Grey Relational Degree Research Articles

On optimization of Spin-Forming Process Parameters for Magnesium Alloy Wheel Hub Based on Gray Relational Analysis.

To study the influencing factors of process parameters on the wall thickness deviation and internal warpage deviation of the workpiece in magnesium alloy wheel hub spin molding, a two-pass heterogeneous spin molding model is proposed. To ensure the accuracy of the simulation results, the stress-strain data of AZ31 magnesium alloy at different temperatures and different strain rates were obtained through tests. Wall thickness deviation and internal warp deviation after molding were used as evaluation indexes of workpiece molding quality. ABAQUS software facilitated the numerical simulation and analysis of the magnesium alloy wheel hub spinning process. Gray relational degree analysis optimized the first-pass process parameters, elucidating the impact of the axial offset, the thinning ratio, and the feed ratio on forming quality. The application of optimized parameters in the hub spinning simulation resulted in a substantial 28.84% reduction in wall thickness deviation and a 4.88% reduction in inner diameter deviation. This study underscores the efficacy of employing Gray Relational Analysis for comprehensive parameter optimization, ensuring wheel hub quality. Moreover, it provides a theoretical foundation for enterprises to expedite research and development cycles and minimize associated costs.

A novel grey relational clustering model under sequential three-way decision framework

In the context of addressing clustering problems with small samples, grey relational clustering (GRC) plays a crucial role. Currently, a three-way GRC has gained popularity, particularly for its ability to handle the uncertain relationship between objects and classes. However, explicit clustering for some objects may not be achievable when employing the one-step decision, which is often a coarse-grained approach. In such cases, re-clustering these objects with a finer granularity becomes necessary. This is where the sequential three-way decision (STWD) proves to be an effective solution. Therefore, this paper is dedicated to designing a novel GRC under the STWD framework, namely GRC-STWD. Specifically, recognizing that some existing threshold calculations in GRCs can be overly subjective and that a pair-wise global threshold may not efficiently leverage available information for accurate object clustering, we introduce a novel threshold algorithm to address these limitations. Furthermore, we propose a conditional probability calculation method combining TOPSIS and grey relational degrees for clustering data lacking decision attributes or class labels. As the development of GRC within portfolio environments remains relatively nascent, we apply the proposed model to portfolio strategy construction. Finally, comparative and experimental analyses validate the model's effectiveness and feasibility. Notably, the developed model serves as a generalization of GRC, contributing to the broader advancement of GRC and STWD methodologies.

Study of Synthesis of Dual-Curing Thermoplastic Polyurethane Hot-Melt Adhesive and Optimization by Using Gray Relational Analysis to Apply in Fabric Industry to Solve Seamless Bonding Issues.

People wear clothes for warmth, survival and necessity in modern life, but in the modern era, eco-friendliness, shortened production times, design and intelligence also matter. To determine the relationship between data series and verify the proximity of each data series, a gray relational analysis, or GRA, is applied to textiles, where seamless bonding technology enhances the bond between components. In this study, a polyurethane prepolymer, 2-hydroxyethyl acrylate (2-HEA) as an end-capping agent and n-octyl acrylate (ODA) as a photoinitiator were used to synthesize a dual-curing polyurethane hot-melt adhesive. Taguchi quality engineering and a gray relational analysis were used to discuss the influence of different mole ratios of NCO:OH and the effect of the molar ratio of the addition of octyl decyl acrylate on the mechanical strength. The Fourier transform infrared spectroscopy (FTIR) results showed the termination of the prepolymer's polymerization reaction and the C=O peak intensity at 1730 cm-1, indicating efficient bonding to the main chain. Advanced Polymer Chromatography (APC) was used to investigate the high-molecular-weight (20,000-30,000) polyurethane polymer bonded with octyl decyl acrylate to achieve a photothermosetting effect. The thermogravimetric analysis (TGA) results showed that the thermal decomposition temperature of the polyurethane hot-melt adhesive also increased, and they showed the highest pyrolysis temperature (349.89 °C) for the polyhydric alcohols. Furthermore, high peel strength (1.68 kg/cm) and shear strength (34.94 kg/cm2) values were detected with the dual-cure photothermosetting polyurethane hot-melt adhesive. The signal-to-noise ratio was also used to generate the gray relational degree. It was observed that the best parameter ratio of NCO:OH was 4:1 with five moles of monomer. The Taguchi quality engineering method was used to find the parameters of single-quality optimization, and then the gray relation calculation was used to obtain the parameter combination of multi-quality optimization for thermosetting the polyurethane hot-melt adhesive. The study aims to meet the requirements of seamless bonding in textile factories and optimize experimental parameter design by setting target values that can effectively increase production speed and reduce processing time and costs as well.

Grey-Taguchi Approach for Optimizing FSW Parameters in Joining AA6262 and AA5083 Alloys

T his study concentrates on the friction stir welding (FSW) joining of dissimilar AA6262 and AA5083 plates. Four input parameters, namely tool rotational speed (TRS), welding speed (WSP), tool tilt angle (TTA), and pin depth (PDP) are chosen to analyze the output responses of tensile strength (UTS), hardness (HBN), and wear rate (WTR) in welding. The primary objective is to optimize the variables of the FSW process for AA6262 and AA5083 alloys using a combination of Taguchi and Grey Relational Analysis (GRA) methods, while also comprehending the correlations among process variables through the application of these two methods. Utilizing the Taguchi approach, L27 orthogonal array is constructed for conducting FSW experiments. After this, the multi-objective optimization challenge is streamlined into a single-objective optimization task by leveraging the grey relational degree. The significance and impact of the distinct variables are elucidated through the application of Analysis of Variance (ANOVA). Confirmation tests and predictive analysis are performed to validate the optimal variables, which are then compared against the acquired outcomes. The results highlight the welding speed as the paramount parameter, exerting substantial influence over the output responses of the welds in contrast to the other variables under examination. The application of the Grey-Taguchi methodology yields enhancements in Tensile Strength (245.23 MPa) and Hardness (99.2 Hv), while concurrently decreasing the Wear Rate (0.00456 m3/Nm). Furthermore, the Grey Relational Grade exhibits an advancement of 0.1312. These findings collectively signify an increased operational efficiency in the welding process, particularly for dissimilar alloys.

Prediction of Baltic Dry Index Based on GRA-BiLSTM Combined Model

The Baltic dry index (BDI) is not only one of the most important indicators of shipping costs but is also an important barometer of global trade and manufacturing sentiment. The BDI is highly volatile and subject to complex factors, which make it difficult to predict. In this paper, a neural network model-based BDI forecasting system was proposed to effectively forecast the BDI. We used the gray relational degree analysis method to select seven factors with higher correlation from 15 factors affecting the variation of BDI index to be used as input indicators for the bi-directional long short-term memory (BiLSTM) model to forecast BDI. From the experimental results, the prediction model proposed in this paper had an excellent prediction effect on the BDI. The mape value of the prediction result was 9.19%. The accuracy was better than the common machine learning models SVR and REG and the neural network model LSTM. In addition, in order to further optimize the prediction performance of the combined model GRA-BiLSTM, this paper introduced the MIV method to conduct an in-depth analysis of the contribution of each variable to the prediction results. Rice price, Shanghai securities composite index and crude oil price were found to be the three most relevant indicators to the prediction accuracy of the model.

A new grey relational analysis model of cross-sequences

PurposeThe purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.Design/methodology/approachThe definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.Findings(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.Practical implicationsThe grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.Originality/valueThe new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

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

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

Grey angle cosine relational degree model based on generalized greyness of interval grey number

PurposeAim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.Design/methodology/approachFirstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.FindingsThe results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.Practical implicationsThe research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.Originality/valueThe paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

Experimental Optimization of High-precision Turning Parameters of AL6061 Materials for Automotive Industry Based on Grey Relational Analysis

This research article aims to explore the relationship between the machining parameters of a Slant Bed Turning Centre Computer Numerical Control (SB/C/CNC) precision lathe and surface microhardness, dimensional error and surface roughness of AL6061. A technique called the central composite design (CCD) method with 13 experiments was used to evaluate the surface microhardness, dimensional error, and surface roughness after a turning operation using a micro-grooved texture tool. Separate prediction models were developed for each of these characteristics using the response surface method (RSM) in order to find the optimal process parameters for each characteristic. The analysis of variance revealed that the prediction models for surface microhardness, dimensional error, and surface roughness were highly significant, with p-values less than 0.0001. The process parameters that resulted in the highest surface microhardness were a cutting speed (Vc) of 154.363 m/min and a feed rate (fz) of 0.231 mm/rev. On the other hand, the process parameters that led to the lowest dimensional error and surface roughness were Vc = 154.363 m/min, fz = 0.1389 mm/rev, and Vc = 152.081 m/min, fz = 0.1025 mm/rev, respectively. The multi-objective prediction model based on gray relational analysis showed an error range of 1.5% to 3.1% and a minimum gray relational degree value of 0.3503 within the feasible process parameter range. The accuracy of this multi-objective prediction model was higher, with a stronger response to the cutting speed Vc compared to the feed rate fz. The determined feasible process parameter range serves as a useful reference for engineers working with AL6061 materials in turning operations.

Effect of polypropylene fiber and nano-silica on the compressive strength and frost resistance of recycled brick aggregate concrete

Abstracts Wasted clay bricks as coarse aggregate of recycled concrete is an effective solution to save energy and reduce CO2 emissions in the construction industry. However, the mechanical properties and frost resistance of recycled brick aggregate (RBA) concrete are inferior to those of ordinary concrete, which limits its widespread application. In this research, the effects of RBA, polypropylene fiber (PPF) and nano-silica (NS) on the mechanical properties and frost resistance of concrete were investigated. The effect of RBA, PPF, and NS on the compressive strength was quantitatively analyzed, and microstructural analysis and fractal dimension calculation of the concrete were performed. The results show that the concrete compressive strength decreased with the increase in RBA replacement rate, and it was effectively improved by adding PPF and NS (PPF-NS). The compressive strength first increased and then decreased with the increase in PPF and NS. The improvement effect of 0.12% PPF and 2% NS on the compressive strength of 50% replacement rate of RBA concrete was most effective. The gray relational degrees between the compressive strength and RBA, PPF, and NS were 0.6578, 0.8297, and 0.5941, respectively. The frost resistance of PPF-NS modified concrete was better than that of ordinary concrete, mainly manifested in its superior apparent phenomena, mass loss, and strength loss. Compared with normal concrete, the microstructure was denser and the fractal dimension of the cross-section was higher for RBA concrete modified with PPF-NS before and after freeze–thaw cycles.

DYNAMIC MULTI-ATTRIBUTE EVALUATION OF DIGITAL ECONOMY DEVELOPMENT IN CHINA: A PERSPECTIVE FROM INTERACTION EFFECT

This study aims to reflect the grey information coverage and complex interactions effect in digital economy development. Therefore, a multi-attribute decision making method based on the grey interaction relational degree of the normal cloud matrix (GIRD-NCM) model is proposed. First, the original information coverage grey numbers are transformed into normal cloud matrixes, and then a novel Minkowski distance between normal clouds is proposed by using different information principles. Second, the GIRD-NCM model is established according to the Choquet fuzzy integral and grey relational degree. Finally, the dynamic comprehensive evaluation of digital economy development in China from 2013 to 2020 is conducted. The implementation, availability, and feasibility of the GIRD-NCM model are verified by comparative analysis with three existing evaluation models. The empirical findings reveal a stable growth trend in China’s digital economy, with an annual growth rate of 7.87%, however, there are notable regional development disparities. The change in interaction degree has no effect on the rankings of provinces that are in the lead or have a moderately high level of digital economy development, but has a positive and negative impact on the rankings of these provinces with high and low levels of digital economy development, respectively.

Quality analysis of range hood impeller automated assembly based on generalized grey relational degree

PurposeThis paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.Design/methodology/approachFirst, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.FindingsThe research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.Practical implicationsThe newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.Originality/valueThis study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

Grey Relational Degree-based Chinese Competitive Sports Developmental Study

Competitive sports are an important part in Chinese sports, which would constantly develop with social economic development. Development of competitive sports marks upward human spiritual civilization and their pursuit of healthy life. Thus, Chinese competitive sports development has become hot spot in the present. The paper started from numerous Chinese competitive sports development influence factors, combined with previous scholars’ investigation results, took economy, amount of excellent sports team members and total funding as relevant major influence factors, and established grey relational degree model regarding Chinese competitive sports development influence. We reached the conclusion that main factor that affected Chinese competitive sports development was total funding, followed by amount of excellent sports team members and economy. On this basis, we made comprehensive evaluation on various factors, analyzed Chinese competitive sports development status and put forward feasible suggestions.

Energy-Efficient Computing and Green Computing Techniques

Energy-efficient computing and strategies for environmentally sustainable computing have become indispensable in the contemporary world, given the escalating demand for computing power and the growing emphasis on ecological conservation. This summary delves into diverse facets of energy efficient computing and green computing methodologies, elucidating their significance, hurdles, and potential remedies. The primary objective of energy-efficient computing is to curtail the energy consumption of computing systems while either maintaining or enhancing their performance. This proves pivotal in alleviating the ecological ramifications of computing, especially as data centers and similar facilities consume substantial amounts of energy. Green computing techniques encompass a broad spectrum of practices geared towards diminishing the environmental footprint of computing. These encompass the utilization of renewable energy sources, the crafting of energy-efficient hardware, the optimization of software algorithms, and the cultivation of energy consciousness among users. Despite the advantages associated with energy-efficient computing and green computing methodologies, various challenges necessitate attention. These encompass the delicate balance between energy efficiency and performance, the intricacies involved in designing energy-efficient systems, and the absence of standardized metrics for gauging and comparing energy efficiency. Exploration in this domain propels technological progress, resulting in the creation of more effective hardware, software, and systems. These advancements not only positively impact the environment but also propel the overall evolution of computing technology. The Information and Communication Technology (ICT) sector substantially adds to worldwide carbon emissions. Energy-efficient computing and environmentally conscious computing approaches can play a crucial role in mitigating this impact, decreasing energy usage, and advocating for the adoption of renewable energy sources. The aim of this research is to investigate the complexities associated with multiple attribute decision-making when confronted with intuitionist fuzzy information. In this context, the weights of attributes are not entirely known, and the attribute values are expressed using intuitionist fuzzy numbers. To ascertain the attribute weights, an optimization model is formulated based on the foundational principles of traditional grey relational analysis (GRA). The proposed approach entails computing the grey relation degree between each alternative and the positive-ideal solution as well as the negative-ideal solution. This degree is subsequently utilized to establish a relative relational degree, facilitating the simultaneous ranking of all alternatives concerning both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result Efficient Algorithms is ranked at first position and Green Data Centers is ranked at fifth position.

Assessing the influence of water fertilizer, and climate factors on seed cotton yield under mulched drip irrigation in Xinjiang Agricultural Regions

Cotton is an important raw material, and its production has a profound impact on global economic and social development. Developing a further understanding of the optimal growing environment for cotton would help establish a reasonable management system and improve seed cotton yield. In the present study, we established a water-fertilizer coupling mathematical model based on the comprehensive analysis of existing field experimental data; this model was used to determine the optimal water and fertilizer requirements when the seed cotton yield reached a maximum in different regions. In addition, the grey relational degree between the climate factors and seed cotton yield during the cotton-growing period were analyzed using grey relational analysis, including average air pressure (AAP), sunshine duration (SD), average relative humidity (ARH), average maximum temperature (MAXT), average temperature (AT), average minimum temperature (MINT), and rainfall (RF). The results showed that the optimal irrigation levels were determined as 675.61 mm for southern Xinjiang and 345.59 mm for northern Xinjiang. In addition, the ideal fertilizer application rates (N −P −K) were found to be 150.26–115.66–76.28 kg/ha for southern Xinjiang and 290.32–162.57–72.22 kg/ha for northern Xinjiang. These optimal practices resulted in yield potentials of 7140.30 kg/ha for northern Xinjiang and 8693.69 kg/ha for southern Xinjiang. Simultaneously, AAP, MAXT, SD, AT, and ARH exhibited a strong influence on seed cotton yield in Xinjiang. Conversely, RF and MINT had minimal impact on seed cotton yield. This research determined the optimal water and fertilizer requirements and highlighted the significance of climate factors in Xinjiang agricultural regions. These findings offer a scientific foundation and practical guidance to enhance seed cotton yield, water and fertilizer efficiency, optimize management practices, and address climate fluctuations during the cotton-growing season.

Multiscale cyclic frequency demodulation-based feature fusion framework for multi-sensor driven gearbox intelligent fault detection

Accurate fault detection is extremely important to ensure stable gearbox operation. Data-driven schemes using cyclic spectral have received significant attention due to their robust demodulation performance. However, these schemes are mainly applied to process single sensor signals, and they are unable to accurately obtain precise fault features. This paper proposed a novel multiscale cyclic frequency demodulation (MCFD)-based feature fusion framework for multi-sensor driven gearbox intelligent fault diagnosis. Firstly, the MCFD is proposed to analyze the vibration signals from multi-sensor driven gearbox, which acquires the multi-sensor mode information without setting parameters in advance. Thereafter, the grey relational degree between the multi-sensor mode information and original signal is calculated, and its results are normalized to obtain the relationship coefficients. Finally, the acquired coefficients are performed for multi-sensor information fusion to form the covariance matrix for gearbox fault diagnosis. The effectiveness of the proposed feature fusion framework is validated using the gearbox case. The comparative experiments indicate that this framework outperforms comparative algorithms for multi-sensor driven gearbox fault diagnosis.

Assessment of Human Settlement Quality Based on the Population Exposure Risk to PM2.5 Pollution in the Mid-Spine Belt of Beautiful China

Human settlements are areas closely related to human production and life, and their quality directly affects people’s physical health and quality of life. However, with air pollution continuing to worsen, people are becoming increasingly concerned about the exposure to air pollution in their residential regions. In addition, many studies ignore the long-term impact of environmental factors on the evolution of human settlement quality (HSQ). This study aims to assess the HSQ in the Mid-Spine Belt of Beautiful China (MSBBC) from 2000 to 2020 and to analyze the changes in its driving forces during different time periods. We divided the HSQ into five dimensions: terrain conditions, climate conditions, hydrological conditions, ground cover conditions, and air pollution exposure. The Entropy-TOPSIS and grey relational degree model were used to assess the HSQ in the MSBBC. To analyze the driving forces of HSQ, the optimal-parameters-based geographic detector model was utilized. The results show the following: (1) Within the study area, the degree of the population exposure risk to PM2.5 and its change trend were significantly different on both sides of the Hu Line, with the east showing higher levels than the west. (2) The HSQ of the MSBBC decreased from east to west. Moreover, the HSQ in the metropolitan area of the urban agglomeration was characterized from low to high. The HSQ underwent three phases, consisting of an increase (2000–2010), a decrease (2010–2015), and an increase again (2015–2020). (3) Among the driving factors, the impact of PM2.5 on the HSQ weakened year by year. The driving force of meteorological parameters on the HSQ was the strongest. Furthermore, the interactions of each factor could enhance the HSQ. The results of this study not only provide a strong reference for creating livable areas and promoting sustainability in the MSBBC but also contribute to addressing challenges such as pollution and climate change.

The principal factor–a three-dimensional golden-section drill bit optimizing menthod based on formation anti-drilling ability

The use effect of a bit has a direct influence the on rate of penetration, drilling cost, and wellbore quality. Conventional drill bit selection methods suffer from uncertainties in formation anti-drilling ability, lack of reference data in new areas, and overreliance on data, which results in overfitting in machine learning, inaccurate selection, and difficulty in meeting on-site construction requirements. In light of these challenges, this paper proposes a novel drill bit selection method based on the principal-factor three-dimensional golden-section method and considering the drillability similarity of formation resistance to drilling. First, grey relational analysis is employed to study the drillability similarity of encountered formations in each spud and formations with high grey relational degree and good drillability similarity are divided into the same category to expand the drill bit selection database. Second, a principal-factor three-dimensional golden-section drill bit optimization model is developed by comprehensively considering the effects of mechanical and hydraulic parameters on drill bit performance, and a three-dimensional golden-section discriminant surface is plotted. Finally, the preferred drill bit is selected from the region above the golden-section surface for on-site applications. The results indicate that formations with a grey relational degree exceeding 0.55 exhibit good similarity of anti-drilling ability. The TS1678BP drill bit, selected through the principal-factor three-dimensional golden-section method, exhibits exceptional performance in a two-step drilling operation. It attains an IADC wear grade of 1-2-BU-A-X-IX-WT-PR, achieves an average mechanical drilling speed of 10.50 m/h, surpassing adjacent wells by 31.25% (8 m/h), and exceeding construction requirements by 16.67% (9 m/h). These findings illustrate that the drill bit selection method proposed in this paper provides scientific guidance for an on-site drill bit selection.

Research on the Optimization of Open-Pit Mine Cast Blasting Parameters Based on the Optimal Economy of Dragline Stripping Technology

As the key technology of high-step stripping and blasting engineering, throwing blasting will be the development trend of the production optimization and benefit maximization of the open-pit mine in the future. In order to break with the research idea of optimizing the cast blasting effect by improving the effective throwing rate for a long period of time, maximizing the economic benefit of the subsequent dragline stripping technology and stabilizing the engineering connection, this paper takes the Heidaigou open-pit mine as the research object. Through data acquisition, analysis, and data fusion of the heaped-up bulk characteristics, the heaped-up bulk profile can be obtained to characterize the cast blasting effect. Secondly, the key factors influencing the cast blasting effect are determined based on the gray relational degree theory. Then, the black box principle of the system control theory is adopted as the guiding principle of the blasting optimization. Based on the production data of field cast blasting, the dynamic change law between the controllable variables of system input and the observable variables of system feedback in the black box system are studied and explored without consideration of the internal structure or the energy transformation relationship of the cast blasting system. Finally, the optimal blasting parameter combination under different working conditions is obtained. If the research results are successfully applied, it is expected that the dragline stripping technology cost of the Heidaigou open-pit mine can be reduced by 5~10%, and the results can also provide new ideas and new ways of optimizing the design of cast blasting.

Deformation prediction model of concrete face rockfill dams based on an improved random forest model

The unique structure and complex deformation characteristics of concrete face rockfill dams (CFRDs) create safety monitoring challenges. This study developed an improved random forest (IRF) model for dam health monitoring modeling by replacing the decision tree in the random forest (RF) model with a novel M5' model tree algorithm. The factors affecting dam deformation were preliminarily selected using the statistical model, and the grey relational degree theory was utilized to reduce the dimensions of model input variables. Finally, a deformation prediction model of CFRDs was established using the IRF model. The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm. The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models. At point ES-10, the performance evaluation indices of the IRF model were superior to those of the M5' model tree and RF models and the classical support vector regression (SVR) and back propagation (BP) neural network models, indicating the satisfactory performance of the IRF model. The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10, demonstrating its strong anti-interference and generalization capabilities. This study has developed a novel method for forecasting and analyzing dam settlements with practical significance. Moreover, the established IRF model can also provide guidance for modeling health monitoring of other structures.