Search Results (34)

The purpose of this study is to analyze the relationship between the key factors and the output results, and to determine the feasible prediction method of the elastic modulus of calcium hydroxide in oil well cement. Combining the first-principles calculation method with machine learning, Material Studio (MS) was used to simulate calcium hydroxide at different temperatures and pressures, obtain the microstructure parameters of the mechanical properties of calcium hydroxide, and construct the initial data set. At the same time, the random forest feature importance analysis method is used to screen the input parameters, remove the weak correlation variables, and reduce the complexity of the prediction model. On this basis, three prediction models, the BP neural network (BP), radial product function neural network (RBF), and random forest model (RF), are constructed. The hidden layer of the prediction model was adjusted by orthogonal test. The results of different performance evaluation methods are compared, the regression ability of each model is evaluated comprehensively, and the optimal algorithm model is selected. The results show that the determination coefficient of the RBF model is 0.9988, the root mean square error is 0.04331, the average absolute error is 0.02995, the mean square error is 0.01876, and the prediction ability is the best. This method can be used to predict the elastic modulus of calcium hydroxide and provide a reliable method for predicting the elastic modulus of each phase of oil well cement. Full article

Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen. This study uses ML and ANN models to predict shear force and nugget diameter responses to RSW parameters. The RSW analysis was executed on similar and dissimilar AISI 304 and grade 2 titanium alloy joints with equal and unequal thicknesses. The input parameters included welding current, pressure, welding duration, squeezing time, holding time, pulse welding, and sheet thickness. Linear regression, Decision tree, Support vector machine (SVM), Random forest (RF), Gradient-boosting, CatBoost, K-Nearest Neighbour (KNN), Ridge, Lasso, and ElasticNet machine learning algorithms, along with two different structures of Multilayer Perceptron, were utilized for studying the impact of the RSW parameters on the shear force and nugget diameter. Different validation metrics were applied to assess each model’s quality. Two equations were developed to determine the shear force and nugget diameter based on the investigation parameters. The current research also presents a prediction of the Relative Importance (RI) of RSW factors. Shear force and nugget diameter predictions were examined using SHapley (SHAP) Additive Explanations for the first time in the RSW field. Trainbr as the training function and Logsig as the transfer function delivered the best ANN model for predicting shear force in a one-output structure. Trainrp with Tansig made the most accurate predictions for nugget diameter in a one-output structure and for shear force and diameter in a two-output structure. Depending on validation metrics, the Random forest model outperformed the other ML algorithms in predicting shear force or nugget diameter in a one-output model, while the Decision tree model gave the best prediction using a two-output structure. Linear regression made the worst ML predictions for shear force, while ElasticNet made the worst nugget diameter forecasts in a one-output model. However, in two-output models, Lasso made the worst predictions. Full article

In the field of oil and gas well perforation, it is imperative to accurately forecast the casing entry hole diameter under full downhole conditions. Precise prediction of the casing entry hole diameter enhances the design of both conventional and limited entry hydraulic fracturing, mitigates the risk of proppant screenout, reduces skin factors attributable to perforation, guarantees the presence of sufficient flow areas for the effective pumping of cement during a squeeze operation, and reduces issues related to sand production. Implementing machine learning and deep learning models yields immediate and precise estimations of entry hole diameter, thereby facilitating the attainment of these objectives. The principal aim of this research is to develop sophisticated machine learning-based models proficient in predicting entry hole diameter under full downhole conditions. Ten machine learning and deep learning models have been developed utilizing readily available parameters routinely gathered during perforation operations, including perforation depth, rock density, shot phasing, shot density, fracture gradient, reservoir unconfined compressive strength, casing elastic limit, casing nominal weight, casing outer diameter, and gun diameter as input variables. These models are trained by utilizing actual casing entry hole diameter data acquired from deployed downhole cameras, which serve as the output for the X’ models. A comprehensive dataset from 53 wells has been utilized to meticulously develop and fine-tune various machine learning algorithms. These include Gradient Boosting, Linear Regression, Stochastic Gradient Descent, AdaBoost, Decision Trees, Random Forest, K-Nearest Neighbor, neural network, and Support Vector Machines. The results of the most effective machine learning models, specifically Gradient Boosting, Random Forest, AdaBoost, neural network (L-BFGS), and neural network (Adam), reveal exceptionally low values of mean absolute percent error (MAPE), root mean square error (RMSE), and mean squared error (MSE) in comparison to actual measurements of entry hole diameter. The recorded MAPE values are 4.6%, 4.4%, 4.7%, 4.9%, and 6.3%, with corresponding RMSE values of 0.057, 0.057, 0.058, 0.065, and 0.089, and MSE values of 0.003, 0.003, 0.003, 0.004, and 0.008, respectively. These low MAPE, RMSE, and MSE values verify the remarkably high accuracy of the generated models. This paper offers novel insights by demonstrating the improvements achieved in ongoing perforation operations through the application of a machine learning model for predicting entry hole diameter. The utilization of machine learning models presents a more accurate, expedient, real-time, and economically viable alternative to empirical models and deployed downhole cameras. Additionally, these machine learning models excel in accommodating a broad spectrum of guns, well completions, and reservoir parameters, a challenge that a singular empirical model struggled to address. Full article

This article analyzes the factors causing the precision error of the robot joint module, such as gear meshing disturbance, output elastic deformation, and load mutation. An improved active disturbance rejection control (ADRC) algorithm is proposed to overcome the uncertainty of nonlinear factors and variables of this research topic. The output precision loss of the joint module is introduced as a new input variable of ADRC, combined with the input variable of torque current of the joint module. The extended state observer is redesigned, and the online estimation of disturbance is realized. According to the disturbance estimation results, the current loop algorithm of permanent magnet synchronous motors is improved to compensate the torque disturbance of the robot joint module. The experimental results show that the improved ADRC algorithm can obviously suppress the disturbance of the joint module, weaken the meshing error and torque output deformation of the harmonic reducer gear, and improve the control accuracy of the joint module. Full article

In the digital transformation era, digitalization integrates deeply into production, bolstering output efficiency and economic value. Through stochastic frontier analysis (SFA), this research positions digitalization as an input in the production function, dissecting its elasticity impact on capital, labor, and output. The effect of digitalization on total factor productivity change (TFPC) is explained by comparing TFPC with and without digitalization. Findings reveal that digitalization’s integration into economic growth displays a U-shaped trajectory, with initial productivity setbacks transitioning to long-term benefits as industries adapt. The periodic complementarity and substitution between digitalization and labor, along with a weak substitution relationship with capital, illustrate that, as a production factor, digitalization dynamically interacts with other factors, both complementing and substituting them. This dynamic interplay highlights the intricate role that digitalization plays within the production function. Furthermore, digitalization has played a crucial role in China’s TFP growth, which also highlights the lack of other technological progress. Meanwhile, the pace of digital transformation presents scalability challenges, evident in the fluctuating scale efficiency change (SEC). Policymakers are advised to address these early stage challenges through supportive measures, ensuring smoother digital transitions. Concurrently, industries should embrace this non-linear transformation, emphasizing adaptability to maximize digitalization’s long-term advantages. Full article

In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric dimensions and the material parameters of the overburden, including upstream and downstream slope coefficients of the first and the second stage of the dam body, the elastic modulus and the Poisson’s ratio of the overburden on the dam’s settlement and compressive stress are studied. An orthogonal experiment with six factors and three levels is conducted for a grey relational analysis of the dam’s maximum settlement and maximum compressive stress separately on these six parameters. Based on the BP neural network, the six selected factors are used as input layers for the neural network prediction model, and the maximum settlement and compressive stress of the dam are taken as the result to be output. The mapping relationship between the geometric dimensions of the dam body and the maximum settlement and the maximum compressive stress in the trained prediction model is combined with the global optimization tool Pattern Search in the MATLAB toolbox to optimize the section design of the dam. The results reveal that the six selected factors have a high correlation degree with the dam’s maximum settlement and maximum compressive stress. In dimension parameters, the downstream slope coefficient of the second stage of the dam has the greatest impact on the maximum settlement, with a grey correlation degree of 0.7367, and the upstream slope coefficient of the second stage of the dam has the greatest impact on the maximum compressive stress, with a grey correlation degree of 0.7012. The influence of the elastic modulus of the overburden on the maximum settlement and maximum compressive stress of the dam body is greater than its Poisson’s ratio. The BP neural network is applicable for predicting the dam’s settlement based on geometric dimension parameters of the dam and material parameters of the surrounding environment, with R² reaching 0.9996 and RMSE only 0.0109 cm. Based on the optimization method combined with BP neural network, the material consumption is saved by 11.83%, the maximum settlement is reduced by 2.6%, the maximum compressive stress is reduced by 37.35%, and the optimization time is shortened by 40.92%, compared to the traditional method. The findings have certain reference value for site selection, dimension design, overburden treatment, and design optimization of CSG dams. Full article

Sensitivity analysis of influencing factors on frost resistance is carried out in this paper, and a two-stage neural network model based on grey theory and Back Propagation Neural Networks (BPNNs) is established for the sake of predicting the frost resistance of active-admixture recycled concrete quickly and accurately. Firstly, the influence degree of cement, water, sand, natural aggregate, recycled aggregate, mineral powder, fly ash, fiber and air-entraining agent on the frost resistance of active-admixture recycled-aggregate concrete was analyzed based on the grey system theory, and the primary and secondary relationships of various factors were effectively distinguished. Then, the input layer of the model was determined as cement, water, sand, recycled aggregate and air-entraining agent, and the output layer was the relative dynamic elastic modulus. A total of 120 datasets were collected from the experimental data of another author, and the relative dynamic elastic modulus was predicted using the two-stage BPNN prediction model proposed in this paper and compared with the BPNN prediction results. The results show that the proposed two-stage BPNN model, after removing less-sensitive parameters from the input layer, has better prediction accuracy and shorter run time than the BPNN model. Full article

In the current literature, there is no practical formula to calculate the horizontal displacement of cantilever walls. To fill this gap, in the present study, eight formulae for the estimation of wall displacement were developed based on 431 FE wall model configurations. Each formula considers factors such as the wall height, embedment depth, surcharge load, unit weight, internal friction angle, elastic modulus of the surrounding soil, and flexural rigidity of the wall. The FE model, which was used in the development of the formula, was also validated against a physical laboratory study. In addition, the outputs obtained from the formulae were compared with the results of two laboratory studies and a real site study. Finally, a parametric study was performed to estimate the influence of formula input parameters on wall displacement. Full article

In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures. Full article

Input–output analysis finds widespread application in estimating the shock effects on production networks within both local and global economies. We are developing a new technology for intersectoral analysis that takes into account the substitution of production factors within a complex supply network triggered by external or internal shocks. This technology is based on the explicit solution of a pair of convex programming problems: the resource allocation problem under the assumption of Constant Elasticity of Substitution (CES) technologies and the special dual Young problem. Solving these problems, we can ascertain the equilibrium inputs and price indexes of goods within the production network. In this paper, we apply this technology to analyze the economy of Kazakhstan in the context of the COVID-19 pandemic. Our calculations provide us with the means to discuss the macroeconomic responses of the multi-sectoral production network in Kazakhstan to both external and internal shocks stemming from the pandemic. Full article

China is the world’s largest potato producer, and the potato’s role in ensuring food security and rural development is irreplaceable. Therefore, how to achieve sustainable growth in potato production has attracted widespread attention from academia. However, few existing studies have analyzed how to achieve sustainable growth in main potato-producing areas based on farmers’ micro perspectives in terms of both technical efficiency and output elasticity of input factor. This paper investigates the output elasticities of input factors, technical efficiency, and its influencing factors among 398 potato farmers from China’s main potato-producing regions in 2021 to fill this knowledge gap. The stochastic frontier production is applied to calculate the technical efficiency and elasticities of input factors in main potato-producing areas. The Tobit model is utilized to analyze influencing factors of technical efficiency. Our findings indicate that the technical efficiency of the main potato production regions is 0.67, with an efficiency loss of 0.33. And, the output elasticity of land input and labor input is negative, and the output elasticity of capital input is positive. Moreover, the factors that affect the technical efficiency in main potato-producing areas include age, whether to be a village leader, income from other crops, labor input, potato price, and disaster impact. Our findings suggest that the agricultural authorities should strengthen the cultivation of potato producers, control the scale of potato production, and optimize the allocation of input factors. Full article

This research addresses the challenge of determining the optimal parameters for the selective laser sintering (SLS) process using thermoplastic polyurethane elastomer (TPU) flexa black powder to achieve high-quality SLS parts. This study focuses on two key printing process parameters, namely layer thickness and the laser power ratio, and evaluates their impact on four output responses: density, hardness, modulus of elasticity, and time required to produce the parts. The primary impacts and correlations of the input factors on the output responses are evaluated using response surface methodology (RSM). A particular response optimizer is used to find the optimal settings of input variables. Additionally, the rationality of the model is verified through an analysis of variance (ANOVA). The research identifies the optimal combination of process parameters as follows: a 0.11 mm layer thickness and a 1.00 laser power ratio. The corresponding predicted values of the four responses are 152.63 min, 96.96 Shore-A, 2.09 MPa, and 1.12 g/cm³ for printing time, hardness, modulus of elasticity, and density, respectively. These responses demonstrate a compatibility of 66.70% with the objective function. An experimental validation of the predicted values was conducted and the actual values obtained for printing time, hardness, modulus of elasticity, and density at the predicted input process parameters are 159.837 min, 100 Shore-A, 2.17 MPa, and 1.153 g/cm³, respectively. The errors between the predicted and experimental values for each response (time, hardness, modulus of elasticity, and density) were found to be 4.51%, 3.04%, 3.69%, and 2.69%, respectively. These errors are all below 5%, indicating the adequacy of the model. This study also comprehensively describes the influence of process parameters on the responses, which can be helpful for researchers and industry practitioners in setting process parameters of similar SLS operations. Full article

Electroactive polymer (EAP) actuators are an example of a novel soft material device that can be used for several applications including artificial muscles and lenses. The field of EAPs can be broken down into a few fields; however, the field that will be discussed in this study is that of Soft Electrohydraulic (SEH or EH) actuators. The device that will specifically be studied is the Hydraulically Amplified Self-Healing Electrostatic (HASEL) actuator. The design of the HASEL actuator is simple. There are two compliant films that house a dielectric liquid, and with the application of a voltage potential, there is an output displacement and force. However, the actuation mechanism is more complex, thus there is a need to understand theoretically and experimentally how the actuator works. This study analytically describes the electrode closure and the experimental testing of the actuators. Then, dimensional analysis techniques are used to determine what factors are contributing to the function of the actuator. For this study, eight dimensionless Π groups were found based on the derived analytical equation. These Π groups were determined based on the input voltage, density, viscosity, and elastic modulus of the materials; these were chosen because of their major contribution to the experimental data. The Π groups that are of particular importance are related to the characteristic length, which is directly related to the displacement of the fluid, the fluid velocity, the fluid pressure, and the dielectric constant. From this study, relationships between the output force, the electrostatic contributions, and other parameters were determined. All in all, this type of analysis can provide guidance on the development of high-performance HASEL actuators. Full article

This study proposes a methodology to predict the damage condition of Reinforced Concrete (RC) resisting-moment frame buildings using Machine Learning (ML) methods. Structural members of six hundred RC buildings with varying stories and spans in X and Y directions were designed using the virtual work method. Sixty thousand time-history analyses using ten spectrum-matched earthquake records and ten scaling factors were carried out to cover the structures’ elastic and inelastic behavior. The buildings and earthquake records were split randomly into training data and testing data to predict the damage condition of new ones. In order to reduce bias, the random selection of buildings and earthquake records was carried out several times, and the mean and standard deviation of the accuracy were obtained. Moreover, 27 Intensity Measures (IM) based on acceleration, velocity, or displacement from the ground and roof sensor responses were used to capture the building’s behavior features. The ML methods used IMs, the number of stories, and the number of spans in X and Y directions as input data and the maximum inter-story drift ratio as output data. Finally, seven Machine Learning (ML) methods were trained to predict the damage condition of buildings, finding the best set of training buildings, IMs, and ML methods for the highest prediction accuracy. Full article

In order to save the time and cost of friction and wear experiments, the coating composition (different contents of Al, Ti, and Cu elements), ratio of hardness and elastic modulus (H³/E²), vacuum heat treatment (VHT) temperature, and wear form were used as input variables, and the wear rates of high-entropy alloy (HEA) coatings were used as output variables. The dataset was entirely obtained by experiment. Four machine learning algorithms (classification and regression tree (CART), random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting (AdaBoost)) were used to predict the wear resistance of HEA coatings based on a small amount of data. The results show that except for the GBDT model, the other three models had good performance. Because of the small amount of data, the CART model demonstrated the best prediction performance and can provide guidance for predicting the wear resistance of AlCoCrFeNi-X (Ti, Cu) HEA coatings for drilling equipment. Furthermore, the contribution of different factors to the wear rate of AlCoCrFeNi-X (Ti, Cu) HEA coatings was obtained. Al content had the greatest influence on wear rate, followed by H³/E², wear form, and VHT temperature. Full article