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Penetration quality prediction of asymmetrical fillet root welding based on optimized BP neural network

JOURNAL OF MANUFACTURING PROCESSES 2020年 50卷 247-254页

作者： Chang, Yushuo Yue, Jianfeng Guo, Rui Liu, Wenji Li, Liangyu Tianjin Polytech Univ Sch Mech Engn Tianjin Peoples R China

Penetration morphology has an important influence on the weld quality. Due to the nonlinear and strong coupling characteristics of welding process, neural network is often used to predict weld formation and quality. However, the prediction method of fillet weld penetration needs further exploration due to the difficulty in quality evaluation. More intricately, fillet welds of the medium-thickness plate with one-side V-groove are structurally asymmetrical, which makes the penetration quality difficult to guarantee. In this study, the penetration quality of asymmetrical fillet welds is depicted by two characteristic quantities: penetration depth and penetration deflection. The penetration deflection can be reflected by leg length on both sides. After correlation analysis, a back-propagation neural network(BPNN) optimized by mind evolutionary algorithm (MEA) is proposed. The welding current, welding speed, torch work angle and real-time molten pool width were chosen as input parameters, and the penetration of blunt edge and the leg length on both sides of the weld were chosen as output parameters. The results demonstrated that it is feasible and reasonable to predict the penetration of asymmetrical fillet welds by this model. Experimental comparison showed that the optimized model has a significant improvement in predictive performance. The prediction error of blunt edge penetration is controlled within 0.1 mm, and the prediction error rate of leg length is less than 7 %. It satisfies the accurate prediction of penetration quality of asymmetrical fillet welds, and lays the foundation for the study of penetration morphology control of asymmetrical fillet automatic welding.

关键词： Asymmetrical fillet weld Penetration forming BPNN mind evolutionary algorithm

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Aging prognosis model of proton exchange membrane fuel cell in different operating conditions

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第20期45卷 11761-11772页

作者： Chen, Kui Laghrouche, Salah Djerdir, Abdesslem CNRS UMR 6174 FEMTO ST F-90000 Paris France Univ Bourgogne Franche Comte CNRS FR 3539 FCLABBelfort UTBM F-90000 Paris France

The aging prognosis model of Proton Exchange Membrane Fuel Cell (PEMFC) can predict the aging state of PEMFC to develop an effective prognostic maintenance plan. This paper proposes an aging prognosis model of PEMFC in different operating conditions based on the Backpropagation (BP) neural network and evolutionary algorithm. The influence of PEMFC current, hydrogen pressure, temperature, and relative humidity on the aging of PEMFC can be considered by the proposed method. Firstly, the aging prognosis model of PEMFC is built by the BP neural network. Then, the evolutionary algorithm including mind evolutionary algorithm (MEA), Particle Swarm Optimization (PSO), and Genetic algorithm (GA) is used to optimize the parameters of the established aging prognosis model of PEMFC. Finally, the accuracy of the proposed aging prognosis model is validated by 3 PEMFC aging experiments in different operating conditions. The results show that MEA, GA, and PSO can greatly improve the accuracy of the aging prognosis model of PEMFC. The MEA improves the accuracy by 10 times, while the computing time increases by 0.085s. The proposed MEA-BP that has a very short computing time can be applied to online applications. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cell Aging prognosis model mind evolutionary algorithm Particle swarm optimization Genetic algorithm Backpropagation neural network

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Established prediction models of thermal conductivity of hybrid nanofluids based on artificial neural network (ANN) models in waste heat system

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2020年 110卷 104444-104444页

作者： Wang, Jiang Zhai, Yuling Yao, Peitao Ma, Mingyan Wang, Hua Kunming Univ Sci & Technol State Key Lab Complex Nonferrous Met Resources Cl Kunming 650093 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650093 Yunnan Peoples R China

The properties of water (W)/ethylene glycol (EG) mixtures vary significantly with the proportion of EG and temperature, so it is suitable to use such fluids as exchange heat mediums in a waste heat system with temperature fluctuations. The experiments were conducted with 1.0 wt% Cu/Al2O3- EG/W hybrid nanofluids at temperatures ranging from 20 to 50 degrees C, where the base fluid (EG/W) mixture ratio was varied from 20:80 to 80:20. To search individuals which contain optimal weights and thresholds, a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) coupled with a back-propagation neural network (GA-BPNN and MEA-BPNN, respectively) were used to improve the accuracy in the predicted thermal conductivity. The results show that the thermal conductivity increases nonlinearly with the ratio of water to ethylene glycol and temperature, due to the higher thermal conductivity of water and stronger collision frequency between molecular and nanoparticles. Binary Polynomial Regression (BPR) was fit with (coefficient of determination) R-2 = 0.9984 as functions of temperature and mixture ratio. Comparisons of the prediction performance and capability of BPR, the performance of R-2 increases by 0.11% and 0.13% for GA-BPNN and MEA-BPNN. It indicates that the combined BPNNs both predicate more accurately, particularly MEA-BPNN has the highest prediction accuracy.

关键词： Hybrid nanofluids Thermal conductivity Artificial neural network Genetic algorithm mind evolutionary algorithm

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Development of a Surface Roughness Prediction Model for Slow Tool Servo Turning Machining

MANUFACTURING TECHNOLOGY

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MANUFACTURING TECHNOLOGY 2022年第1期22卷 111-122页

作者： Zhou, Wei Kang, Min Guo, Hangyan Nanjing Agr Univ Coll Engn 40 Dianjiangtai Rd Nanjing 210031 Peoples R China

To investigate the effect of slow tool servo turning process parameters on surface roughness, we established a high precision surface roughness prediction model. A guide to the selection of turning process parameters was compiled, and a turning test was conducted based on a response surface method (RSM) central composite design. ANOVA explores the influence law of process parameters on surface roughness. A RSM BP neural network model, and MEA-BP surface roughness model were established and the prediction performance of the three models was evaluated. The results show that the significant process parameters affecting surface roughness are tool radius, discrete angle, feed rate, and cutting depth in descending order;and the prediction errors of RSM, BP, and MEA-BP are 11.41%, 19.67%, and 5.54%. This suggests that the MEA-BP model has the highest prediction accuracy with the same test data, RSM is second, whilst the single BP model struggles to capture multiple data characteristics and its prediction accuracy is poor. In addition, MEA can effectively solve the BP model falling into local optimum and improve the model prediction accuracy.

关键词： Process parameters Surface roughness Response surface method BP neural network mind evolutionary algorithm

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Introduce canopy temperature to evaluate actual evapotranspiration of green peppers using optimized ENN models

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JOURNAL OF HYDROLOGY 2020年 590卷 125437-125437页

作者： Liu, Jingran Meng, Xiankuan Ma, Yingjie Liu, Xin Hebei Univ Engn Sch Water Conservancy & Hydroelect Power Handan 056038 Peoples R China Xinjiang Agr Univ Urumqi 830052 Peoples R China

Canopy temperature (T-c) was introduced using the Elman Neural Network (ENN) optimized by mind evolutionary algorithm (MEA) and Genetic algorithm (GA) methods to improve the predicting accuracy of actual evapotranspiration (ET) of green peppers. The improving planting methods, rainwater harvesting partial plastic film mulching and regulated deficit irrigation (MFR-RDI) with straw mulling, had been employed to save irrigation water. The results indicated that MEA-Elman1 (RMSE = 0.349 mm/d, MAE = 0.303 mm/d, NS = 0.951) and GA-Elman1 (RMSE = 0.487 mm/d, MAE = 0.404 mm/d, NS = 0.913) models can accurately estimate the ET of green peppers, indicating that the T-c can well improve the accuracy of both MEA-Elman and GA-Elman models in predicting ET under the condition of MFR-RDI. Furthermore, the results showed that the performance of MEA-Elman model was better than GA-Elman model under the same factors. Based on the above results, different input factors in different growth periods of crops has been adopted using the MEA-Elman4 model to further improve the predicting accuracy of ET. Indeed, the MEA-Elman4 model exerted the best performance (RMSE = 0.339 mm/d, MAE = 0.285 mm/d, NS = 0.954), which should be given priority to predict the ET of green peppers.

关键词： Green peppers Evapotranspiration Canopy temperature Elman Neural Network mind evolutionary algorithm Genetic algorithm

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Estimation of the SOC of Energy-Storage Lithium Batteries Based on the Voltage Increment

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IEEE ACCESS 2020年 8卷 198706-198713页

作者： Zhao, Bo Hu, Juan Xu, Shouping Wang, Jiangzhao Zhu, Yanqing Zhang, Li Gao, Chaofei Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing 100192 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China

State of charge (SOC) estimations are an important part of lithium-ion battery management systems. Aiming at existing SOC estimation algorithms based on neural networks, the voltage increment is proposed in this paper as a new input feature for estimation of the SOC of lithium-ion batteries. In this method, the port voltage, current and voltage increment are taken as inputs and the current SOC is used as output to train a neural network. Different from the adaptive filtering algorithm, which requires complex equivalent circuit parameter identification, this algorithm uses the voltage increment instead of the open circuit voltage (OCV);hence, the complexity of the SOC estimation algorithm is reduced, and the problem of inaccurate estimation caused by neural network algorithms without considering the internal structure of the battery is avoided. The experimental results show that compared with the traditional neural network algorithm, the neural network SOC estimation algorithm based on the voltage increment could improve the accuracy of SOC estimation.

关键词： State of charge Estimation Batteries Neural networks Machine learning algorithms Filtering Autoregressive processes Lithium battery state of charge neural network mind evolutionary algorithm voltage increment

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Feed intake prediction model for group fish using the MEA-BP neural network in intensive aquaculture

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Information Processing in Agriculture 2020年第2期7卷 261-271页

作者： Lan Chen Xinting Yang Chuanheng Sun Yizhong Wang Daming Xu Chao Zhou Beijing Research Center for Information Technology in Agriculture Beijing 100097China Tianjin University of Science and Technology Tianjin 300222China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China National Engineering Laboratory for Agri-product Quality Traceability Beijing 100097China

In aquaculture,the accurate prediction of feed intake for group fish is considered to be crucial to any feeding *** studies mainly used mathematical statistics to establish the mapping relationship between feed intake and influencing *** result was easily influenced by subjective *** solve the above issues,this paper proposed a feed intake prediction model for group fish using the back-propagation neural network(BPNN)and mind evolutionary algorithm(MEA).Firstly,four factors,including water temperature,dissolved oxygen,the average fish weight and the number of fish were selected as the input of the BPNN ***,the initial weight and threshold of the BPNN were optimized by the MEA to improve the matching ***,the prediction model was achieved after *** results showed that the correlation coefficient between the predicted and measured values reached *** the root mean squared error,mean square error,mean absolute error,mean absolute percent error of the model was 6.89,47.53,6.17 and 0.04,*** addition,the proposed method also had the better nonlinear fitting ability than BPNN and *** using an intelligent optimization algorithm,the mapping relationship between fish intake and environmental factors was automatically established,thus avoiding the subjectivity of traditional ***,it can lay a theoretical foundation for the development of intelligent feeding equipment and meet the needs of the smart fishery.

关键词： BP neural network Feed intake prediction Group fish mind evolutionary algorithm

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An Improved Model Combining evolutionary algorithm and Neural Networks for PV Maximum Power Point Tracking

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IEEE ACCESS 2019年 7卷 2823-2827页

作者： Wang, Haixin Shen, Jianxin Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

Aiming at the large error of the traditional constant control method in predicting the maximum power of solar UAV, this paper proposed an improved mind evolutionary algorithm combined with BP neural network (BPNN), in which the improved mind evolutionary algorithm optimizes the BPNN. The optimized model is used to predict the voltage at the maximum power of the panel in the UAV. The constant voltage parameter based on the conventional constant pressure control algorithm is replaced by this value. At the same time, a new control simulation model of constant voltage solar panel maximum power tracking based on the improved mind evolution algorithm optimize BPNN was built. At last, the algorithm was simulated and validated in the MATLAB/Simulink environment. The simulation results show that this algorithm is better than simply using BPNN and genetic algorithm optimize BPNN stability and higher accuracy.

关键词： Simulation photovoltaic maximum power tracking artificial neural networks mind evolutionary algorithm

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The Intelligence mind evolutionary algorithm Based on Difference

The Intelligence Mind Evolutionary Algorithm Based on Differ...

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24th Chinese Control and Decision Conference (CCDC)

作者： Guo, Hongge Xie, Keming Taiyuan Univ Technol Coll Informat Engn Taiyuan 030024 Peoples R China

ISBN: (纸本)9781457720727

In order to add population diversity, The intelligence mind evolutionary algorithm (IMEA) based on difference is presented. It uses difference information of subpopulations to share social information and comparing fitness to consideration. The improved algorithm makes mind evolutionary algorithm(MEA) perfect in theory and achieves the best balance between the searching velocity and global convergence in practice. Optimum experiments by the testing functions show the effectiveness and feasibility of the IMEA.

关键词： mind evolutionary algorithm Sharing social information Difference

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The Intelligence mind evolutionary algorithm Based on Difference

The Intelligence Mind Evolutionary Algorithm Based on Differ...

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The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Hongge Guo Keming Xie College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China. Coll College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China.

关键词： mind evolutionary algorithm Sharing social information Difference

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