Taguchi-Grey Relational Analysis Method Research Articles

Multi response optimization of recycled aggregate based alkali-activated concrete using Taguchi-Grey Relational Analysis Method

The use of cement in construction causes large amounts of CO2 emissions into the atmosphere. Moreover, the use of large amounts of natural coarse aggregate causes environmental damage, and the availability of such natural aggregate is also depleting. The present experimental study explores and optimizes the impact of various parameters on the fresh, mechanical, and durability properties of recycled waste concrete coarse aggregate (RWCA)-based geopolymer concrete (GPC) in combination with ground-granulated blast furnace slag (GGBFS) and fly ash (FA) using the Taguchi-Grey Relational Analysis Method (TGRAM). The coarse aggregates in GPC were fully replaced with RWCA produced by crushing waste concrete. The effects of FA inclusion (%), Na2SiO3/NaOH ratio (SS/SH), molarity of NaOH (SH), dosages of superplasticizer (%), and alkaline solution to binder (Al/Bi) ratio are investigated under ambient curing conditions. The obtained maximum compressive strength and split tensile strength along with minimum water absorption of GPC at 28 days were 67.90 MPa, 3.85 MPa and 2.92 % respectively. The microstructural studies of GPC having low, high, and optimal strength are carried out using XRD, FE-SEM, and EDAX analysis. These studies confirmed the presence of calcium-based hydration products such as CSH and N(C)ASH, which makes the matrix of optimized GPC more compact.

Parametric optimization in drilling of sisal–glass reinforced epoxy composites using Taguchi grey relational analysis method

This research work intends to study the effect of hybridization of glass and sisal fiber, stacking sequence and tensile properties of the composite. The sisal-glass fiber hybrid composites laminates are prepared using reinforced plain woven sisal fabric (unidirectional) and plainwoven glass fabric. In this research study, 27 experiments are conducted as per L27 orthogonal array. Five process parameters are selected and three responses are considered in this work. The drilling of the composite specimen is considered and the drilling process parameters such as speed, feed rate, drill diameter, material thickness, and drill point angle are selected. The responses considered in this work are delamination factor, thrust force, and torque. Taguchi analysis is performed and the response table for means for the responses is determined, and the most influencing parameter in the drilling of the composite specimen is analyzed. The grey relational coefficients are computed and followed with the computation of the grey relational grade. The grey relational grades are calculated for determining the highest contributing parameter in the drilling of the sisal fiber and glass fiber reinforced hybrid composite specimen. The optimum drilling process parameters are ranked and the ranks presented represent the sequence of run resulting in optimum solutions.

Multi-Response Optimization of High-Performance Low-pH Grouting Materials by Using Taguchi-Based Grey Relational Analysis.

The most accepted approach to sealing in high-level radioactive waste repositories (HLRWs) is to develop a low-pH grouting material with a pH of the pore solution of less than 11. Currently, the most widely used binary low-pH grouting material is MCSF64, which comprises 60% microfine cement (MC) and 40% silica fume (SF). In this study, a high-performance MCSF64-based grouting material was developed by incorporating naphthalene superplasticizer (NSP), aluminum sulfate (AS), and united expansion agent (UEA) to enhance the slurry's shear strength, compressive strength, and hydration process. Orthogonal experiments were conducted to measure the flow time, yield stress, plastic viscosity, initial setting time, shear strength, and compressive strength of the MCSF64-based slurry, and the optimal mix proportion was determined using the Taguchi-Grey relational analysis method. The pH variation of the pore solution, shrinkage/expansion, and hydration products of the optimal hardened slurry were evaluated using simplified ex-situ leaching (S-ESL), a length comparometer, and scanning electron microscopy (SEM), respectively. The results demonstrate that the Bingham model effectively predicted the rheological properties of the MCSF64-based slurry. The optimum ratio for the MCSF64-based slurry was water/binder (W/B) ratio of 1.4, and the contents of NSP, AS and UEA by mass of binder were 1.9%, 3.6% and 4.8%, respectively. The optimal mix exhibited a pH value below 11 after curing for 120 days. The addition of AS and UEA facilitated hydration, shortened the initial setting time, improved early shear strength, and enhanced the expansion ability of the optimal mix under water curing conditions.

Optimizing a hybrid mortar for an alkali-activated material by papermaking process wastes and blast furnace slag utilizing Taguchi–Grey relational analysis method

Different types of papermaking process wastes (PPWs) are generated in the paper industry. The compositions of the waste materials vary significantly depending on the source of the waste materials. To date, a standard methodology for the comprehensive assessment of the recovery potential of the waste materials generated has not been established. This research adopted the Taguchi–Grey relational analysis method to evaluate and efficiently recycle and reuse these wastes systematically. With regard to the development of the PPW- blast furnace slag (BFS) hybrid system, four types of PPWs, namely lime mud (LM), primary sludge (PS), fly ash (FA), and bottom ash (BA), were systematically utilized. Herein, the setting time, compressive strength, flexural strength, and shrinkage of the mortars that were based on the PPW-BFS hybrid system are analyzed. The results revealed that different factors affected different mortar properties. The setting time was primarily affected by the PS content, the BA content significantly influenced the compressive and flexural strengths, and the BA and LM contents exerted effects on the degree of shrinkage of the materials. The optimal PPW–BFS mortar composition was determined (LM: 5%; PS: 5%; FA: 30%; BA: 10%). It was observed that the optimal mortar composition attained a strength grade of M30, with successful mitigation of premature stiffening and volume shrinkage in the hybrid system.

Exergoeconomic analysis and multi-objective optimization of ORC configurations via Taguchi-Grey Relational Methods

Recovery of low-grade waste heat in industrial processes is an essential energy management topic. Yet, most low-temperature heat sources discharge their heat directly into the environment. The Organic Rankine Cycle (ORC), which has the benefits of being energy-efficient, enabling investment savings, and being ecologically friendly, is crucial in recycling energy from low-temperature waste heat. Both the application of the optimum cycle design and the provision of optimum working conditions are the issues that need to be focused on efficiently using energy. This study performs the energy, exergy, and exergoeconomic analysis of four different organic Rankine cycle configurations operating with renewable or low grade waste heat. The effect degrees and ratios of selected control factors are calculated using Taguchi and variance analysis methods to compare thermal and exergy efficiencies, total system cost, and unit cost of electricity produced by the system. The objective function of the multi-objective optimization problem is defined, and its solution is realized with the Taguchi-Grey Relational Analysis method. The best thermodynamic and exergoeconomic performance result is calculated for the configuration of ORC with Feed Fluid Heater-Internal Heat Exchanger (IHE–FFH–ORC). As a result of Taguchi and ANOVA analysis, the factors that most affect the thermal efficiency of the system, the exergy efficiency, the total system investment cost, and the unit cost of the electricity produced are, respectively, the evaporation temperature (∼50%), turbine efficiency (∼25%), working fluid (∼20%), subcooling (∼4%), pump efficiency (∼0.05%), and superheating (∼0.05%). As a result of the optimization process, the thermal and exergy efficiencies, the total system cost and the unit cost of produced electricity for the IHE–FFH–ORC power system are calculated as 22.6% and 73.5%, 1.06 $/h, 0.039 $/kWh and 2.9 years, respectively.

Research on Cement Slurry Using Silica Fume Instead of Fly Ash.

Ordinary cement is not environmentally friendly, has high cost and lacks superior performance. Many scholars use various admixtures to adjust the properties of cement slurry, but admixtures are usually not environmentally friendly, and it is difficult to ensure that the properties after deployment meet engineering requirements. In this study, a variety of admixtures were obtained using the environmental protection method, and the optimal mixing ratio was analyzed by combining the entropy weight method and the Taguchi grey relational analysis method. The developed cement slurry was compared with conventional slurry from both macroscopic and microscopic aspects. Aiming at the problem that previous scholars lacked the engineering feasibility verification of the developed slurry, this study combined the constitutive equation regression analysis method, discrete element numerical simulation and other methods to study various actual engineering conditions. The results show that the optimal mix ratio of silica fume cement slurry has good permeability characteristics under the conditions of different roughness, grouting pressure and confining pressure. At the same time, under different geological temperatures and different erosive liquid states, the cement slurry stone body shows good properties of reinforcement materials.

Multi-Response Optimization of Ultrafine Cement-Based Slurry Using the Taguchi-Grey Relational Analysis Method.

The grouting technique is an important method in underground engineering that prevents water seepage and reinforces fractured rock mass. In this research, ultrafine cement-based grouting material, including ultrafine cement (UC), ultrafine fly ash (UFA), polycarboxylate superplasticizer (SP), colloidal nanosilica (CNS), sodium sulfate solution (SS) and water, was developed. The flow time, viscosity, bleeding, setting time and uniaxial compressive strength of the UC-based slurry were measured by orthogonal experiments, and the optimal mix proportion of the UC-based slurry was obtained based on the Taguchi-Grey relational analysis method. Microstructure analysis of the UC-based slurry was conducted using scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) tests. The results showed that the Bingham model could provide a satisfactory description of the rheological properties of the UC-based slurry. The addition of CNS and SS could promote the hydration of the UC-based slurry and improve the microstructure of the hardened slurry, thereby increasing the strength of the hardened slurry. The optimum ratio for the UC-based slurry was water/solid (W/S) ratio of 1.0, and the contents of UFA, SP, CNS and SS by mass of UC were 40%, 0.2%, 4% and 4%, respectively.

Optimization of Production Activated Carbon for Removal of Pharmaceuticals Waste Using Taguchi Method and Grey Relational Analysis

The development of the pharmaceutical industry has led to increased environmental pollution by pharmaceutical waste. This encourages efforts to develop effective and inexpensive pharmaceutical waste management. One effort to handle pharmaceutical waste is to use activated carbon. In the manufacture of activated carbon there are several factors that affect the quality and performance of activated carbon produced. This research seeks to determine the optimum factors in making activated carbon and study its performance in adsorbing pharmaceutical waste. Multi-response analysis based on the Taguchi Grey relational analysis method is used to determine the optimum conditions. The most influential factors in the production of activated carbon, respectively, are pyrolysis temperature (800°C), ratio of precursors and activating agents (1:1), residence time (150 minutes) and finally the type of activator (KOH).

Design Optimization of a Single-Strand Tundish Based on CFD-Taguchi-Grey Relational Analysis Combined Method

A novel digital design methodology that combines computational fluid dynamics (CFD) modelling and Taguchi-Grey relational analysis method was presented for a single-strand tundish. The present study aimed at optimizing the flow control device in the tundish with an emphasis on maximizing the inclusion removal rate and minimizing the dead volume fraction. A CFD model was employed to calculate the fluid flow and the residence-time distribution of liquid steel in the tundish. The Lagrangian approach was applied to investigate the behavior of non-metallic inclusions in the system. The calculated residence-time distribution curves were used to analyze the dead volume fraction in the tundish. A Taguchi orthogonal array L9(3^4) was used to analyze the effects of design factors on both single and multiple responses. Moreover, for the purpose of meeting the multi-objective target functions, grey relational analysis and analysis of variance were used. The optimum positions of the weir and the dam were obtained based on the design targets. A special focus of this study was to demonstrate the capabilities of the Taguchi-Grey relational analysis method as a powerful means of increasing the effectiveness of CFD simulation.

Processing, Properties, and Microstructure of Recycled Aluminum Alloy Composites Produced Through an Optimized Stir and Squeeze Casting Processes

Abstract This work focuses on the processing, properties, microstructure, and optimization of squeeze and stir casted samples of scrap aluminum alloy wheel aluminum matrix composites reinforced with alumina. The Taguchi-Grey relational analysis method was used to optimize the stir and casting process parameters, namely, squeeze pressure, squeeze time, die preheating temperature, and stirrer speed. These stir-casted composites were analyzed based on their microstructure, hardness, tensile strength, compression strength, and wear/tribological performance. Adding alumina to an aluminum matrix improved the mechanical and tribological properties. The results showed that out of nine experiments (L1–L9) obtained from Taguchi analysis, experiments L5 and L6 exhibited the best mechanical properties. Microstructural observations revealed different morphologies in the distribution of Al2O3 and porosity in the Al matrix, depending on the process parameters. Finally, the Taguchi-GRA method was used to find the optimized process parameters and was experimentally verified. The optimized sample (M2) showed the lowest porosity (5.29%) and significantly higher ultimate compression strength (433 MPa). However, it exhibited slightly lower hardness and ultimate tensile strength when compared with the L6 and L5 samples, respectively.

Optimization of wear parameters using Taguchi grey relational analysis and ANN-TLBO algorithm for silicon nitride filled AA6063 matrix composites

The current research work aims to synthesis AA6063-Si3N4 composites by stir casting technique and analyzing their wear behavior using optimization techniques. The selected composition of the proposed composites are AA6063, AA6063-5 wt% Si3N4 and AA6063-10 wt% Si3N4. The Taguchi grey relational analysis (TGA) was used to analyze the wear performance of the synthesized composites. Further, using the measured experimental data, an artificial neural network (ANN) model was developed to predict the wear rate and teaching learning based optimization (TLBO) algorithm was implemented to obtain the optimized parameters value with the objective of minimizing the wear rate. In the existing work, the multiple linear regression model (MLRM) was developed using the measured values of both dependent (response) and independent (process parameter) variables and these models are used in the optimization technique to optimize the process parameters. From the TGA technique a minimum wear rate of 0.0002 mm3 min−1 was obtained for 10% wt reinforcement, 29.43N load, 3 m s−1 is sliding velocity and 1500 mm sliding distance and from the ANN model and TLBO algorithm, it is probable to attain 0.000183 mm3 min−1 wear rate for the process parameter values of 9.892% of wt reinforcement, 9.837N of load, 2.936 m s−1 sliding velocity and 1130.7 mm sliding distance. As compared to TGA method, an amount of 8.4% improvement in wear rate is achieved by implementing ANN–TLBO method.

Multi parameter optimization of deep drawing for cylindrical cup formation on brass sheets using Grey Relational Analysis

Abstract Deep drawing is the widely used process for producing products from sheet metals. There are many effectible process parameters in the process. Proper selection of these parameters leads to sound forming of the components. The aim of the present paper is to analyze the influences of the forming parameters in deep drawing process on the outcomes like drawing force and thinning ratio in cylindrical cup forming of brass sheets. Three Control parameters are considered in the present study such as blank diameter, punch velocity and lubricant with three levels each. The experiment is conducted according Taguchi's DOE methodology and experimental data is analysed by using Taguchi Grey Relational analysis Method for the identification of optimal process parameters. The optimal values are confirmed and found that it is satisfactory. The ANOVA analysis also conducted to determine the influences of control parameters.

Part load and full load multi-objective performance optimization of a single-cylinder diesel engine operating on Mangifera indica biodiesel as biofuel

ABSTRACTThis experimental study was carried out on a CI engine using Mangifera indica biodiesel for performance optimization using the Taguchi method and grey relational analysis (GRA). This paper presents the combined effect of six input parameters, namely compression ratio, injection pressure, injection nozzle geometry, additive, fuel fraction and exhaust gas recirculation (EGR), in controlling seven output variables, namely brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), CO, HC, CO2, NOx and smoke. Five levels were selected for each input parameter and unequal weightage assigned to each output variable. Taguchi's L25 orthogonal array has been applied for reducing the number of experiments. The data obtained from the experiments has been analyzed using S/N ratio and GRA to obtain the optimum combination of six input parameters. The results of the analysis were further confirmed through experiments. The tests were conducted on a CI engine at part load and full load to optimize the performance. Deviations in predicted values of grey relational grade (GRG) are 4.48 and 2.61% of the experimental values at part load and full load, respectively, justifying the application of the Taguchi GRA method for multi-objective optimization of CI engine performance. The GRG improvements with Mangifera indica biodiesel blend are 57.5% at part load and 68.3% at full load compared to GRG at initial settings of the diesel engine.

Optimization of process parameters in the high-speed milling of titanium alloy TB17 for surface integrity by the Taguchi-Grey relational analysis method

TB17 is an ultra-high-strength titanium alloy, a material typically difficult to machine. The surface quality and integrity of titanium alloy TB17 are poor after machining, which can seriously affect its service performance and restrict its fields of application. Therefore, the optimization of high-speed milling process parameters for the new ultra-high-strength titanium alloy TB17 was investigated based on multiple performance characteristics, including surface roughness, surface microhardness, and surface residual stress. The Taguchi method with grey relational analysis was utilized for the experiments. Additionally, analysis of variance was employed to evaluate the most influential factors for surface integrity in the high-speed milling of titanium alloy TB17. The results of the analysis using the Taguchi-Grey relational analysis method indicate that the preferred combination of high-speed milling process parameters are as follows: the cutting fluid condition of H-1 fine grinding fluid, using a milling speed of 100 m/min, a feed per tooth of 0.02 mm/z, an axial depth of cut of 1 mm, a radial depth of cut of l.5 mm, a rake angle of 18°, a clearance angle of 12°, and a helix angle of 60°. Moreover, the analysis of variance reveals that milling speed has the greatest effect on the surface integrity in the high-speed milling of the TB17 titanium alloy, and the contribution percentages for each factor are as follows: the cutting fluid condition (3.98%), milling speed (25.89%), feed per tooth (8.96%), axial depth of cut (1.29%), radial depth of cut (13.71%), rake angle (17.17%), clearance angle (6.62%), and helix angle (15.64%).