Taguchi-based Grey Relational Analysis Research Articles

Multi-objective optimization of micro crystalline cellulose and montmorillonite filled poly lactic acid bio–composite and its characterizations

In this research study, the synthesis of poly lactic acid (PLA) based bio composite material factors contributions were investigated through the Taguchi-based grey relational analysis (GRA) technique. Effects of micro crystalline cellulose (MCC) and montmorillonite (MTT) nano clay filler, sorbitol (S) plasticizer, and temperature (T) operating factor on the PLA matrix through melt-mixing preparation method. The tensile strength (TS), Young modulus (YM), flexural strength (FS), flexural modulus (FM), hardness, impact strength (IS), water absorption (WA), and density properties of bio composite material were investigated for each experimental setup (orthogonal array, L16). Additionally, neat PLA and optimal sample structural, thermal, and morphological properties were examined through Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (X-RD), thermal gravimetry analysis/differential thermal gravimetry (TGA/DTG), and DSC and SEM analyses. The obtained result for optimal mechanical and physical properties of MCC/MTT/S/PLA bio composite was MCC at level 3 (6%), MTT at level 4 (9%), S at level 2 (10%), and T at level 4 (175 °C). Analysis of variance (ANOVA) shows that MTT has the greatest significant effect on mechanical and physical properties of MCC/MTT/S/PLA bio composite followed by MCC, T, and S. The confirmation test indicates that the improvement of weighted grey relational grade (GRG) from 0.7896 to 0.846 and the FTIR, XRD, thermal gravimetry/differential scanning calorimetry (TG/DSC), and scanning electron microscopy (SEM) results indicates that the good interaction between PLA and fillers, improvement of thermal and morphological properties of optimal (6MCC/9MTT/10S/175 °C) bio composite samples. Therefore, the multi-response characteristics of MCC/MTT/S/PLA bio composite can be highly improved by this technique.

Experimental investigation into friction stir welding of AA6061-T6 and magnesium AZ31B alloy using copper backing plate

Aluminium-magnesium alloys are lightweight and possess superior properties, making them ideal for applications in aerospace, railway, automotive, and marine structures. However, these alloys are challenging to fabricate using traditional fusion welding due to various metallurgical issues. This study investigates the feasibility of joining 3 mm thick dissimilar alloys of aluminium AA6061-T6 and magnesium AZ31B using the friction stir welding (FSW) process with copper as a backing plate. The effects of FSW process parameters, including tool rotational speed (380, 545, 765 rpm), weld speed (20, 31.5, 50 mm min−1), and tilt angle (1°, 2°, 3°), on tensile properties were examined using Taguchi-based grey relational analysis. Temperatures were recorded during the FSW process under different conditions on both sides of the joint. The highest tensile strength of 130.72 MPa, with a joint efficiency of 67.85%, was achieved at a tool rotational speed of 765 rpm, a welding speed of 50 mm min−1, and a tilt angle of 2°. Analysis of variance (ANOVA) for the grey relational grade indicated that the tilt angle had the highest percentage contribution of 43.60%. This study demonstrates the significant influence of FSW parameters on the mechanical properties of aluminium-magnesium joints and highlights the optimal conditions for achieving high joint strength and efficiency.

Evaluating the effects of pineapple fiber, potato waste filler, surface treatment, and fiber length on the mechanical properties of polyethylene composites for biomedical applications

This study investigates the development of a biocompatible polyethylene composite incorporating natural fiber (pineapple fiber) and potato waste filler for potential use in biomedical applications. The composite was fabricated using compression molding and optimized through Taguchi-based grey relational analysis (GRA), TOPSIS, and artificial neural network (ANN) prediction models. The hybrid combination of these materials has been minimally explored in the biomedical field. Fourier Transform Infrared (FTIR) analysis of the potato filler revealed a peak at 1336 cm⁻1, indicating asymmetric deformation of C-H and C-O functional groups, contributing to enhanced compatibility with the polyethylene matrix. The natural fiber showed uniform distribution, improving stress transfer and flexural properties. The ANN model demonstrated high accuracy, with an r-value of 0.9972. Multi-response optimization identified the best configuration as 3 % NaOH treatment, 3 wt% potato filler, 30 wt% natural fiber, and a fiber length of 1.5 cm, yielding a tensile strength of 22.3 MPa, flexural strength of 23.8 MPa, and impact strength of 18.3 kJ/m2. This fabricated composites were lightweight and biocompatible materials, suitable for biomedical applications.

Emerging eco-friendly fiber-reinforced concrete with shaped synthetic aggregates using Taguchi grey relational analysis and utility concept

The present study assessed the applicability of employing Taguchi-based Grey relational analysis (T-GRA) and the Taguchi approach combined with the utility concept (T-UC) for enhancing the mix design parameters of fiber-reinforced synthetic aggregate concrete (FRSAC). Cement was supplemented with binders such as ground granulated blast furnace slag (GGBFS), metakaolin (MK), and lime powder (LP). The optimized mix proportion of FRSAC was determined to enhance the fresh and mechanical performance. Furthermore, this investigation used two weighting approaches: equally weighted and maximum deviation methods. An established L16 (45) Taguchi orthogonal array was used with five different factors, namely, cement replacement (40 %, 50 %, 60 % and 70 %), binder content (B1, B2, B3, B4), water-to-cement ratio (0.31, 0.32, 0.33, 0.34), fiber dosage (0.1 %, 0.2 %, 0.3 % and 0.4 %), and synthetic aggregate replacement (25 %, 50 %, 75 %, and 100 %) were employed to design the experiments. Then, T-GRA and T-UC were used to determine the optimum mix proportions for five response parameters: workability, compressive strength, split tensile strength, elastic modulus, and impact ductility index. Results indicated that the two methods, GRA and UC, can be successfully integrated with the Taguchi method to derive the optimized FRSAC mixture. Confirmation experiments were performed after determining the optimum mix design parameters, including cement replacement of 60 %, binder content of B4, the water-to-cement ratio of 0.34, fiber dosage of 0.1 %, and synthetic aggregate replacement of 75 %. The optimum FRSAC mix was examined for its fresh (workability) and hardened (compressive strength, split tensile strength, elastic modulus, impact ductility index) properties.

Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings

This paper deals with the topic of reducing drag force acting on aircraft wings by incorporating novel winglet designs, such as multi-tip, bird-type, and twisted. The high-speed NASA common research model (CRM) was selected as the baseline model, and winglet designs were retrofitted while keeping the projected wingspan constant. Computational analysis was performed using RANS coupled with the Spalart–Allmaras turbulence model to determine aerodynamic coefficients, such as CL and CD. It was observed that the multi-tip and bird-type designs performed exceptionally well at a low angle of attack (0°). A parametric study was conducted on multi-tip winglets by tweaking the parameters such as sweep angle (Λ), tip twist (Є), taper ratio (λ), and cant angle (Φ). The best combination of parameters for optimal aerodynamic performance while maintaining the wing root bending moment was determined using both the Taguchi method and Taguchi-based grey relational analysis (T-GRA) coupled with principal component analysis (PCA). Also, the percentage contribution of each parameter was determined by using the analysis of variance (ANOVA) method. At the design point, the optimized winglet design outperformed the baseline design by 18.29% in the Taguchi method and by 20.77% in the T-GRA coupled with the PCA method based on aerodynamic efficiency and wing root bending moment.

Taguchi-Based Grey Relational Analysis on Multiple Damping Characteristics of a Hydraulic Engine Mount

Taguchi-Based Grey Relational Analysis on Multiple Damping Characteristics of a Hydraulic Engine Mount

Multi-objective Optimization of Process Parameters for Surface Quality and Geometric Tolerances of AlSi10Mg Samples Produced by Additive Manufacturing Method Using Taguchi-Based Gray Relational Analysis

AbstractIn this study, it has been focused on examining the effects of production parameters on quality parameters such as surface roughness and geometric tolerances in the production of AlSi10Mg samples by the additive manufacturing method. The experimental design has been prepared according to the Taguchi L27 orthogonal array. As a result, in the production of samples, increasing laser power (P) contributed positively to surface roughness and diameter change, and increasing scanning distance (SD) negatively contributed to circularity change and concentricity. Further, it has been determined that increasing the scanning speed (SS) negatively affects the concentricity change of the produced samples. The optimum production parameters for surface roughness and diameter variation has been determined as A1B1C3. The optimum production parameters for circularity variation and concentricity have been determined as A3B3C1 and A3B1C1, respectively. According to the ANOVA analysis results, the most effective parameters for surface roughness, diameter change, circularity change and concentricity have been 53.22% P, 62.45% SD, 37.23% SS and 40.41% SD, respectively. Furthermore, as a result of the gray relationship analysis (GRA) performed for the output parameters, the optimum production parameter has been determined as A2B1C3.

Multi-objective parametric optimization process of hybrid reinforced titanium metal matrix composite through Taguchi-Grey relation analysis (TGRA)

Hybrid titanium metal matrix composites (HTMMCs) are advanced composite materials that can be tailored to a variety of applications. Because of their decreased fuel consumption and cost, they are popular in the transportation industry. Using multi-objective optimization and Taguchi-based Grey relational analysis (TGRA), this study investigates the impact of hybrid reinforced HTMMCs synthesized using powder metallurgy on their physic mechanical properties. The research investigates reinforcements such as B4C, SiC, ZrO2, and MoS2 at various compaction pressures, milling durations, and sintering temperatures. The best powder metallurgy control parameters for HTMMC synthesis, with a milling time of 5 h, a compaction pressure of 40 MPa, a sintering temperature of 1200 °C, and a sintering time of 1 h, and a compaction time of 40 min. According to validation results, HTMMC material with optimized process parameters had experimental densities, porosities, hardness, compressive strength, and wear rates of 4.29 gm/cm3, 0.1178%, 71.53RHN, 2782.36 MPa, and 0.1519 mm3 correspondingly. The material hardness was increased by 1.99% and compressive strength by 2.87%. The use of Taguchi and GRA techniques strongly verified that the impact of milling duration and sintering temperature was the greatest of all five factors. The novel synthesized hybrid reinforcing HTMMCs outperformed pure Ti grade 5 and single and double fortified HTMMCs in terms of physic mechanical characteristics. As a result, the newly developed tetra hybrid reinforced HTMMC material is expected to be used in heavy-duty vehicles, aerospace, automobiles, maritime, and other industries.

Mechanical properties of Sr inoculated A356 alloy by Taguchi-based gray relational analysis

Abstract In this study, Sr inoculated A356 alloy casted by sand-casting technique. Production parameters such as Sr concentration (wt.%), aging temperature (°C), aging time (h), and constant cooling rate were used. The effect of heat treatment on the microstructure and mechanical features of inoculated A356 materials was examined by using scanning electron microscopy, optical microscopy, and the Taguchi-based gray relational analysis method. The optimum production parameters for A356 alloy were determined as 0.03 Sr concentration, aging 300 °C temperature, and 3 h aging time. Multiple response optimization based on the interaction of these parameters provided a 30.15 % improvement in performance. Gray relational grade (GRG) experimental results showed that the most important parameter was Sr concentration, with a contribution of 76.51 %, according to the analysis by ANOVA statistical method.

An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process

An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process

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.

Multi-response optimization of reinforcement parameters of aluminum alloy composites by Taguchi method and grey relational analysis

The present work describes the optimization of reinforcement parameters for hardness, thermal conductivity, and coefficient of thermal expansion while developing LM6 alloy/soda-lime glass particulate composite through Taguchi-based Grey Relational Analysis (GRA). Soda-lime glass particle weight % (1.5, 3.0 and 4.5 %), particle size (100, 150 and 300 μm) and pre-heat temperature (260, 380 and 500oC) are varied accordingly to explore the effect of reinforcement parameters on LM6 alloy/soda-lime glass composite properties. Composites are developed through stir casting based on the L9 Taguchi orthogonal array approach. The properties such as hardness, thermal conductivity and coefficient of thermal expansion of developed composites are assessed. Signal to Noise Ratios (S/N ratios) are calculated and used for the optimization of parameters. GRA is employed for multi-response optimization to find the levels of parameters that affect the desirable properties of the composite. Thus, the reinforcement parameters are optimized for attaining the combined objectives of higher hardness, higher thermal conductivity and lower coefficient of thermal expansion values considered in this investigation. The analysis shows that 4.5 wt %, particle size of 200 μm and pre-heat temperature of 380oC are optimal parameter levels. A confirmation test is carried out with the optimal parameter levels and the GRG value of 0.7778 is obtained. The GRG with the initial parameter settings is 0.4711, and the improvement of GRG is found to be 65.1 %. ANOVA is performed on GRG to find out significant parameters and the contribution of each parameter is identified. The wt.% of soda-lime glass is the most significant parameter and its contribution is 92.6 %.

Investigation and Optimization on Parameters of Gas Additive Powder Mixed Near Dry –EDM (GAPMNDEDM) by using Taguchi based- Grey Relational Optimization

The Gas Additive Powder Mix near Dry Electric Discharge Machining (GAPMND-EDM) serves a manufacturing purpose, specifically for cutting hard materials. This involves the removal of material from a workpiece via fusion, ablation, and evaporation caused by the heat energy generated through electric sparks during energy supply. The process offers several advantages in terms of performance and characteristics. This research project aims to optimize key process parameters, including material removal rate, surface roughness, residual stresses, and microhardness. The study employs a methodology that combines the standard deviation-based objective weighting method with GRA (Gray Relational Analysis) optimization to enhance the hybrid GAPMND-EDM process when applied to EN-31 material. Experimental runs were conducted to evaluate the impact of various input factors, such as pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration. The taguchi-based GRA method was utilized for this purpose, and the experimental design followed an L-27 orthogonal array with the assistance of Minitab-19 software. A total of 27 experiments were performed, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was executed to analyze the influence of pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration on Material Removal Rate (MRR), Surface Roughness (SR), Residual Stress (RS), and Microhardness (MH). The result shows the optimal combination of parameters, denoted as A2B2C2D1, was identified as the preferred configuration.

A STUDY ON MULTI-OBJECTIVE OPTIMIZATION OF PULSED TIG WELDING PARAMETERS AND THEIR EFFECT ON JOINT PROPERTIES OF HIGH MANGANESE STEELS USING TAGUCHI-BASED GRA APPROACH

This investigation focuses on the study of the effect of process parameters like peak current ([Formula: see text], base current ([Formula: see text], pulse frequency ([Formula: see text], shielding gas flow rate ([Formula: see text] on mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and flexural strength (FS) of the welded joints during pulsed TIG welding of SAILMA 450 and EN14 B steels. Taguchi’s L[Formula: see text] orthogonal array has been used for conducting the tests. Multi-objective optimization has been performed using Grey relational analysis (GRA) in order to maximize the mechanical strength and to find out the optimal set of parameters. The optimum parametric combination is obtained at a peak current of 220[Formula: see text]Amps, base current of 120[Formula: see text]Amps, pulse frequency of 5[Formula: see text]Hz and shielding gas flow rate of 17[Formula: see text]l/min. Analysis of variance (ANOVA) is used to predict the significant process parameters. It has been observed from the ANOVA analysis that peak current and pulse frequency have more influence on the output responses than the shielding gas flow rate and base current. The results of the confirmatory test show an improvement of 0.5801 in the GRG, which is satisfactory. A microstructure study has been performed using scanning electron microscopy (SEM) for the optimal set of process parameters.

Multi-objective optimization of temperature uniformity in the immersion liquid cooling cabinet with Taguchi-based grey relational analysis

Single-phase immersion liquid cooling technology can meet the cooling needs of high-density Data Centers. To improve the temperature uniformity in an immersion liquid cooling cabinet and prevent local hot spots that reduce the efficiency of servers, the structural parameters of the cabinet were optimized in this study. The fluid path is mainly affected by the position of inlet and outlet, length, Angle and number of baffles. To evaluate the effects of these factors on the temperature uniformity in the cabinet, Taguchi-based grey relational analysis method and computational fluid dynamics were used to optimize the system, the standard deviation and maximum temperature difference are determined as evaluation indices, and L16 (44) orthogonal array was used for experimental design. The baffle Angle is judged to be the most important structural parameter by the Main Effect Analysis, whose contribution rate is 49.23%. The multi-objective optimization is transformed into a single objective by the grey relational analysis, and the influence rate of each structural parameter is shown. The optimal combination of structural parameters is determined. The results show that the temperature uniformity of the optimized cabinet is improved by 46%, which provides a reference for the design of immersion liquid cooling Data Centers.

Optimizing the five magnetic abrasive finishing factors on surface quality using Taguchi-based grey relational analysis

The effectiveness of the magnetic abrasive finishing (MAF) process relies on several factors, including the brush’s flexibility that varies across tools. This study aimed to optimize the results of five key parameters (voltage, finishing time, gap distance, rotating speed, and particle size) on surface roughness (SR) and microhardness (HV) using the grey relational analysis (GRA) method. Experimental work employed the Taguchi design with L27 trials in Minitab 17, involving five variables with three levels for each. The impact of these parameters on microhardness and surface roughness for stainless steel SUS420 bubble cups was assessed using Taguchi and regression analyses. The best roughness improvement and the most substantial enhancement in microhardness were individually obtained with the GRA method. This method assigned the best results for both surface roughness and microhardness. According to Taguchi analysis, the voltage parameter has the main or maximum parameter effect on grade, followed by gap distance, time, spindle speed, and particle size. It was found that the optimal parameters were the same as the input parameters.

Multi Objective Optimization of FDM Parameters Using Taguchi Grey Relation Analysis for PLA Specimen

Polylactic acid (PLA), is a thermoplastic polyester that has many uses in both consumer goods and industrial settings. The mechanical characteristics of PLA specimens created using Fused deposition modeling (FDM), a cost-effective 3D printing process, including tensile strength, shore hardness, and dimensional precision, have been studied for use in specialised engineering applications. Layer height, infill density, and printing speed are the choices made for the specimen’s 3D printing. Design of experiments use Taguchi’s L9 orthogonal array. Using analysis of variance (ANOVA), designers can determine the relative importance and percentage contribution of each process parameter to each answer. Using Taguchi method while conducting test for individual responses result shows that for tensile strength printing speed is 70 mm/s, layer height 0.2mm and 40% infill density as optimum parameters while for the hardness it is 60 mm/s,0.3mm and 40%, and for the dimensional deviation found 60 mm,0.2 mm, 40% respectively. Proposed TGRA method found optimum parameter for all the responses in single test as printing speed is 70 mm/s, layer height 0.3 mm and 40% infill density also validated by conducting confirmation test. Finally, a superior specimen with all-around mechanical characteristics is fabricated using Taguchi based grey relational analysis (TGRA) as a multi-objective optimization technique.

Multi-objective optimization of window configuration and furniture arrangement for the natural ventilation of office buildings using Taguchi-based grey relational analysis

Multi-objective optimization of window configuration and furniture arrangement for the natural ventilation of office buildings using Taguchi-based grey relational analysis

Optimum process parameters of IN718 alloy fabricated by plasma arc additive manufacturing using Taguchi-based grey relational analysis

The process parameters of IN718 alloy fabricated by plasma arc additive manufacturing (PAAM) were evaluated by Taguchi-based grey relational analysis, and the microstructure and mechanical properties of the PAAM alloy were analyzed to verify the optimized process parameters. The results show that the influence of each process parameter on the dilution ratio and forming coefficient is consistent. Based on the results of Taguchi-based grey relational analysis, the optimized process parameters for PAAM IN718 alloy are plasma arc current 115 A, scanning rate 1000 mm/min, powder feeding rate 16 g/min, and shielding gas flow 800 L/h. The plasma arc current and powder feeding rate play primary roles in the dilution rate and forming coefficient of PAAM IN718 alloy. With the optimized process parameters, the forming and internal crack of the PAAM IN718 alloy is controlled. The microstructure of PAAM IN718 alloy is dominated by temperature gradient and constitutional supercooling, which is sequentially transformed from cellular crystal in the bottom region to coarse columnar dendrite and equiaxed crystal in the top region. Laves phase exhibits a concentration gradient that gradually increases from top to bottom on the cross-section from the range 11.3–18.6 vol%. Due to the solid solution strengthening of Nb/Mo elements and dispersion strengthening by γ″ phase, the microhardness and tensile strength of PAAM alloy after full heat treatment reach about 500 HV and 1284 MPa, respectively. These are satisfactory mechanical properties compared to other additive manufactured IN718 alloys, and PAAM is suitable to prepare IN718 alloy to obtain favorable microstructure and mechanical properties.

Multi-response optimization on the effect of wet and eco-friendly cryogenic turning of D2 steel using Taguchi-based grey relational analysis

Multi-response optimization on the effect of wet and eco-friendly cryogenic turning of D2 steel using Taguchi-based grey relational analysis