Grey Relational Analysis Research Articles

A flexible group decision-making method for informationization teaching ability evaluation of university teachers integrating ExpTODIM and GRA under interval neutrosophic environment

Today, information technology has penetrated into various fields of universities, and the development of information technology in teaching, scientific research, management, and services has become a catalyst for promoting changes in universities. In terms of teaching informatization, the Internet provides a powerful tool for knowledge dissemination and a huge platform for learning and communication between university teachers and students. Knowledge sharing has become easier, and the era of mutual interaction between teachers and students has arrived. University teachers need to quickly face this challenge, adapt to the new teaching and learning environment, improve their own literacy, enhance their information-based teaching ability, change their teaching behavior, and thereby improve the quality of university education and meet the needs of society for talent cultivation. The informationization teaching ability evaluation of university teachers is a classical MAGDM problems. Recently, the Exponential TODIM(ExpTODIM) and (grey relational analysis) GRA method has been used to cope with MAGDM issues. The interval neutrosophic sets (INSs) are used as a tool for characterizing uncertain information during the informationization teaching ability evaluation of university teachers. In this manuscript, the interval neutrosophic number Exponential TODIM-GRA (INN-ExpTODIM-GRA) method is built to solve the MAGDM under INSs. In the end, a numerical case study for informationization teaching ability evaluation of university teachers is given to validate the proposed method.

Lagrange-enhanced GRA framework for probabilistic simplified neutrosophic multiple-attributes decision-making and applications to painting teaching quality evaluation

Universities are important talent training bases in China and the main driving force for achieving the strategic layout of “revitalizing the country through science and education” and “strengthening the country through talent". Oil painting is a global art with rich humanistic and artistic value. Most art colleges in China have set up oil painting courses. Analyze the current situation and value of oil painting course teaching in local art (teacher training) majors, and leverage the educational role of oil painting courses by enriching course offerings, emphasizing the integration of humanistic innovation, improving teacher literacy, and striving to further improve the quality and efficiency of oil painting course teaching. The quality evaluation of oil painting teaching in universities is viewed as multiple-attribute decision-making (MADM). The grey relational analysis (GRA) is a useful tool to cope with the MADM issue. The probabilistic simplified Neutrosophic set (PSNSs) is easy to characterize uncertain information during the quality evaluation of oil painting teaching in universities. In this paper, in order to obtain the weight information, an optimization model implemented to obtain a simple and exact formula which can be employed to derive the attribute weights values based on the Lagrange function and the probabilistic simplified neutrosophic number grey relational analysis (PSNN-GRA) technique is implemented for MADM to rank the alternatives. Finally, a numerical example for quality evaluation of oil painting teaching in universities is used to verify the practicability of the PSNN-GRA technique and compares it with other techniques.

Multi-objective analysis of solid-state joints: Effect of tool pin shoulder clearance on thick dissimilar aluminum plates

ABSTRACT Friction stir welding is a well-established technique developed extensively over the past two decades for various applications. This study investigates the effect of repositioning the tool pin shoulder (0.18 mm) above the surface of a 12 mm thick dissimilar aluminum alloy (AA6063-AA5083) on tensile strength, welding temperature, and micro-hardness. Trials were conducted according to the L9 Taguchi method and further simulated using ABAQUS software. The input parameters spindle speed, tool pin length, and traversing speed were each tested at three levels. The impact of these parameters on weld quality was analyzed using ANOVA. Optimization of the process to achieve the desired temperature and tensile strength was carried out using grey relational analysis for both experimental and simulated results. Mathematical models were generated using the multi-variable regression method and the response surface method (RSM). Predicted data from the RSM model were compared to experimental outcomes, revealing a maximum deviation of 8.69% for temperature and 5.17% for tensile strength. Furthermore, the study demonstrated that ABAQUS accurately simulates the friction stir welding process with an accuracy of up to 93.71%.

Application of machine learning and grey Taguchi technique for the development and optimization of a natural fiber hybrid reinforced polymer composite for aircraft body manufacture

Abstract The rapid expansion of the air transport industry raises significant sustainability concerns due to its substantial carbon emissions and contribution to global climate change. These emissions are closely linked to fuel consumption, which in turn is influenced by the weight of materials used in aircraft systems. This study extensively applied machine learning tools for the optimization of natural fiber-reinforced composite material production parameters for aircraft body application. The Taguchi optimization technique was used to study the effect of sisal fibers, glass fibers, fiber length, and NaOH treatment concentration on the performance of the materials. Multi-objective optimization methods like the grey relational analysis and genetic algorithm (using the MATLAB programming interface) were employed to obtain the best combination of the studied factors for low fuel consumption (low carbon emission) and high-reliability structural applications of aircraft. The models developed from regressional analysis had high accuracy of prediction, with R-Square values all >80%. Optimization of the grey relational analysis of the developed composite using the genetic algorithm showed the best process parameter to achieve low weight material for aircraft application to be 40% sisal, 5% glass fiber at 35 mm fiber length, and 5% NaOH concentration with grey relational analysis at the highest possible level, which is unity.

Damage Identification Method of Tied-Arch Bridges Based on the Equivalent Thrust-Influenced Line

Early tied-arch bridges cannot satisfy the current traffic load demand due to their load grades and maintenance levels. Also, these tied-arch bridges have accumulated structural damage with increasingly prominent safety risks. In order to accurately evaluate the characteristics of tied-arch bridges’ structural-influenced lines and identify damage of their arch ribs and suspenders, two analytical solutions are derived and established in this paper for the thrust-influenced lines of parabola and catenary two-hinged arches with a tie beam. A new method and an index for identifying damage of arch ribs and suspenders of tied-arch bridges are proposed. The results of numerical simulation in this paper verified that the proposed analytical solution has good analytical accuracy in practice on those two-hinged nonflat arches. With the use of equivalent thrust-influenced line difference curvature, the effectiveness of damage identification and the verification method were verified on the tie beam, suspender, and arch rib of plane tie arch structure as well as the suspender and arch rib of tied-arch bridges in this research. Furthermore, combining with grey relation analysis, the noise immunity of the proposed index method can be verified. Also, thrust-influenced line recognition based on VMD (variational mode decomposition) is introduced, and a practical process of bridge health assessment based on the quasistatic-influenced line loading of three-axle heavy vehicles is established. Theoretical analysis and numerical verification show that the calculated error of the analytic solution of two-hinged arches with a rise-span ratio greater than 1/8 is less than 9.57%, and the error decreases with the increase of the rise-span ratio. Therefore, it can be applied to the calculation and analysis of tied-arch bridges with a rise-span ratio range between 1/4 and 1/5. With the equivalent thrust-influenced line index proposed in this paper, the damage of suspenders and arch ribs of tied-arch bridges can be accurately located. It has been found that the proposed method is more effective to identify the damage of suspenders than the damage of arch ribs does, showing good noise immunity. In summary, this research has provided theoretical support for arch bridge design and evaluation. Combining with existing bridge-monitoring methods, the new bridge damage identification method proposed in this paper has the prospect of realizing the normal health status assessment of existing tied-arch bridges.

Study on Machining Quality in Abrasive Water Jet Machining of Jute-Polymer Composite and Optimization of Process Parameters through Grey Relational Analysis

Abrasive Water Jet Machining (AWJM) is a popular machining method used to machine polymer matrix composites that are sensitive to temperature. This method is non-thermal, and each input parameter has a significant effect on output parameters, such as material removal rate, kerf width, surface roughness, and the potential for delamination. To ensure high-quality machining, it is crucial to set these input parameters at their optimal level. This paper proposes a simple approach to predict the optimum process parameters of water jet machining operations on jute fiber-reinforced polymer composite (JFRPC). The process parameters considered are standoff distance (SOD), traverse speed (TS), and abrasive material flow rate (MFR). Conversely, surface roughness (Ra) and delamination (Da) are the output parameters. Process parameters are set using Taguchi’s L27 array, with consideration given to three levels of each input parameter. The best value for process parameters is found using grey relational analysis (GRA), and an ANOVA on GRA illustrates the impact of each input variable. After a confirmation test, it was found that the suggested parameters guarantee the best possible results.

State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine

Lithium-ion battery State of Health (SOH) estimation is an essential issue in battery management systems. In order to better estimate battery SOH, Extreme Learning Machine (ELM) is used to establish a model to estimate lithium-ion battery SOH. The Swarm Optimization algorithm (PSO) is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy. Firstly, collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve. Use Grey Relation Analysis (GRA) method to analyze the correlation between battery capacity and five characteristic quantities. Then, an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics, and a PSO is introduced to optimize the parameters of the capacity estimation model. The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions. The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation, and the average absolute percentage error is less than 1%.

Experimental investigation of 3D taper profile machining of SS304 using WEDM

ABSTRACT Wire electrical discharge machining (WEDM) is an outstanding variety of EDM processes in which the tool is a wire electrode. The medical, electronics, tool, and die industries use stainless steel 304 (SS304). However, there are traditionally many complications that manufacturers face, while machining SS304. Also, the creation of 3D shapes using WEDM is challenging. Due to difficulties in normal machining and producing 3D profiles using conventional machining of SS304, this research aims to utilize the application of WEDM for creating complex shapes or products with top as square and bottom as rectangular shape to get a rectangular frustum. A ϕ 0.25 mm brass wire was used, and experiments were conducted as per L9 array. Further, the determination of the best process parameter (Ton, Toff, Voltage, current) combination for achieving the highest MRR, hardness, and minimal surface roughness (Ra) and kerf with analysis of machined surfaces using an SEM microstructure image was tried. The results indicated that MRR and Ra values varied from 7.05 to 47.29 mm3/min and 1.251 to 2.019 µm, respectively. Furthermore, the kerf ranged from 0.294 to 0.475 mm and hardness started at 164 to 233 HV. To optimize WEDM results Grey relational analysis (GRA) was utilized.

Bionic topology optimization design and multi-objective optimization of guide arm

Lightweight design is universally recognized as a critical criterion for many engineering problems. In addition to the well-developed topology optimization (TO) method, structural bionics is also considered an effective approach to developing innovative structure designs with lightweight. In the process of natural evolution, bamboo has developed a unique hollow structure with ingenious mechanical properties. Inspired by these characteristics, this paper selected bamboo as a bionic prototype to carry out bionic structure optimization of guide arm. First, the initial guide arm was modeled and simulated for its mechanical behavior. Secondly, similarity analysis between bamboo and guide arm was performed from three aspects, and the parametric bionic guide arm model was established step by step. Then, the key parameters affecting the performance of the bionic guide arm were verified by the parameter sensitivity analysis method. The multi-objective optimization was carried out with the minimum of the total mass and the maximum deformation of guide arm as the optimization objectives. The optimal solution of Pareto solution set was determined by multi-objective decision methods of the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Gray Relational Analysis (GRA). Finally, finite element (FE) method was used to make a comparison between the initial model and the optimal bionic model in terms of mechanical performance. According to the results, under the premise of the mass of the optimized bionic model decreased by 17.44%, the maximum deformation was decreased by 9.24%, the equivalent stress was decreased by 17.33%, and the first-order frequency was increased by 22.92%. Comparison results showed that the proposed bionic model provided the best lightweight solution for guide arm. This study reveals that structural bionics provides a new solution for the lightweight design of guide arm and similar beam structural components.

Statistical modelling of depth milling in Ti-6AL4V using abrasive water jet machining

Multi-objective grey relational analysis optimization technique and multiple regression analysis were employed to determine the optimum values for depth of cut, surface roughness ( Ra), and kerf at entry and exit ([Formula: see text] and [Formula: see text]), for abrasive waterjet machining of Ti6AL4V materials. This method highlights a new process to extend the grey relational analysis technique for determining the optimum conditions for obtaining the best quality characteristics. The input parameters of the study were water pressure ( Wp), transverse speed ( Ts), abrasive mass flow rate ( Amf), abrasive orifice size ( Aos), nozzle/orifice diameter ratio ( N/Odia). The experiments were conducted as per the Taguchi-based L27 orthogonal array. The grey relational analysis technique found that Ts was the most significant parameter on the combined outputs. The regression models developed had an R2 of 81.58%, 79.79%%, 77.20%, and 74.39% for depth of cut, Ra, [Formula: see text] and [Formula: see text], respectively. Additionally, the analysis of variance showed that Wp and Aos had a significant influence on the output parameters. The predicted values were found to be reasonably close with the experimental values, and the maximum average deviation was 8.15% for [Formula: see text].

Improving Short-Term Traffic Flow Prediction using Grey Relational Analysis for Data Filtering and Stacked LSTM Modeling

Traffic flow prediction is one of the critical measures to alleviate traffic congestion. Currently, traffic flow prediction research has made some achievements, but there are still some deficiencies. In order to solve the problems of low prediction accuracy, poor real-time performance, and high data dimensions. This paper proposes a new traffic flow prediction method that employs Grey Relation Analysis (GRA) to detect the correlation between detection points, remove insignificant or uncorrelated traffic flow data points, and hence reduce the data dimensionality of the prediction model. Multiple Long Short-Term Memory (LSTM) models are then stacked to establish the traffic flow prediction model, considering that traffic flow is affected by multi-dimensional spatiotemporal factors, incorporating vehicle speed, occupancy, and traffic volume as inputs. We conducted experiments on real datasets, and the results showed that our GRA-SLSTM model improved prediction accuracy by 3.6% compared to other models, while reducing model prediction time by 27.33%. The proposed model’s generalization ability is validated by predicting other detection points, which provides significant references for traffic flow prediction research and practical applications.

Discovery of antitussive material basis and mechanisms in Citri Sarcodactylis Fructus by coupling UHPLC-Q/Orbitrap HRMS combined spectrum-effect relationship and metabolomics analyses

Citri Sarcodactylis Fructus (CSF) is widely used as food raw material and traditional Chinese medicine. Fingerprints of different fractions of CSF were established for spectrum-effect relationship analysis, and the main compounds were identified by UHPLC Quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q/Orbitrap HRMS). The antitussive effect was evaluated using a classical mouse model of cough induced by ammonia water. One-way ANOVA was used to determine differences in efficacy. The potential active compounds were screened by spectrum-effect relationship with grey relational degree analysis (GRA), Pearson bivariate correlation analysis (Pearson's), and partial least squares analysis (PLS) analyses. Differential metabolites associated with cough in serum were screened and identified using orthogonal partial least squares-discriminant analysis, HMDB database, and UHPLC-Q/Orbitrap HRMS. Metabolic pathway analysis was performed using MetaboAnalyst 5.0. Results indicate that 70 % ethanol elution fraction (70 % EF) is the major active fraction, and 8 components were identified to possess antitussive effects. Metabolomic analysis showed that 19 metabolites are potential biomarkers related to cough, and 70 % EF can remarkable restore 13 of them to normal levels (P < 0.05). These biomarkers are mainly involved in glycerophospholipid metabolism and sphingolipid metabolism. This study aims to reveal the main pharmacodynamic active sites and potential active ingredients of CSF's antitussive effect. In addition, metabolomics was used to preliminarily elucidate the in-vivo regulatory mechanism of the antitussive effect of the 70 % EF of CSF.

Elucidating the anti-inflammatory activity of platycodins in lung inflammation through pulmonary distribution dynamics and grey relational analysis of cytokines

Ethnopharmacological relevancePlatycodonis Radix (PR) is a traditional herbal remedy used to prevent and treat lung inflammation, and platycodins are speculated to be the major active constituents. However, concrete experimental verification for this assertion remains absent thus far. Aim of the studyThis study aims to compare the pulmonary distribution dynamics of five platycodins and analyze their effects on cytokines. Through the grey relational analysis (GRA) between pulmonary active components and cytokines, the study ascertains platycodins as the potential effective component against lung inflammation. Materials and methodsA rat lung inflammation model was created using lipopolysaccharides (LPS). Pulmonary distribution dynamics were analyzed via LC-MS/MS. Cytokine changes and distribution patterns in lung tissues were studied by multi-factor reagent kit. GRA was applied to determine correlations between pulmonary components and cytokines. Finally, the anti-inflammatory properties of platycodins were further studied using LPS-induced BEAS-2B cells in vitro. ResultsThe results showed that five platycodins (Platycodin D, Platycodin D3, Deapio Platycodin D, 3-O-β-D-Glucopyranosyl Platycodigenin, and Platycodigenin) featured fast absorption rate, short time to peak, and slow metabolism rate. The pulmonary distribution dynamics were significantly affected within 2 h after LPS modeling. At the same time, PR altered the relationships among different cytokines induced by LPS stimulation, particularly inflammatory cytokines IL-6 and IFN-γ. The GRA results indicated good correlation between the pulmonary distribution dynamics of the five platycodins components and the changing patterns of cytokine levels, with Platycodin D3 contributing the most. Additionally, Platycodin D3 exhibited a protective role against LPS-induced inflammation by reducing the production of pro-inflammatory mediators such as IL-1β, IL-8, and ROS, as well as increasing the expression of the anti-inflammatory mediator IL-10. ConclusionsPlatycodins are the main anti-inflammatory agents in PR and there is a good correlation with cytokines. This contributes to the anti-pneumonia effect of PR.

Preparation and Performance of Bitumen Modified by Melt-Blown Fabric of Waste Mask Based on Grey Relational and Radar Chart Analysis.

To effectively utilize waste mask materials in road engineering and minimize resource waste, the melt-blown fabric (MBF) of waste masks was utilized to modify the virgin bitumen. The preparation process of MBF-modified bitumen was investigated, and the physical and rheological properties of bitumen were measured. Subsequently, the blending mechanism during preparation and the dispersion morphology of the modifier were explored. Finally, the pavement performance of the mixture was investigated, and a radar chart analysis was performed to quantitatively assess the effects of MBF modification. Results suggested that the recommended preparation process of shear time, shear rate, and shear temperature was 170 °C, 4000 r/min, and 15 min, respectively. MBF enhanced the high-temperature stability of the binder and weakened the temperature susceptibility. The modification was primarily a physical process. No network structure and agglomeration formed in the bitumen after modification. The addition of MBF significantly improved the resistance of the asphalt mixture to a high-temperature deformation and water damage but harmed its low-temperature crack resistance. The comprehensive assessment results of 0% (f1), 1% (f2), 3% (f3), and 5% (f4) MBF to improve the properties of the mixture were in the following order: f3>f4>f2>f1, where the impact of 3% MBF was the most significant, followed by 5% and 1% MBF.

Forecasting the containerized freight index with AIS data: A novel information combination method based on gray incidence analysis

AbstractThis paper uses the container shipping capacities of 11 major trade lanes, obtained from automatic identification system (AIS), to construct a common factor based on gray incidence analysis (GIA) in the aim of improving the predictability of containerized freight index. Our results show that the common factor generated by GIA consistently exhibits better out‐of‐sample prediction performances than principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO), meaning that GIA can extract more useful information for forecasting freight index. Our main findings are first, GIA can evaluate the similarity between the predictors and the predicted value. Unlike popular information combination method PCA, which cannot extract the relevant information from the predictors, GIA can extract the most relevant information of the predictors to the predicted value. Second, different from LASSO, which drops some information, GIA maintains the most information, because the container shipping capacities of different lanes all impact the freight index. Third, AIS data do provide information increments for freight rate forecasting. This research explores a new field application of gray relational analysis in information combination and presents one application of GIA in big data processing. This research shows the usefulness of AIS information in predicting freight index. Additionally, this research enlightens the prediction of freight rate based on big data from AIS.

Application of Taguchi Technique and Grey Relational Analysis for Multi-Target Optimization of Two-stage Helical Gearboxes

Application of Taguchi Technique and Grey Relational Analysis for Multi-Target Optimization of Two-stage Helical Gearboxes

Assessing quality in extrusion based additive manufacturing technologies

Quality is a magic word that has fascinated the people since the early days of civilization and today, quality is the only mantra to survive. Quality is the only relevant attribute associated with product and processes that makes any technology in-demand. The quality of the parts produced by Additive Manufacturing (AM) Technologies depends on many factors, like print speed, nozzle temperature etc. The aim of this work is the identification of influence factors that affect the performance measures of extrusion based Additive Manufacturing (AM) technologies, namely, Fused Deposition Modelling (FDM). These processes can be characterized by a set of influence factors, like print speed, infill density, temperature; layer thickness and build orientation are responsible for quality of the product. In this work, Grey relational Analysis (GRA) is employed. GRA is a method which enables determination of the relational degree of every factor in the system. The method can be used for systems that are not completely described and with relatively few available data. Reliable technique of optimization GRA is clubbed with another established technique; called Interrelation ship Diagram (ID), to identify and optimize the influence factors, that yields highest performance characteristics. Manufacturing Combinations are evaluated against performance measures. In the earlier research, the authors have applied same tool, GRA clubbed with 7QC tools, to evaluate extrinsic and intrinsic qualities of the parts produced by FDM, In this research, the entire focus is paid to the intrinsic properties of the parts produced by FDM. This research will have a greater impact on the quality of the parts produced by the FDM.

Sustainable Optimization of Drilling Parameters for AA2017/AlN Composite Materials: A Grey Relational Analysis Approach

Modern engineering materials have seen remarkable development. Because conventional materials can no longer meet the needs of modern applications, numerous composites are being employed as viable alternatives. Drilling is the most important production step for most uses, and the resulting holes are high-stress zones that must be handled carefully. Scientists and engineers have long been interested in the challenge of finding the best combination of drilling settings for cutting-edge composite materials. As part of this research, AA2017/AlN composites with matrices made of AA2017 aluminium alloy and reinforcements made of 5, 10, and 15 wt % AlN were produced using stir casting. By experimenting with different input parameters, we were able to utilise the L9 OA to determine the best machining arrangement for drilling materials. The investigation focused on critical drilling characteristics, including burr height (H), thrust force (T), and surface roughness (SR), with a keen emphasis on sustainability.By considering the burr height (H), thrust force (T) and surface roughness (SR) and this work used grey relational analysis (GRA) to establish the optimum cutting parameters for drilling holes in the cutting-edge composite AA2017/AlN. The significance of different machining settings and their effect on the typical characteristics of the drilling were analysed using GRA. However, a confirmation experiment was conducted to ensure the highest quality outcomes. Test results and GRA indicate that the best grey relational grade is achieved with the following parameters: spindle speed (S) of 3500 rpm, feed rate (F) of 60 m/s, drill material (D) of Tungsten Carbide, and reinforcement (R) of 10%.. Analysis of variance (ANOVA) revealed statistically significant impacts on GRG from the drill material (29.08%), the feed rate (24.24%), and the spindle speed (19.52%). The error term was a function of the reinforcement percent and its interactions with all other parameters; however, the influence of the interaction between feed rate and drill material on GRG was small. The GRG is 0.856, which is higher than the prediction of 0.824. The calculated and measured values agree quite well and 3.7% is a negligible margin of error. The mathematical models for all reactions depending on the drill bits employed was also constructed.

Multi-objective optimization of one-part alkali-activated mortar mixes using Taguchi-Grey relational analysis

In the context of the contemporary emphasis on sustainability within the realm of construction, there is a notable surge in attention towards one-part alkali-activated (OP-AA) materials. This is primarily attributed to their enhanced performance and reduced carbon emissions as compared to conventional OPC-based concrete. In the present investigation, Taguchi and Taguchi- Grey relational analysis (GRA) methodologies were employed to execute the experimental design, involving three input parameters, each considered at three levels, to generate an L9 orthogonal array. An attempt was made to assess the impact of different parameters, such as ground granulated blast furnace slag (GGBFS) to fly ash (FA) ratio - (S/F), water-to-binder ratio - (W/B), and percentage of Na2O - (N), on the slump flow, setting time, and compressive strength characteristics and hence to optimize the proportions of the OP-AA mortar blends. The results revealed that optimum parameter levels for multi-objective optimization corresponded to S/F = 1, W/B = 0.45, and N = 5%. For these parameter levels specified, the corresponding values of slump flow, initial setting time, final setting time, and 28 days compressive strength were 208 mm, 285.4 min, 990.4 min, and 36.52 MPa, respectively. In addition, to gain insights into their mineral composition, morphology, and chemical bond characteristics, microstructural characterization such as X-ray diffraction (XRD), Field emission scanning electron microscope (FESEM), and Fourier transform infrared spectroscopy (FTIR) were also conducted on selected OP-AA mortar mixes. The microstructural examination unveiled the predominant formation of hydration products, such as C/N -A-S-H gels, in OP-AA mortar blends, resembling those found in conventional alkali-activated materials (AAMs). During the validation phase, an assessment was conducted by comparing the actual experimental results with the predicted values obtained through regression equations. The outcome of this comparison revealed that the proposed optimum mix parameter levels demonstrated the effectiveness of both the Taguchi and Taguchi-GRA approaches.

Study on Machinability Behaviour and Simultaneous Optimisation of Multiple Responses Using Taguchi‐Based Grey Relational Analysis in End Milling of Aluminum Hybrid Composites

The present work reveals the machinability characteristics of Al7068/Si3N4/BN hybrid composite materials. The composite materials were prepared through the liquid metallurgy technique with various weight fractions (0, 5, 10) of silicon nitride and hexagonal boron nitride particles. The experimental studies were performed on a CNC milling machine by varying cutting speed (600, 1200 and 1800 rpm), feed rate (150, 300 and 450 mm/min), and depth of cut (0.8, 1.2 and 1.6 mm). Taguchi’s plan of experiments (L27 orthogonal array design) and analysis of variance (ANOVA) were employed to investigate the effect of process parameters on surface roughness (Ra), cutting temperature, and material removal rate (MRR). Taguchi‐based grey relational analysis (GRA) was performed to identify the best process parameters combination for optimising the multiple responses simultaneously. ANOVA results revealed that the weight fraction of Si3N4 is the most contributing parameter for multiple response characteristics.