Most recent issue published online in the International Journal of Wireless and Mobile Computing.
International Journal of Wireless and Mobile Computing
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International Journal of Wireless and Mobile Computing
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International Journal of Wireless and Mobile Computing
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http://www.inderscience.com/browse/index.php?journalID=46&year=2024&vol=26&issue=2
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Fracture constitutive study of spring-beam model applied to slurry anchor connection
http://www.inderscience.com/link.php?id=137180
In order to prove the feasibility of selecting the beam element attributes of the spring-beam model by parameter prediction algorithm, this study proposes a beam elements fracture constitutive model that simulates the fracture of mortar and concrete at the slurry anchor connection. By adjusting the elastic modulus, plastic strain, fracture modulus and section properties of beam elements, the results of literature tests are compared with the finite element calculation and the effects of key parameters in the beam elements fracture constitutive calculation model are analysed. The results show that the calculated slip deformation of the slurry anchor connection is controlled by the plastic strain and fracture modulus of beam elements. The mortar entry to the plasticity calculation stage is controlled by section properties of beam elements. The overall deviation of calculation from experimental results is controlled by the elasticity modulus of beam elements, and the parameter optimal solution has uniqueness.
Fracture constitutive study of spring-beam model applied to slurry anchor connection
Wenjun Zhou; Shipian Shao; Hongxin Nie; Li Ma
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 107 - 114
In order to prove the feasibility of selecting the beam element attributes of the spring-beam model by parameter prediction algorithm, this study proposes a beam elements fracture constitutive model that simulates the fracture of mortar and concrete at the slurry anchor connection. By adjusting the elastic modulus, plastic strain, fracture modulus and section properties of beam elements, the results of literature tests are compared with the finite element calculation and the effects of key parameters in the beam elements fracture constitutive calculation model are analysed. The results show that the calculated slip deformation of the slurry anchor connection is controlled by the plastic strain and fracture modulus of beam elements. The mortar entry to the plasticity calculation stage is controlled by section properties of beam elements. The overall deviation of calculation from experimental results is controlled by the elasticity modulus of beam elements, and the parameter optimal solution has uniqueness.]]>
10.1504/IJWMC.2024.137180
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 107 - 114
Wenjun Zhou
Shipian Shao
Hongxin Nie
Li Ma
School of Civil Engineering, Jilin University of Architecture and Technology, Changchun, Jilin, China ' School of Management, Shenyang Jianzhu University, Shenyang, Liaoning, China ' School of Civil Engineering, Jilin University of Architecture and Technology, Changchun, Jilin, China ' China State Construction Railway Investment & Engineering Group Co., Ltd., Changchun, Jilin, China
spring-beam
fracture of beam element
slurry anchor
mortar
concrete
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Copyright © 2024 Wenjun Zhou et al
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Aggregation techniques in wireless communication using federated learning: a survey
http://www.inderscience.com/link.php?id=137135
With the recent explosive rise in mobiles, IoT devices and smart gadgets, the data generated by these devices has grown exponentially. Given that the data generated by these devices is private, transmitting large amounts of private data is not practical. So, a new learning paradigm has been introduced known as federated learning, which is a machine learning technique. In this technique, user data is not transmitted to the base server as in centralised approach but only the locally updated model is transmitted. These model updates generated by the devices are aggregated at the server which updates its global model according to the local models and transmits back to the devices for next round. This technique reduces the privacy risk and also decreases the communication overhead. Various aggregation schemes are proposed in the literature for increasing the performance and accuracy of the system while also increasing the security and reliability. This paper presents a survey of the latest advances in research of such aggregation techniques.
Aggregation techniques in wireless communication using federated learning: a survey
Gaganbir Kaur; Surender K. Grewal
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 115 - 126
With the recent explosive rise in mobiles, IoT devices and smart gadgets, the data generated by these devices has grown exponentially. Given that the data generated by these devices is private, transmitting large amounts of private data is not practical. So, a new learning paradigm has been introduced known as federated learning, which is a machine learning technique. In this technique, user data is not transmitted to the base server as in centralised approach but only the locally updated model is transmitted. These model updates generated by the devices are aggregated at the server which updates its global model according to the local models and transmits back to the devices for next round. This technique reduces the privacy risk and also decreases the communication overhead. Various aggregation schemes are proposed in the literature for increasing the performance and accuracy of the system while also increasing the security and reliability. This paper presents a survey of the latest advances in research of such aggregation techniques.]]>
10.1504/IJWMC.2024.137135
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 115 - 126
Gaganbir Kaur
Surender K. Grewal
Department of Electronics & Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology (DCRUST), Murthal, Sonepat, Haryana, India ' Department of Electronics & Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology (DCRUST), Murthal, Sonepat, Haryana, India
federated learning
machine learning
stochastic gradient descent
aggregation techniques
federated averaging
data privacy
wireless communication
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Research on employment quality evaluation system of skilled talents
http://www.inderscience.com/link.php?id=137136
This paper constructs an indicator system of employment quality and skilled talent supply from the macro-level, evaluates the employment quality and skilled talent supply in the two years before and after the outbreak of COVID-19 by using the entropy method and calculates the coupling coordination and correlation degree between the two systems. The research shows that the level of economic development is an important dimension affecting the employment quality, and the education level has the least influence on the employment quality of skilled talents. After the outbreak of the epidemic, employment training and employment opportunities have a greater impact on the quality of employment, and lead to a more serious shortage of skilled talents. The antagonistic coupling between the quality of employment and the supply of skilled talents has become more serious due to the impact of the epidemic.
Research on employment quality evaluation system of skilled talents
Guojun Zheng
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 127 - 136
This paper constructs an indicator system of employment quality and skilled talent supply from the macro-level, evaluates the employment quality and skilled talent supply in the two years before and after the outbreak of COVID-19 by using the entropy method and calculates the coupling coordination and correlation degree between the two systems. The research shows that the level of economic development is an important dimension affecting the employment quality, and the education level has the least influence on the employment quality of skilled talents. After the outbreak of the epidemic, employment training and employment opportunities have a greater impact on the quality of employment, and lead to a more serious shortage of skilled talents. The antagonistic coupling between the quality of employment and the supply of skilled talents has become more serious due to the impact of the epidemic.]]>
10.1504/IJWMC.2024.137136
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 127 - 136
Gaganbir Kaur
Surender K. Grewal
Zhejiang Yuying College of Vocational Technology, Hangzhou, Zhejiang, China
employment quality evaluation
skilled talents
COVID-19
economic development
2024-03-04T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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Data survivability in unattended wireless sensor networks with optimal clustering and hybrid encryption algorithm
http://www.inderscience.com/link.php?id=137176
This research proposes an energy- and security-aware data survivability solution for Unattended Wireless Sensor Networks operating in hostile environments. The multi-objectives like energy consumption, delay, distance, communication overhead, inter-cluster distance, and intra-cluster-distance, are taken into consideration. The projected hybrid optimisation approach is referred to as Aquila Updated Candidate Selection Optimiser. To solve this issue, a novel hybrid cryptographic model denoted as Two-Fold Advanced Triple Data Encryption Standard (TF-A3DES) is developed. The TF-A3DES is developed by hybridising the concepts of the 'Triple Data Encryption Standard (triple DES) algorithm and Advanced Encryption Standard'. As per the recorded outcomes, the projected model has utilised the lowest energy (5.015 µJ), which is better than AHE = 5.9 µJ, CHTP = 5.5 µJ, SAPDA = 6.2 µJ, BDLA = 5.98 µJ, AO = 6.0 µJ, EBOA = 6.15 µJ, BOA = 6.2 µJ and MFO = 6.25 µJ. Thus, the projected model is said to be much more applicable for secure data transmission.
Data survivability in unattended wireless sensor networks with optimal clustering and hybrid encryption algorithm
Nischay Kumar Hegde; Linganagouda Kulkarni
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 137 - 156
This research proposes an energy- and security-aware data survivability solution for Unattended Wireless Sensor Networks operating in hostile environments. The multi-objectives like energy consumption, delay, distance, communication overhead, inter-cluster distance, and intra-cluster-distance, are taken into consideration. The projected hybrid optimisation approach is referred to as Aquila Updated Candidate Selection Optimiser. To solve this issue, a novel hybrid cryptographic model denoted as Two-Fold Advanced Triple Data Encryption Standard (TF-A3DES) is developed. The TF-A3DES is developed by hybridising the concepts of the 'Triple Data Encryption Standard (triple DES) algorithm and Advanced Encryption Standard'. As per the recorded outcomes, the projected model has utilised the lowest energy (5.015 µJ), which is better than AHE = 5.9 µJ, CHTP = 5.5 µJ, SAPDA = 6.2 µJ, BDLA = 5.98 µJ, AO = 6.0 µJ, EBOA = 6.15 µJ, BOA = 6.2 µJ and MFO = 6.25 µJ. Thus, the projected model is said to be much more applicable for secure data transmission.]]>
10.1504/IJWMC.2024.137176
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 137 - 156
Nischay Kumar Hegde
Linganagouda Kulkarni
Visvesvaraya Technological University, Belagavi, Karnataka, India ' B.V. Bhoomaraddi College of Engineering and Technology (BVBCET), Hubli-Dhanwad, Karnataka, India
UWSN
data survivability
optimal clustering
encryption algorithm
TF-A3DES
AUCSO
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Finger vein recognition based on efficient channel attention and GhostNet
http://www.inderscience.com/link.php?id=137172
The deep convolutional network (DCN) suffers from drawbacks such as high computational complexity and slow speed. To address these issues and facilitate the deployment of DCNs in embedded devices, we propose a finger vein recognition method based on lightweight Efficient Channel Attention (ECA) mechanism and GhostNet. By combining the ECA mechanism with the GhostNet's G-bneck, we create a new module called ECAGhostNet. Additionally, we establish a more realistic FV-UST dataset for finger vein door locks which includes images with various challenges like rotation, stains, skin damage, hand sweat, temperature variations, and illumination differences. Experimental results demonstrate that ECAGhostNet outperforms GhostNet on the public FV-USM dataset, improving accuracy by 0.82% with minimal parameter increase (1.9 M). On the self-built FV-UST dataset, ECAGhostNet achieves a 0.63% accuracy improvement over GhostNet. Furthermore, we validate the effectiveness of our proposed model on the Jetson Nano device, confirming its suitability for real-world embedded applications.
Finger vein recognition based on efficient channel attention and GhostNet
Yintao Ke; Hui Zheng; Jing Jie; Beiping Hou; Yuchuan Chen
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 157 - 167
The deep convolutional network (DCN) suffers from drawbacks such as high computational complexity and slow speed. To address these issues and facilitate the deployment of DCNs in embedded devices, we propose a finger vein recognition method based on lightweight Efficient Channel Attention (ECA) mechanism and GhostNet. By combining the ECA mechanism with the GhostNet's G-bneck, we create a new module called ECAGhostNet. Additionally, we establish a more realistic FV-UST dataset for finger vein door locks which includes images with various challenges like rotation, stains, skin damage, hand sweat, temperature variations, and illumination differences. Experimental results demonstrate that ECAGhostNet outperforms GhostNet on the public FV-USM dataset, improving accuracy by 0.82% with minimal parameter increase (1.9 M). On the self-built FV-UST dataset, ECAGhostNet achieves a 0.63% accuracy improvement over GhostNet. Furthermore, we validate the effectiveness of our proposed model on the Jetson Nano device, confirming its suitability for real-world embedded applications.]]>
10.1504/IJWMC.2024.137172
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 157 - 167
Yintao Ke
Hui Zheng
Jing Jie
Beiping Hou
Yuchuan Chen
School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China
deep convolutional neural network
efficient channel attention mechanism
finger vein recognition
GhostNet
2024-03-04T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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Effective IoT-based crop disease prediction using localise search traversing coupled with deep convolutional neural network classifier
http://www.inderscience.com/link.php?id=137173
Predicting crop disease on the image obtained from the affected crop has been a potential research topic. In this research, the Localise Search Optimisation Algorithm (LSOA) enabled deep Convolutional Neural Network (deep CNN) is used to predict the crop disease for which the dominant statistical and texture features are utilised and LSOA as a training algorithm. The experiments were done on an apple data set and a corn data set, and the results show that the LSOA-deep CNN model attains 98.474% of accuracy, 92.837% of sensitivity and 99.00% of specificity in <em>k</em>-fold training data and 94.683% of accuracy, 95.489% of specificity and 99.00% of specificity with 80% training data for the corn data set. With the apple data set, the developed method achieves 94.587% of accuracy, 99.00% sensitivity and 99.00% specificity under <em>k</em>-fold training, while for the 80% of training, 97.959% accuracy, 96.233% sensitivity and 99.005% specificity are attained.
Effective IoT-based crop disease prediction using localise search traversing coupled with deep convolutional neural network classifier
B.V. Vani; C.D. Guruprakash
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 168 - 181
Predicting crop disease on the image obtained from the affected crop has been a potential research topic. In this research, the Localise Search Optimisation Algorithm (LSOA) enabled deep Convolutional Neural Network (deep CNN) is used to predict the crop disease for which the dominant statistical and texture features are utilised and LSOA as a training algorithm. The experiments were done on an apple data set and a corn data set, and the results show that the LSOA-deep CNN model attains 98.474% of accuracy, 92.837% of sensitivity and 99.00% of specificity in <em>k</em>-fold training data and 94.683% of accuracy, 95.489% of specificity and 99.00% of specificity with 80% training data for the corn data set. With the apple data set, the developed method achieves 94.587% of accuracy, 99.00% sensitivity and 99.00% specificity under <em>k</em>-fold training, while for the 80% of training, 97.959% accuracy, 96.233% sensitivity and 99.005% specificity are attained.]]>
10.1504/IJWMC.2024.137173
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 168 - 181
B.V. Vani
C.D. Guruprakash
Information Science and Engineering, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India ' Department Computer Science and Engineering, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India
deep convolutional neural network
optimisation
IoT sensor
wireless sensor network
smart irrigation
2024-03-04T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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A scalable multimodal ensemble learning framework for automatic modulation recognition
http://www.inderscience.com/link.php?id=137175
The Automatic Modulation Recognition (AMR) method based on Deep Learning (DL) has achieved excellent performance and gradually become a hot spot of research. Most researches have designed complex structures or supplemented feature information to achieve the recognition of modulation signals, which cannot fully combine the advantages of different models to extract features, resulting in poor recognition accuracy of modulated signals. To solve the problem, we propose a Scalable Multimodal Ensemble Learning Framework (SMELF), which trains various models with multimodal information including In-phase Quadrature (I/Q) and Amplitude Phase (A/P) information to supplement feature information. The meta-model is used as a combined strategy to correlate the feature extraction advantages of each model. The simulation results show that SMELF not only achieves superior classification accuracy, but also is the most robust under different Signal-to-Noise Ratios (SNRs) environments and the training sample sizes. In addition, our method can further improve the classification accuracy by combining more diverse and better performance models, which reflects the great potential of the framework.
A scalable multimodal ensemble learning framework for automatic modulation recognition
Jian Shi; Guangxue Yue; Shengyu Ma; Tianjun Peng; Bolin Ma
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 182 - 197
The Automatic Modulation Recognition (AMR) method based on Deep Learning (DL) has achieved excellent performance and gradually become a hot spot of research. Most researches have designed complex structures or supplemented feature information to achieve the recognition of modulation signals, which cannot fully combine the advantages of different models to extract features, resulting in poor recognition accuracy of modulated signals. To solve the problem, we propose a Scalable Multimodal Ensemble Learning Framework (SMELF), which trains various models with multimodal information including In-phase Quadrature (I/Q) and Amplitude Phase (A/P) information to supplement feature information. The meta-model is used as a combined strategy to correlate the feature extraction advantages of each model. The simulation results show that SMELF not only achieves superior classification accuracy, but also is the most robust under different Signal-to-Noise Ratios (SNRs) environments and the training sample sizes. In addition, our method can further improve the classification accuracy by combining more diverse and better performance models, which reflects the great potential of the framework.]]>
10.1504/IJWMC.2024.137175
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 182 - 197
Jian Shi
Guangxue Yue
Shengyu Ma
Tianjun Peng
Bolin Ma
College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China; Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Jiaxing University, Jiaxing, Zhejiang, China ' College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China; Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Jiaxing University, Jiaxing, Zhejiang, China ' College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China; Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Jiaxing University, Jiaxing, Zhejiang, China ' College of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China; Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Jiaxing University, Jiaxing, Zhejiang, China ' College of Data Science, Jiaxing University, Jiaxing, Zhejiang, China
automatic modulation recognition
multimodal information
ensemble learning
vision transformer
2024-03-04T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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Optimised design of cross-shaft parameters based on response surface optimisation model with MOGA
http://www.inderscience.com/link.php?id=137165
Cross-shaft is the core component of the cross-type universal coupling and has a vital transmission function. This paper proposes Sparse Grid and the Kriging interpolation to construct a response surface model to solve the problem of long design cycles, low reliability and high susceptibility to cross-shaft fatigue deformation. The critical dimensions of the cross-shaft are used as design variables, and the maximum equivalent force and deformation are reduced as the optimisation objective. Then experimental points are obtained by Sparse Grid Initialisation and then the response surface model is obtained with high accuracy by Kriging interpolation, and finally, the optimised design of the cross-shaft is completed using MOGA in this paper. Compared with the original structural solution, the maximum deformation of the cross-shaft is reduced by 0.4717 mm (22.35%), the maximum equivalent force is reduced by 130.35 Mpa (17.21%) and the mass increased by only 4.17%.
Optimised design of cross-shaft parameters based on response surface optimisation model with MOGA
Sijie Xiong; Yuanmin Xie; Chunlong Zou; Yanfeng Mao; Yongcheng Cao
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 198 - 206
Cross-shaft is the core component of the cross-type universal coupling and has a vital transmission function. This paper proposes Sparse Grid and the Kriging interpolation to construct a response surface model to solve the problem of long design cycles, low reliability and high susceptibility to cross-shaft fatigue deformation. The critical dimensions of the cross-shaft are used as design variables, and the maximum equivalent force and deformation are reduced as the optimisation objective. Then experimental points are obtained by Sparse Grid Initialisation and then the response surface model is obtained with high accuracy by Kriging interpolation, and finally, the optimised design of the cross-shaft is completed using MOGA in this paper. Compared with the original structural solution, the maximum deformation of the cross-shaft is reduced by 0.4717 mm (22.35%), the maximum equivalent force is reduced by 130.35 Mpa (17.21%) and the mass increased by only 4.17%.]]>
10.1504/IJWMC.2024.137165
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 198 - 206
Sijie Xiong
Yuanmin Xie
Chunlong Zou
Yanfeng Mao
Yongcheng Cao
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Hubei University of Automotive Technology, Shiyan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Technology Research and Development, Hubei Jingmen Wusan Machinery Equipment Manufacturing Co., Ltd., Jingmen, Hubei, China
cross-shaft
Kriging interpolation
MOGA
multi-objective optimisation
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Copyright © 2024 Inderscience Enterprises Ltd.
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Multi-objective optimisation design of cross-shaft based on Kriging response surface optimisation model
http://www.inderscience.com/link.php?id=137166
Cross-universal coupling is a key component of the mechanical transmission system and the cross-shaft is the core component of the coupling for torque transmission. Under normal circumstances, cross-shafts are most susceptible to fatigue and deformation, mainly due to the large torques they carry and the irrationality of their structure. Traditional design methods rely on practical experience to determine the key dimensions of the cross-shaft, resulting in long design cycles and low reliability. To address this problem, parametric modelling of the cross-shaft is carried out in this paper and imported into ANSYS Workbench. In addition, static and finite element analyses are carried out to find the weak parts of the cross shaft as the objective function. Finally, sensitivity analysis is carried out using the main structural parameters of the cross-shaft as design variables. Based on the linear correlation matrix and sensitivity graph, the three design variables that have the greatest impact on the objective function, journal height, thickness and body length, are selected.
Multi-objective optimisation design of cross-shaft based on Kriging response surface optimisation model
Yuanmin Xie; Sijie Xiong; Juntong Yun; Yanfeng Mao; Boao Li; Xinjie Tang; Yongcheng Cao
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 207 - 214
Cross-universal coupling is a key component of the mechanical transmission system and the cross-shaft is the core component of the coupling for torque transmission. Under normal circumstances, cross-shafts are most susceptible to fatigue and deformation, mainly due to the large torques they carry and the irrationality of their structure. Traditional design methods rely on practical experience to determine the key dimensions of the cross-shaft, resulting in long design cycles and low reliability. To address this problem, parametric modelling of the cross-shaft is carried out in this paper and imported into ANSYS Workbench. In addition, static and finite element analyses are carried out to find the weak parts of the cross shaft as the objective function. Finally, sensitivity analysis is carried out using the main structural parameters of the cross-shaft as design variables. Based on the linear correlation matrix and sensitivity graph, the three design variables that have the greatest impact on the objective function, journal height, thickness and body length, are selected.]]>
10.1504/IJWMC.2024.137166
International Journal of Wireless and Mobile Computing, Vol. 26, No. 2 (2024) pp. 207 - 214
Yuanmin Xie
Sijie Xiong
Juntong Yun
Yanfeng Mao
Boao Li
Xinjie Tang
Yongcheng Cao
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei, China; Research Centre for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, Hubei, China ' Technology Research and Development, Hubei Jingmen Wusan Machinery Equipment Manufacturing Co., Ltd., Jingmen, Hubei, China
cross shaft
sensitivity analysis
multi-objective optimisation
ANSYS workbench
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