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This study investigates the effects of sintering parameters on the mechanical properties and microstructure of spark plasma sintered aluminium hybrid composites reinforced with 10 wt% SiC and 4 wt% kaolin. Using Taguchi–grey relational analysis (TGRA), the sintering temperature, compaction time, and compaction pressure were optimized based on their influence on density, ultimate tensile strength (UTS), and compression strength. Experiments were designed using an L9 orthogonal array, and ANOVA analysis was performed to determine the percentage contribution of each parameter. The optimal sintering conditions were found to be at a temperature of 570°C, a compaction time of 5 min, and a pressure of 20 MPa, resulting in a maximum density of 2.72 g/cc, UTS of 313 MPa, and compression strength of 379 MPa. Microstructural analysis through SEM revealed a homogeneous distribution of reinforcements at the optimal conditions, while the presence of Al2Cu intermetallic compounds was detected near the grain boundaries at non-optimal conditions. These results confirm that optimized sintering parameters significantly enhance the mechanical properties of the composite. [ABSTRACT FROM AUTHOR]

The issue of deformation during the secondary processing of plastic plate is becoming increasingly concerning with the expanding use of plastic-shaped appliances. This is especially evident due to work-hardening and thermal softening, leading to problems such as hot-melt adhesion and distortion. To tackle this challenge, a study employed the Theory of Inventive Problem Solving (TRIZ) to conduct a systematic analysis of the problem. It identified contradictions and conflicts therein and proposed an innovative optimization strategy based on the Improved Coati Optimization Algorithm (ICOA). Initially, five crucial processing parameters—spindle speed (AS), cutting tool material (ACM), feed rate (Avf), plastic plate material (APM), and cutting depth (Aap)—are determined through grey relational analysis and utilized as input for the prediction model. Subsequently, a processing state prediction model is constructed on the MATLAB software platform, and the ICOA algorithm is used to optimize the model parameters of the convolutional gated recurrent unit network (CNN-GRU), thereby enhancing its robustness, generalization ability, and prediction accuracy. Experimental validation on the PT-1325 CNC machine tool demonstrated that the CNN-GRU model optimized by ICOA attained a prediction accuracy of 96.67% and effectively enhanced machining efficiency through optimizing the machining parameters. [ABSTRACT FROM AUTHOR]

Cold extrusion is a fundamental metal forming technique and involves subjecting a block of material to high pressure as it passes over a die orifice of exact profile. The method remains influenced by numerous parameters including die angle, ram speed, lubricant type, extrusion ratio, die land height, workpiece geometry and material properties. Accurate estimation of extrusion force is critical, as it significantly impacts product quality. The current study explores the effects of die angle (15°, 25° and 35°), ram speed (1.5 mm/min, 3 mm/min and 4.5 mm/min) and lubricant (coconut oil, petroleum jelly and grease) on extrusion force, compressive strength, time and displacement. L27 orthogonal array design is used to conduct the tests, and aluminium 2014 alloys are chosen as the workpiece material. Analysis of variance (ANOVA) is employed to assess the significance of extrusion process parameters. Results depict that die angle has a greater impact on extrusion force compared to ram speed and lubricant. Multi-parameter optimization using Grey relation analysis with equal weighting for extrusion force, compressive strength, time and displacement have been considered. The optimal conditions for minimizing extrusion force, compressive strength and time, while maximizing displacement, are obtained at a die angle of 15°, ram speed of 3 mm/min and lubricant grease. [ABSTRACT FROM AUTHOR]

This study presents a hybrid methodology for planning green spaces to enhance urban sustainability and livability, evaluating the impacts of climate change on cities. Cities, once accommodating a small population, have become major centers of migration and development since the eighteenth century. Rapid urban growth intensifies infrastructure, environmental, and social challenges. Fossil fuel reliance and deforestation increase greenhouse gas emissions, exacerbating climate change, impacting ecosystems, and urban livability. This research assesses green spaces in Izmir's Bayraklı district by assigning weights to site selection criteria via the analytic hierarchy process (AHP), entropy weight method (EWM), and Game Theory. The weighted linear combination (WLC) method integrates these weights to produce decision maps. Game Theory harmonizes discrepancies between AHP and EWM, influencing the decision maps. Notably, incorporating climate change criteria reduced highly suitable areas from 50.3 to 41.5%, stressing climate considerations in planning. Grey relational analysis (GRA) prioritizes investment areas, showing objective, criterion-based planning's importance in sustainable urban development. [ABSTRACT FROM AUTHOR]

Down fabric is the most natural form of thermal insulation due to its significant breathability, rendering down jackets excellent warmth retention effect. This study focused on the down heat storage technology to increase the heat storage temperature. The single quality optimization using the process parameters of the Taguchi method was discussed in the first stage. The experimental down heat storage process parameters included carbon powder, bridging agent, resin adhesive, and ethyl acetate. The corresponding down fabric quality characteristics were temperature rise, dust, and oxygen number. The Taguchi method was combined with the grey relational analysis (GRA) in the second stage to determine the grey relational grade (GRG) and perform multi-quality optimization. The optimized process parameters simultaneously achieved higher temperature rise, lower dust, and lower oxidation value, which were verified by verification experiments. The optimal process parameters are: carbon powder content at 1.2%, resin adhesive at 6%, bridging agent at 2.0%, and ethyl acetate at 0.4%. These yield a temperature rise of 7.8 °C, dust level 2, and oxygen number 1.6 mg/100 g, which are exceeding the industry standards: Temperature rise ≥ 3 °C, dust < Grade 4, oxygen number < 10 mg/100 g. [ABSTRACT FROM AUTHOR]

The battery's health status is fundamental to battery health management. Accurately estimating the health status of lithium-ion batteries is crucial for ensuring their safe, reliable, and long-term operation. In this paper, a novel method for estimating the health state of lithium-ion batteries, which is based on grey relational analysis (GRA), Savitzky Golay (SG) filter, and Elman neural network enhanced by sparrow search algorithm (SSA). Firstly, multiple representative health features (HFs) are extracted from the charge and discharge curves. In order to reduce computational complexity, the GRA method is employed for feature analysis and screening, resulting in reasonable, highly relevant, and explanatory HFs. Secondly, to improve the correlation by reducing unstable factors in HF curves, SG filter is utilized for noise reduction and data smoothing, effectively mitigating the influence of data noise and short-term fluctuations resulting from capacity regeneration. Thirdly, in order to accurately estimate the state of health (SOH) of lithium-ion batteries, a SOH estimation model based on SSA-Elman neural network is proposed. The neural network characteristics are optimized to effectively mitigate the issue of Elman network being prone to local optima. Finally, the proposed method's effectiveness is validated by comparing it with several other methods using NASA dataset. The results show that the RMSE and MAE of the model are controlled within 0.0045 and 0.0038 respectively, and the R2 is maintained above 99.79%, which significantly improves the accuracy and reliability of SOH estimation. [ABSTRACT FROM AUTHOR]

This paper describes about experiment conducted on End milling of AISI D2 steel material under Dry, traditional flooded and cold air assisted micro-lubrication. Tool wear and surface roughness were selected as metrics for evaluating the performance of the said lubrication systems. Dry machining and micro-lubrication methods are considered as cleaner production strategy in context of sustainable manufacturing, aiming to reduce harmful impact of mineral based lubricants over humans as well as environment. Experiments were conducted based on Taguchi's orthogonal matrix L27. Lubrication method, cutting speed and feed rate were selected as controlled variables. Dry machining, MQL and Vortex tube generated cold air with MQL were selected as lubrication methods. ANOVA of L27 Experiments found that coolant and feed rate affects the surface roughness the most contributing 41 and 32% respectively. Similarly, same factors contribute 33 and 41% for flank wear. Desirability function analysis and Grey Relational analysis are used to find optimal setting of process parameters. Study under DFA indicated that the coolant and feed rate are the most influencing factors contributing 45.17 and 42.18% for Composite desirability Index. Similarly, In GRA, for maximization of grey relational grade, coolant method and feed rate contributed 40 and 42.37%, respectively. [ABSTRACT FROM AUTHOR]

This study optimized minimum quantity lubrication (MQL) parameters to enhance the machining performance of AISI 630 steel, focusing on reducing tool-work interface temperature and surface roughness. An in-house developed MQL system utilizing vegetable oils (Azadirachta Indica, Ceiba Pentandra, Calophyllum Inophyllum) was employed. The experiments varied flow rates (700–900 ml/h), pressures (0.06–0.18 bar), and nozzle diameters (0.35–1.05 mm) on a center lathe using carbide tools. Taguchi's L18 orthogonal array and Grey Relational Analysis (GRA) were applied for optimization, while Analysis of Variance (ANOVA) supported predictive modeling. The optimal parameters for minimizing temperature were identified as 90 ml/h flow rate, 2.8 bar pressure, 1.5 mm nozzle diameter, and Calophyllum Inophyllum coolant, achieving a significant reduction to 118°C—a 40% improvement over dry machining. For surface roughness, the optimal parameters of 90 ml/h flow rate, 2.8 bar pressure, 2 mm nozzle diameter, and Calophyllum Inophyllum coolant yielded a minimum value of 0.672 µm, marking a 28% improvement. Regression models with R2 values exceeding 0.98 demonstrated strong predictive accuracy for machining responses. This research underscores the potential of MQL systems to enhance machining sustainability and performance by reducing temperature, surface roughness, and coolant usage. The study highlights the viability of vegetable oil-based coolants as eco-friendly, cost-effective alternatives, advancing sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

Rubber pad forming is one of the primary ways of manufacturing aviation sheet metal components. In addition to reducing forming die costs, using elastic rubber in rubber pad forming prevents scratching on sheet metal, which is crucial for flight safety. However, during the forming process, the rubber pad primarily applies a vertical force to the sheet metal and cannot effectively exert pressure on the sheet metal laterally. Consequently, the contours of the finished product could fall outside specifications. This can cause severe manufacturing defects such as wrinkling or springback and therefore require a significant amount of labor to remedy and prevent any defects. As such, improvements in production efficiency are necessary. This study presents a rubber pad forming die with a lateral forming force mechanism that provides a lateral compressive stroke to the forming process of sheet metal via wedge-shaped structures; in other words, a lateral forming force to rubber pad forming is applied. In our exploration of stacking rubber pads of varying hardnesses in different orders, the maximum force was a larger-the-better objective, and the thickness compression ratio of the rubber pad was a smaller-the-better objective. We then combined grey relational analysis and the entropy method to identify the rubber pad stacking order and thicknesses with the optimal forming results. Subsequently, we constructed an actual rubber pad forming die with a lateral forming force mechanism as well as performed experiments and verification using 6061-T6 sheet metal with a thickness of 1 mm for aircraft wing reinforcement. The results demonstrated that employing rubber pad forming with a lateral forming force mechanism could reduce the amount of springback and surface wrinkling, meet blueprint requirements, and provide a new method and application of rubber pad forming for aviation sheet metal. [ABSTRACT FROM AUTHOR]

This study used Taguchi-based Grey relational analysis to optimize the bead geometry (bead width and length) and the hardness of laser welded 3 mm thick Ti6Al4V alloy sheets joined using the butt configuration. Firstly, the L9 (32) orthogonal array, with varying laser power and welding speed, was employed to optimize the input parameters (bead length, width, and hardness). The Taguchi optimization showed that the welding speed and laser power have a significant effect on the hardness of the welds and that laser power has a significant impact on the bead length. Meanwhile, the welding parameters had little effect on the bead width. These results were further affirmed with the Analysis of Variance (ANOVA). The Grey relational multi-objective optimization shows the optimized parameters for all responses to be 3 kW laser power and 2.2 m/min welding speed. It is important to note that the combination of Taguchi and Grey relational analysis and ANOVA is useful in optimizing welding parameters to achieve suitable weld properties. [ABSTRACT FROM AUTHOR]

A sophisticated manufacturing technique called 3D-metal printing uses digital drawings to create 3D-metal items layer by layer. This study presents an empirical investigation and analysis of the dimensional accuracy in SS316L metal 3D printing utilizing Direct Metal Laser Sintering. Grey Relational Analysis (GRA) and ANOVA are the main tools used in this study to assess how process factors affect dimensional accuracy. The three most important process variables are layer thickness, scan speed, and laser power. Various values of these factors were examined in a series of trials, and the resulting dimensional accuracy was evaluated. Using GRA analysis, the experimental results were optimized, and it was discovered that the ideal combination of laser power of 360 W, scan speed of 800 mm/s, and layer thickness of 80 μm was found. Additionally, the research findings showed that the level of scan speed significantly affected the precision of dimensions (43.99%) due to its sensitivity to changes and potential influence from the interaction effect between layer thickness and laser power. The investigation was able to determine the best parameter combinations that resulted in increased dimensional accuracy. [ABSTRACT FROM AUTHOR]

In this study, the authors divided the work into two parts. The first part involved the development and validation of a thermal model for electric discharge machining (EDM) using Ansys software. Three different values of fraction of heat transferred to workpiece (Fc) were used in simulation to determine which one is better at correctly predicting MRR and SR. Three workpiece materials, namely Al-10%SiCmicro-3%SiCnano, Al-10%SiCmicro-4.5%SiCnano and Al-10%SiCmicro-6%SiCnano, were considered. The results showed that the Fc value calculated using the Shabdard's function provided the best prediction of MRR and SR, with a MRR ratio range of 1.20-8.00, followed by Fc taken from Dibitonto (MRR ratio range − 0.28 to 12.95) than Fc calculated from Ming et al. (MRR ratio range 0-18.56). Further, ANOVA showed that the percentage of SiC nanoparticles has 1.44% contribution in MRR and 31.80% contribution in SR, whereas pulse current has 86.10% contribution in MRR, 50.09% contribution in SR, while pulse-on time has 8.79% contribution in MRR and 9.14% contribution in SR. The second part involved the parametric study of the experimental results using ANOVA, main effect plot and grey relational analysis. The workpiece in this study were manufactured using stir casting. The EDM process was accomplished using a L9 orthogonal array, and the performance parameters were measured in terms of MRR, TWR, SR, dilation in hole diameter and hole taper. The most important parameters affecting MRR were pulse current (89.68%) and pulse-on time (8.32%), SR has percentage of SiC nanoparticles (32.92%) and pulse-on time (31.21%), TWR has percentage of SiC nanoparticles (32.75%) and pulse current (31.59%), dilation in hole diameter has percentage of SiC nanoparticles (52.25%), and pulse current (24.41%) and hole taper have percentage of SiC nanoparticles (49.96%) and pulse current (11.62%). Dilation in hole diameter and hole taper increased with an increase in the percentage of SiC nanoparticles, while TWR increased with an increase in the pulse current and hole taper decreased with increase in pulse-on time. The best EDM machining results were obtained at 6 A pulse current, 20 pulse-on time, and 6% SiC nanoparticles in the hybrid composite. A validation experiment was also performed, and it was found that the MRR, dilation in hole diameter, and hole taper improved at 6% SiC nanoparticles, 6 A pulse current, and 30 pulse-on time. [ABSTRACT FROM AUTHOR]

Due to the extensive use of explosives, the failure to identify hazards and assess risks in blasting may lead to catastrophic consequences. However, classical risk assessment approaches are limited in their ability to address ambiguity and uncertainty, as well as in assigning weights to the criteria involved in the risk assessment process. This study employs a multi-criteria decision-making system to address these limitations and assess the risks associated with blasting. The proposed model integrates Grey Relational Analysis (GRA) to prioritize risks and the Fuzzy Best-Worst Method (FBWM) to assign weights to the criteria critical to the risk assessment process. The findings indicated that "not using personal anti-static protection devices during blasting (R12)", "placing the explosive fuse near explosive materials (R15)", and "bringing explosive materials to the explosion site before completing drilling and blasting operations (R23)" were the most significant blasting risks, respectively. These risks stem from operational processes, human factors, and the working environment, thus requiring special attention. The weighting of the study criteria, including Consequence (C), Probability (P), and Exposure (E), revealed that the C criterion, with a final weight of 0.538, was the most influential in the risk assessment process. The P and E criteria, with weights of 0.294 and 0.167, respectively, ranked second and third in importance among the assessment criteria. To ensure the applicability and accuracy of the proposed method, a validation study comprising two distinct parts—sensitivity analysis and comparative analysis—was conducted. The results of these evaluations highlighted the appropriate and reliable performance of the proposed approach. This approach can assist decision-makers, managers, and risk analysts in more accurately identifying and assessing risks by addressing some of the limitations inherent in classical risk assessment methods. [ABSTRACT FROM AUTHOR]

Nowadays, sustainable Minimum Quantity Lubrication (MQL) technology has significantly enhanced machinability, surface quality, and productivity comparing to dry machining. Furthermore, it is considered as a key factor that respects the ecological trends in machining and also contributes in reducing the consumption of cutting fluid and machining costs. The present paper brings a comparative research between sustainable dry machining and sustainable MQL machining while turning AISI 52100 steel with CVD coated (GC-2015) metal carbide inserts. First, parametric tests were carried out to evaluate the impact of machining parameters (CS, f, and Doc) on performance factors in terms of tool wear, cutting force, surface roughness, energy consumption, and cutting temperature. Then, a Taguchi L9 (3^3) experimental design was implemented in order to carry out a statistical analysis based on ANOVA and performance modelling (Ra, Fz, Pc, Kc, and MRR) by applying the (RSM) method. Finally, a comparative multi-criteria optimization was performed using the DF method and four other MCDM methods (GRA, EAMR, FUCA, and EDAS) combined with Taguchi's signal-to-noise-ratio (S/N). The results confirmed the effectiveness of MQL technique in enhancing machining performance over dry machining. Furthermore, the EAMR, EDAS, and GRA methods identified the same optimal combination (CS = 300 m/min, Doc = 0.45 mm, and f = 0.08 mm/rev), yielding the best results for (Ra, Kc, and MRR). The FUCA method promotes improvement of energy efficiency during machining by achieving minimal values for (Pc and Fz). Finally, the DF method stands out for its ability to produce balanced results across all performances. [ABSTRACT FROM AUTHOR]

Although the concept of circular economy is a frequently encountered effective tool of sustainable development, its social dimension, the social circular economy, is a topic that has only begun to be discussed. Assessing the social circular economy performance of European Union countries from the sustainable development perspective is critical for monitoring their progress. In this study, a model that evaluates the social circular economy performances of countries for sustainable development is proposed. The importance weights of the evaluation criteria are determined using the interval-valued intuitionistic fuzzy VIKOR method's linguistic scale. Then , the countries are ranked using the grey relational analysis method. In this study, the social circular economy performance of EU member states for 2021 are investigated with the proposed model. According to the results obtained, social circular economy performances are directly proportional to the success of countries in achieving sustainable development goals. The success rating achieved in this study may vary with the improvement activities of the relevant countries. The proposed model updates the ranking, considering each improvement. The findings of this study can help scholars and policymakers better understand the social circularity capabilities of the European Union member countries in the context of sustainable development. In the limited literature, there is no other study in which the social circular economy performance of EU member countries is measured with the relevant evaluation criteria. A model that can evaluate the social circular economy performances of the European Union member countries is proposed to fill the profound gap in this field. We contributed to the literature with a new model that differs with evaluation criteria, real and current data sets. [ABSTRACT FROM AUTHOR]

The current study proposes a new approach that is a combination of methods: fuzzy and decision-making trial and evaluation laboratory (DEMATEL) and distance-based approximation (DBA) so that decision-makers can determine the relationship structures between criteria, how the criteria affect each other, and the weight of each criterion. From that, managers can quickly and easily evaluate and select the best alternative. A previous study using DEMATEL and grey relational analysis (GRA) is presented as an example. Data from this previous study are used to present the implementation process and results of the proposed method and simultaneously show the results of the current problem solved by the fuzzy DEMATEL—TOPPSIS (technique for order of preference by similarity to ideal solution) method. The comparison of results between three methods including DEMATEL—GRA, fuzzy DEMATEL—TOPPSIS, and fuzzy DEMATEL—DBA is performed to demonstrate the efficiency of the proposed method. The results show that there is a slight difference in weights between fuzzy DEMATEL and DEMATEL but the error is extremely low. In addition, the correlation of results between methods is very high. Particularly, correlation coefficients between fuzzy DEMATEL and DEMATEL, between fuzzy DEMATEL—TOPPSIS and fuzzy DEMATEL—BDA, and between DEMATEL—GRA and fuzzy DEMATEL—BDA are 0.99, 0.95, and 0.78 respectively. Finally, all three methods suggest alternative 4 as the best option. [ABSTRACT FROM AUTHOR]

This study proposes an ideal milling process parameter setup for multi-responses. Due of this problem, Grey relational analysis with Taguchi approach is used. These approaches helped determine ideal machining conditions to improve surface roughness, material removal rate, cutting force, and tool wear. Compare results from wet and minimal amount lubrication studies. Further multiple flow rates of Minimum Quantity Lubrication were tested to find the "best" setting. The best gray relational analysis setting is used in tests with varied minimal Quantity Lubrication flow rates. ANOVA for MPCI showed that depth of cut affects several performance factors the most, followed by spindle speed, feed, tool diameter, and tool type. The response table and S/N ratio main effect plot show that 1500 rpm spindle speed, 0.5 mm depth of cut, 550 mm/min feed, 12 mm end mill cutter with coated (PVD), and minimal amount lubrication are the ideal machining settings. After experimenting with minimum amount lubricating flow rates, 80 ml/hr is the ideal setting. Finally, experimentation tests conformance. [ABSTRACT FROM AUTHOR]

Sisal fiber/polycaprolactone (SF/PCL) composites are a new and exciting development in the field of sustainable materials research. Enhancing the physical strength of these SF/PCL composites has been the primary emphasis of the present study work. The most efficient strategies to increase the chemical treatment process have been identified using methodologies such as Taguchi's and Grey Relational Analysis (GRA). The injection molding technique was used to make the composites while important variables were changed. Chemical treatments solutions (potassium hydroxide, lithium hydroxide, and potassium acetate), chemical concentration (2%, 4%, and 6% w/v), and treatment duration (3, 6, and 9 h) were the variables for this work. Various SF/PCL composite variations were produced as a result of these adjustments. A number of physical properties were measured to assess the improved composites' strength, including modulus of elasticity, flexural strength, impact resistance, deformability under bending, elongation under tension, and tensile strength. To examine the examined and treated materials in detail, we employed a scanning electron microscope (SEM). Using GRA and mean plots, the composite's ideal values were the same. The optimal parameters for X1-Y2-Z2 were determined to enhance the mechanical properties of SF/PCL, which are KOH, 4%, and 4 h. However, the microstructure of the PCL matrix bio-composite strengthened with sisal fiber was affected by a variety of surface treatments. [ABSTRACT FROM AUTHOR]

Mode I fracture toughness is the most common amongst the three fracture modes because of its applications in rocks/materials engineering. There are different experimental methods that are available for its measurements and the selection of the available procedures to be used is usually a difficult task and highly multi criteria in nature. Therefore, this study proposed novel applications of two fuzzy-based multi criteria decision making (MCDM) methods for prioritising the experimental procedures. Thirteen different experimental methods were identified and evaluated under three different criteria by the experts. The weights were computed using both expert-assigned weights to each criterion and the fuzzy intuitionistic entropy measure obtained weights. This study reveals that ISRM-suggested cracked chevron notched Brazilian disc method is the most preferred follow by the semi-circular bend specimen and Brazilian disc method. The Pearson's correlation between the models is very strong (˃ 0.9), indicating that either of the two proposed approaches is suitable for this purpose. The sensitivity analysis was conducted by generating twenty-one sets of weights and the outcome ranked ISRM-suggested cracked chevron notched Brazilian disc, the most suitable follow by semi-circular bend specimen and Brazilian disc methods. The correlations amongst the MCDM methods for the sensitivity analysis are also very strong as previously observed. This study shows that the proposed models are suitable in prioritising the most appropriate experimental procedures for mode I fracture toughness. Highlights: Experimental procedures for mode I fracture toughness are found in the literature but users are facing difficulties in selecting the most suitable. Novel fuzzy TOPSIS and fuzzy GRA MCDM methods are used for selecting the best experimental procedure. The ISRM-suggested cracked chevron notched Brazilian disc method is found to be most suitable by the novel methods. [ABSTRACT FROM AUTHOR]

Cauliflower is an important winter crop grown in India, its curds are rich in nutritional profile, containing valuable minerals and vitamins. However, cauliflower harvesting is mainly accomplished by hands, which is time-consuming and requires a high labour force. On the other hand, most developed cauliflower harvesters are once over or single pass type, which harvests all plants irrespective of their maturity. So, the selective harvester could improve the cauliflower curds yield, and then decrease the labour requirement. To improve the cutting performance of the selective cauliflower harvester, the working parameters of the chainsaw cutting mechanism need to be considered and optimized. This research investigates the impact of cutting height, feed (push) force, and cutting speed on the efficiency of the cutter during harvest. The Taguchi approach, together with grey relational analysis (GRA), was employed to identify the most favorable combination of operational parameters. In addition, the variance analysis was conducted to statistically examine the impact of multiple parameters. The findings indicated that the feed force was the major parameter that influenced the cutting force, splitting failure levels, and cutting time. The most effective parameter combination consisted of a cutting height of 15 mm, a feed force of 10 N, and a cutting speed of 5 m/s. The grey relational grade of the ideal parameter combination has shown a 0.322 increase in comparison to the grade achieved with the initially selected parameter combination. This setting was further incorporated in the developed selective cauliflower harvester to improve the performance of its cutting mechanism. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), also known as 3D printing, is a recent innovation in manufacturing, employing additive techniques rather than traditional subtractive methods. This study focuses on Directed Energy Deposition (DED), utilizing a blend of nickel-based superalloy IN 718 and stainless steel SS316 powders in varying ratios (25%+75%, 50%, and 75%+25%). The objective is to assess the impact of process parameters on quality and optimize them. Mechanical properties of the different powder mixtures are compared. In the study, Taguchi-grey relational analysis is employed for parameter optimization, with four key factors identified: laser power, overlap ratio, powder feed rate, and scanning speed, affecting cladding efficiency, deposition rate, and porosity. Verification experiments confirm optimization repeatability, and further fine-tuning is achieved through one-factor-at-a-time experiments. Optimized parameters yield varied tensile properties among different powder mixtures; for example, a 25% SS316L and 75% IN718 blend demonstrates the highest ultimate tensile strength (499.37 MPa), while a 50% SS316L and 50% IN718 blend exhibits the best elongation (13.53%). This study offers an effective approach for using DED technology to create mixed SS316 and IN718 powders, enabling tailored mechanical performance based on mixing ratios. [ABSTRACT FROM AUTHOR]

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement. [ABSTRACT FROM AUTHOR]

Slope stability prediction based on the grey entropy relational analysis (GERA) was carried out. First, the entropy weight method was applied to determine the weight of each influencing factor, and the GERA model was established. Then, 30 slope engineering cases were collected from the literature as a reference sample database, and another ten slope engineering cases were used as prediction samples. Finally, the GERA method was used to calculate the grey entropy relational degree between each prediction sample and 30 reference samples to evaluate the stability of the prediction sample slope. The results show that the stability predicted results of 10 slopes obtained by GERA are entirely consistent with the actual slope stability state. Case analysis confirms that the GERA model can effectively predict the slope stability state. [ABSTRACT FROM AUTHOR]

This study aimed to extend the storage time of fresh-cut lotus roots. A quantitative evaluation was conducted using the grey–correlation analysis method based on the variation–coefficient weight to observe the treatment of acetic acid (0, 2%, and 5%) and ozone (0, 0.3 mg/L, and 0.6 mg/L). Their effects on weight loss rate, browning degree, and the content of reducing sugar, ascorbic acid, and free amino acid were determined. Under varied treatments and storage time, the weight loss rate, browning degree, and content of nutritional components in fresh-cut lotus root differed. Result of the weighted correlation degree showed that for fresh-cut lotus root stored for more than 4 days, 0.3 mg/L ozone could be used as preservative to ensure the storage quality. These findings could provide a scientific basis for using acetic acid and ozone as preservative to extend the storage time of fresh-cut lotus root. [ABSTRACT FROM AUTHOR]

The selective catalytic reduction (SCR) system plays a crucial role in reducing diesel exhaust emissions. To further enhance the performance of the SCR system, a three-dimensional numerical simulation model was established and experimentally validated for its reliability. Subsequently, the length (L1) and porosity (ε1) of carrier 1, as well as the length (L2) and porosity (ε2) of carrier 2, were varied to achieve an axial non-uniform distribution of the catalysts. The impact of this axial non-uniform distribution on the SCR system's performance was investigated. Finally, a fuzzy grey relational analysis method was employed to evaluate and optimize the influence of the aforementioned four individual parameters on the SCR system's denitrification (de-NOx) efficiency and pressure drop. The results indicate that variations of L1 have the greatest impact on de-NOx efficiency, while changes of ε2 have the most significant effect on pressure drop. The fuzzy grey relational degrees of L1, L2, ε1, and ε2 on de-NOx efficiency are 0.8267, 0.6288, 0.7457, and 0.8233, respectively. Similarly, the fuzzy grey relational degrees of L1, L2, ε1, and ε2 on pressure drop are 0.3552, 0.4894, 0.1045, and 0.7931, respectively. Compared with uniformly distributed catalysts, axially non-uniform distributed catalysts can improve de-NOx efficiency by 2.22% and reduce pressure drop by 46 Pa. This study provides theoretical and engineering references for improving the SCR catalyst coating process. [ABSTRACT FROM AUTHOR]

Defect Density (DD) is a cornerstone metric in software quality assessment, influencing decisions across quality planning, testing strategies, and resource allocation. However, inherent uncertainties within software module data significantly impede accurate prediction of DD. This paper proposes a novel predictive framework that leverages the strengths of grey system theory and fuzzy logic to address data imprecision in software defect measurement. Our approach introduces a fuzzy grey relational similarity metric that capitalizes on the benefits of both fuzzy logic membership functions and grey relational analysis for similarity assessment. We perform a rigorous evaluation comparing our model with eight established machine learning algorithms across 28 publicly available software datasets. The proposed framework demonstrates superior performance, particularly in scenarios characterized by high sparsity levels in the DD variable. We further analyze the model's efficacy across varying data sparsity levels, categorizing datasets into four groups based on sparsity ratios. Our findings reveal the model's dominance over alternative prediction methods in datasets exhibiting high and very high sparsity. Notably, traditional techniques like multi-linear regression and multi-layer perceptron exhibit limitations in handling such challenges within this specific problem domain and data landscape. Conversely, ensemble learning techniques emerge as viable alternatives for datasets with lower sparsity levels. [ABSTRACT FROM AUTHOR]

Predicting regional carbon dioxide (CO2) emissions is essential for advancing toward global carbon neutrality. This study introduces a novel CO2 emissions prediction model tailored to the unique environmental, economic, and energy consumption of Shanghai Chongming. Utilizing an innovative hybrid approach, the study first applies grey relational analysis to evaluate the influence of economic activity, natural conditions, and energy consumption on CO2 emissions. This is followed by the implementation of a dual-channel pooled convolutional neural network (DCNN) that captures both local and global features of the data, enhanced through feature stacking. Gated recurrent unit (GRU) network then assesses the temporal aspects of these features, culminating in precise CO2 emission predictions for the region. The results indicate: (1) The proposed hybrid model achieves accurate predictions based on accounting data, with high precision, low error, and good stability. (2) The study found an overall increase in Chongming's carbon emissions from 2000 to 2022, with the prediction results being generally consistent with existing research findings. (3) The proposed method, based on Chongming's CO2 emission predictions, addresses issues such as the scarcity of effective accounting data and inaccuracies in traditional calculation methods. The results can provide effective technical support for local government policies on carbon reduction and promote sustainable development. [ABSTRACT FROM AUTHOR]

In the present study, a wear-resistant AA7075-Al2O3 composite coating was efficiently developed on an AA6082 substrate via the friction surfacing technique. Micro-sized Al2O3 particles were used as reinforcement material by filling in the blind holes drilled on the face of the consumable rod (AA7075) to develop the composite coating. The consumable rod rotational speed (1000–1400 rpm), number of blind holes (1–3) and travel speed (140–180 mm/min) were considered as input parameters. The composite coating's dry sliding wear, microhardness and bond strength were chosen as output response characteristics. Grey relational analysis was utilized to multi-objectively optimize the input parameters while the experiments were carried out in accordance with the Taguchi L9 orthogonal array. The multi-objective optimized input parameters were found to be the rod rotational speed of 1200 rpm, a travel speed of 140 mm/min, and three holes, respectively. Under optimal conditions, the bond strength of the AA7075-SiC composite coating is 170.4 MPa, and its wear (weight loss) is 56.02% and 41.6% less than that of AA7075 and AA6082, respectively. The microhardness of the composite coating developed at optimal parametric setting (174.2 HV0.1) was 99.98% and 11.31% higher than that of the substrate (AA6082) material (87.11 HV0.1) and rod (AA7075) material (156.5 HV0.1) respectively. [ABSTRACT FROM AUTHOR]

The present study aims to explore the suitable welding technique for Austenitic Stainless Steel (ASS) material through multi-criteria decision making approach i.e. Grey Relational Analysis (GRA). For this purpose, seven selection criteria are identified with the help of literature and expert' suggestions and assigned the same weightage to all selection criteria. We have identified eight most widely used welding techniques through the comprehensive literature review for stainless steel material. From the application of GRA approach, it is observed that Tungstun Inert Gas welding obtained 1st rank and found the most suitable welding process for ASS material as per selected criteria. This welding exhibits the minimal heat affected zone and better weld strength for ASS material. This study would facilitates to industrial person and engineers in selection of appropriate welding technique for ASS material with the specified performance characteristics. [ABSTRACT FROM AUTHOR]

The main objective of this study was to optimize the friction stir process parameters to fabricate agate particle reinforced AA6061-T6 Aluminum alloy surface composite for hardness and tensile strength. The surface composite samples were made using a CNC milling machine based on Taguchi's mixed L16 orthogonal array. The process parameters such as tool rotational speed, traverse speed, percent of reinforcement, number of passes and pin profiles were selected in this study. The optimal properties for the developed surface composite were obtained when surface composites having 18% reinforcement were fabricated by a two-pass FSP using a squared pin tool with a rotational speed of 1400 rpm and traverse speed of 60 mm/min. At the optimal process parameters, the tensile strength and hardness of the fabricated surface composite were found to be 312.23 MPa and 74.82 HRB respectively. ANOVA analysis performed at a 95% confidence level revealed that all process parameters were significant and the contribution of the number of passes on the properties of surface composite was higher than other process parameters. Microstructural analysis revealed that medium rotational speed and high traverse speed produced a uniform distribution of agate particles which helped to improve the properties. Article Highlights: A new aluminum alloy surface composite produced with friction stir processing. Studied the effects of process parameters on tensile strength and hardness of the surface composite. Grey relational analysis (GRA) identified the best process parameters for optimum properties. [ABSTRACT FROM AUTHOR]

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

We attempted to optimize the process-based parameters during friction stir welding of AZ80A - AZ31B Mg alloys with the objective of enhancing the mechanical properties of the fabricated joints. A response surface method based grey relational analysis was employed using three factors and three distinctive levels. A central composite design based multi-objective numerical model using the technique of grey relational analysis was formulated for optimizing the tool dependent parameters, namely tool's rotational speed, its speed of traverse and geometry of the pin. Grey relational grade was determined for all the responses: tensile strength and elongation percentage. Analysis of variance was employed for attaining grey relational grade to determine the most influential parameter of the FSW process. It was observed that the geometry of the tool pin had a greater impact in ascertaining the quality of the fabricated Mg alloy joints, and the tool possessing tapered cylindrical pin geometry exhibited larger values of grey relational grade. Optimized process parameter settings based on the attained GRG values were recorded to be 1100 rpm rotational speed, speed of traverse of 1.5 mm/sec and a tool with taper cylindrical pin geometry. The anticipated values were validated through confirmation investigational runs performed during employment of optimized parameter combinations, which exhibited a perfect agreement with the investigational run values and the confirmatory joint exhibited a tensile strength of 260.42 MPa and elongation percentage of 6.53. [ABSTRACT FROM AUTHOR]

With the increasing number of students majoring in art in universities, the improvement of the quality of art teaching in universities is becoming increasingly important. However, due to the unique characteristics of art teaching compared to other majors, in order to improve the quality of art teaching, it is necessary to follow the individual characteristics of art majors and students, and establish corresponding systems to ensure the steady improvement of teaching quality, while taking students as the main body. Thus, the overall quality of cultivating art talents in universities can be improved. The art education quality evaluation in higher education institutions is MAGDM. In recent years, the Exponential TODIM (ExpTODIM) and grey relational analysis (GRA) technique was employed to deal with MAGDM. The 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs) are employed as an effective tool for expressing uncertain decision information during the art teaching quality evaluation in higher education institutions. In this paper, the 2-tuple linguistic Pythagorean fuzzy number Exponential TODIM-GRA (2TLPFN-ExpTODIM-GRA) technique is implemented to deal with MAGDM under 2TLPFNs. Finally, a numerical example for art teaching quality evaluation in higher education institutions is implemented to verify the proposed technique. The research contributions of this work are outlined: (1) the entropy technique is employed to conduct the attribute's weight; (2) the 2TLPFN-ExpTODIM-GRA technique is implemented to implement the MAGDM under 2TLPFNs; (3) a numerical example for art teaching quality evaluation in higher education institutions and several comparative analysis is employed to validate the 2TLPFN-ExpTODIM-GRA technique. [ABSTRACT FROM AUTHOR]

The present research examines the impact of Si3N4–BN hybrid reinforcement on the machinability of a magnesium hybrid composite in wire electrical discharge machine (WEDM). The composite is fabricated through inert gas assisted stir casting route with silicon nitride and boron nitride as reinforcement with varying weight percentages of 0%, 5%, and 10%. Subsequently, the fabricated composites were machined through WEDM according to Taguchi 27 orthogonal array with varying pulse on time (PON), pulse off time (POFF), wire feed rate (WFR), and wire tension (WT). The machinability of the composite was evaluated by measuring Surface Roughness (SR), Kerf Width (KW), and Cutting Velocity (CV) during WEDM. Results reveals that the % of Si3N4 has greater influence over kerf width and cutting velocity whereas BN % has higher influence over surface roughness. The optimization of process parameters using the Taguchi method resulted in different combinations of parameters for each output response. Therefore, Grey Relational Analysis was applied to determine the common optimal process parameters for all three considered output responses. The identified input parameters that yielded the higher CV, minimal SR and KW were as follows: 0% Si3N4 and BN, 6 µs PON, 14 µs POFF, 6 m/min WFR, and 10 g WT. Artificial Neural Network model has been developed to predict the output response CV, SR and KW. The 6–8–3 network model predicts the output responses with better accuracy with overall R2 value of 99.3%. [ABSTRACT FROM AUTHOR]

In order to study the relationship between China's safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China's safety production, and the country's three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research. [ABSTRACT FROM AUTHOR]

Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River. [ABSTRACT FROM AUTHOR]

Hastelloy B2 is a high-performance nickel-molybdenum alloy and has been used in micro reactor, pump, pipe, and corrosion-resistant equipment. Square holes are required in the above application components. Although square holes are easily produced by spending more electrodes wear in Electrical Discharge Machining (EDM). Moreover, the square hole with small electrode wear achieved is a challenging task in thermal machining. Hence, in this work, the effects of EDM process parameters on electrode taper angle, electrode radius, electrode wear length and cutting time of Hastelloy B2 are studied. Taguchi based VišekriterijumskoKompromisnoRangiranje (VIKOR) method is used to find the optimum process parameters and compared with Taguchi based Grey Relational Analysis (GRA) method for validation. The results showed that current and pulse duration greatly affects the electrode wear out surface and edges. The square electrodes with sharp edges and surfaces are fully transformed into hemispherical shapes at electrode ends due to an unstable spark with diffusion of heat. Also, the VIKOR method provides an alternative multi-criteria optimization method that enhances the process performance over the grey relational analysis due to comparability, uniqueness, assessment and prediction. [ABSTRACT FROM AUTHOR]

This study investigates the effects of laser power, scan speed, and hatch distance on the features of aluminium specimens produced using direct metal laser sintering (DMLS). By systematically varying these parameters, we identified optimal combinations for producing high-quality metal components. Our findings were validated through reproducible printing processes. Analysis of variance (ANOVA) and grey relational analysis (GRA) were employed to optimize the production parameters further. We found a significant trade-off between laser power, tensile strength, and fatigue resistance, with laser power having the most substantial impact on mechanical properties, microstructure, and surface roughness. Statistical analysis confirmed that higher laser power improves mechanical characteristics but may increase surface roughness. These insights are crucial for enhancing the efficiency and quality of DMLS-produced metal components. [ABSTRACT FROM AUTHOR]

Accurately predicting the state of health (SOH) of lithium-ion batteries is fundamental in estimating their remaining lifespan. Various parameters such as voltage, current, and temperature significantly influence the battery's SOH. However, existing data-driven methods necessitate substantial data from the target domain for training, which hampers the assessment of lithium-ion battery health at the initial stage. To address these challenges, this paper introduces the multi-head attention-time convolution network (MHAT-TCN), amalgamating multi-head attention learning with random block dropout techniques. Additionally, it employs grey relational analysis (GRA) to select health indicators (HIs) highly correlated with battery capacity, thereby enhancing the accuracy of the model training. Employing leave-one-out crossvalidation (LOOCV), the MHAT-TCN network is pre-trained using data from batteries of the same model to facilitate comprehensive prediction of the target battery throughout its operational period. Results demonstrate that the MHAT-TCN network trained on HIs outperforms other models, enabling precise predictions across the entire operational period. [ABSTRACT FROM AUTHOR]

The current study explores the precision enhancement of AISI420 Martensitic Stainless Steel (MSS) using sinking-electrical discharge machining (S-EDM) with Copper electrodes, which is a unique combination of materials and machining process and conducts a comprehensive multivariate analysis to investigate the correlation between machine control variables (MCV) and measured machining performance (MMP) in the context of AISI420 Martensitic Stainless Steel and Sinking-Electrical Discharge Machining. The analysis of variance (ANOVA) establishes the hierarchy of machine control variables influence: Pulse current (B) > Gap voltage (A) > Pulse on Time (C). Remarkably, Pulse current (B) emerges as the paramount parameter, thus constituting a cornerstone of this study's findings. This research article utilizes the RSM–GRA–PCA methodology, which combines response surface methodology (RSM), grey relational analysis (GRA), and principal component analysis (PCA) to optimize the machining process. Using traditional RSM–GRA technique and RSM–GRA–PCA methodology, the experimental Grey Relational Grade (GRGexperiment) are achieved 0.8048 and 0.9817, respectively. The validation test has been performed to confirm the fittest method positions. The percentage significance of significant factor is also improved from 64.63 to 79.71% and error is reduced from 5.22 to 1.68% using RSM–GRA–PCA methodology with improved GRG of 0.068. This integrated approach improves the grey relational grade (GRG) and reduces errors, leading to more accurate and efficient machining. [ABSTRACT FROM AUTHOR]

Drilling precise micro-holes in glass material has brought a new challenge, primarily due to its application in microfluidic devices. It is required to reduce machining time and simultaneously achieve repeatability of the process. The machined hole should have minimum overcut to get desired hole diameter. Maximum hole circularity and minimum heat-affected zone are the essential hole characteristics to achieve controlled machining. The multi-criteria decision-making (MCDM) method is quite effective in selection of best possible combination of outputs from several alternative solutions. The experimental data from the previously published paper is used in the current study. As input parameters, the experiment data include voltage, tool feed rate, and machining time. The radial overcut (ROC), circularity of the machined hole and heat-affected zone (HAZ) were calculated as output responses. The experiments were conducted using copper and nickel-coated copper tools in previous study and response data were used in current work. The present study uses the methods which include hybrid grey relational analysis (GRA), technique for order performance by similarity to ideal solution (TOPSIS), and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods for the selection of the best combination of process parameters in drilling to obtain the optimal geometric characteristic of a hole, i.e. optimal values of ROC, circularity and HAZ, in glass using electrochemical discharge machining (ECDM). In this study, weight calculation for MCDM was proposed using entropy and analytic hierarchy process (AHP) methods and combined to get final weights using the fuzzy logic tool, which determines the importance of AHP and entropy weights using the expert opinion. The final weights were used to calculate the ranks from each MCDM method and determine optimal process parameter selection in ECDM hole drilling with copper and nickel-coated copper tools. The fuzzy logic method determined that the weight contribution from AHP is 0.586 in the present study. Genetic algorithm-based multi-objective optimization was conducted, and a non-dominated Pareto front was generated. The Spearman correlation method was used to determine the relationship between different MCDM methods. The current study will be helpful in selecting the best combination of process parameters in drilling holes during ECDM machining using MCDM methods. [ABSTRACT FROM AUTHOR]

The present work deals with the influence of the graphite and tin particles in the wear behavior of A356 alloy-based composites. The A356 alloy consists of 92% aluminum and 7% silicon. The A356/Gr/Sn composites were fabricated with the proportion of 10 wt.% Gr and varying wt.% of Sn (0, 5, and 10 wt.%) particles through the bottom pouring stir casting route. The inclusion of Sn with Gr in A356 alloy matrix took significant improvement in the wear characteristics of the composites. Process parameters such as wt.% of tin, normal load, and sliding speed have been considered for performing the wear test. The Box–Behnken design in response surface methodology was chosen for conducting the wear study because of its ability to efficiently explore the response surface with fewer experiments, ensuring statistical rigor in optimizing multiple variables within a balanced experimental domain. Grey relational analysis (GRA) has been used to optimize the wear study parameters. Grey-fuzzy reasoning grade analysis was used to check the uncertainty in the GRA results. The study used main effect plots to analyze the GFRG values for the various input variables. An artificial neural network model was created to create the relationship model between the input conditions and output responses. Scanning electron microscope micrographs were used to test the worn samples' surfaces. [ABSTRACT FROM AUTHOR]

Glass fiber-reinforced polymer (GFRP) composites are extensively used in many sectors because of their high strength, low weight, and resistance to corrosion properties. Drilling operations are needed to make holes for the joining of the components. Various defects were reported during the drilling, and the authors in this study tried to provide an approach to minimize the drilling defects. Selection of the optimum level of the input parameters can reduce defects generated during drilling. This study used grey relational analysis to optimize input parameters during glass fiber-reinforced composite drilling. Three tools (T1, T2, T3) with point angles (118°, 135°, 140°), feed rate in mm/min (10, 20, 30), and spindle speed in rpm (1000, 2000, 3000) were taken as input parameters. Taguchi L27 orthogonal arrays were selected for the three factors at three levels. Machining force (Fm), surface roughness (Ra), peel-up delamination factor (DF1), push-out delamination factor (DF2), hole size error, and circularity error were taken as output responses. Drilling GFRP composite by considering these input parameters combined with all these output responses using grey relational analysis was not explored before. The effect of input parameters on individual output response was analyzed using Taguchi and analysis of variance. Feed rate influenced machining force the most (65.34%), tool geometry influenced surface roughness the most (38.32%), and spindle speed influenced the push-out delamination factor the most (18.11%). SEM analysis was done to explain the aftereffects of drilling. [ABSTRACT FROM AUTHOR]

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research. [ABSTRACT FROM AUTHOR]

The competition between real estate enterprises in the future is likely to be between supply chains, and strategic general contracting is an important part. Implementing strategic construction general contracting procurement is beneficial for improving procurement efficiency and transparency, saving procurement costs, improving procurement quality, achieving economies of scale, and opening up or enhancing the core competitiveness of real estate enterprises. It has practical and guiding significance for the long-term and good development of real estate enterprises. The capability evaluation of real estate general contractors is a multiple-attribute decision-making (MADM) problem. Recently, the Exponential TODIM(ExpTODIM) and (grey relational analysis) GRA technique has been used to cope with MADM issue. The hesitant triangular fuzzy sets (HTFSs) are used as a tool for characterizing uncertain information during the capability evaluation of real estate general contractors. In this paper, the hesitant triangular fuzzy Exponential TODIM-GRA (HTF-ExpTODIM-GRA) technique is built to solve the MADM under HTFSs. In the end, a numerical case study for capability evaluation of real estate general contractors and some comparative analysis is given to validate the proposed technique. The main contribution of this paper is managed: (1) The MEREC technique is constructed to obtain weight values under HTFSs; (2) an integrated HTF-ExpTODIM-GRA technique is constructed to manage the MADM issue; (3) An illustrative example for capability evaluation of real estate general contractors has accomplished to verify the HTF-ExpTODIM-GRA technique. [ABSTRACT FROM AUTHOR]

Fluid leakage caused by debonding at the cement interfaces driven by low-temperature fluids is recognized as a significant challenge to wellbore integrity. Previous studies have primarily focused on casing/cement interface debonding, without thoroughly analyzing the simultaneous propagation of debonding fractures at the casing/cement interface and cement/formation interface. Furthermore, prior investigations have been conducted based on hydro-mechanical coupling analysis, neglecting the influence of injection fluid temperature on interface debonding. Therefore, this paper aims to reveal the debonding mechanism of the two cement interfaces using a coupled thermal–hydrological–mechanical (THM) modeling approach. Considering the effect of injected fluid temperature, a 3D numerical model of casing-cement-formation is developed for the simulation of the fluid-driven debonding based on the coupled pore pressure cohesive zone method. The influences of various factors including injection fluid temperature, flow rate, viscosity, casing pressure and cement Young's modulus on the cement sheath debonding are studied, and grey relational analysis is carried out on the influence degree of different factors. The results indicate that the fracture propagation pressure (FPP) is higher, and the fracture height is lower at the casing/cement interface compared to the cement/formation interface. When the fluid temperature is considered by the model, the FPP at the cement sheath interface is lower, and fracture initiation and propagation are more likely to occur. The results also demonstrate that higher fluid temperature, flow rate, viscosity and casing pressure increase the FPP and reduce the risk of cement sheath interface debonding. Casing pressure and fluid temperature exhibit a more prominent influence on FPP. The method can effectively predict the propagation of debonding failures at the cement sheath interface, and provide a foundation for the optimization of injection parameters. Highlights: This paper investigates the simultaneous propagation of the fluid-driven debonding of two cement interfaces considering the injected fluid temperature. A novel numerical model considering the fully coupled thermal–hydrological–mechanical processes is developed. Effects of injection fluid temperature, flow rate, viscosity, casing pressure and cement Young's modulus on the interface debonding are examined. A grey relational analysis is conducted to rank the influence degree of different factors. [ABSTRACT FROM AUTHOR]

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

Improving the machining efficiency in form of better surface quality and improved tool life is always challenging. This is due to the high amount of heat produced during machining. Hence effective measures are required to control the heat generated. In this response, conventional coolants are applied to achieve cooling and lubricating effect in machining. However, these cutting fluids affect the operator's health and environmental resources. Considering this, the aim of the work is to assess the role of minimum quantity lubrication (MQL) compared to dry and flood cooling. The turning tests are performed under the defined machining environments to find the most suitable combination of parameters to minimize the tool wear and surface roughness. The Taguchi orthogonal arrays are incorporated to evaluate the 27 different combinations of variable working parameters and assess them. The lubricating conditions are varied as per the experimental design. Three different lubricating conditions are used: dry lubrication, flood lubrication and MQL. A statistical analysis of obtained results is performed to understand the effect of machining variables on output responses. A multi-response optimization is conducted to minimize surface roughness and tool wear by utilizing Grey relational analysis. Finally, the most suitable combination of parameters is suggested based on the obtained grey relational grade. [ABSTRACT FROM AUTHOR]

The Gas Tungsten Arc Welding (GTAW) approach is employed in this research study to join the thin sheet (1.6 mm) of Super Duplex Stainless Steel (SDSS). Process parameters such as welding current (65–85 A), voltage (12–20 V), and welding speed (250–450 mm/min) are used to compute heat input. To anticipate all potential experiments and outcomes, an artificial neural network is deployed. The Taguchi technique and grey relational analysis (GRA) is employed to optimize the welding conditions. To identify the best suitable parameters, the Taguchi L25 orthogonal array technique is employed. The parameters 1-3-4-5 have been established by GRA. The welding current, voltage, welding speed, and gas flow rate were discovered to be 65 A, 16 V, 400 mm/s, and 12 L/min, respectively. After determining the optimal conditions, the butt joint of SDSS material is fabricated using the GTAW technique. The welded joint's microstructure, mechanical characteristics (tensile test, three-point bending, and hardness), and corrosion rate has been investigated. Based on the acquired experimental data, artificial intelligence tools have evolved and the capability of evolved tools has been analyzed. The outcomes of the analysis prove that the model is competent enough to predict the desired process variables. [ABSTRACT FROM AUTHOR]