Grey Relational Analysis Research Articles

Multi-Objective Optimization of Tribological Characteristics for Aluminum Composite Using Taguchi Grey and TOPSIS Approaches

In this study, a multi-objective optimization regarding the tribological characteristics of the hybrid composite with a base material of aluminum alloy A356 as a constituent, reinforced with a 10 wt.% of silicon carbide (SiC), size 39 µm, and 1, 3, and 5 wt.% graphite (Gr), size 35 µm, was performed using the Taguchi method, gray relational analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making methods. Tribological tests were carried out on a “block on disc” type tribometer with lubrication. Load, sliding speed, and graphite mass concentration were analyzed as input parameters. As output parameters, wear rate and coefficient of friction were calculated. An analysis of variance (ANOVA) was conducted to identify all parameters that have a significant influence on the output multi-response. It was found that the normal load has the highest influence of 41.86%, followed by sliding speed at 32.48% and graphite addition at 18.47%, on the tribological characteristics of composites. Multi-objective optimization determined that the minimal wear rate and coefficient of friction are obtained when the load is 40 N, the sliding speed is 1 m/s, and the composite contains 3 wt.% Gr. The optimal combination of parameters achieved by GRA was also confirmed by the TOPSIS method, which indicates that both methods can be used with high reliability to optimize the tribological characteristics. The analysis of worn surfaces using scanning electron microscopy revealed adhesive and delamination wear as dominant mechanisms.

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

Study on the effect of county e-commerce development on rural residents' income growth

Against the background of rural revitalization and the development of county-level e-commerce industry, based on the data of Caoxian County of Heze, Shandong Province from 2017 to 2021, this paper studies the promotion effect of county-level e-commerce industry development on urban and rural residents' income through grey relative correlation analysis. The results show that the impact of the development of county e-commerce industry on the operational income of urban and rural residents is similar, and the impact on the operational income of rural residents is partly greater than that of urban residents, and the effect is not obvious. The influence of the development of county e-commerce industry on the wage income of rural residents is greater than that on the wage income of urban residents.

Forecast combination using grey relational analysis and Choquet fuzzy integral for container throughput forecasting

Utilizing the accurate and reliable demand forecasting model for the port logistics industry decision makers, can prevent misguided decisions ensuing from inaccurate forecasting. Current forecasting models of container throughput forecasting focus on the development of single or related hybrid models. However, these studies focus on improving the forecast accuracy, while neglecting the risk of failure. Combination forecasting can improve the forecast accuracy and reduce the risks from model selection failure. The previous research on port container throughput (PCT) combination forecasting focused on using a small number of linear models to generate the combined values, and neglected the nonlinear combined models reflecting the interaction between variables. Given the significance of combination forecasting for the PCT prediction, this study establishes a nonlinear combined model using the grey relational analysis (GRA) and Choquet fuzzy integral. The empirical results indicate that the proposed combined model outperforms both the other considered linear and nonlinear models. The findings assist the port decision-makers in both the public and private sectors for understanding the future PCT, and devising timely response plans aligned with actual circumstances and trends, besides formulating the appropriate development plans and decisions to modify the current situation or sustain competitive advantages.

Correlation between high temperature performance of gussasphalt mixture for steel bridge decks and rheology of asphalt mastic

Rutting is a prevalent issue in the Gussasphalt (GA) mixture layer of steel bridge deck pavements. This study aims to investigate the correlation between the rheology of GA mastic and the high-temperature performance of the GA mixture. To achieve this objective, GA mastic underwent temperature and frequency sweep tests, along with multiple stress creep and recovery tests employing a dynamic shear rheometer. Additionally, indentation tests, uniaxial penetration tests, and Lueer fluidity tests were conducted on the GA mixture. The Grey Relational Analysis method was employed to evaluate the relationship between asphalt mastic rheology and the high-temperature deformation resistance of the GA mixture. The findings reveal that the addition of mineral filler enhances the deformation resistance of both GA mastic and the mixture. The percent difference in non-recoverable creep compliance (Jnr-diff) was minimized when the weight ratio of filler to asphalt binder (F/B) was 2.5. Furthermore, increasing the F/B ratio from 2.0 to 3.5 resulted in a more than twofold increase in the GA mixture’s shear strength. Asphalt type emerged as a significant factor affecting the indentation parameter of the GA mixture, with an F-value reaching 8.82 based on ANOVA analysis. The rheological properties of GA mastic exhibited a significant correlation with the high-temperature performance of the mixture. The highest Grey Relational Grade of 0.87 was observed between the non-recoverable creep compliance at a 3.2 kPa stress level (Jnr3.2) and indentation. In future research, greater emphasis can be placed on exploring the micromechanical properties of the GA mastic-aggregate interface.

Machining Performance Analysis of Ti6-Al-4V in Powder-Mixed EDM Using Green Dielectric Oil

In this investigation, the factors were oil type ([Formula: see text]), powder type ([Formula: see text]), and powder concentration ([Formula: see text]) level, and the machining parameters were the voltage (V), capacitance (pf), pulse-on time ([Formula: see text]), and pulse-off time ([Formula: see text]). The output parameters were the material removal rate (MRR), and tool wear rate (TWR). Three types of oils and powders, along with their respective particle concentrations, were selected for this experiment. Neem, Karanja, and sesame oils were mixed with aluminum oxide (Al2O3), chromium (Cr), and nickel (Ni) powder at 4, 6, and 8[Formula: see text]gm/l, respectively, using an overhead stirrer to investigate the conditions under which the green oil powder mixed mixture yielded a higher MRR and lower TWR during the micro-EDM process of titanium alloy (Ti-6Al-4V) using a 0.8[Formula: see text]mm tungsten carbide (WC) tool with fixed machining parameters (240[Formula: see text]V, 100[Formula: see text]pf, 20[Formula: see text][Formula: see text]s [Formula: see text], and 5[Formula: see text][Formula: see text]s [Formula: see text]). All the powders used in this experiment had a 32-micron (450 mesh) size. The Taguchi L[Formula: see text] orthogonal array and grey relational analysis methods were used to design and optimize the input process parameters for both responses. SEM, optical microscopic analysis, and EDS analysis were performed to examine the surface morphology, characteristics of the WC tool, and elemental composition of both the tool and the workpiece material. The optimum conditions for Karanja oil with a Cr powder mixture concentration of 4[Formula: see text]g/l resulted in higher MRR and lower TWR. However, machining with neem and sesame oils resulted in higher TWR and lower MRR.

Matrix‐phase material selection for shape memory polymer composites: A comparative analysis of multi‐criteria decision‐making

AbstractIn the present study, the selection of suitable shape memory polymers (SMPs) to be used as the matrix‐phase material with various (In)organic filler materials to achieve the required optimum multi‐stimuli response in shape memory polymer composites (SMPC) systems is analyzed. The selection of these materials is based on their mechanical and physical properties as well as other underlying factors such as cost, availability, shape recovery rate, and aesthetic characteristics. In this study, the entropy method was applied to estimate the weightages of the various criteria while the gray relation analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS). Multi‐criteria decision‐making (MCDM) approaches have been used to rank the suitable matrix‐phase polymer materials for manufacturing shape memory polymer composites (SMPC) system. A total of eight alternative SMP matrix‐phase materials based on a set of nine criteria were analyzed and ranked. The proposed methodology and the result obtained thereof have been illustrated in detail. The results obtained from TOPSIS and GRA methods have been compared to conclude the effects of the material properties on the ranking and the selection of the SMP materials. Among all the eight alternatives considered, thermoplastic polyurethane (TPU) was found to be the best material in both the MCDM methods. The material cost, resistivity, % elongation, and hydrophobicity present the most influencing properties on the SMP material selection, whereas density presents no effect on the SMP matrix material selection. The robustness of the results for the comparative analysis was verified using TOPSIS methodology to validate its reliability. It was revealed that the TPU, polycarbonate, polypropylene, and epoxy‐resin/poly(lactic acid) are the most dominant matrix‐phase SMP material alternatives when a deviation in the entropy weights of the primary evaluation criteria is applied. The novelty of this study is the exploration and application of statistical MCDM methods of engineering material selection problems based on a set of decision criteria, which can be time‐consuming and costly while using experimental analysis methods.Highlights SMPCs are applicable engineering materials thus making their research viable. SMPC systems can undergo various shape changes under external stimuli. Selection of matrix‐phase polymer is critical in achieving desired objectives. GRA and TOPSIS MCDM approaches have been applied in the selection process. TPU was found as the best material in both methods.

Multi-response optimisation of wire-arc additive manufacturing process parameters for AISI 4130 steel during remanufacturing process

Wire-arc additive manufacturing (WAAM) has emerged as a critical tool for remanufacturing industrial components. A limited understanding of this technique for quality product manufacturing has hindered its utilisation for industrial applications. This study reports on the optimisation of WAAM process parameters for AISI 4130 steel towards remanufacturing of high-quality products for industrial applications. AISI 4130 steel was selected for this study due to its high strength-to-weight ratio, excellent weldability, and suitability for the WAAM process. Taguchi’s Grey Relational Analysis (GRA) used four factors and three levels in the multiple response optimisation process. The study considered process parameters voltage, current, travel speed and gas flow in the gas metal arc welding (GMAW)-based WAAM technique. Analysis of Variance (ANOVA) results show that voltage, travel speed and gas flow significantly affect material deposition. Voltage had the highest significance (31.61%) compared to other parameters. The optimised process parameters were found to be: voltage –23 V, current –100 A, travel speed −350 mm min−1, and gas flow −10 L min−1. These parameters resulted in tensile residual stresses of 25 ± 74 MPa, microhardness of 171.4 ± 12.2 HV0.3, and a relative density of 98.21%. The microstructural analysis reveals the existence of predominant ferritic and pearlitic colonies. This is due to compounded thermal stresses during the deposition process and alloy composition resulting in tailored microstructure and mechanical properties. The study provides some insights into the WAAM remanufacturing process for producing highly quality industrial components.

Grey-Taguchi approach to optimize the tribological performance of hybrid composites

Modern manufacturing technology is evolving, necessitating the creation of materials with higher wear resistance. This research endeavors to forecast the dry sliding wear performance of AL6061-B4C-RM hybrid composites by employing Taguchi and grey relational analysis approaches. Present study applies the L16 Taguchi approach to optimize the process parameters specific wear, and coefficient of friction (COF) at four different levels. Using multiple responses for optimization for both responses, a single process parameter setting is obtained via grey relational analysis. In AL6061 composites, varying weight proportions of Boron Carbide particles (B4C) were added as reinforcements while keeping the red mud (RM) content constant at 2 %. To recognize the dominant factor, analysis of variance (ANOVA) was implemented, and the outcomes of the assessment showed speed plays a crucial role, followed by the weight percent of reinforcement and the load in determining the specific wear rate and COF. A research trial was completed to confirm the outcomes obtained from the combination of optimal parameters.

Investigating the Laser engraving on Polymethyl methacrylate sheets and process parameters optimization using GRA technique.

Abstract Polymethyl methacrylate (PMMA) is sound-resistant because it can lessen the transmission of outside sound waves. PMMA is widely used in many applications, such as automotive lighting, electronics and energy, health, sanitation, etc. Laser engraving for PMMA sheet panels with process parameters of good quality is a major problem in the process industry. There is a demand for surface roughness that is affected by the volume of material removal. Therefore, in this work, multi-value response optimization was performed on MRR and surface roughness during laser engraving of PMMA sheets using an optical fiber diode in a dry environment through gray relational analysis. Also, regression models are developed, and their responses are compared for their correlation. The GRA combined with the Taguchi method has proved efficient in the decision-making of multi-responses in a laser engraving environment.

Intelligent prediction and evaluation method of optimal frequency based on PSO-BPNN-AdaBoost model

To achieve rapid and accurate determination of the optimal compaction frequency for high-speed railway subgrade materials, a method based on the PSO-BPNN-AdaBoost model for intelligent frequency estimation is proposed. Firstly, the Particle Swarm Optimization (PSO) algorithm is introduced to obtain the optimal hyperparameters of the Backpropagation Neural Network (BPNN), and then the PSO-BPNN-AdaBoost model is established by integrating the AdaBoost ensemble algorithm. Secondly, taking graded gravel fill material as an example, the Grey Relational Analysis algorithm (GRA) is employed to identify the main controlling features affecting f op as input features for the model, and the predictive performance of the model is evaluated. Finally, the model’s reliability is verified through ablation analysis. The results indicate that the PSO-BPNN-AdaBoost model demonstrates higher predictive accuracy. The main controlling features influencing f op are revealed to be the maximum particle size (d max), gradation parameters (b, m), coarse aggregate elongation index (EI), Los Angeles Abrasion (LAA), water absorption rates (W ac, W af).

Performance and Emission Analysis of Ethanol-Petrol Blends in Single-Cylinder Engines: A Sustainability Perspective

This study investigates the blending of ethanol and petrol in the Indian subcontinent, addressing the urgent need for sustainable fuel alternatives amid rising energy demands and environmental concerns. Ethanol, as a renewable biofuel, presents a viable option for enhancing the efficiency of internal combustion engines while reducing harmful emissions. Given the significant air pollution issues prevalent in urban areas of India, optimizing fuel blends can substantially improve engine performance and contribute to cleaner air. The primary objectives of this research include analyzing the impact of different ethanol-petrol blends on the performance of a single-cylinder petrol engine, assessing key performance metrics such as brake power, Indicated Mean Effective Pressure (IMEP), Brake Mean Effective Pressure (BMEP), and Brake Thermal Efficiency (BTHE). Emission analysis was conducted to measure hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO₂), oxygen (O₂), and nitrogen oxides (NOₓ) across various blends, quantifying the environmental impact of each. Grey Relation Analysis (GRA) was employed to rank the fuel blends based on their performance and emissions, helping identify the most effective blend for optimal engine operation. Additionally, the sustainability of ethanol blending as an alternative fuel solution in the Indian automotive sector was assessed. The study recorded key performance indicators and emissions using standardized techniques, with GRA calculating Grey relational coefficients to measure the degree of similarity between observed data and desired outcomes. This approach facilitated the ranking of blends, ultimately identifying the optimal ethanol-petrol blend. The findings revealed significant variations in both performance and emissions across different blends, indicating that ethanol blending holds substantial promise as a sustainable solution for the Indian automotive sector. These insights align with recent studies on the benefits of biofuels in enhancing engine performance and reducing emissions, providing valuable guidance for optimizing ethanol-petrol blends to improve sustainability in energy consumption.

Evaluation of the financial performance of electricity generation companies using the Grey relational analysis method

In the study, 10 electricity generation companies on the Fortune 500 list were examined using nine criteria. These companies are listed in the top 40 in terms of installed power. The aim of the study was to determine the financial performances of the companies with the entropy-weighted Grey Relationship Analysis (GRA) method by obtaining the 2021 data of the criteria from the websites of the relevant companies. As a result of the weighting made by the entropy method, it was determined that the most important criterion was the export income. These criteria were determined as equity, total assets, net sales, asset turnover, equity turnover, profit before interest/tax , change in profit before interest/tax, and as change in net sales respectively. After the weights of the criteria were calculated with the entropy method, the GRA method was used to rank the alternatives. Çelikler company was determined to be the best electricity generation company with the highest degree of relationship. This company was followed by Odaş, Gama, İçdaş, Limak, Akenerji, Zorlu, Enerjisa, Aksa and Enka respectively.

Evaluation of Goalkeepers’ Performance via Grey Relational Analysis Method: Example of Africa Cup of Nations

Aim: The aim of the study is to evaluate the technical performance of goalkeepers via the gray relational analysis method. Materials and Method: The sample of the study consisted of 21 goalkeepers competing in the African Cup of Nations 2023. Data was obtained from the official website of the Confederation of African Football and Transfermarkt. Technical performance indicators were determined as “save percentage”, “claimed crosses” and “passing accuracy”. The data were evaluated by the grey relational analysis method among the quantitative research methods. In this context, six steps of the grey relational analysis method were applied respectively. These are "preparation of the data set and generation of the decision matrix", "normalization of the data", "generation of the reference series", "generation of the absolute value table", "generation of the grey relational coefficient matrix", "calculation and ranking of the grey relational degrees". Results: According to the analysis results, the highest technical performance value was calculated as 0.7654, while the lowest technical performance value was calculated as 0.3949. Thus, the top 3 goalkeepers were determined as Ronwen Williams (South Africa), Edouard Mendy (Senegal), and Babacar Niass (Mauritania). The last 3 goalkeepers were determined as Richard Ofori (Ghana), Ibrahim Koné (Guinea), and Baboucarr Gaye (Gambia). Conclusion: According to the results of the research, Ronwen Williams (South Africa), who won the Golden Glove Award and was selected for the team of the tournament at the Africa Cup of Nations, was determined as the goalkeeper with the highest performance. His national team South Africa completed the tournament in 3rd place, despite being ranked 19th among 24 teams in terms of market value. Similar conclusions were obtained for the remained goalkeepers. It is expected that this conclusion will provide significant contributions for stakeholders such as researchers, goalkeepers, coaches, scouts, and trainers.

Data reconstruction for tunnel structural health monitoring: An updated KNN model with gray relational analysis

Structural health monitoring (SHM) system is an important way to evaluate the tunnel structural performance. In practice, the missing data is inevitably induced in the SHM dataset, which may cause deviation or even misleading results of the data analysis, and hence, need to be accurately reconstructed. This study proposes a new KNN data reconstruction method based on a gray correlation measure (GRA-KNN). Compared to the traditional KNN, the GRA-KNN can measure the structural similarity of data better, and the pre-filling can make full use of the information of known data to estimate missing data. By comparing with the other five machine learning methods (i.e., Ridge Regression, Support Vector Regression, Multilayer Perceptron, RandomForest, and XGBoost), the imputation performance of this method is examined using two real-time SHM datasets from Nanjing Yangtze River tunnel and Hong Kong-Zhuhai-Macao Bridge Undersea Tunnel. Results show that the GRA-KNN method possesses a better performance and stronger robustness under a wide range of missing ratios from 10% to 90%, showing great potential in SHM data reconstruction. In particular, when the missing ratio is larger than 60%, the imputation error of the proposed method is the smallest compared to that of the other machine learning methods.

Modified calculation model of train-induced aerodynamic pressure on vertical noise barriers considering the train geometry effect

High-speed trains (HSTs) generate air disturbance, leading to significant aerodynamic pressure on the noise barriers. Differences in train geometry result in variations in the aerodynamic pressure on noise barriers, implying that existing European standard calculation models may not necessarily be suitable for all types of HSTs. In this paper, the influence of the width, height, and nose length of the train on the aerodynamic pressure on vertical noise barriers was studied using computational fluid dynamics (CFD) simulations. Results showed that taller and wider trains result in greater aerodynamic loads on noise barriers. Conversely, an increase in the nose length of a train leads to a reduction in such pressure. Using grey relational analysis, correlation of various factors with the train-induced aerodynamic pressure is, from strong to weak: distance to the track center, width, height, and nose length of the train. Building upon the EN 14067-4 calculation model, the shape coefficients of trains with varying geometric characteristics were derived using the simulation data obtained in this study. A modified pressure calculation model was established accounting for the differences in geometric features of HSTs and pressure distribution in the vertical direction of noise barriers and validated using relevant data from the literature.

Comparison of Machinability of Al-4.5%Cu/TiB2/3p MMC for Multi-Layer Coated Insert: Validated FEM and Statistical Approaches

Aluminum-based Metal Matrix Composites (MMC) are commonly used in metal-cutting applications due to their better mechanical and physical properties, such as high strength, hardness, and low weight. Also, modern coating applications, especially multi-layer coated tools, have the cutting-edge potential for relieving the difficulties of machining MMCs to improve insert performances. Therefore, this study aimed to reveal the turning Al-4.5%Cu/TiB2/3p performance of the multi-layer coated cemented carbide insert with verified FEM and statistical approaches. Different coating materials, two and three of which were soft and hard, were appointed at different thicknesses and sequences in the design of experimentally calibrated simulations. The Grey Relation Analysis (GRA) was set to investigate the multi-layer coated insert performance for turning the MMC concerning the resultant cutting forces (FR) and maximum insert temperature (Tmax). The optimal multi-layered coating was found at levels 4-2-4-3-2 for the factors of coating materials: tungsten disulfide (WS2), molybdenum disulfide (MoS2), titanium nitride (TiN), aluminum oxide (Al2O3), and titanium carbo-nitride (TiCN), respectively. The contribution rates of each factor were significant concerning General Linear Model (GLM) at 47.13% and 24.43% for WS2 and Al2O3 coatings materials, respectively. In the future, multi-layered coatings can be a valuable solution for the difficulties of machining the MMCs.

A spatiotemporal synergetic operating performance assessment based on reconstructed correlation matrix and dissimilarity analytics for plant-wide hot strip mill process

Operating performance assessment (OPA) of process industries plays a significant role in improving the product quality and pursuing the best economic benefits. With the continuous improvement of industrial intelligence, massive data has been collected. Variable time-delay issue is brought by the different spatial and temporal distributions of production units, which affects the actual causal relationship of collected data and assessment accuracy. Therefore, a spatiotemporal synergetic operating performance assessment (SSOPA) method is proposed in this paper. First, a reconstructed correlation matrix is carefully crafted in both time and space, leveraging the intricate multi-correlated spatiotemporal-delay (MCSD) parameters. To precisely quantify the multi-correlated relationships, a mixed gray relation analysis (MGRA) is employed. Subsequently, a predatory search-based genetic algorithm (PSGA) is utilized to meticulously search for the optimal MCSD parameters. Once these parameters are determined, an OPA model is formulated for each subsystem, drawing upon aligned process data and a distributed dissimilarity analysis (DISSIM). Finally, based on the subsystem-level OPA, a comprehensive assessment of the global operating performance level is conducted. The feasibility and effectiveness of the proposed method are verified by the plant-wide hot strip mill process (PHSMP).

Target Cell Extraction and Spectrum-Effect Relationship Coupled with BP Neural Network Classification for Screening Potential Bioactive Components in Ginseng Extract with a Protective Effect against Myocardial Damage.

Cardiovascular disease has become a common ailment that endangers human health, having garnered widespread attention due to its high prevalence, recurrence rate, and sudden death risk. Ginseng possesses functions such as invigorating vital energy, enhancing vein recovery, promoting body fluid and blood nourishment, calming the nerves, and improving cognitive function. It is widely utilized in the treatment of various heart conditions, including palpitations, chest pain, heart failure, and other ailments. Although numerous research reports have investigated the cardiovascular activity of single ginsenoside, there remains a lack of systematic research on the specific components group that predominantly contribute to cardiovascular efficacy in ginseng medicinal materials. In this research, the spectrum-effect relationship, target cell extraction, and BP neural network classification were used to establish a rapid screening system for potential active substances. The results show that red ginseng extract (RGE) can improve the decrease in cell viability and ATP content and inhibit the increase in ROS production and LDH release in OGD-induced H9c2 cells. A total of 70 ginsenosides were identified in RGE using HPLC-Q-TOF-MS/MS analysis. Chromatographic fingerprints were established for 12 batches of RGE by high-performance liquid chromatography (HPLC). A total of 36 common ingredients were found in 12 batches of RGE. The cell viability, ATP, ROS, and LDH of 12 batches RGE were tested to establish gray relationship analysis (GRA) and partial least squares discrimination analysis (PLS-DA). BP neural network classification and target cell extraction were used to narrow down the scope of Spectral efficiency analysis and screen the potential active components. According to the cell experiments, RGE can improve the cell viability and ATP content and reduce the oxidative damage. Then, seven active ingredients, namely, Ginsenoside Rg1, Rg2, Rg3, Rb1, Rd, Re, and Ro, were screened out, and their cardiovascular activity was confirmed in the OGD model. The seven ginsenosides were the main active substances of red ginseng in treating myocardial injury. This study offers a reference for quality control in red ginseng and preparations containing red ginseng for the management of cardiovascular diseases. It also provides ideas for screening active ingredients of the same type of multi-pharmacologically active traditional Chinese medicines.

Optimization of Machining Parameters on The Geometry Precision of Cortical Screw of Ti-6Al-4V ELI Using Gray Relational Analysis Method

Titanium and its alloys have been widely developed in aerospace, biomedical, electronics, sports, and offshore. In the biomedical field, titanium alloy that has been widely used, based on its properties, is biocompatible for bone implants and not becoming a foreign thing in the body. This study aims to optimize to determine the optimal conditions for cutting parameters for the precision of the cortical thread screw geometry referring to the ISO 5835 standard. In this study, the cutting was carried out using a CNC lathe, where the machining parameters used were spindle rotation of 100, 200, and 300 rpm, depth of cut of 0.01, 0.02, and 0.03 mm, and types of lubricants in the form of synthetic oil, virgin palm oil, and virgin coconut oil. Data processing in this study is analyzed using the Gray Relational Analysis Method to get an optimal combination of cutting conditions. The significant factor that influences the precision of cortical thread is the depth of cut, which is a contribution of 98%. It can illustrate that the smaller depth of cut will produce better screw geometry. The optimum conditions for cutting parameters that deliver the best level of precision were obtained at a spindle speed of 100 rpm, depth of cut of 0.01 mm, and type of lubricant of coconut oil. Meanwhile, the screw surface damage is stacked chips as a result of the high temperature generated during the cutting process.