taguchi method with Taguchi Grey Relational Analysis. The preparation of bio fluids has three levels of input control parameters such as Type of oil, Percentage of concentration and Stirrer speed. Output optimization indicators for the experiment were chosen as Thermal conductivity, Density and Viscosity. The comparative study made consists of coconut oil, sunflower oil and castor oil in the bio fluids preparation. These oils are available readily in the market and are quite inexpensive. The bio fluids are prepared by mixing the above oil with water in appropriate concentration. The experiment is run for the optimal solution using Taguchi analysis using L9 orthogonal array. The influential parameters which are significant in contributing the performance of bio fluids were studied using ANOVA table. After multi objective parametric optimization, the control parameters optimization level shows the use of Sunflower oil with 15% concentration at 15000 RPM stirrer speed produces the optimal performance. Based on the Ranking and Sum of squares from ANOVA table, it is evident that the most influential parameter is the type of oil followed by Percentage of concentration and Stirrer speed respectively.

Multi objective taguchi–grey relational analysis and krill herd algorithm approaches to investigate the parametric optimization in abrasive water jet drilling of stainless steel

This novel study deals with the investigation of abrasive mixtures on abrasive water jet (AWJ) drilling of stainless steel 304 using multi objective soft computing techniques. In this study, the drilling parameters such as abrasive mixture, stand-off distance and feed rate were varied. The abrasive mixture was prepared with the composition of different types of abrasives such as silicon carbide & garnet, and aluminium oxide & garnet, and different mixture ratios. This mixing ratio was done based on the total mass of the abrasive mixture. The effect of abrasive water jet drilling parameters was examined on the hole features such as the hole diameter, circularity, cylindricity, and surface roughness. Multi objective optimization and algorithm techniques were employed in this study, namely Taguchi–Grey Relational Analysis (TGRA) and Krill Herd Algorithm (KHA). In this research work, the performance of KHA method was also compared with another recent metaheuristic technique i.e. grey wolf optimization (GWO) based on the quality measurement tools such as Spacing and Inverted generational distance. For this approach, the main parameters of metaheuristics algorithms were tuned using a robust design approach to acquire the best feasible solution. Besides, different multiple linear regression model equations were established to determine the best model for the KHA method based on the similarity between experimental and calculated attributes. With the assistance of these approaches, it is found that the abrasive mixtures have improved the performance of the AWJ drilling process in SS 304 rather than the use of a single type abrasive such as 100% Garnet. The results of this study proved that the KHA optimization technique is successfully utilized to find the best configuration parameter setting for AWJ drilling process, and that results are found to be efficient than the TGRA. To validate the predicted results of KHA, confirmation test was conducted. The results of the confirmation test showed that the predicted hole features of KHA were acceptable as that the error deviation was found as less than 2% with the experimental results. It is also noticed that the computational time and the selected quality metrics of KHA are found to be lower than the GWO method. Hence, it is confirmed that a new metaheuristic algorithm namely, KHA was found suitable for AWJ drilling process. The outcome of the present work explores a new paradigm to the AWJ machining to improve performance features in various operations.

Multiple response optimization of ultrasonic assisted electric discharge Machining of Nimonic 75: A Taguchi-Grey relational analysis approach

The present work proposes the multiple response optimization of ultrasonic assisted electric discharge machining (UAEDM) process through Taguchi-grey relational analysis (TGRA) approach, during machining of Nimonic 75 superalloy. Nimonic 75 finds its vast applications in aerospace, nuclear, chemical industry, notably in highly corrosive and oxidizing environments. Three response variable namely material removal rate (MRR), surface finish (SF) and tool wear rate (TWR) were optimized. Based on the average responses computed from Taguchi-grey relational analysis, the tool electrode material-copper, gap current of 12 A, discharge voltage of 45 V, pulse on duration of 200 µs, pulse off duration of 15 µs and tool lift time of 2 sec were estimated as optimal parametric settings for UAEDM process. Predicted results have been verified by conducting confirmation experiments indicating an overall improvement 2.02% on MRR, 5.74% on SF and a reduction of 3.98% on TWR.

Multi performance optimisation of wire-cut EDM process parameters of Incoloy 800 alloy using grey relational analysis

Abstract Wire Electric Discharge Machining is one of the non-conventional machining processes, used to machine alloy materials with high mechanical strength and also cuts intricate shapes. This paper presents a study in multi performance optimization of machining parameters of Wire electric discharge machining of Incoloy 800 alloy using Taguchi method and grey relational analysis. Incoloy 800 alloy is a nickel, iron and chromium based alloy. The variation in process parameters affects the response variables such as Surface roughness and Material removal rate. Taguchi L27 Orthogonal array is used to perform the experiments. Experiments are performed by considering machining parameters such as Pulse on time, Pulse off time, Gap Voltage, Peak current. By varying the machining parameters accordingly the optimum values are obtained for performance characteristics. The optimization is performed in order to obtain best Surface Roughness and maximum Material Removal Rate. By Grey relational analysis the optimal combination of machining parameters is obtained. ANOVA reveals the most influential factor for Surface roughness and MRR. The results show that the machining parameter Pulse ON time has the greater impact on the performance characteristics.

Using the Taguchi Method and Grey Relational Analysis to Optimize the Performance of a Solar Air Heater

Solar energy is regarded as one of the promising renewable energy sources in the world.The main aim of this study is to use the Taguchi-Grey relational grade analysis to optimize the performance of two Solar Air Heaters (SAHs). A typical Grey–Taguchi method was applied. The Orthogonal Array, Signal-to-Noise ratio, Grey Relational Grade, and Analysis of Variance were employed to investigate the performance characteristics of SAH. Experimental observations were made in agreement with Jordanian climate 32°00′ N latitude and 36°00′ E longitude with a solar intensity of 500 W\m2. The operating factors selected for optimization are the tilt angle (T) with three levels (0°, 22°, 45°), inlet velocity (V) with two levels (1.2, 1.8 m/s), and absorber plate material (M) with two levels (Aluminum, wood). In this study, the Grey–Taguchi approach is validated by performing 12 individual experiments. The results show that the process factors sequence required for a maximum SAH efficiency (SAH µ) is V > T > M. Using this approach, we combined the Orthogonal Array design with Grey Relational Analysis. As a result of that, the level of each operating conditions which optimizes both process responses (Temperature difference, ∆T and Solar air heater efficiency, SAH µ) can be specified with a minimum number of tests compared with classic Grey Relational Analysis. The optimal operating conditions of a SAH for multiple performance characteristics are determined as T2, M2, and V2, respectively, which are in congruence with the experimental results.

Taguchi grey relational analysis for optimal process parameter of low carbon steel welded by GTAW by using ER309L filler material

Welding is one of oldest method of joining and it has lot of application in structures, automobiles, metal fabrication industries etc, thus the mechanical properties like hardness, tensile strength of these joints is an important GTAW is one of the suitable welding technique used for joining stainless steel due to concentrated heat source and having stable arc. In this present work, TIG welding technique is used to join low carbon steel by using filler material ER309L. The experimentation is performed to get Maximum tensile strength and harness. Though the hardness and tensile values are inversely proportional for this Grey taguchi method is used to evaluate the optimal parameter. The parameter like welding current, voltage and traverse speed are selected as a input parameter. An approach has been made to compare the responses with Taguchi L9 orthogonal Array with GRA. Methodology of grey-taguchi method starts with GRA. The optimal setting for the selected parameter is obtained by the help of GRA and the contribution of process parameter to the output response also achieved by using main effect plot of S/N ratio.