Grey Relational Grade Analysis Research Articles

Effect of process variables on Ultimaker 2+ 3D FDM printed Tough PLA parts

ABSTRACT Fused Deposition Modeling (FDM) is pivotal in creating functional prototypes and end-use parts, with Tough PLA emerging as a favored material due to its durability. However, fine-tuning printing process variables for Tough PLA on the Ultimaker 2 + 3D printer remains challenging. This study employs Taguchi Grey relational analysis to optimize these process variables. L09 Taguchi’s systematic approach and Taguchi Grey Relational Grade Analysis (TGRGA) investigated Layer Thickness (LT), Infill Density (ID), and Nozzle Temperature (NT) at varying levels for which response variables such as Ultimate Tensile Strength (UTS), Average hardness (AH), and Surface Roughness (Ra) are evaluated. Utilizing Taguchi Grey Relational Grade Analysis (TGRGA), enhancements in ultimate tensile strength from 43.182 N/mm2 to 46.56 N/mm2, average hardness from 64 to 67, and Surface roughness from 8.60 µm to 8.35 µm are achieved. This study contributes to advancing additive manufacturing optimization, offering insights into refining Tough PLA part production on the Ultimaker 2 + 3D printer for bio implants like stent application.

Physics-informed spatio-temporal hybrid neural networks for predicting remaining useful life in aircraft engine

Aircraft engines have complex structures and various operating conditions, which leads to the health monitoring data of aircraft engines’ high coupling in the spatial domain and time invariance in the temporal domain. Existing methods for remaining useful life prediction of aircraft engines are deficient in handling spatial data and spatial–temporal data fusion. To address this problem, a novel method named physics-informed spatio-temporal hybrid neural networks (PI-STHNN) is proposed. In this study, a spatial domain data association graph for aircraft engines is constructed by utilizing a thermal cycle physical model as prior knowledge and combining it with the grey relational grade analysis method. Specifically, the proposed spatio-temporal hybrid neural network framework extracts the temporal features and spatial coupling features of the data in parallel. Then attention mechanism is employed to weigh and fuse all feature representations. Finally, the method is validated on the two aircraft engine datasets, the results show that PI-STHNN outperforms current state-of-the-art methods. Furthermore, our analysis sheds light on the advantages of incorporating physical information into graph neural network models and visually presents the attention weights to elucidate the contributions of time-domain and spatial-domain models within our hybrid approach.

Study on Settlement of Self-Compacting Solidified Soil in Foundation Pit Backfilling Based on GA-BP Neural Network Model

In order to predict the settlement of self-compacting solidified soil in foundation pit backfilling, finite element software is used to study the influence of soil properties and the surrounding structural properties of the foundation pit on the settlement of backfilled self-compacting solidified soil based on a foundation pit project in the city of Nanjing. The degree of influence of various factors influencing settlement is considered, a grey relational grade analysis is conducted, and input layer parameters of the neural network are determined based on the results of the grey relational grade analysis. Based on the GA-BP neural network model, the settlement of soil is predicted using numerical simulation results. The results reveal that the settlement and structural disturbance of self-compacting solidified soil after backfilling are smaller than those of fine silty sand; self-compacting solidified soil significantly improves the engineering performance of excavated soil. In the grey relational grade analysis, the six influencing factors that have high correlation with soil settlement can be used as input layer parameters for the neural network model. Among them, the correlation degree between elastic modulus and soil settlement is the highest, reaching 0.8402. The correlation degrees of the remaining five influencing factors are above 0.5, and the values are close. The GA-BP neural network can improve the overfitting situation of a BP neural network trapped in local optima, with R2 reaching 0.9999 and RMSE only 0.0018 mm, achieving high-precision prediction of settlement of self-compacting solidified soil.

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

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

Optimization of process parameters in ECDM machining using Taguchi based grey relation analysis

Electrochemical discharge machining (ECDM) is a hybrid machining process capable of micromachining conductive and nonconductive materials. The recent development and application of nonconductive materials, such as glass, composites, and ceramics in MEMS and microfluidic devices, have emphasized studying the ECDM process for micro holes and channel formation. The current study uses Taguchi's L27 orthogonal array to design the experiments, evaluate responses by the signal-to-noise ratio, analyze the variance of process parameters (ANOVA), and predict optimal settings for a multi-objective response using grey relation analysis (GRA). The experiments were conducted using two tools, a bare copper tool and Nickel coated copper tool, over a glass substrate. The machining process's output characteristics (radial overcut, heat-affected zone, and hole circularity) were studied by varying input parameters such as applied voltage, tool feed rate, and machining time. The experimental results revealed that Nickel coated Cu tool reduces the average hole overcut and heat-affected zone width and increases the overall circularity of the hole as compared to the bare Cu tool. Based on grey relational grade analysis, the optimal parameters were identified: the applied voltage of 36 V, tool feed rate of 110 µm/min, and one-minute machining time with Nickel coated copper tool. In contrast, a bare copper tool electrode provides optimal process parameters as an applied voltage of 36 V, 110 µm/min tool feed, and a machining time of two minutes for drilling holes in borosilicate glass.

Parametric Optimization In Drilling Process Parameters For Machining Of Glass Fibre Reinforced Composites Using Grey Relational Grade Analysis

The aim of the research is to optimize the drilling process parameters while drilling of glass fi bre reinforced composites using solid carbide drill by integrating response surface methodology and Taguchi technique. Drilling process parameters includes cutting speed, Feed rate and point angle with three levels. The responses measured against these drilling parameters are; material removal rate, surface roughness, oversize and delamination factor. The ANOVA analysis is carried out for the responses and the regression equations are developed and checked for its adequacy. The R-squared value for material removal rate is found to be 99.40%, for surface roughness is found to be 96.81% , for over size is found to be 98.2% and for delamination factor is found to be 96.46% and these values are acceptable. Also Taguchi grey relational grade analysis is carried and the grey relational coeffi cients are determined and they are ranked. The optimal parameter is fi nally determined using the grey relational analysis and the experiments are validated.

Optimization of WEDM Process Parameters in Al2024‐Li‐Si3N4 MMC

The present study focuses on optimization of operating parameters in wire electric discharge machining of AA2024 aluminium alloy reinforced with lithium and silicon nitride particles. Aluminium composite was produced through the two‐step stir casting route with the combination of 2% lithium and 10% silicon nitride reinforcements. Experiments were performed using the Taguchi design of experiments to optimize the selected input parameters such as pulse on time, pulse off time, current and wire feed for the response parameter, material removal rate, and surface roughness. An ANOVA‐based regression equation with genetic algorithm was used to optimize the input variables. The gray relational grade was also performed to optimize multiple performance characteristics. Taguchi‐based optimization analysis results in wire feed as the domination factor for material removal rate and surface roughness. Increased wire feed increases the material removal rate with good surface finish as confirmed from gray relational grade analysis. Regression equation generated results with minimum error (<2%) proving the accuracy of the investigation. A genetic algorithm‐based study also confirms the analysis of Taguchi and gray relational grade. The wire feed rate at 3 m/min and pulse on time of 120 microseconds were found to be similar for material removal rate and surface finish. Current at 50 A increases the material removal rate and current at 30 A results in good surface finish.

Research on displacement prediction of step-type landslide under the influence of various environmental factors based on intelligent WCA-ELM in the Three Gorges Reservoir area

Landslides are one of the most destructive geological disasters and have been caused many casualties and economic losses every year in the world. The reservoir area formed by the world's largest hydropower project, Three Gorges Hydropower project of China, has become a natural testing ground for landslide prediction in the hope of reducing losses. In this paper, a new algorithm with strong optimization ability, the water cycle algorithm (WCA), is combined with the extreme learning machine (ELM) to improve the prediction accuracy of step-wise landslide. The gray relational grade analysis method was adopted to determine the main influencing factors of the landslide's periodic displacement. Then, the determined factors were used as the input items of the proposed WCA-ELM model, and the corresponding periodic displacement was used as the model output item. Taking the Liujiabao landslide in the Three Gorges Reservoir area as a case history, the proposed model was verified through a comparison with the measurements. The results showed that the model has a faster convergence rate and higher prediction accuracy than the traditional back-propagation neural network model and ELM-model. The water cycle algorithm is suitable for optimizing the accuracy of the extreme learning machine model in landslide prediction.

Novel application of graphite-talc hybrid nanoparticle enriched cutting fluid in turning operation

In this study, the influence of hybrid nanocutting fluid (both graphite and talc nanoparticles dispersed in a base fluid) in turning of Titanium alloy grade 5. The hybrid nanocutting fluid was developed by the blending of graphite and talc nanoparticles in a constant volumetric proportion (50:50) in pure coconut oil as a base fluid. The prepared hybrid nanocutting fluid has been investigated for its tribological behaviour using a pin-on-disc machine. The Gray relational analysis (GRA) is applied as a conservative approach in the optimization of process variables of Titanium alloy with multiple performance characteristics. The turning performance of the hybrid nanocutting fluid is compared with that of pure coconut oil in terms of cutting force and surface roughness. From the Gray relational grade analysis, it is obtained that the feed rate has a larger influence on responses as compared to cutting speed and nanoparticle concentration as well. By the application of hybrid nanocutting fluid, it is obtained a significant reduction in cutting force and surface roughness compared to pure coconut oil by 21.19 % and 18.9 %, respectively.

Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction

The landslide caused a huge disaster to the living environment and seriously threatened the lives and property safety of nearby residents. Assess or predict landslide-susceptible the landslide displacement through monitoring are great beneficial to guide landslide control and mitigate these hazards by taking appropriate preparatory measures. In this paper, a new water cycle algorithm optimization BP neural network (BPNN) dynamic prediction model (WCA-BPNN) was established to make up for the shortcoming of BPNN convergence speed. A typical step-wise landslide——Langshuwan Landslide happened in the Three Gorges Reservoir area of China is taken as a case, and the displacement monitoring data of 4 years was used for time series analysis and modeling. The long-term creep effect of the landslide and the short-term acceleration effect of the climate are considered in the model, and the accumulative displacement is divided into two kinds of trend displacement and periodic displacement. The key influencing factors of landslide periodic displacement were screened by gray relational grade analysis method, and then used as learning data. In addition to comparing the predicted value of the model with the measured value, it also compares the accuracy of the three models of BPNN, support vector machine, extreme learning machine under the training conditions of the same learning data set. The results show that the WAC-BPNN model has faster convergence speed and higher prediction accuracy than the traditional BPNN model, and it is also the most accurate of the four models.

A Multi-Criteria Decision-Making Approach for Process Improvement of E-Jet: An Experimental Investigation

E-jet is a novel microfabrication technology of producing high-resolution patterns for various application areas like printed electronics, biotechnology, etc. Judicious selection of the operating scenario can improve the quality of the fabrication performance of E-jet. The objective of this study is to experimentally evaluate different operating scenarios of the E-jet microfabrication process while considering the deposited droplet diameter and droplet ejection frequency as performance characteristics simultaneously. Experimentations were carried out on the developed E-jet setup according to the design of experiment technique considering nozzle stand-off height, applied voltage, and ink flow rate as process control parameters. The effect of each control parameter on the process response is investigated. The relative weight values of each performance characteristic or response variable are determined by principal component analysis, which makes the weight evaluation procedure more rigorous and eliminates the dependence on the practitioner’s judgment. A hybrid grey relational grade analysis and technique for order preference by similarity to ideal solution methodology is employed to evaluate the optimal operating scenario of E-jet. Both methodologies indicated a similar desirable operating condition for E-jet. Moreover, the variance study called analysis of variance is employed to discover the pattern in which the control parameters affect the fabrication process. The variance study suggests that the ink flow rate is the most dominant parameter in the experimental domain.

Multiresponse Parameter Optimization for the Composite Tape Winding Process Based on GRA and RSM

Composite tape winding is an important forming process of composite materials, which can ensure good performance of composite products. Selection and control of key process parameters in the winding process have a great influence on the properties of products, such as void content and residual stress of products. Through experimental analysis, the residual stress and void content of the composite products are not the minimum when the prepreg winding process is carried out by using the empirical process parameters. To solve this multiobjective optimization problem, experiments were conducted using the Box–Behnken design. The multiobjective optimization problem is converted to a single-objective problem using grey relational analysis (GRA). Principal component analysis (PCA) is used to quantify the relative contributions of residual stress and void content. Regression analysis of grey relational grade (GRG) based on the experimental data was used to develop a second-order GRG prediction model. The winding process parameters were optimized with response surface methodology (RSM), and the winding experiments were carried out with these parameters. The experimental results show that the best combination of process parameters yields the best GRG results with better void content and residual stress.

Parametric design analysis of meridional deflection stresses in metal expansion bellows using gray relational grade

Unpredictable and uncertain failure of the bellows needs the attention of the designer toward maximum meridional deflection stresses generated on the U-shaped convolutions. Evaluation of these maximum stress points on the flexible convolutions is multifaceted. This paper deals with the identification of these stresses induced on the bellows convolutions by analytical and experimental methods. Further, the influence of the pivotal design parameters on meridional deflection stresses is studied mainly in two aspects: (1) effect of number of convolutions (N) and pitch diameter (dp) on the meridional deflection stresses and (2) the determination of the optimized design parameters to reduce the stress levels using effective multi-response gray relational grade (GRG) optimization technique. Three bellows of stainless steel material (Grade SS321) are used for the experimentation with a different number of convolutions (N) and pitch diameters (dp). The optimal design parameter setting is found by using GRG analysis through ANOVA and regression mathematical model. The effect of design factors on GRG is analyzed. Besides, using GRG, the regression model enhances to give alternative optimal solutions; in turn, it increases the designer’s choice for the selection of the parametric levels. In the confirmation test, it is seen that the average improvement in GRG for all selected Taguchi orthogonal runs is 23%.

Multiresponse Optimization of Cutting Force and Cutting Power in Chopping Agricultural Residues Using Grey-Based Taguchi Method

Agricultural residues are the most abundant biomass and forage resources. Chopping is a precondition for either use, and reducing the shearing force has been considered as one of the most effective way to save energy. A smaller force can also permit more compact mechanical parts of chopping devices. In several cases, power consumption may be large, even when the shearing force is small, if the corresponding cutting velocity is high. Consequently, it is important to minimize both cutting force and power consumption simultaneously. This paper presents a practical approach to solve the multi-objective optimization problem for chopping processes of corn stalks. Applying the Taguchi design of experiments, a plan of 27 chopping tests was conducted. Based on the grey relational grade analysis and analysis of variance technique, optimum levels as well as the percentage contribution of such parameters were identified. Experimental results showed that the multi-response optimization problem in chopping corn stalks can be effectively addressed through the combination of Taguchi design and grey relational analysis. The results can be expanded for practical applications in design and operation of chopping machines for agricultural residues.

Research on Investment Demand Forecast of Power Grid Based on Grey System Theory

The forecast of power grid investment demand is of great significance for reasonably arranging the investment of power grid construction funds and reducing the cost of funds. In this paper, a forecasting model of power grid investment demand based on grey system theory is proposed. Firstly, the correlation coefficient method is used to analyze the correlation between the investment demand of power grid and its influencing factors, and the key factors affecting the investment demand of power grid are selected. Then the grey relational grade analysis theory is used to construct the investment demand forecasting model of power grid. The validity and rationality of the model are verified by an example of power grid investment prediction in a certain area. The model is of great theoretical and practical significance for the coordination of power grid investment and the scientific planning of power grid investment scale.

An NIRS-based assay of chemical composition and biomass digestibility for rapid selection of Jerusalem artichoke clones

BackgroundHigh-throughput evaluation of lignocellulosic biomass feedstock quality is the key to the successful commercialization of bioethanol production. Currently, wet chemical methods for the determination of chemical composition and biomass digestibility are expensive and time-consuming, thus hindering comprehensive feedstock quality assessments based on these biomass specifications. To find the ideal bioethanol feedstock, we perform a near-infrared spectroscopic (NIRS) assay to rapidly and comprehensively analyze the chemical composition and biomass digestibility of 59 Jerusalem artichoke (Helianthus tuberosus L., abbreviated JA) clones collected from 24 provinces in six regions of China.ResultsThe distinct geographical distribution of JA accessions generated varied chemical composition as well as related biomass digestibility (after soluble sugars extraction and mild alkali pretreatment). Notably, the soluble sugars, cellulose, hemicellulose, lignin, ash, and released hexoses, pentoses, and total carbohydrates were rapidly and perfectly predicted by partial least squares regression coupled with model population analyses (MPA), which exhibited significantly higher predictive performance than controls. Subsequently, grey relational grade analysis was employed to correlate chemical composition and biomass digestibility with feedstock quality score (FQS), resulting in the assignment of tested JA clones to five feedstock quality grades (FQGs). Ultimately, the FQGs of JA clones were successfully classified using partial least squares-discriminant analysis model coupled with MPA, attaining a significantly higher correct rate of 97.8% in the calibration subset and 91.1% in the validation subset.ConclusionsBased on the diversity of JA clones, the present study has not only rapidly and precisely examined the biomass composition and digestibility with MPA-optimized NIRS models but has also selected the ideal JA clones according to FQS. This method provides a new insight into the selection of ideal bioethanol feedstock for high-efficiency bioethanol production.

Microchannels Fabrication in Alumina Ceramic Using Direct Nd:YAG Laser Writing.

Ceramic microchannels have important applications in different microscale systems like microreactors, microfluidic devices and microchemical systems. However, ceramics are considered difficult to manufacture owing to their wear and heat resistance capabilities. In this study, microchannels are developed in alumina ceramic using direct Nd:YAG laser writing. The laser beam with a characteristic pulse width of 10 µs and a beam spot diameter of 30 µm is used to make 200 µm width microchannels with different depths. The effects of laser beam intensity and pulse overlaps on dimensional accuracy and material removal rate have been investigated using different scanning patterns. It is found that beam intensity has a major influence on dimensional accuracy and material removal rate. Optimum parameter settings are found using grey relational grade analysis. It is concluded that low intensity and low to medium pulse overlap should be used for better dimensional accuracy. This study facilitates further understanding of laser material interaction for different process parameters and presents optimum laser process parameters for the fabrication of microchannel in alumina ceramic.

Investigation of drilling parameters on hybrid polymer composites using grey relational analysis, regression, fuzzy logic, and ANN models

Among many machining operations, drilling has become one of the important machining operations performed in polymer composites. The quality of the drilled hole is closely associated with the drilling parameters and conditions. The current work focuses on the optimization of multiple response characteristics during drilling of hybrid glass fiber reinforced polymeric nanocomposites. Taguchi’s L25, orthogonal array is used to conduct the experiments and for optimization of the process parameters. The machining parameters such as spindle speed, feed rate, and drill diameter are optimized for the response which includes delamination, thrust force and torque via grey relational analysis technique. From the grey relational grade analysis, it is clear that the drill diameter is the most influencing factor followed by the feed rate and the spindle speed. The optimized process parameter settings were found as spindle speed of 2700 rpm, the feed rate of 30 mm/min and drill diameter of 4 mm, respectively, for lower delamination, torque and thrust force. Among the various modeling techniques used, ANN is found to be suitable for the process with minimum error percentage of 0.526.

Comparison and Identification of Suitable Multi-Response Optimization Technique for Laser Welding Process Parameters

In the present research work, comparison of four different multi-response optimization techniques viz., multi-response signal-to-noise ratio (MRSN), weighted signal-to-noise ratio (WSN), grey relational grade analysis (GRA) and the technique for order preference by similarity to ideal solution method (TOPSIS) have been applied in a case study of laser welding process for joining automotive gears of 16MnCr5 Alloy Steel. It has been observed that (MRSN) method gives the closest results and can be considered as the best suitable method for this case. The authors conclude that this approach could be applicable for any manufacturing application.

Investigation of Dimensional Accuracy and Material Performance in Fused Deposition Modeling

This paper presents the research work done to determine the influence of process parameters and their interactions on the dimensional accuracies of the specimens fabricated using two different materials namely Acrylonitrile Butadiene styrene (ABS) and Poly-Lactic Acid (PLA) of Fused Deposition Modeling (FDM) process. Three process parameters namely, Layer thickness, Raster angle and Build orientation are considered to determine the dimensional accuracy of each specimen. Grey relational grade analysis is used to determine the optimum parameter level setting so as to minimize the percentage change in length (L), width (W), and thickness (T) of the parts. The dimensional accuracy of the parts fabricated is within the desired range and can be improved by appropriate control of machine process parameters