Article
Engineering, Manufacturing
Behnoud Salehebrahimnejad, Ali Doniavi, Mehran Moradi, Mehrdad Shahbaz
Summary: A detailed finite element model was developed to estimate the thermo-mechanical stress and residual stress components of an industrial hot rolling work roll. A semi-analytical procedure was also developed to study the effect of initial residual stress on maximum von Mises stress. Residual stress components were measured using the ring-core method and compared with the estimated values to validate the finite element model. Artificial neural network and genetic algorithm were used to predict the maximum von Mises stress and optimize the initial residual stress components to minimize stress at the work roll surface where fatigue cracks initiate and propagate.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Chemistry, Physical
Liguo Xu, Shuangxi Shi, Bin Kong, Deng Luo, Xiaoyong Zhang, Kechao Zhou
Summary: The globularization of the lamellar alpha phase is achieved through thermomechanical processing and subsequent annealing, resulting in a well-balanced strength and plasticity of titanium alloys. A high-throughput experimental method called wedge-shaped hot-rolling is developed to obtain samples with gradient true strain distribution. The samples are annealed to obtain the gradient distribution of globularized alpha phase, and the static globularization behavior under various parameters is systematically studied.
Article
Thermodynamics
Dongwoo Kim, Taesu Yim, Jae Yong Lee
Summary: COVID-19 has changed the daily peak time and amount of domestic hot water usage, with active case number serving as a good indicator for correlating changes. A machine learning model was developed to predict hot water demand based on the severity of COVID-19, showing an increase in demand with higher active cases.
Article
Automation & Control Systems
Dong Chen, Rui Zhang, Zhenlei Li, Yunjie Li, Guo Yuan
Summary: The paper proposes a temperature distribution prediction method based on recurrent neural network, fully considering the dynamic characteristics of variable-velocity rolling. By evaluating the temperature distribution prediction performance of the model with different recurrent cells and time steps, the results show that the proposed model can achieve temperature distribution prediction. The model based on bi-LSTM and 48 timesteps has the highest determination coefficient value of 0.976, the lowest root mean square error of 8.03, and a mean absolute error of 5.7.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Materials Science, Multidisciplinary
Mengtao Ning, Xiaomin Chen, Yongcheng Lin, Hongwei Hu, Xiaojie Zhou, Jian Zhang, Xianzheng Lu, You Wu, Jian Chen, Qiang Shen
Summary: The hot deformation behavior of AZ42 alloy was studied, and an optimized artificial neural network model and a hot processing map based on the material model were established to characterize the mechanical properties and thermal processing of the material. The research results are of great significance for optimizing the processing technology of AZ42 alloy.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2023)
Article
Chemistry, Multidisciplinary
Fengwei Jing, Junliang Li, Shimeng Hao, Jie Li, Jing Wang
Summary: This study focuses on the hot strip rolling process and proposes optimal rolling suggestions using neural networks and genetic algorithms. The research shows that the optimized process parameters can improve the rolling stability and meet the limit specifications.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Ibham Veza, Asif Afzal, M. A. Mujtaba, Anh Tuan Hoang, Dhinesh Balasubramanian, Manigandan Sekar, I. M. R. Fattah, M. E. M. Soudagar, Ahmed EL-Seesy, D. W. Djamari, A. L. Hananto, N. R. Putra, Noreffendy Tamaldin
Summary: Artificial Neural Network (ANN) is considered as a beneficial prediction tool in automotive applications, especially when the system is complicated and costly to model using simulation programs. However, further examination and improvement are required for the use of ANN in engine applications.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Qingquan Xu, Jie Dong, Kaixiang Peng, Xuyan Yang
Summary: This paper proposes a novel method of neural network model predictive control integrated process monitoring (PM-NNMPC) for stable control of product quality in the presence of abnormal working conditions in the industrial production process. The method extracts features using a combination of gated recurrent unit and convolutional neural network (GRU-CNN), and utilizes principal component analysis combined with deep neural network and XGBoost (PCA-DNN-XGBoost) for process monitoring and fault diagnosis. A new operation control framework integrating process monitoring and fault diagnosis is designed to address equipment operation failure and ensure stable product quality. Experimental results demonstrate the effectiveness of PM-NNMPC in controlling the system under normal working conditions and in case of system failure.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Chemistry, Multidisciplinary
Xiaobei Liang, Jinyong Yao, Weifang Zhang, Yanrong Wang
Summary: In this paper, a bearing fault diagnosis model based on VMD and ANN was designed, which achieved higher performance through fault feature extraction and neural network structural parameter optimization.
APPLIED SCIENCES-BASEL
(2023)
Article
Thermodynamics
Ali Sohani, Siamak Hoseinzadeh, Saman Samiezadeh, Ivan Verhaert
Summary: An enhanced design for a solar still desalination system was employed to develop artificial neural network (ANN) models, with FF and RBF types identified as the best structures for predicting distillate production and water temperature. Error analysis on data not used for ANN model development showed varying errors in different months.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2022)
Article
Multidisciplinary Sciences
Tiankuang Zhou, Wei Wu, Jinzhi Zhang, Shaoliang Yu, Lu Fang
Summary: We propose a spatiotemporal photonic computing architecture to achieve dynamic processing, matching highly parallel spatial computing with high-speed temporal computing. A unified training framework is devised to optimize the physical system and the network model. The proposed architecture paves the way for ultrafast advanced machine vision and will find applications in unmanned systems, autonomous driving, ultrafast science, etc.
Article
Automation & Control Systems
Wenbo Lu, Yong Zhang, Peikun Li, Ting Wang
Summary: This paper proposes a multi-time granularity passenger flow data fusion forecasting method, which utilizes MulDesLSTM model to fuse passenger flow features with different time granularities. Experimental results on the urban rail transit (URT) system in Shanghai, China show that compared to traditional single-granularity LSTM network, the proposed method achieves a reduction in mean absolute error, root mean square error, and symmetric mean absolute percentage error by 51%, 63%, and 15% respectively. This research provides a reference and basis for the operation and management of URT systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Materials Science, Multidisciplinary
Hyungsoo Lee, Hi Won Jeong, Seong Moon Seo, Dae Won Yun, Kyungmi Park, Kwang Hyuk Yim, Young Soo Yoo
Summary: The study investigated the optimal process parameters of grade 250 maraging steel for hot forging using a process map and microstructure analysis. Flow stress-strain curves were calibrated by Bayesian artificial neural network modeling to compensate the heat generated by dynamic deformation. The Ni and Mo segregation during solidification impacted the recrystallization behavior during hot compression, revealing favorable process conditions for wide process windows.
METALS AND MATERIALS INTERNATIONAL
(2021)
Article
Multidisciplinary Sciences
Xiangyu Zheng, Rong Jia, Linling Gong, Aisikaer, Xiping Ma, Jian Dang, Zhihan Lv
Summary: This paper discusses the weaknesses of traditional relay protection technology and proposes the concept of relay protection based on artificial intelligence. Through simulation experiments, it is validated that the system has good performance and high reliability.
Article
Engineering, Mechanical
Luigi Romano, Michele Maglio, Stefano Bruni
Summary: This paper provides an overview of different theories, including exact formulation, simplified models and a recent two-regime model, to analyze unsteady rolling contact phenomena between wheel and rail. The classic solution to the transient problem and the more complicated situation of combined creepages and spin using simplified models are discussed. Analytical solutions and qualitative results for time-varying and constant creepages are reported. Finally, a novel theory using ordinary differential equations is introduced to describe the transient evolution of force-creepage characteristics.
TRIBOLOGY INTERNATIONAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Ismail Esen, Alaa A. Abdelrahman, Mohamed A. Eltaher
Summary: This paper presents a modified mathematical model for investigating the dynamic behavior and response of perforated microbeams under moving mass/load. The results obtained from the size-dependent finite element model provide valuable insights for the design and production of MEMS structures with perforation.
ENGINEERING WITH COMPUTERS
(2022)
Article
Mechanics
Ismail Esen, Mohamed A. Eltaher, Alaa A. Abdelrahman
Summary: This article investigates the dynamic responses of symmetric and sigmoid FG Timoshenko beam subjected to moving mass. The study explores the influences of gradation type, gradation index, elastic foundation stiffnesses, inertia, and variable velocity of the moving mass on the dynamic response. The Hamilton principle and finite element method are used for modeling and solving the system.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Engineering, Mechanical
Mustafa Eroglu, Mehmet Akif Koc, Ismail Esen
Summary: This paper uses a train-track-bridge interaction system to assess the dynamic performance of railway bridges exposed to a high-speed train and magnetic field. A 24 degrees of freedom 3D train model and thin steel bridge beam are considered. In the interaction of train and bridge, a new six-parameter track system consisting of rail, sleeper, and ballast is modeled. The obtained results are helpful for the design of railway bridges and the safe and comfortable ride of high-speed trains over flexible structures.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Materials Science, Multidisciplinary
Mehmet Akif Koc, Ismail Esen, Mustafa Eroglu
Summary: This study examines the thermal vibration and buckling behavior of a functionally graded nanoplate. The nanoplate consists of a silicon nitride/stainless steel core plate and two cobalt-ferrite/barium-titanate face plates. Four different porosity models were used to simulate the nanoplate's porosity, and various variables affecting the nanoplate's behavior were considered. The study found that the thermomechanical behavior of nanoplates with magneto-electro-elastic face layers and a functionally graded porous core plate is influenced by material gradation indices, porosity ratios, nonlocal variables, and different core plate material porosity models.
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
(2023)
Article
Chemistry, Physical
Aboujaila A. M. Soltan, Ismail Esen, Seyit Ali Kara, Hayrettin Ahlatci
Summary: Corrosion and wear tests were conducted on NiTi alloy samples with shape memory effect. The corrosion test revealed a proportional reduction in thickness of the samples with the change in corrosion current values. Moreover, the weight loss of the samples in corrosive wear was found to be 20% less than that in dry wear.
Article
Chemistry, Physical
Hamza A. H. Abo Nama, Ismail Esen, Hayrettin Ahlatci, Volkan Karakurt
Summary: In this study, Al7075+0%Ti-, Al7075+2%Ti-, Al7075+4%Ti-, and Al7075+8%Ti-reinforced alloys were prepared and examined for their microstructure, mechanical behavior, and dry-wear behavior. The addition of Ti increased the peak hardness and wear resistance of the Al7075 alloy, attributed to the formation of oxide films, precipitation hardening, secondary hardening, grain refinement, and solid-solution-hardening mechanisms.
Article
Engineering, Multidisciplinary
Abdulmuaen Sager, Ismail Esen, Hayrettin Ahlatci, Yunus Turen
Summary: This paper investigates the microstructure, mechanical, immersion, and potentiodynamic corrosion behaviors of extruded ZK60 matrix composites reinforced with SiC and AlN particles. The results show that increasing the percentages of SiC and AlN reinforcement elements in the matrix improves corrosion resistance.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2023)
Article
Chemistry, Physical
Mohamed Ali Ibrahim Alwakwak, Ismail Esen, Hayrettin Ahlatci, Esma Keskin
Summary: This study investigates the microstructural properties and corrosion behavior of magnesium with added RE elements (Y, La) in varying minors after casting and heat treatment. The results show the presence of three-phase structures, such as α-Mg, lamellae-like phases, and network-shaped eutectic compounds. The corrosion behavior is affected by the content of lanthanum, with an increase in crater structures and a decrease in corrosion resistance observed. The corrosion products formed by Y2O3 and Y(OH)3 contribute to the thickness of the corrosion film and act as a protective barrier.
Article
Chemistry, Physical
Masoud M. M. Elhasslouk, Ismail Esen, Hayrettin Ahlatci, Bengu Akin
Summary: This study focuses on the microstructure, hardness, corrosion behavior, and rotary bending fatigue properties of rolled Al5083-H111 materials. It is the first to investigate the fatigue behavior of corroded Al5083 samples in aggressive corrosion environments. The microstructure of Al5083-H111 consists of grains oriented towards the rolling direction, with various precipitates distributed randomly at the grain boundary. The corrosion resistance of the samples varies in different NaCl and NaCl + HCl solutions, and corroded samples show lower fatigue life compared to non-corroded samples.
Article
Construction & Building Technology
Ozge Ozdemir, Ismail Esen, Huseyin Ural
Summary: This paper focuses on the free vibration behavior of rotating composite beams reinforced with carbon nanotubes under uniform thermal loads. By considering different distribution patterns of carbon nanotubes, a new finite element formulation is proposed for the first time. The effects of various parameters and the positive influence of carbon nanotube addition in improving the dynamic performance of the system are presented.
STEEL AND COMPOSITE STRUCTURES
(2023)
Article
Engineering, Multidisciplinary
Samet Karabulut, Ismail Esen
Summary: In this study, the mechanical properties of SCGADUB1180 high-strength sheet were determined and the effects of various parameters on springback were experimentally investigated. The results showed that the springback values increased with an increase in punch speed at different bending angles. A decrease in springback was observed with an increase in holding time. The experimental results obtained will contribute to understanding the springback behavior of high strength sheets in the bending process.
PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI
(2023)
Article
Engineering, Mechanical
Bahadir Furkan Kinaci, Huseyin Botsali, Cevat Ozarpa, Ismail Esen, Hayrettin Ahlatci
Summary: This study investigated the fatigue occurring in the draw hook body and its influencing factors experimentally and numerically. The material's microstructure characterization and mechanical properties were examined. The fatigue behavior and endurance limit were obtained through testing and computer analysis.
ENGINEERING FAILURE ANALYSIS
(2024)
Article
Construction & Building Technology
Alaa A. Abdelrahman, Rabab A. Shanab, Ismail Esen, Mohamed A. Eltaher
Summary: This manuscript investigates the dynamic response of nanoscale carbon nanotubes embedded in an elastic media under moving load using doublet mechanics theory. The size effect of the nanotubes is captured by simulating nano-mechanics through a bottom-up approach. Different configurations of the nanotubes are considered, and the influence of these configurations on the dynamic behavior is explored. The accuracy of the developed procedure is verified by comparing the results with previous algorithms, showing good agreement.
STEEL AND COMPOSITE STRUCTURES
(2022)
Article
Construction & Building Technology
Ismail Esen, Mashhour A. Alazwari, Mohamed A. Eltaher, Alaa A. Abdelrahman
Summary: This study comprehensively investigates the free and live load-forced vibration behavior of porous functionally graded higher order nanobeams in thermal and magnetic fields using nonlocal strain gradient theory. Various factors such as porosity parameter, porosity distribution, temperature rise, magnetic field intensity, material gradation index, non-classical parameters, and applied moving load velocity were found to significantly affect the dynamic behavior of nanobeams.
STEEL AND COMPOSITE STRUCTURES
(2022)
Article
Nanoscience & Nanotechnology
Mashhour A. Alazwari, Ismail Esen, Alaa A. Abdelrahman, Azza M. Abdraboh, Mohamed A. Eltaher
Summary: The dynamic behavior of temperature-dependent Reddy functionally graded nanobeam under the action of moving point load is investigated in this study. The effects of material distribution, beam aspect ratio, temperature, magnetic field, and size parameters on the dynamic behavior are examined. The introduced magnetic effect creates a hardening effect, leading to higher natural frequencies and smaller transverse deflections.
ADVANCES IN NANO RESEARCH
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
