Taguchi-based Grey Relational Analysis Research Articles

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.

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

A multi-criterion optimization of mechanical properties and sustainability performance in friction stir welding of 6061-T6 AA

Nowadays, the transportation industry is curious about fabricating lightweight vehicles under a sustainable manufacturing process to minimize fuel consumption costs and environmental footprints. Assessing the sustainability performance metrics of the friction stir welding (FSW) process reduces the sawing costs and environmental impacts. However, the evaluation and assessment of the sustainability performance metrics integrated with the mechanical properties of the weld joint have not been sufficiently studied. Thus, this study investigates the multi-criterion process parameter optimization of friction stir welding (FSW) to attain an optimum parameter to enhance the mechanical properties of the weld joint and decrease energy consumption and carbon dioxide emission. Experimentations are conducted using a vertical CNC milling machine on a 5 mm 6061-T6 AA material with a butt joint orientation. Tool rotational speed, traverse speed, and tool profile were used as processing parameters. Response of the study, such as transient temperature and energy consumption, was measured during the joining process, whereas hardness, tensile strength, and CO2 emission were examined post-processing. The multi-criterion optimization was done using the Taguchi-based grey relational analysis method. In addition, the transient temperature was predicted by an auto-regressive moving average (ARIMA) and Ansys simulation. The finding showed that higher rotational and minimum welding speeds had imparted a sound weld. Besides, the analysis of variance results showed that welding speed has a significant parameter at a 95% confidence interval. The proposed ARIMA model's result showed that the transient temperature prediction accuracy reached 99.922%, and the error percentage between the FEA simulation and experimental work reached 2.067%.

Investigation of machinability indicators during sustainable milling of 17-4PH stainless steel under dry and MQL environments

17-4PH steel, which has the perfect combination of corrosion resistance and high mechanical properties, is especially preferred in defense and aerospace applications, but its machinability is poor. Thus, an extensive research has been conducted on its milling under sustainable cutting regimes (dry and minimum quantity lubrication_MQL) to contribute to both more efficient use and sustainable machining. First, the changes in resultant cutting force ( Fr), the average surface roughness ( Ra), the mean roughness depth ( Rz) and total energy consumption ([Formula: see text]) were investigated after the experiments performed by applying the L18 orthogonal array. Subsequently, machining conditions were optimized for the minimization of machinability indicators with the Taguchi-based grey relational analysis technique. Finally, the predictive models for these indicators were developed by regression analysis. The order of importance for Fr and [Formula: see text] was the depth of cut and feed, while for Ra and Rz this ordering was found to be feed rate and cutting regime. Short curved chips formed in MQL cutting regime contributed positively to the minimization of the considered machinability indicators. Although the energy consumption due to spindle speed increased with increasing cutting speed in dry cutting environment, the decrease in material strength resulted in a decrease in [Formula: see text]. Since the cooling effect of MQL reduces the cutting temperature, material softening and thus the expected decrease in cutting resistance could not be achieved, so the decrease in [Formula: see text] was not as much as dry cutting. Optimum machining conditions were determined as MQL cutting regime, the cutting speed of 120 m/min, the cutting depth of 0.5 mm and feed rate of 0.05 mm/rev. The determination coefficients of the predictive models developed by regression analysis showed that these models can be used safely in up milling.

Performance assessment of some selected vegetable oils as lubricants in turning of AISI 1045 steel using a Taguchi-based grey relational analysis approach

ABSTRACT This study is focused on the performance evaluation of three cutting lubricants, which are neem, palm kernel and mineral oil in the turning of AISI 1045 with MQL. Optimization of machining parameters with multi-response signals using Taguchi-based grey relational analysis was done. For experimental design, tests were developed using Taguchi's L9 orthogonal array and the signal-to-noise ratio was obtained using the smaller-the-better approach to achieve the optimal combination. Performance indicators including surface roughness and cutting temperature were measured throughout the machining process. Neem oil performed best among the oils for surface roughness while mineral oil outperformed them all for cutting temperature, according to the experimental results. The optimum spindle speed, feed rate and depth of cut for neem, palm kernel and mineral oils are 870 rev/min, 0.25 mm/rev and 1.25 mm; 870 rev/min, 0.10 mm/rev and 0.75 mm; and 415 rev/min, 0.10 mm/rev and 1.00 mm, respectively.

Multi-objective optimization of tool nose radius and machining conditions employing Taguchi-based grey relational analysis in milling of AISI 304

Multi-objective optimization of tool nose radius and machining conditions employing Taguchi-based grey relational analysis in milling of AISI 304

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.

Multi-objective parametric optimization of AWJM process using Taguchi-based GRA and DEAR methodology

Abrasive waterjet machining (AWJM) is a well-known non-traditional cutting process that is widely used to shape materials such as metals, ceramics, alloys, and composites. AWJM was utilized to manufacture titanium alloy in this study, with influencing parameters such as standoff distance (SOD), abrasive flow rate (AFR), and traverse speed (TS) being varied. Material removal rate (MRR) and surface roughness (SR) have been used to quantify the impact of these variables on machining quality. The effects of process factors on output responses were investigated using a Taguchi study approach with an L9 factorial design. SOD of 1 mm, AFR of 300 g/min, and TS of 519 m/s yield the best combination for greater MRR. A combination of SOD of 3 mm, AFR of 400 g/min, and TS of 519 m/s yields the best SR results. Simultaneous optimization of both outputs was also carried out using Taguchi-based Grey relational analysis (GRA) and data envelopment analysis-based ranking (DEAR) methodologies. PCA is utilized for considering the weights of responses and the correlation among them. Both methods obtained similar results, but there is a difference in the middle rankings in the nine experimental trials. Validation trials were run on the acquired combination of parameters, and the error was found to be within acceptable bounds.

Experimental and numerical investigation of dry turning AISI 1030 carbon steel using CNC lathe machining

Nowadays, modern metal industries have difficulty obtaining the required surface quality during machining. This is because various process parameters affect the quality of the surface. The aim of study to examine and enhance the impact of cutting-speed, cutting-depth, and feed rate during dry turning of AISI 1030 carbon steel experimentally and numerically (by DEFORM 3D) to get a better output response like minimal surface roughness, tool temperature, and maximum MRR. Taguchi-based grey relational analysis optimization technique was used for the experimental design and to determine the optimum solution of the multi-response. ANOVA was utilized to assess the contribution of the cutting parameters. Based on the results, cutting speed was the most important parameter that influenced the multiple responses of the grey-relational analysis, with a significance of 56.85%. The optimum parametric combination of multi-responses was 90 m min−1, 0.25 mm, and 0.15 mm/rev. With a minimum average relative error, the Taguchi prediction and finite element simulation were in excellent agreement with the experimental result.

Multi-Objective Optimization of Fiber Laser Cutting of Stainless-Steel Plates Using Taguchi-Based Grey Relational Analysis

Stainless-steel has become a widely preferred material type in the marine, aerospace, sanitary, industrial equipment, and construction industries due to its superior corrosion resistance, high mechanic properties, high strength, formability, and thermal and electrical conductivity. In this study, a multi-objective optimization method based on grey relational analysis was employed to optimize the fiber laser-cutting parameters of cutting speed, focal position, frequency, and duty cycle. Surface roughness and kerf width, which are the two most important parameters that determine laser-cutting quality, were simultaneously optimized. In order to assign the optimum level of each parameter individually, the Taguchi technique was applied. The cutting surface morphology was examined according to the grey relational grade with a 3D optical profilometer, and maps of the cutting surfaces were created. According to the results achieved using Analysis of Variance (ANOVA), it was seen that the parameters that affected surface roughness and kerf width the most were duty cycle, with a contribution rate of 49.01%, and frequency, with a contribution rate of 31.2%. Frequency was the most important parameter in terms of multiple responses, with a contribution rate of 18.55%. Duty cycle and focal position were the second and third most effective parameters, respectively. It was determined that the optimum parameter values for minimum surface roughness and minimum kerf width that could be obtained with the fiber laser cutting of 20 mm thick AISI 304L (DIN EN 1.4301) material were 310 mm/min cutting speed, −11 mm focal position, 105 Hz frequency, and 60% duty cycle.

Optimizacija PMEDM sa više odgovora na Inkonel 718 koristeći hibridni T-GRA, TOPSIS i ANN model

Inconel 718 is one of the Nickel-based superalloys considered one of the most difficult-to-machine materials owing to its property to retain hardness at higher temperatures. This study examined the performance of Edcutor-based PMEDM machining on Inconel 718. Taguchi L9 OA has been used with current, Pulse-OFF time & Pulse-ON time as process parameters with a delivery side pressure of 6 bar for the Alumina mixed dielectric. Material removal rate (MRR), Tool wear rate (TWR), & surface roughness (SR) have been taken as output responses. The results have been investigated by MADM techniques, namely Taguchi-based Grey Relational Analysis (T-GRA) & TOPSIS analysis. Furthermore, the ranks obtained from T-GRA & TOPSIS have been validated by developing a single layered ANN model. Ranks predicted by the ANN model are the same for T-GRA and TOPSIS and the R-values are 0.924 & 0.871, respectively. ANOVA has also been used to analyze parameter effects on output responses.