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Artificial Neural Network-Based Prediction and Validation of Drill Flank Wear in GFRP Machining for Sustainable and Smart Manufacturing 认领 引用 被引量：1
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作者 Sathish Rao Udupi Gururaj Bolar +1 位作者 Manjunath Shettar Ashwini Bhat 《Computers, Materials & Continua》 SCIE EI 2026年第6期903-918,共16页
Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear duri... Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear during drilling.Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy.This research investigates the impact of spindle speed,feed rate,and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel(HSS)twist drills.A full-factorial design with 81 experiments is used to create a comprehensive dataset.ANOVA indicates that spindle speed is the dominant factor affecting wear changes,accounting for 74.43%,followed by feed rate(15.80%)and drill diameter(6.16%).A linear regression model demonstrates reasonable statistical sufficiency(R2=0.964),but it falls short in reflecting nonlinear interactions.Hence,an artificial neural network(ANN)model is developed to improve prediction.The multilayer feed-forward ANN with a 3-10-6-1 architecture,trained using the Levenberg-Marquardt optimization algorithm,achieves excellent predictive accuracy,with high correlation and low root-mean-square error.Model validation was achieved through independent confirmation experiments,yielding a mean absolute percentage error of only 2.27%,with all predictions falling within the permissible wear range.The findings indicate that ANN-based modeling provides a reliable framework for capturing the complex nonlinear relationships governing tool wear in GFRPC drilling and serves as a viable soft sensor for tool condition monitoring,process optimization,and sustainable,data-driven manufacturing. 展开更多
关键词 Glass fiber-reinforced polymer composites machining and drilling drill flank wear prediction artificial neural networks(ANN) soft computational modeling
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Intelligent segment typesetting in shield tunneling based on artificial neural networks and transfer learning 认领 引用
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作者 Rui LIU Quanyong ZENG +1 位作者 Haibo XIE Guoli ZHU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期200-214,共15页
Shield tunneling is the method most commonly used for underground projects.Segment typesetting,which involves determining the optimal assembly point for each segment ring and sequentially assembling them into a comple... Shield tunneling is the method most commonly used for underground projects.Segment typesetting,which involves determining the optimal assembly point for each segment ring and sequentially assembling them into a complete tunnel,is a critical step in shield tunneling.Currently,this typesetting process relies heavily on the operator’s experience at construction sites,which does not guarantee quality.Furthermore,research focuses mainly on the commonly used 16-point segment typesetting,largely ignoring other segment types.In addition,the reliability of these studies in site applications remains unsatisfactory.To address these issues,we propose an intelligent method for segment typesetting using an artificial neural network(ANN)and transfer learning.Due to insufficient historical data for ANN training,a dataset creation method was devised based on the Monte Carlo method and manual annotation.An ANN model was then developed to typeset 16-point segments,with its hyperparameters optimized through Bayesian optimization.Subsequently,the trained model was adapted to other segment types via transfer learning,using 10-point segments as a case study.Based on the test set established in this study,our proposed method showed superior performance compared with several commonly used machine learning methods and a representative and well-validated segment typesetting method.It was also validated using real data collected from construction sites,achieving an accuracy of 93.75%for 16-point segments and 91.43%for 10-point segments,both of which significantly surpass the results from manual typesetting on-site.The proposed method achieves accurate,rapid,and intelligent segment typesetting,which is adaptable to various segment types. 展开更多
关键词 Shield tunneling Segment typesetting Assembly points Artificial neural network(ANN) Bayesian optimization Transfer learning
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Artificial Neural Network-Based Flow and Heat Transfer Analysis of Williamson Nanofluid over a Moving Wedge:Effects of Thermal Radiation,Viscous Dissipation,and Homogeneous-Heterogeneous 认领 引用 被引量：2
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作者 Adnan Ashique Nehad Ali Shah +3 位作者 Usman Afzal Yazen Alawaideh Sohaib Abdal Jae Dong Chung 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期642-664,共23页
There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reac... There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems. 展开更多
关键词 Williamson fluid thermal radiation viscous dissipation Artificial Neural Networks(ANNs) homogeneous-heterogeneous reactions
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Artificial neural network based stamping process design for three-point bending 认领 引用
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作者 Thamer Sami Alhalaybeh Yilihaer Muhamaiti +3 位作者 Hongchun Shang Liucheng Zhou Xiaoqing Liang Yanshan Lou 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期354-392,共39页
This research encompasses three-point bending based on artificial neural networks(ANNs)for a simple accurate process design.Uniaxial tensile tests are carried out for 22 steel and aluminum sheet metals with different ... This research encompasses three-point bending based on artificial neural networks(ANNs)for a simple accurate process design.Uniaxial tensile tests are carried out for 22 steel and aluminum sheet metals with different thicknesses to characterize their mechanical properties,such as the Young’s modulus,yield stress,strength,strain hardening,etc.Approximately 20-30 three-point bending tests are conducted for each sheet metal with different gaps and punch strokes to obtain different bending angles before and after spring-back ranging from 60°to 165°.The angles after spring-back are modeled by an ANN as the output.The inputs for the ANN model include the mechanical properties obtained from uniaxial tensile tests,as well as gap and punch stroke used in three-point bending.The angles after spring-back predicted by the ANN model trained by 22 materials are compared with experimental results to evaluate its performance.The comparison shows that the trained ANN model can precisely predict the angle after spring-back with a maximum error of less than 3.7%.The trained ANN model is also tested for unseen gap and stroke,to design the processing parameters in three-point bending of advanced high-strength steel(DP980)and an aluminum alloy(AA6K21-T4).The application demonstrates that the trained ANN model can design the process parameters with high accuracy even for unseen data.This study shows that the ANN model is strongly suggested to be used in process and tool design/optimization of metal forming processes to achieve high accuracy and generalizability. 展开更多
关键词 Spring-back Three-point bending Artificial neural network Machine learning
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Artificial Neural Network Surrogate Modelling for Predicting and Optimising CO2 Conversion to Methanol Under Uncertainty 认领 引用
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作者 Muhammad Zulkefal Iftikhar Ahmad +2 位作者 Hakan Caliskan Hiki Hong Farooq Ahmad 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期633-645,共13页
This study introduces a data-driven surrogate modelling framework that combines an artificial neural network(ANN)with particle swarm optimisation(PSO)and a genetic algorithm(GA)to optimise methanol production under un... This study introduces a data-driven surrogate modelling framework that combines an artificial neural network(ANN)with particle swarm optimisation(PSO)and a genetic algorithm(GA)to optimise methanol production under uncertain conditions.A steady-state Aspen Plus model was developed and converted into dynamic mode by applying±5%uncertainty across 12 key process variables,generating 3880 data points that reflect realistic operational variability.The ANN model was trained and validated on the samples,achieving predictive accuracy(R2=0.988,RMSE=28.59)on unseen test data.Key features of the work include the use of the ANN as a surrogate model,its integration within PSO and GA optimisation frameworks and its application alongside Sobol and Fourier amplitude sensitivity test(FAST)methods to identify the most influential process variables affecting the methanol production rate.The proposed framework resulted in performance improvements,with PSO achieving an increase of 38.63%and GA 33.14%in methanol production.Cross-validation with the Aspen Plus model confirmed the reliability of the optimised operating conditions,with relative errors ranging from 0.07%to 2.15%.Overall,the study demonstrates the effectiveness of integrating surrogate modelling with intelligent optimisation techniques to improve the efficiency and robustness of methanol production processes under uncertainty. 展开更多
关键词 artificial neural networks(ANN) CO2hydrogenation genetic algorithm methanol production particle swarm optimisation surrogate modelling
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Enhanced geometrical parameters prediction in nanometrology:A hybrid metrology approach using AFM and SEM with an artificial neural network 认领 引用
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作者 Bixuan Huang Xianmin Jin +1 位作者 Yangwen Li Yiting Wu 《ENGINEERING Mechanical Engineering》 SCIE CAS CSCD 2026年第2期37-47,共11页
With the development of nanofabrication technologies,decreasing structural sizes,feature miniaturization,three-dimensional stacking,and concurrent increasing dimension characterize the measurement tasks for nano-measu... With the development of nanofabrication technologies,decreasing structural sizes,feature miniaturization,three-dimensional stacking,and concurrent increasing dimension characterize the measurement tasks for nano-measuring systems.Atomic Force Microscopy(AFM)and Scanning Electron Microscopy(SEM)are the most used metrology methods in nanometrology.However,each of the techniques has its inherent strengths and limitations;no single technique can provide the full capabilities,such as resolution,accuracy,and speed,to tackle the challenges of increasingly complex measurement tasks in nanometrology.In this study,a hybrid metrology approach using an Artificial Neural Network(ANN)is proposed to combine the advantages of AFM and SEM for the accurate and efficient measurements of geometrical parameters.To improve measurement efficiency,an automated measurement process utilizing deep learning has also been proposed.AFM and SEM measurement models are established to simulate training data for the ANN.This network can predict geometrical parameters more accurately with high efficiency,which can be achieved through individual techniques.Finally,the effectiveness of this method is validated by exemplary measurements for the determination of step height and pitch.This proposed approach also provides a promising solution for the laboratory-to-fab transition of metrology for semiconductors,for which automation and hybrid metrology are necessary. 展开更多
关键词 hybrid metrology artificial neural networks nanometrology SEM AFM
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Artificial Neural Network Model for Thermal Conductivity Estimation of Metal Oxide Water-Based Nanofluids 认领 引用
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作者 Nikhil S.Mane Sheetal Kumar Dewangan +3 位作者 Sayantan Mukherjee Pradnyavati Mane Deepak Kumar Singh Ravindra Singh Saluja 《Computers, Materials & Continua》 SCIE EI 2026年第1期316-331,共16页
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n... The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids. 展开更多
关键词 Artificial neural networks nanofluids thermal conductivity prediction
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An Improved PID Controller Based on Artificial Neural Networks for Cathodic Protection of Steel in Chlorinated Media 认领 引用
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作者 José Arturo Ramírez-Fernández Henevith G.Méndez-Figueroa +3 位作者 Sebastián Ossandón Ricardo Galván-Martínez Miguel Ángel Hernández-Pérez Ricardo Orozco-Cruz 《Computers, Materials & Continua》 SCIE EI 2026年第3期624-640,共17页
In this study,artificial neural networks(ANNs)were implemented to determine design parameters for an impressed current cathodic protection(ICCP)prototype.An ASTM A36 steel plate was tested in 3.5%NaCl solution,seawate... In this study,artificial neural networks(ANNs)were implemented to determine design parameters for an impressed current cathodic protection(ICCP)prototype.An ASTM A36 steel plate was tested in 3.5%NaCl solution,seawater,and NS4 using electrochemical impedance spectroscopy(EIS)to monitor the evolution of the substrate surface,which affects the current required to reach the protection potential(Eprot).Experimental data were collected as training datasets and analyzed using statistical methods,including box plots and correlation matrices.Subsequently,ANNs were applied to predict the current demand at different exposure times,enabling the estimation of electrochemical parameters(limiting voltage values)that can be used to optimize a self-regulating ICCP system.The obtained electrochemical parameters were then used,through Particle Swarm Optimization(PSO),to fine-tune an ANN-based proportional-integral-derivative(PID)controller for the ICCP system. 展开更多
关键词 Artificial neural networks(ANNs) corrosion impressed current cathodic protection(ICCP) proportional integral derivative(PID)corrosion control particle swarm optimization(PSO) statistical analysis
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A framework for automatic discontinuity trace extraction using multi-scale surface variation index and transfer-learning enhanced artificial neural network 认领 引用
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作者 Mingming Ren Hongru Li +1 位作者 Jie Hu Manchao He 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期1794-1810,共17页
Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface var... Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface variation index(MsSVI)and transfer-learning enhanced artificial neural network(ANN)for efficient discontinuity trace extraction from rock mass point clouds.Leveraging the similarity between regular geometric bodies and engineering rock masses,we extract trace feature points without manual threshold selection.Our contributions include:(1)An adaptive radius MsSVI calculation method based on density information;(2)a universal trace feature point classification model trained using MsSVI and ANN via inductive transfer learning;and(3)a random sampling L1-medial skeleton algorithm for precise trace feature point extraction,bypassing point cloud triangulation.Experimental results show that our model achieves a 90.2%F1-score on test sets,demonstrating its accuracy and robustness.Furthermore,our method excels in trace detail extraction on two datasets,surpassing existing models and highlighting its potential for rock mass structural analysis. 展开更多
关键词 Rock discontinuity trace extraction Transfer learning Artificial neural network(ANN) Rock mass point cloud
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Free Software and Artificial Neural Networks in Educational Contexts:A Psychological Analysis of Organizational Violence in Public Security Administration 认领 引用
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作者 Cruz Garcia Lirios Javier Carreon Guillen +5 位作者 Arturo Sanchez Sanchez Gilberto Bermúdez Ruíz PabloÁlamo Hernández Eva Isabel Lombana Paz Jorge E.Chaparro Medina Zulma Delgadillo González 《Psychology Research》 2026年第1期1-11,共11页
This article analyzes the relationship between the use of free software and artificial neural networks and the presence of organizational violence in educational settings of public security administration.From a psych... This article analyzes the relationship between the use of free software and artificial neural networks and the presence of organizational violence in educational settings of public security administration.From a psychological perspective,organizational violence is conceptualized as a multidimensional construct involving structural,symbolic,and interpersonal dynamics that affect learning environments and institutional functioning.A cross-sectional and correlational design was employed with participants enrolled in public security training programs.Data were collected through validated instruments measuring organizational violence,digital autonomy in open-source environments,and analytical competencies in artificial intelligence(AI).Results indicate that higher levels of digital autonomy and analytical competencies are associated with lower levels of perceived organizational violence.The artificial neural network model demonstrated strong predictive capacity,revealing both direct and nonlinear relationships among variables.Findings suggest that the integration of open technologies and advanced analytical skills contributes to more transparent,participatory,and less coercive educational environments.The study highlights the importance of aligning technological innovation with institutional transformation to address organizational violence in highly structured public sector contexts. 展开更多
关键词 organizational violence public security education free software artificial neural networks digital autonomy analytical competencies educational psychology
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Artificial neural network validation of MHD natural bioconvection in a square enclosure:entropic analysis and optimization 认领 引用 被引量：3
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作者 Noura Alsedais Mohamed Ahmed Mansour +1 位作者 Abdelraheem Mahmoud Aly Sara I.Abdelsalam 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第9期17-35,共19页
This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and sol... This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and solved using the finite volume method.The simulations examine the impact of key parameters such as heat source length and position,Peclet number,porosity,and heat generation/absorption on flow patterns,temperature distribution,concentration profiles,and microorganism rotation.Results indicate that extending the heat source length enhances convective currents and heat transfer efficiency,while optimizing the heat source position reduces entropy generation.Higher Peclet numbers amplify convective currents and microorganism distribution complexity.Variations in porosity and heat generation/absorption significantly influence flow dynamics.Additionally,the artificial neural network model reliably predicts the mean Nusselt and Sherwood numbers(Nu&Sh),demonstrating its effectiveness for such analyses.The simulation results reveal that increasing the heat source length significantly enhances heat transfer,as evidenced by a 15%increase in the mean Nusselt number. 展开更多
关键词 Artificial neural network Gyrotactic microorganisms Magnetohydrodynamics Natural convection Square enclosure
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Largely tunable compensation temperature in a rare-earth ferrimagnetic metal and deterministic spin-orbit torque switching for artificial neural network application 认领 引用
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作者 Li Liu Yuzhou He +13 位作者 Yifei Ma Peixin Qin Hongyu Chen Xiaoning Wang Xiaorong Zhou Ziang Meng Guojian Zhao Zhiyuan Duan Dazhuang Kang Yu Liu Shuai Ning Zhaochu Luo Qinghua Zhang Zhiqi Liu 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2025年第31期15-23,共9页
Ferrimagnets are important for next-generation high-density ultrafast spintronic device applications.Magnetization compensation temperature(TM)is a fundamentally critical magnetic parameter for ferrimagnets besides th... Ferrimagnets are important for next-generation high-density ultrafast spintronic device applications.Magnetization compensation temperature(TM)is a fundamentally critical magnetic parameter for ferrimagnets besides their Curie temperature.Around TM,the spin-orbit switching efficiencies are extraordinarily high.Therefore,the accurate manipulation of TM from the material fabrication process is essential for the electrical steering of ferrimagnetic spins.In this work,CoTb thin films,with the 3 d and 4 f magnetic sublattices antiferromagnetically coupled to each other,are deposited at different temperatures.The magnetotransport and magnetic properties of these films are systematically investigated.It was found that the TM of this rare-earth ferrimagnet largely depends on the growth temperature and it can be tuned by over 100 K.Accordingly,the spins of an optimized ferrimagnetic CoTb thin film with its TM close to room temperature can be efficiently switched by the current-pulse-induced spin-orbit torque.Moreover,an artificial neural network utilizing the spin-orbit torque device was constructed,demonstrating an image recognition accuracy of approximately 92.5%,which is comparable to that of conventional software solutions.Thus,this work demonstrates the large tunability of TM of a rare earth ferrimagnet by chemical ordering and the great potential of such a ferrimagnet for electrically operated spintronic devices. 展开更多
关键词 Ferrimagnetic metals CoTb Spin-orbit torque Compensation temperatures Artificial neural network
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Artificial neural networks applied to photo-Fenton process:An innovative approach to wastewater treatment 认领 引用
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作者 Davide Palma Kevin U.Antela +3 位作者 Alessandra Bianco Prevot MLuisa Cervera Angel Morales-Rubio Roberto Sáez-Hernández 《Water Science and Engineering》 EI CAS CSCD 2025年第3期324-334,共11页
Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols ... Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods.A notable application of AI is in the photoFenton degradation of organic compounds.Despite the high novelty and recent surge of interest in this area,a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking.This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks(ANN)in the photo-Fenton process,with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables.It examines the types and architectures of ANNs,input and output variables,and the efficiency of these networks.The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process.This review also discusses the benefits and drawbacks of using ANNs,emphasizing the need for further research to advance this promising area. 展开更多
关键词 Artificial neural networks Degradation Machine learning Optimization Persistent organic pollutants Wastewater
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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques 认领 引用
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作者 Abdul Ahad Ira Puspitasari +4 位作者 Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires 《Computers, Materials & Continua》 SCIE EI 2025年第3期5115-5134,共20页
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and... This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes. 展开更多
关键词 Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke
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Phase selection prediction and component determination of multiple-principal amorphous alloy composites based on artificial neural network model 认领 引用
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作者 Lin WANG Pei-you LI +5 位作者 Wei ZHANG Xiao-ling FU Fang-yi WAN Yong-shan WANG Lin-sen SHU Long-quan YONG 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2025年第5期1543-1559,共17页
The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy... The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti34Cu17Ni31.36Hf17.64and Ti36Cu18Ni29.44Hf16.56alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions. 展开更多
关键词 multiple-principal amorphous alloy composites Ti−Cu−Ni−Hf alloy phase selection artificial neural network machine learning
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Review of the Mechanical Performance Prediction of Concrete Based on Artificial Neural Networks 认领 引用
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作者 Yidong Xu Weijie Zhuge +2 位作者 Jialei Wang Xiaopeng Yu Kan Wu 《Structural Durability & Health Monitoring》 EI 2025年第6期1507-1527,共21页
The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.Th... The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.The rise of artificial intelligence provides a way to meet the above challenges.This article elaborates on research overview of artificial neural network(ANN)and its prediction for concrete strength,deformation,and durability.The focus is on the comparative analysis of the prediction accuracy for different types of neural networks.Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the practical engineering applications.To further improve the applicability of ANN in concrete,the model can consider the combination of multiple algorithms and the expansion of data samples.The review can provide new research ideas for development of concrete performance prediction. 展开更多
关键词 Concrete performance prediction artificial neural network strength deformation durability
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A comparison between artificial neural network and random forest on predicting ferrofluids viscosity under magnetic field application 认领 引用 被引量：1
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作者 Walaeddine Maaoui Zouhaier Mehrez Mustapha Najjari 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第6期50-62,共13页
This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magn... This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magnetic nanoparticles,temperature,and magnetic field intensity as inputs.A comprehensive database of 333 datasets sourced from various literatures was utilized for training and validating models.The ANN model demonstrated high accuracy,with root mean square error(RMSE)values below 0.033 and mean absolute percentage error(MAPE)not exceeding 3.01%,while the RF model achieved similar accuracy with RMSE under 0.052 and MAPE below 4.82%.Maximum deviations observed were 9.14%for ANN and 16.48%for RF,confirming that both models accurately learned the underlying patterns without overestimating viscosity.Additionally,the ANN model successfully captured intricate physical relationships between input parameters and viscosity when it was used to predict viscosity for random input data,confirming its ability to generalize beyond the training dataset.The RF model,however,showed limitations in extrapolating beyond the range of the training data.This research study demonstrates machine learning models’effectiveness in capturing intricate relationships governing the viscosity of ferrofluid for different types,paving the way for an improved understanding of ferrofluid’s viscosity behavior. 展开更多
关键词 Viscosity Ferrofluids Magnetic field Artificial neural networks
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An artificial neural network-based data-driven constitutive model of shape memory alloys 认领 引用 被引量：1
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作者 Xingyu Zhou Ziang Liu +1 位作者 Chao Yu Guozheng Kang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第8期108-125,共18页
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio... The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models. 展开更多
关键词 Shape memory alloys Constitutive model Data-driven Artificial neural network
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Enhancing cyber threat detection with an improved artificial neural network model 认领 引用 被引量：1
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作者 Toluwase Sunday Oyinloye Micheal Olaolu Arowolo Rajesh Prasad 《Data Science and Management》 EI CSCD 2025年第1期107-115,共9页
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec... Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method. 展开更多
关键词 Cybersecurity Intrusion detection Deep learning Artificial neural network Imbalanced data classification
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A Comprehensive Numerical and Data-Driven Investigations of Nanofluid Heat Transfer Enhancement Using the Finite Element Method and Artificial Neural Network 认领 引用
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作者 Adnan Ashique Khalid Masood +4 位作者 Usman Afzal Mati Ur Rahman Maddina Dinesh Kumar Sohaib Abdal Nehad Ali Shah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3627-3699,共73页
This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless e... This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless equations of mass,momentum,and energy are solved using the finite element method as implemented in the COMSOL Multiphysics 6.3 software in different rotating Reynolds numbers and cylinder geometries.An artificial Neural Network that is trained using Bayesian Regularization on data produced by the COMSOL is utilized to estimate the average Nusselt numbers.The analysis is conducted for a wide range of rotational Reynolds numbers(Rew=0-100),with the fixed Prandtl number.Results are presented in terms of streamline patterns,isotherm contours,and Nusselt numbers to assess heat transfer behavior.Findings revealed that increasing the number of cylinders and optimizing their placement significantly enhances convective mixing and thermal transport.The ANN model accurately predicts the Nusselt numbers across all configurations with negligible errors.Among all configurations,the third arrangement in Scenario 5 exhibits the highest heat transfer rates,attributed to intensified vortex interaction and reduced thermal resistance.Artificial neural networks and finite element-based models will be of great value to the design of miniature and energy-efficient enclosures and electronics cooling mechanisms that make use of nanofluids. 展开更多
关键词 Cu-water nanofluid rotational Reynolds number heat transfer enhancement COMSOL Multiphysics artificial neural network
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