Grey Relational Analysis Approach Research Articles

Material Selection for Unmanned Aerial Vehicles (UAVs) Wings Using Ashby Indices Integrated with Grey Relation Analysis Approach Based on Weighted Entropy for Ranking

The designer must find the optimum match between the object's technical and economic needs and the performance and production requirements of the various material options when choosing material for an engineering application. This study proposes an integrated (hybrid) strategy for selecting the optimal material for an engineering design depending on design requirements. The primary objective is to determine the best candidate material for the drone wings based on Ashby's performance indices and then rank the result using a grey relational technique with the entropy weight method. Aluminum alloys, titanium alloys, composites, and wood have been suggested as suitable materials for manufacturing drone wings. The requirements for designing a drone's wings are to make them as light as possible while meeting the stiffness, strength, and fracture toughness criteria. The conclusion indicates that Carbon Fiber-Reinforced Polymer (CFRP) is the best material for producing drone wings. In contrast, wood and aluminum alloys were the cheapest materials when the design had to be inexpensive.

Optimization of multi‐response biodiesel fueled diesel engine parameters using Taguchi grey relational analysis and analytical hierarchy process

AbstractCompression ignition engines are currently optimized to run on petrodiesel fuel, but due to its increasing demand and depletion of fossil fuel reserves, alternative fuels must be utilized. In this article, an attempt was made to optimize the operating parameters of an unmodified compression ignition engine fueled with linseed methyl ester biodiesel. The input parameters were compression ratio, load, and fuel blend, while output responses included brake‐specific fuel consumption (BSFC), brake thermal efficiency (BTE), and exhaust gas temperature (EGT), peak cylinder pressure (PCP), the rate of pressure rise (RPR), and net heat release rate (NHR), hydrocarbon (HC), carbon monoxide (CO), NOx, and Smoke. The aim was to determine the optimal combination of input parameters for minimal emissions and better performance and combustion parameters, with a minimum number of experiments. The study used Taguchi's L9 orthogonal array with a grey relational analysis approach (GRA) to obtain the optimum combination of input parameters. The study found that fuel blend B10, CR 18, and load 100% were the optimal input parameter combinations using the TGRA method. The S/N ratio analysis of grey relational grade (GRG) showed the optimal input factor levels to be fuel B10, CR 16, and load 100%, which was confirmed by the AHP method. The analysis of variance (ANOVA) showed that the load was the most influential factor, followed by CR and fuel. The confirmatory results showed an improvement of 56.1%, indicating that biodiesel can be efficiently used in an unmodified compression ignition engine by varying these influencing factors.

BORSA İSTANBUL’DA ENERJİ ŞİRKETLERİNİN NAKİT AKIM ORANLARINA DAYALI FİNANSAL PERFORMANS ANALİZİ

The purpose of this paper is to evaluate the financial performance of the sixteen energy companies listed in Borsa Istanbul in terms of cash sufficiency and cash efficiency and benchmark them in 2020 and 2021; during the COVID-19 period by using multi-criteria decision making methods. This paper utilizes the Method Based on the Removal Effects of Criteria (MEREC) technique to determine the objective weights of cash ratios. Energy companies’ ranking is estimated using the Grey Relational Analysis (GRA) approach. In the case of the financial performance evaluation problem, 7 performance attributes and 16 alternative layouts were considered. The results show that most of the energy companies have weak financial performance in 2020 and then improve their financial performance in 2021.

A novel model based on the fuzzy Grey Relational Analysis (F-GRA) approach for selecting the appropriate high-speed train set

A novel model based on the fuzzy Grey Relational Analysis (F-GRA) approach for selecting the appropriate high-speed train set

Consumer decisions toward halal purchase before and during COVID-19 pandemic: a grey relational analysis approach

PurposeThe main objective of this research is to investigate the factors that influence consumer purchase decisions for halal products before and during the COVID-19 pandemic, based on the Engel-Kollat-Blackwell (EKB) theory.Design/methodology/approachThe research was conducted as a survey. The influencing factors were determined based on the grey relational analysis (GRA) approach.FindingsThe findings indicate before the COVID-19 pandemic, consumers mainly purchased halal products based on four key factors: purchasing experience, certification label, Internet searches and past consumption experience. However, during the pandemic, the ranking and factors have changed to six indicators, which are past consumption experience, purchasing experience, certification labels, standardized specifications, Internet searches and halal certification labels.Research limitations/implicationsThe study was limited by the sample size and geographical area. Nevertheless, the findings could be further explored by expanding related theories toward understand human decisions based on spiritual beliefs.Practical implicationsThe findings of this study have important implications for research, practice and society. Understanding the factors influencing halal purchase decisions before and during the pandemic can help businesses, policymakers and halal certification bodies to better cater to consumers' needs and preferences and ensure the continued growth and development of the halal industry.Originality/valueThis study evaluates halal purchasing decisions between periods of certainty and uncertainty by using the GRA. Changes in halal consumption and purchase decisions in response to COVID-19 pandemic have become an emerging topic of discovery. The study addresses the gap in the literature regarding changes in consumer decision pattern.

Investigation and optimization of wear properties of flax fiber reinforced Delrin polymer composite

Nowadays, the significance of natural fiber hybrid composites shows increasing trend in the automobile industry. New high-performance polymer materials can be produced from natural fibers which are inexpensive, renewable, eco-friendly, and biodegradable. Because of renewability, natural fibers can be an alternative source to synthetic fibers as a reinforcing agent. This research work looks at flax fiber natural composite material reinforced with matrix material. For a wide range of applications, delrin offers viable replacements for metal composites. In the present work, composites were prepared by injection molding and investigated. The composite constituents were chopped natural flax fiber as reinforcement (5%, 10%, 15% and 20%) and delrin as the matrix (95%, 90%, 85% and 80%). Pin-on-disc (POD) wear tests were conducted to reveal wear strength of the composite under various POD parameters, optimization is done through the hybrid grey relational and principal component analysis approach. The results showed that composite material (Flax+Delrin) shows improved wear strength compared to the non-reinforced material.

Grey Relational Analysis and Grey Prediction Model (1, 6) Approach for Analyzing the Electrode Distance and Mechanical Properties of Tandem MIG Welding Distortion

The tandem metal inert gas (MIG) process uses two wires that are continuously fed through a special welding torch and disbursed to form a single molten pool. Within the contact tip of the modern approach, the wires are electrically insulated from one another. This study identified the effect of welding electrode spacing on the distortion of AA5052 aluminum plates and different mechanical properties including hardness and thermal cycle using grey relational analysis. Plate distortion was subsequently predicted using the grey prediction model GM (1, 6). This research used a pair of 400 mm × 75 mm × 5 mm of AA5052 plates and electrode distances of 18, 27, and 36 mm. The welding current, voltage, welding speed, and argon flow rate were 130 A, 23 V, 7 mm/s, and 17 L/min, respectively. The temperature was measured using a type-K thermocouple at 10, 20, 30, and 40 mm from the center of the weld bead. The smallest distortion at an electrode distance of 27 mm was 1.4 mm. At an electrode distance of 27 mm, the plate may reach a proper peak temperature where the amount of heat input and dissipation rate are similar to those for electrode distances of 18 mm and 36 mm. The highest relative VHN of 57 was found in the BM, while the lowest, 46, was found in the WM, showing good agreement with their respective grain sizes. Six parameters were designed using grey relational analysis (GRA) and subsequently employed in the grey prediction model GM (1, 6). Process evaluation results show that predictions for welding distortions are consistent with actual results, thus, the GM (1, 6) model can be used as a predictive model for welding distortions of 5052 aluminum plates.

Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach

Natural fibre reinforced polymer composite (NFRPC) materials are gaining popularity in the modern world due to their eco-friendliness, lightweight nature, life-cycle superiority, biodegradability, low cost, and noble mechanical properties. Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification, material selection has become a crucial component of design for engineers. This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC, and GRA approaches. The results are based on integrating two decision methods implemented utilising two distinct decision-making approaches: qualitative and quantitative. This study suggested thermoplastic polyethylene as a particularly ideal matrix in composite cyclist helmets during the selection process for the best thermoplastic matrices material using the 6σ technique, with the decision based on the highest performance, the lightest weight, and the most environmentally friendly criteria. The DMAIC and GRA approach significantly influenced the material selection process by offering different tools for each phase. In the future study, selection technique may have been more exhaustive if more information from other factors had been added.

Tribological behaviour of Al6061/Gr/WC hybrid MMCs using multi-response optimisation

The current investigation is motivated to examine the improvement in mechanical properties and tribological behaviour of Al6061/Gr/WC. Initially, the mechanical and tribological properties were evaluated after adding the reinforcement that is, graphite (Gr) at 3, 6, 9 and 12 wt.% compositions. Therefore, 9 wt.% of Gr generated superior properties than others. Consequently, the hybrid composites were fabricated by reinforcing tungsten carbide (WC) at 1, 2, 3 wt.% and Gr at 9 wt.%. Successful fabrication of the hybrid composites was confirmed through SEM examinations for microstructural characterisation. Further, the concept of design of experiments (DOEs) has been used to obtain the number of experiments. Later on, two nature inspired optimisation algorithm that invasive weed optimisation (IWO) and particle swarm optimisation (PSO) were implemented to determine the optimal wear properties and the results were compared with grey relational analysis (GRA) approach.

Influence of process parameters on machining studies on stir casted MMCs with AA6351 and TiO2 by grey and desirability approaches

In this paper stir casting technique is an efficient method to improve the mechanical characteristics. It was done only on the aluminium magnesium alloy with their ceramic reinforced materials. Therefore, Al 6351 is a base matrix and ceramic strengthening reinforcement particles Titanium oxide (TiO2) makes the metal matrix composites. It was mainly related with aerospace, automotive and ship industries applications. The TiO2 was increasing the weight fraction 4, 8 and 12 % with Al6351. After processing, the processed MMCs specimens was utilized to investigating the machining studies form their appropriate process parameters like speed, feed and depth of cut. The machining performances are related with surface roughness (SR) and Material removal rate (MRR). In this research, two multi-objective techniques grey relational analysis and desirability approaches was involved to identify the optimal machining parameters. From the outcomes, both output responses are improved with their process parameters. There is major reason behind that the increasing of TiO2 enhances the material properties.

Application of Grey Relational Analysis to Predict Dementia Tendency by Cognitive Function, Sleep Disturbances, and Health Conditions of Diabetic Patients.

Background: The number of elderly diabetic patients has been increasing recently, and these patients have a higher morbidity of dementia than those without diabetes. Diabetes is associated with an increased risk for the development of dementia in elderly individuals, which is a serious health problem. Objectives: The primary aim was to examine whether diabetes is a risk factor for dementia among elderly individuals. The secondary aim was to apply grey theory to integrate the results and how they relate to cognitive impairments in elderly diabetic patients and to predict which participants are at high risk of developing dementia. Methods: Two hundred and twenty patients aged 50 years or older who were diagnosed with diabetes mellitus were recruited. Information on demographics, disease characteristics, activities of daily living, Mini Mental State Examination, sleep quality, depressive symptoms, and health-related quality of life was collected via questionnaires. The grey relational analysis approach was applied to evaluate the relationship between the results and health outcomes. Results: A total of 13.6% of participants had cognitive disturbances, of whom 1.4% had severe cognitive dysfunction. However, with regard to sleep disorders, 56.4% had sleep disturbances of varying degrees from light to severe. Further investigation is needed to address this problem. A higher prevalence of sleep disturbances among diabetic patients translates to a higher degree of depressive symptoms and a worse physical and mental health-related quality of life. Furthermore, based on the grey relational analysis, the grey relation coefficient varies from 0.6217~0.7540. Among the subjects, Participant 101 had the highest value, suggesting a need for immediate medical care. In this study, we observed that 20% of the total participants, for whom the grey relation coefficient was 0.6730, needed further and immediate medical care.

Statistical analysis and multi-criteria optimization of fused deposition additive manufacturing process for acrylic butadiene styrene parts

The current market trend and customer requirement are to find excellent quality products within shortest manufacturing time. Therefore, it is essential to analyze the processing conditions that affect the product’s quality before the actual manufacturing of the product. This study aims to experimental analysis and multi-criteria optimization of fused deposition additive manufacturing (FDAM) process for differently shaped acrylic butadiene styrene material parts. Five process variables such as raster angle, infill density, layer thickness, nozzle temperature and print head speed are considered which vary at three levels. A total of 33 experiments have been designed based on central composite design method. The surface roughness, manufacturing time and improved dimensional accuracy are considered as the response parameters in this study. Mitutoyo SJ-410 roughness tester, stopwatch and electronic digital Vernier caliper gauge are used to record the surface roughness, manufacturing time and dimensional variation, respectively. The multi-criteria optimization was performed through the technique for order of preference by similarity to ideal solution (TOPSIS) and Grey relational analysis (GRA) techniques. Concerning TOPSIS, the improvement in responses are obtained as, surface quality of concave (Racc) 8.85%, convex (Racv) 18.43%, manufacturing time (MT) 11.11%, dimensional improvement in length (δl) 1.60%, width (δw) 0.47% and height (δh) 0.46%. Similarly, the improvement found in same responses through GRA approach are Racc 7.85%, Racv 10.34%, MT 7.69%, δl 10.05%, δw 6.50% and δh 28.20%, respectively. The experimental results show that, the optimized parameters provide up to 18.43% improvement in surface quality, 11.11% saving in manufacturing time with a significant reduction in dimensional variation. The findings of this study will help the designers of functional parts to improve the quality of designed product.

EXPERIMENTAL INVESTIGATION AND OPTIMIZATION OF WIRE EDM PARAMETERS FOR SURFACE ROUGHNESS, MATERIAL REMOVAL RATE AND SURFACE TOPOGRAPHY IN MACHINING OF AISI D3

Wire electric discharge Machining is a crucial alternative machining technique. As a consequence of its superior precision, finish, machinability of any hard material, and ability to form complex shapes, demand for it continues to rise. In this research, we apply the Taguchi Method in conjunction with the Grey relational analysis approach to determine the optimal parameters in WEDM for the machining of AISI D3. WEDM machines allow for cutting by adjusting process parameters such as pulse on time, pulse off time, servo voltage, wire feed rate, current, and more to get the desired results in terms of response variables like surface roughness and material removal rate. Grey Relational Analysis is used for optimization of all these parameters with multi-response features. Best WEDM parameters are determined by studying grey relational grade. Specifically, the findings demonstrate its improved surface roughness and material removal rate prediction skills and application to such industrial WEDM cutting, which leads to the effective selection of machining settings for improved Machining quality

Assessment of Sustainable Maintenance Strategy for Manufacturing Industry

This study creates a framework to aid in the sustainability of maintenance strategies. The framework was created using expertise from the industry and academia. Using this knowledge, three multi-criteria tools were chosen for the maintenance strategies evaluation. The tools include grey relational analysis (GRA) techniques, additive ratio assessment (ARAS), and step-wise weight assessment ratio analysis (SWARA). In a production system, they were used to assess four planned maintenance strategies. The strategies are periodic maintenance (S1), meter-based maintenance (S2), predictive maintenance (S3) and prescriptive maintenance (S4). The ARAS approach was used to obtain the strategy rating for the various requirements. This study used the SWARA method to determine the requirements’ importance using an intuitionistic fuzzy triangular number. The ARAS results were combined using the GRA method. This study observed that the criteria utilised to choose a maintenance strategy for equipment depend on the information collected from six specialists in a manufacturing organisation. For instance, it was discovered that S3 was the maintenance approach that best suited the system’s technical needs. At the same time, S2 was found to be less effective. The economic needs analysis showed that S1 is the maintenance strategy that is most appropriate for the system, while S3 is the least appropriate. S1 is the most appropriate maintenance method for the system, given the social requirements, whereas S2 is the least effective. According to the results of the environmental requirements, S2 is the best maintenance plan for the system, while S4 is the worst. According to the GRA approach, the system’s best and least appropriate maintenance strategies are S2 and S4, respectively.

Evaluation of the Forest Quantity, Quality and Management through Gray Relational Analysis Method

Forests cover 30 per cent of the Earth’s land surface, almost four billion hectares. They are necessary to sustain human health, economic growth and the environment. Also, approximately 25 per cent of the global population depends on forests for food and work. The world population is expected to reach 9.6 billion by 2050. Therefore, there needs to be quick action at all levels to make sure that forests are managed in a way that is good for the environment and our way of life in the future. The Sustainable Forest Management Goals are included in the major headings of Sustainable Development Goals and the United Nations Strategic Plan for Forests 2017–2030. The data for the worldwide and six geographical areas were assessed using the Gray Relational Analysis (GRA) approach, which is one of the Multi-Criteria Decision Making methodologies. The major goal of the study is to use the GRA mathematical approach to assess data from 6 geographical areas, totalling 245 regions and nations, and 236 countries and regions worldwide. The second purpose is to contribute to the existing literature by expanding the geographical scope, number of indicators, and the time period covered by the study. The study also aims to provide information on new forest quality and management technologies, as well as the change of geographical areas over 30 years. South America consistently comes out on top in interregional comparisons. On the other hand, Oceania ranks last in the rankings. While the scores for 1990 increased markedly for all regions and worldwide in 2000, the performance values for the years 2000, 2010 and 2020 are fairly close to each other. The findings and methods of this study are aimed to be a useful resource for future researchers and policymakers.

A new fuzzy rule based multi-objective optimization method for cross-scale injection molding of protein electrophoresis microfluidic chips

Injection molding is one of the most promising technologies for the large-scale production and application of polymeric microfluidic chips. The multi-objective optimization of injection molding process for substrate and cover plate on protein electrophoresis microfluidic chip is performed to solve the problem that the forming precision is difficult to coordinate because of the cross-scale structure characteristics for chip in this paper. The innovation for this research is that an optimization approach and a detailed fuzzy rule determination method are proposed in multi-objective optimization for protein electrophoresis microfluidic chip. In more detail, firstly, according to the number and level of process parameters, the orthogonal experimental design is carried out. Then, the experiments are performed. Secondly, the grey relational analysis (GRA) approach is employed to process the response data to gain the grey relational coefficient (GRC). Thirdly, the grey fuzzy decision making method which combines triangular membership function and gaussian membership function is adopted to obtain the grey fuzzy grade (GFG). After that, the optimal scheme of process parameters was predicted by the grey fuzzy grade analysis. Finally, the superiority of Taguchi grey fuzzy decision making method are verified by comparing the results of original scheme, optimal scheme and prediction scheme. As a result, compared with the original design, the residual stress of substrate plate (RSS), residual stress of cover plate (RSC), warpage of substrate plate (WS), warpage of cover plate (WC) and replication fidelity of microchannel for substrate plate (RFM) on the prediction scheme for Taguchi grey fuzzy decision making method were reduced by 32.816%, 29.977%, 88.571%, 74.390% and 46.453%, respectively.

Determination of Optimum Machining Parameters for Face Milling Process of Ti6A14V Metal Matrix Composite.

This paper shows the novel approach of Taguchi-Based Grey Relational Analysis of Ti6Al4V Machining parameter. Ti6Al4V metal matrix composite has been fabricated using the powder metallurgy route. Here, all the components of TI6Al4V machining forces, including longitudinal force (Fx), radial force (Fy), tangential force (Fz), surface roughness and material removal rate (MRR) are measured during the facing operation. The effect of three process parameters, cutting speed, tool feed and cutting depth, is being studied on the matching responses. Orthogonal design of experiment (Taguchi L9) has been adopted to execute the process parameters in each level. To validate the process output parameters, the Grey Relational Analysis (GRA) optimization approach was applied. The percentage contribution of machining parameters to the parameter of response performance was interpreted through variance analysis (ANOVA). Through the GRA process, the emphasis was on the fact that for TI6Al4V metal matrix composite among all machining parameters, tool feed serves as the highest contribution to the output responses accompanied by the cutting depth with the cutting speed in addition. From optimal testing, it is found that for minimization of machining forces, maximization of MRR and minimization of Ra, the best combinations of input parameters are the 2nd stage of cutting speed (175 m/min), the 3rd stage of feed (0.25 mm/edge) as well as the 2nd stage of cutting depth (1.2 mm). It is also found that hardness of Ti6Al4V MMC is 59.4 HRA and composition of that material remain the same after milling operation.

Container vessel selection for maritime shipping companies by using an extended version of the Grey Relation Analysis (GRA) with the help of Type-2 neutrosophic fuzzy sets (T2NFN)

Contrary to expectations, based on occurring changes in customer behaviours related to consuming and shopping triggered by the pandemic, the global container shipping market has continued to grow during COVID 19. Experts estimate that these increases will continue in the future due to changing consumption habits. However, container shipping companies (CSCs) may soon encounter many troubles and challenging situations. They have an extremely fragile structure and may be entirely unprotected when they encounter unexpected situations sourced from external factors. (i.e., due to grounding of a ship, complete blockage of the Suez channel for three weeks can be given as a clear example of that). Hence, selecting an appropriate container vessel type can help construct a healthier container shipping system less influenced by adverse conditions for decision-makers and practitioners. Besides, it can provide a more effective and productive maritime transportation environment for all stakeholders. However, selecting a proper container vessel type is a complicated decision-making problem since many conflicting criteria and complex ambiguities exist. The current paper proposes an extended version of the GRA technique with the help of type-2 neutrosophic fuzzy sets (T2NFN) for capturing and processing uncertainties better than the traditional MCDM frameworks. According to the obtained results, C6, container carrying capacity, is the most influential criterion and the type of post-Suezmax container vessel is the best option for the CSCs, as it provides advantages at a satisfactory level for almost all criteria than others. After the proposed model was applied, a comprehensive sensitivity analysis (SA) was performed to test the validity of the T2NFN GRA approach. The results of SA approve the applicability, effectivity, and robustness of the model.

Multi-response optimisation of machining parameters to minimise the overcut and circularity error during micro-EDM of nickel-titanium shape memory alloy

ABSTRACT Micro-electrical discharge machining (µ-EDM) is one the most favourable process to machine difficult to cut materials in micro-scale with higher accuracy and precision. In this paper, two multi-response optimisation tools called Taguchi’s grey relation analysis (GRA) and response surface methodology (RSM)-based desirability function are used to evaluate optimised micro-machining parameters for minimal overcut and circularity error during µ-EDM of nickel-titanium (NiTi) shape memory alloy (SMA). Taguchi’s GRA revealed that electrode material has the strongest influence on both the responses followed by capacitance and discharge voltage. ANOVA analysis established that electrode material has the highest percentage contribution of 40.15% on both the responses. The optimisation result obtained by Taguchi’s GRA approach is validated by the RSM-desirability function-based technique. The final optimisation result (capacitance 155 pF, electrode material tungsten, discharge voltage 80 V) is a very close agreement with the literature model (capacitance 355 pF, electrode material tungsten, discharge voltage 80 V), with reference to the overcut and circularity error for the improvement of the dimensional accuracy of the micro-holes. Further, the response surface plots disclosed the variation of overcut and circularity error with the process parameters at various levels in a descriptive manner.

Multi response optimization for compressive strength, porosity and dimensional accuracy of binder jetting 3D printed ceramic bone scaffolds

Additively manufactured bone scaffolds have been gaining attention of researchers for past couple of years. Besides biocompatibility, bone scaffolds are expected to exhibit a good combination of porosity, mechanical strength and dimensional accuracy for success of the bone tissue engineering. This paper presents a multi objective optimization for additively manufactured ceramic porous bone scaffold prototypes using hybrid Taguchi based Grey relational analysis approach. Five process parameters namely, layer thickness (LT), build orientation (BO), build position (BP), binder saturation (BS) and delay time (DT) of binder jetting 3D printing process have been optimized for three response parameters, namely compressive strength (CS), measured porosity (MP) and dimensional accuracy (DA) of fabricated porous scaffolds. Initially, experiments are designed and performed as per the Taguchi's L27 orthogonal array and signal-to-noise (S/N) ratios for all the experiments have been calculated. Then Grey relational grades (GRGs) of S/N ratios are computed. Main effect plots for means and S/N ratios of GRGs reveals LT1, BO1, BP2, DT1 and BS2 as optimum levels of process parameters for CS, MP and DA. Analysis of variance of GRGs suggests that BO (40.42%) the most, LT (29.81%) the second most and DT (5.19%) the least significant parameter in affecting the response parameters. The value of GRG at optimum levels of the parameters have been predicted using the mean GRG model and liner regression model. Finally, a confirmation test is performed and result has been compared with the predicted values. A deviation of 3.93% (mean GRG model) and 3.89% (regression model) have been observed between experimental and predicted values of GRGs.