Taguchi Analysis Research Articles

Statistical Assessment of Counterflow Ranque-Hilsch Vortex Tube Performance

The vortex tube, which consists of a simple tube, is a device that can simultaneously heat and cool thanks to environmentally friendly pressurized fluids (air, oxygen, nitrogen, etc.). Many studies have been included in the literature to evaluate the Ranque-Hilsch Vortex tube's performance and to reveal influential factors. Variance analysis, linear regression analysis, and the Taguchi method are primarily used in practice. This study aimed to compare the strengths and weaknesses of the factorial experimental design and Taguchi orthogonal array design in the statistical evaluation of the factors affecting the heat exchange of the Ranque-Hilsch Vortex tube. For this purpose, a detailed theory was created for the appropriate factorial ANOVA model to the data set (4 × 5 × 12 = 240 experiments) containing the Ranque-Hilsch Vortex tube and effective material type (polyamide, steel, brass, and aluminum), nozzle number (2,3,4,5 and 6), and input pressure parameters (1,5-7 bar). Following the factorial ANOVA solution, including all binary interactions, the findings were obtained according to the most suitable L16 Taguchi Orthogonal array, considering the four levels for each material, nozzle, and pressure. As a result of the ANOVA, all parameters were statistically significant on heat change (p < 0.001). On the other hand, the pressure was obtained as the only statistically significant factor according to the Taguchi analysis (F = 35.17, p = 0.008). The advantages and disadvantages of the two methods were compared regarding the test findings and graphical performances.

A Study of Drilling Parameter Optimization of Functionally Graded Material Steel–Aluminum Alloy Using 3D Finite Element Analysis

Composite materials, such as aluminum alloy FGMs, provide advantageous weight reduction properties compared to homogenous pure structures while still preserving sufficient stiffness for diverse applications. Despite various research on drilling simulation concepts and ideas for these materials, there still needs to be an agreement on the process modeling. Researchers have looked into a lot of different numerical methods, including Lagrangian, Eulerian, arbitrary Lagrangian–Eulerian (ALE), and coupled Eulerian–Lagrangian (CEL), to find solutions to problems like divergence issues and too much mesh distribution, which become more of a problem at higher speeds. This research provides a global analysis of bottom-up meshing for eleven 1 mm layers using ABAQUS® software. It combines the internal surface contact approach with the Lagrangian domain’s kinematic framework. The model uses the Johnson–Cook constitutive equation to precisely predict cutting forces, stress, and strain distributions, optimizing cutting parameters to improve drilling performance. According to Taguchi analysis, the most favorable parameters for reducing cutting force and improving performance are a rotational speed of 700 rpm, a feed rate of 1 mm/s, and a depth of cut of 3 mm. The findings suggest that increasing the feed rate and depth of cut substantially affects the cutting force, while the rotational speed has a comparatively little effect. These ideal settings serve as a foundation for improving FGM drilling efficiency.

Removal of methylene blue from aqueous solutions by adsorption onto Carpobrotus edulis biomass

The aim of this work is to investigate the influence of an alkaline pretreatment of the Mediterranean plant Carpobrotus edulis on the adsorption capacity toward the dye methylene blue and to estimate the content of soluble organic matter that can be released into the aqueous solution by this biomaterial. The biomaterial used was characterized by SEM-EDX, SS, FT-IR, pHZ, COD and BOD5. Removal of methylene blue (MB) dye by this biomaterial was carried out and the effects of physicochemical factors such as initial pollutant concentration, adsorbent dose, contact time, pH and temperature were investigated. After adsorbent treatment, the soluble organic matter was reduced by 97%, 96%, and 96% for biological oxygen demand, chemical oxygen demand, and organic matter, respectively. Kinetics and equilibrium studies of MB adsorption on the bioadsorbents were fitted with a pseudo-second order kinetic model and a Langmuir model, respectively. The maximum adsorption amount of the modified C. edulis plant was 153.9 mg/g at 298°K. The thermodynamic parameters show that the adsorption process was possible and spontaneous in nature. The adsorption mechanism of MB on the surface of biomaterials was attributed to the electrostatic interaction and H-bonding. The prepared biomaterial (0.016 U$ per gram) was easily regenerated with aqueous HNO3 solution, with a slight decrease in adsorption capacity up to five cycles. The results of Taguchi experimental design and analysis of variance showed that contact time, adsorbent mass and initial concentration are the most important factors affecting the removal efficiency with a contribution of 43.91%, 32.68%, and 22.95%, respectively. The maximum removal capacity of MB dye in optimal operating conditions was 99.39%, which was obtained at the optimum conditions of m = 1 g, pH 4, T = 35 °C, Ci = 1000 mg/L, tc = 5 min. These results confirm previous results obtained with the batch system and show that this absorbent is also promising for the removal of cationic dyes from wastewater.

Influence of various input factors on the compressive strength properties of 3D printed carbon fiber reinforced PETG samples using Taguchi analysis

Influence of various input factors on the compressive strength properties of 3D printed carbon fiber reinforced PETG samples using Taguchi analysis

Optimization of surface roughness using rotary ultrasonic machine of polyvinyl butyral glass with Taguchi and grey relational analysis

Optimization of surface roughness using rotary ultrasonic machine of polyvinyl butyral glass with Taguchi and grey relational analysis

Experimental investigations and process optimization of SKD61 for sinking EDM

ABSTRACT Electrical parameters are vital for electrical discharge machining (EDM) process performance. The study utilized the combination of Taguchi and grey relational analysis (GRA) to optimize sinking EDM. Optimization of peak current, pulse duration, duty ratio and electrode lifting time affecting material removal rate (MRR), relative electrode wear rate (REWR) and surface roughness (SR) in sinking EDM. Analysis of variance (ANOVA) shows that peak current and pulse duration significantly affect MRR and REWR, respectively. Peak current and pulse duration significantly affect SR. The optimized parameters were subjected to EDM experiments and the results are 8.54% increase in MRR, 11.11% decrease in REWR and 9.42% decrease in SR. It was found that at a peak current of 1A, the discharge pit not only depends on peak current and pulse duration but also on pulse off time. Compared to conventional optimization methods, this study analyzed the microstructure and recast layer of the workpieces.

Optimizing thermal performance of nanofluid impinging upon a nonlinearly stretching sheet using Taguchi technique

The main purpose of this article is to optimize the thermal performance of system involving stagnation point flow of A l 2 O 3 + water based nanofluid over a nonlinearly stretching surface. The mathematical model for the problem under consideration utilizes the Tiwari and Das’s single phase flow model for the nanofluid. The aim is to find a similar solution of the problem using some suitable similarity variables. The partial differential equations dictating the flow situation are converted into dimensionless ordinary differential equations. Numerical solution of the system of dimensionless ordinary differential equations has been obtained with the help of MATLAB’s inbuilt bvp4c package. Further, Taguchi analysis along with ANOVA and regression analysis have been performed against the variable like Pr , γ , n and ϕ corresponding to response variable to predict the optimal heat transfer of the system. Outcomes of ANOVA indicate that Pr has maximum and ϕ has minimum influence over the Nusselt number.

Designing restorative landscapes for students: A Kansei engineering approach enhanced by VR and EEG technologies

This study explores the alignment of specific landscape features within school environments with the core elements of Attention Restoration Theory (ART) that includes Coherence, Fascination, Compatibility, and Being Away. Utilizing Kansei Engineering, this research integrates emotional analysis into landscape design by employing Virtual Reality (VR) and Electroencephalogram (EEG) technologies to record students’ responses to different landscape simulations. Analytical techniques, including the Taguchi Method and Analysis of Variance (ANOVA), were applied to evaluate the data. The findings have revealed that students associate a sense of enclosure with a coherent landscape and openness with a fascinating landscape, the lawn’s significance was also highlighted for coherent landscape. However, limited insights were gained regarding Compatibility and Being Away. The study advocates for diverse cognitive zones within school landscapes to promote mental restoration, emphasizing the need for varied design elements that cater to the elevated experience of students.

Optimized preparation route for polyamide top-coated forward osmosis membrane for enhanced water flux using industrial wastewater as feed.

The forward osmosis (FO) process has recently gained significant interest in treating wastewater, brackish/seawater and concentrating feedstocks for various operations, including desalination. The study investigates the effect of different synthesis conditions of the polyamide-based thin-film composite (TFC) FO membranes on the membranes' final performance. Taguchi statistical analyses were used to fabricate and optimize the polyamide TFC FO membrane. The process parameters as factors were the amount of polyethersulfone (PES), polyethylene glycol 400 (PEG-400), polyvinyl pyrrolidone (PVP), m-phenylenediamine (MPD), and trimesoyl chloride (TMC), and TMC reaction-time (RT). The Taguchi method was adopted to investigate the optimal conditions and the significance of individual factors using an L16 (45) orthogonal array. Another Taguchi analysis (Taguchi 2) was adopted to investigate the influence of other important parameters like optimal conditions for MPD, TMC, and TMC reaction-time factors using an L9 (33) orthogonal array. Confirmation tests validated a maximum water flux of 46.4 ± 2.32 L/m2·h with a specific combination of control factors for membrane synthesis: PES/PEG/PVP/MPD/TMC/TMC RT-16/7/0.5/1/0.05/30. These tests demonstrated a high-water flux of 7.05 ± 0.35 L/m2·h when exposed to industrial wastewater (secondary effluent) as the feed solution (FS) and fertilizer as the draw solution (DS) in the FO process. The R2 values were more than 90%. The experimental validation confirmed the models' predictive ability with different FSs, including industrial wastewater.

Mechanical and vibration characteristics of hybrid natural fibre-reinforced composite using taguchi grey relational analysis

In recent scenarios, the need for lightweight components in aerospace and safety applications results in the formation of new composite materials. The current investigation mainly emphasized the influence of a multi-wall carbon nanotube (0, 0.5 and 1 wt%) with different wt% of natural banana fibre (0, 3 and 6 wt%) / three different resins (Epoxy, Vinylester and General purpose) reinforced in composite using Taguchi’s L9 approach. Tensile, impact and impulsive excitation strengths (Youngs Modulus, Shear Modulus and Poisson’s ratio) are performed to predict the mechanical characteristics of the reinforced composites. ASTM standards are followed for preparation and testing. The outcomes illustrate that the mechanical characteristics are improved by the inclusion of 1 wt% CNT and 6 wt% of banana fibre reinforced in epoxy resin with an increase in impact strength of 5.41 kJ m−2, a tensile strength of 11.25 MPa, Young’s Modulus of 3.54 GPa and Poisson’s ratio of 0.404 when compared to the other composition. The optimal process constraints are found using Hybrid Taguchi-Grey Relationship Analysis. The experiments are carried out using the L9 orthogonal matrix. The results show that experimental trial no 3 could give optimized output. To evaluate the outcomes of the Taguchi optimization and analysis of variance, experimental trials are also carried out. This research aims to increase the strength of the industrial helmet by using this combination.

Optimisation of hybrid process to recover zinc from electric arc furnace dust using design of experiment approach

ABSTRACT Recoverable metals like zinc and iron are present in the dust from electric arc furnaces (EAF), a byproduct of the steelmaking process. These metals are mainly present as zinc ferrite (ZnFe2O4), magnetite (Fe3O4), and zinc oxide (ZnO) compounds. ZnO is easily dissolved in either an acidic or an alkaline solution; however, ZnFe2O4 is more difficult to dissolve. Therefore, our earlier paper suggested the thermodynamic analysis of a preliminary caustic-roasting treatment to transform the ZnFe2O4 from EAF dust into more soluble products of ZnO and NaFeO2. Finally, the present study carried out a hybrid process that involved the caustic roasting of EAF dust and a caustic leaching of the roasted product. At first, the effects of caustic-roasting process parameters on zinc-ferrite conversion were studied using the Taguchi method. The implication of Design of Experiment (DoE)-Taguchi analysis and the analysis of variance (ANOVA) shows the optimum caustic-roasting conditions are: roasting temperature of 350 oC, NaOH/ EAF dust molar ratio of 4, and roasting time of 60 minutes. The effect of temperature, reaction time, NaOH concentration, and solid/liquid (S/L) ratio was also examined using the same method in the subsequent caustic-leaching process. Similarly, the analysis shows that maximum Zn dissolution is possible at a NaOH concentration of 6M, a leaching temperature of 40 oC, a liquid-to-solid ratio of 60 ml/g, and a leaching time of 60 minutes. After the hybrid process, the zinc recovery was 83%, while most iron oxides remained in the leach residue.

Optimization of Savonius rotor blade performance using Taguchi method: Experimental and 3D-CFD approach

The Savonius hydrokinetic turbine (HKT) is both cost-effective and reliable, providing clean energy with minimum environmental impact. The efficiency of the Savonius rotor can be improved by modifying the blade profile to increase the effective torque. However, past research works reported that modifying the blade profile is quite challenging due to small Cp improvement and design constraint. Therefore, the present study proposes a newly developed blade profile blueprint, akin to parameterizable designs such as the modified Bach and Benesh profiles but offering more shape versatility. This new blueprint is optimized with a systematic design of experiment (DOE) using a 3D computational fluid dynamics (CFD) simulation to maximize the Cp. The best design is determined statistically using the Taguchi method and analysis of variance (ANOVA). The optimized rotor profile enhances Cp by approximately 10.9 % compared to the conventional Savonius rotor at the optimal TSR (best Cp = 0.159) and 16.7 % improvement at a higher TSR value of 0.9 (Cp = 0.158). Moreover, the optimized rotor outperforms the Fibonacci rotor (Fibonacci blade profile) by 27 % in terms of efficiency. A thorough CFD analysis suggests that the optimized blade profile has a greater lift generated at the convex side of the advancing blade due to its streamlined shape, which delays the flow separation further up to the root of the blade. The confirmation test (experiment) is also performed to verify the Cp improvement of the optimized rotor, and the results herein concluded that the present CFD prediction is accurate and consistent with the experimental values.

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.

Experimental investigation of mechanical properties and multi-objective optimization of electronic, glass, and ceramic waste-mixed concrete.

The utilization of waste from various sources plays an important role in minimizing environmental pollution and civil construction costs. In this research, the mechanical properties of concrete were studied by mixing electronic waste (EW), glass powder (GW), and ceramic tile waste (CW). The effects of weight percentages of EW, GW, and CW are considered to investigate improvements in mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS) of concrete. Taguchi analysis has been applied to predict the optimum composition of waste mixing percentages. The Multi-Objective Optimization Ratio Analysis (MOORA) techniques are applied to estimate the optimum composition of mixing wastes for maximizing the CS, STS, and FS of concrete. It was observed that 10 wt.% of EW, 15 wt.% of GW, and 30 wt.% of CW are predicted as the optimal mixing combinations to obtain a maximum compressive strength of 48.763 MPa, a split tensile strength of 4.178 MPa, and a flexural strength of 7.737 MPa, respectively. Finally, the predicted optimum waste-mixed weight percentages were used to examine the microstructure and various elements in the concrete using SEM and XRD analysis. When compared to conventional concrete, the optimum waste-mixed concrete has improved its compressive strength (38.453%), split tensile strength (41.149%), and flexural strength (36.215%).

Taguchi-Grey relational analysis driven multi-objective optimization of weld bead geometry and hardness in laser welded Ti6Al4V Alloy

AbstractThis study used Taguchi-based Grey relational analysis to optimize the bead geometry (bead width and length) and the hardness of laser welded 3 mm thick Ti6Al4V alloy sheets joined using the butt configuration. Firstly, the L9 (32) orthogonal array, with varying laser power and welding speed, was employed to optimize the input parameters (bead length, width, and hardness). The Taguchi optimization showed that the welding speed and laser power have a significant effect on the hardness of the welds and that laser power has a significant impact on the bead length. Meanwhile, the welding parameters had little effect on the bead width. These results were further affirmed with the Analysis of Variance (ANOVA). The Grey relational multi-objective optimization shows the optimized parameters for all responses to be 3 kW laser power and 2.2 m/min welding speed. It is important to note that the combination of Taguchi and Grey relational analysis and ANOVA is useful in optimizing welding parameters to achieve suitable weld properties.

Parametric investigation of thermal additive centrifugal abrasive flow machining process for enhancing productivity

Thermal additive centrifugal abrasive flow machining hybrid of abrasive flow machining caters to low material removal issues which enhances the acceptability of the process. The present investigation investigates the different centrifugal force-generating electrode geometry performances in the thermal additive centrifugal abrasive flow machining process. The L27 orthogonal array is used for optimizing the process parameters (electrode type, current supply, and the duty cycle) and their effect on material removal and percentage improvement in surface finish (%Δ Ra) are analyzed. Based on the percentage contribution obtained from Taguchi analysis, electrode type is the prominent parameter for material removal (88.27%) and %Δ Ra (78.54%) followed by current (7.43% for material removal and 13.92% for %Δ Ra) and duty cycle (3.95% for material removal and 6.42% for %Δ Ra). The spline electrode with a curved blade is found optimum with 10 A current and 0.76 duty cycle. The average experimental value based on the design of the experiment and the predicted value for material removal is 10.373 and 10.881 mg, respectively. The average experimental value and the predicted value for percentage improvement in the surface are 42.24% and 43.71%, respectively. The percentage error between the predicted and experimental values for material removal and %Δ Ra is 4.66% and 3.15%, respectively.

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM’s susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D printed parts with the desired mechanical characteristics.

Development of Sustainable Slag-based Geopolymer Concrete Using Different Types of Chemical Admixtures

Geopolymer concrete (GPC) has achieved a wide popularity since innovating it as an alternative to conventional concrete because of its superior mechanical characteristics and durability, in addition to being a green concrete due to its low negative impact on the environment. However, GPC still suffers from the problem of its poor workability which suppresses its spread in construction applications. This study investigated the most effective parameters on the workability of GPC including GGBFS content, water to binder ratio, and dosage of different types of chemical admixtures, Naphthalene-Based Admixture (NPA) and Polycarboxylate-Based Admixture (PCA), using Taguchi approach and Analysis of Variance (ANOVA) analysis considering the compressive strength at the different concrete ages. It was observed that NPA, in the geopolymer concrete, improved the compressive strength compared to PCA. The NPA-based mixes achieved the highest 28-day compressive strength, 69 MPa, with about 27.8% more than the highest 28-day compressive strength achieved by the PCA-based mixes, 54 MPa. The obtained results revealed that the NPA has achieved the best improvement for both the workability, in terms of initial slump value and slump loss rate, and the compressive strength of GPC mixes compared to PCA.

Electrical conductivity enhancement of rGO-MoS2 composite electrode for inverted PSC by parameter optimization

Carbon-based electrodes are widely used in photovoltaic, metal-ion batteries, supercapacitors, and sensors due to their low ohmic resistance and ability to integrate modifiers. Reduced graphene oxide (rGO) represents a promising alternative to graphite in carbon-based electrodes because of its high electrical conductivity, large surface area, and robust mechanical properties. This study aimed to optimize the fabrication parameters of rGO-MoS2 composite electrodes prepared via a one-pot hydrothermal method, focusing on maximizing their electrical conductivity for potential electronic applications. Taguchi analysis was employed to evaluate the impact of three key factors: rGO-MoS2 mixing ratio, annealing temperature, and annealing time. The novelty of this work lies in the systematic optimization approach using the Taguchi method, which allowed for the identification of the most significant factors and their optimal levels. The optimal combination was found to be a 1:1 ratio, 75 °C annealing temperature, and 15-min annealing time, resulting in an impressive electrical conductivity of 11 800 S m⁻1. This optimized rGO-MoS2 composite electrode exhibited an approximate 11 % reduction in sheet resistance compared to the unoptimized composite. Furthermore, all samples show an improvement in sheet resistance values compared to pure rGO and MoS2, which were 82.5 Ω/sq and 48.9 Ω/sq. Numerical simulations further revealed a 13.23 % improvement in device performance when the optimized rGO-MoS2 composite was implemented as the top electrode in an inverted perovskite solar cell, demonstrating the significant potential of this material for various electronic applications.

Optimal design of mixed dielectric coaxial-annular TSV using GWO algorithm based on artificial neural network

The single-objective and single-parameter optimization method is commonly used in the structure optimization of TSV to improve the transmission characteristics, for which a structure design scheme that simultaneously satisfies multiple target requirements is difficult to obtain. Moreover, the method cannot simultaneously optimize different design parameters. Aiming at the above problems, a global optimization method based on the grey wolf optimization (GWO) algorithm and artificial neural network (ANN) model is proposed. With the presented mixed dielectric coaxial-annular TSV model, firstly six key design parameters A-F are selected as optimization variables by the control variable method. The L25(56) orthogonal experiment is designed for Taguchi analysis and analysis of variance (ANOVA). Then, three prediction models, ANN, support vector machine (SVM), and extreme learning machine (ELM), are developed with the extended orthogonal data as the training sets. It is found that the ANN model performed best. To search for the global optimal solution, the genetic algorithm (GA) and GWO algorithm, combined with the ANN model are applied, respectively. The results show that the GWO algorithm is more successful in solving the problem of falling into the local optimum than GA, and the convergence speed is faster and more stable. After GWO-ANN optimization, the performance of each S-parameter index is greatly improved, S11 reduces by 14.05 dB, S21 increases by 0.33 dB, and S31 reduces by 12.50 dB at 30 GHz.