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Research on the precision forming cathode design method of electrochemical machining all-metal screw drill stator

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第7-8期133卷 3663-3671页

作者： Tang, Lin Wang, Yi Zhang, Jingjing Zhang, Jun Cao, Tong Deng, Wen Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China Xian Technol Univ Sch Def Sci &Technol Xian 710021 Peoples R China China Natl Heavy Machinery Res Inst Co Ltd Xian 710021 Peoples R China

In order to shorten the cathode design cycle, reduce design cost and improve forming accuracy for all-metal screw drill stator electrochemical machining (ECM), this paper proposed a precision forming cathode design method based on particle swarm optimization BP neural network (PSO-BP). The cathode design algorithm model of all-metal screw drill stator electrochemical machining was established, completed the camber feed cathode design. By using self-developed large scale horizontal CNC electrochemical machining equipment, under the condition of voltage 19 V, electrolyte 15%NaCl, electrolyte temperature 35 +/- 1degree celsius, electrolyte inlet pressure 1.6 MPa, and feed speed 10 mm/min, the stable and reliable electrochemical machining processing of the 4-m length of 38CrMoAlA all-metal screw drill stator was completed. The contour forming accuracy is +/- 0.03 mm, and the surface roughness is Ra0.848 mu m. Research showed that it is an efficient and feasible method to design the electrochemical machining camber feed cathode of all-metal screw drill stator using PSO-BP neural network.

关键词： All-metal screw drill stator Electrochemical machining (ECM) particle swarm optimization algorithm BP neural network Cathode design

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Path Planning of Agricultural Information Collection Robot Integrating Ant Colony algorithm and particle swarm algorithm

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IEEE ACCESS 2024年 12卷 50821-50833页

作者： Wu, Qiong Chen, Hua Liu, Baolong Xian Technol Univ Sch Comp Sci & Engn Xian 710021 Peoples R China Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China

Faced with complex and ever-changing environmental conditions in the agricultural field, efficient agricultural information gathering is crucial for optimising agricultural output. Therefore, a new path planning algorithm combining ant colony algorithm and particle swarm optimization is proposed in this study. The aim is to achieve fast and accurate path planning for agricultural information gathering robots in diverse agricultural environments. The global search ability of particle swarm optimization algorithm in finding optimal paths and the local search advantage of ant colony algorithm in obstacle avoidance are used to optimise the movement strategy of robots in agricultural environments. The research results showed that the global path planning distance of this method was 19.328m. The execution time was 0.97s. In local path planning, the proposed algorithm had a fitness function value of 30.123 when the number of iterations reached 53. In mixed path planning, the proposed algorithm reduced the movement time by 3.2s. The conclusion shows that the algorithm proposed in this study has high applicability and efficiency in practical applications, providing an effective strategy for path planning of agricultural information gathering robots. It has important practical significance for promoting the development of intelligent agriculture.

关键词： Ant colony algorithm particle swarm optimization algorithm agriculture robot path pheromone

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Nonlinear Analysis of Slope Stability Considering Geomaterial Dilatancy

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INTERNATIONAL JOURNAL OF GEOMECHANICS 2024年第4期24卷

作者： Hu, Shihong Zhao, Lianheng Li, Liang Zuo, Shi Xie, Zhouzhou Qiao, Nan Cent South Univ Sch Civil Engn Changsha 410075 Hunan Province Peoples R China Cent South Univ Sch of Civil Engn Key Lab Heavy Haul Railway Engn Struct Hunan Prov Key Lab Disaster Prevent & Mitigat Rail Changsha 410075 Hunan Province Peoples R China Changsha Univ Coll Civil Engn Changsha 410022 Hunan Province Peoples R China

Shear dilation occurs during the failure of geomaterials subjected to shear stress. To account for the dilatancy and nonlinearity of geomaterials, a nonlinear upper-bound analysis method was proposed for evaluating the stability of homogeneous slopes based on the coaxial nonassociated flow rule. The rotational failure mechanism of a homogeneous slope was established within the kinematic approach of limit analysis, using the Davis approach to convert the nonassociated flow rule into an associated one. By applying the variation principle, ordinary differential equations of the potential sliding surface and its corresponding stress were derived, which were then solved using a fourth-order Runge-Kutta method in conjunction with appropriate boundary conditions. Furthermore, the balance equation was derived from the virtual power principle, and the critical height of the slope was calculated using a particle swarm optimization algorithm. The strength reduction technique was then introduced to determine the factor of safety of the slope. The accuracy and effectiveness of the proposed nonlinear upper-bound variation method for evaluating the stability of homogeneous slopes under nonassociated flow rule and nonlinear failure criteria were verified when compared with existing studies and techniques, such as the finite-element limit analysis and the finite difference method. This study accurately reflects the nonlinearity and dilatancy of geomaterials and avoids assumptions regarding the sliding surface and its corresponding stress, making it a valuable reference for future research.

关键词： Slope stability Nonassociated flow rule Nonlinear Mohr-Coulomb failure criterion Kinematic approach of limit analysis Strength reduction Variation principle particle swarm optimization algorithm

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Research on pulse laser detection anti-interference method in smoke environment

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INFRARED PHYSICS & TECHNOLOGY 2025年 145卷

作者： Qu, Qinyang Su, Zerun Xu, Xiaobin Li, Moyuan Chen, Shanshan Ran, Yingying Tan, Zhiying Hohai Univ Coll Mech & Elect Engn Chang Zhou 213200 Peoples R China Hohai Univ Coll Informat Sci & Engn Chang Zhou 213200 Peoples R China

To address the problem of smoke medium interference on pulse laser detection signals, this study employs Monte Carlo method to precisely establish the backscattered signals model of pulsed lasers in smoke conditions, and proposes an anti-interference strategy combining particle swarm optimization (PSO) and convolutional neural network (CNN) by echo signals collected experimentally. Specifically, this study uses the Monte Carlo method and scattering phase function to simulate particle collisions and constructs a sampling model of reflected photons based on the target surface's reflective properties. Semi-analytical reception technology is used to extract the received backscattered photon signals. Finally, a CNN is built taking all the backscattered data points as the input. The parameter of the network is optimized using PSO algorithm. To verify the accuracy of the simulated backscattered echo signals and the effectiveness of the anti-interference algorithm, a laser detection experiment in a smoke environment was conducted. The experimental results show that the simulated detection back- scattered signals and the experimental backscattered signals have a high degree of consistency, with a maximum root mean square error of only 0.043. The recognition accuracy of the CNN optimized by the PSO algorithm is 96.154 %, with a computational parameter size of 193.008 KB. This study provides solid theoretical support and technical assurance for the pulse laser detection technology in the anti-interference field.

关键词： Pulse laser Smoky environment Echo model Monte Carlo method particle swarm optimization algorithm

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Comparative study on constitutive models of a near β titanium alloy TC18 during thermoplastic deformation based on machine learning

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MATERIALS TODAY COMMUNICATIONS 2025年 42卷

作者： Ding, Shaoling Gao, Shuai Jiang, Xiang Shi, Shuangxi Liang, Yaobiao Guilin Univ Technol Sch Math & Stat Guilin 541006 Peoples R China Guangxi Coll & Univ Key Lab Appl Stat Guilin 541006 Peoples R China Guilin Univ Aerosp Technol Guangxi Key Lab Special Engn Equipment & Control Guilin 541004 Peoples R China

The hot deformation behavior of TC18 alloy has been systematically studied at the temperature of 993-1113 K and strain rate of 0.001-1 s-1. Based on the stress-strain data obtained under the above process parameters, machine learning models describing the thermoplastic constitutive relationship of the alloy were established by using random forest (RF), support vector regression (SVR), back propagation artificial neural network (BPANN) and Gaussian process regression (GPR). The hyper-parameters of these models were optimized by adaptive inertial weighted particle swarm optimization (APSO) algorithm, and the models were further evaluated by statistical analysis and cross-validation. The results show that the prediction ability of the developed four machine learning models was ranked as GPR>BPANN>RF>SVR. Then APSO was applied to four models to further enhance their prediction accuracy, and the prediction accuracy of these APSO models was ranked as APSOGPR>APSO-BPANN>APSO-RF>APSO-SVR. The developed APSO-GPR model has the highest R2 (>0.999), as well as the lowest RMSE (<1.77) and MAPE (<0.63 %) on both the training set and the testing set, demonstrating its strong predictive performance. Sixteen cross-validation tests also confirm the APSO-GPR model has high prediction accuracy.

关键词： TC18 alloy Hot deformation Flow stress Machine learning particle swarm optimization algorithm

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Vibration separation method for permanent magnet guideway irregularity measurement

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 223卷

作者： Luo, Yi Yuan, Yuhang Zeng, Peiyang Liu, Jianru Deng, Bin Deng, Zigang Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Sichuan Peoples R China

High-temperature superconducting (HTS) pinning maglev has the potential to achieve high operational speed while having a low maintenance cost, attracting a lot of attention. The HTS pinning maglev system realizes the levitation of the vehicle through the flux pinning characteristics between the high-temperature superconducting bulk and the magnetic field of the permanent magnet guideway (PMG). Thus, the profile of the magnetic field of the PMG can significantly affect the levitation status of the vehicle, which makes the PMG irregularity become one of the main excitation sources of the system. The magnetic induction intensity on the surface of the PMG can be used to characterize the PMG irregularity, but the non-contact dynamic measurement may produce large errors by the vibration of the measurement system during the process. In order to evaluate the PMG irregularity, the vibration component needs to be separated from the overall signal. In this paper, through the synchronous measurement of the Hall sensor and accelerometer, the vibration separation method of the PMG irregularity measurement is simplified to the optimal filter estimation. The particle swarm optimization (PSO) algorithm is used to calculate the parameters of the optimal filter based on the correlation coefficient criterion. The effectiveness of the proposed method is verified by the test. The separated signal is more accurate in both time domain and frequency domain for the characterization of PMG irregularity.

关键词： High-temperature superconducting pinning maglev Permanent magnet guideway irregularity Vibration separation Correlation coefficient particle swarm optimization algorithm

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Research on Intelligent optimization of Wellbore Trajectory in Complex Formation

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1364-1364页

作者： Gu, Haipeng Yan, Tie Wu, Yang Stat Northeast Petr Univ Sch Math Daqing 163318 Heilongjiang Peoples R China Northeast Petr Univ NEPU Sanya Offshore Oil & Gas Res Inst Sanya 572000 Peoples R China

Borehole trajectory optimization is a key issue in oil and gas drilling engineering. The traditional wellbore trajectory design method faces great challenges in optimizing the trajectory length and complexity, and it is difficult to meet the actual engineering requirements. In this paper, the three-stage wellbore trajectory optimization problem is studied, and a multi-objective optimization model including two objective functions of trajectory length and trajectory complexity is constructed. In this paper, an improved multi-objective particle swarm optimization algorithm is proposed, which combines the clustering strategy to improve the diversity of solutions, and enhances the local search ability and global convergence performance of the algorithm through the elite learning strategy. In order to verify the performance of the algorithm, comparative experiments were carried out using classical multi-objective benchmark functions. The results showed that the improved algorithm is superior to the traditional method in terms of diversity and convergence of solutions. Finally, the proposed algorithm was applied to the actual three-stage wellbore trajectory optimization problem. In summary, the research results of this paper provide theoretical support and engineering practice methods for wellbore trajectory optimization, and serve as an important reference for further improving the efficiency and quality of wellbore trajectory design.

关键词： wellbore trajectory multi-objective optimization particle swarm optimization algorithm elite learning strategies

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Integrated Control Strategy of Bearingless Switched Reluctance Motor Based on Active Disturbance Rejection Control

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第7期72卷 7320-7328页

作者： Wang, Xuan Wang, Xilian Huang, Zhuliang Wang, Baohua Yang, Ruikang Beijing Jiaotong Univ Coll Elect Engn Beijing 100044 Peoples R China

Traditional proportion integration differentiation (PID) controller is difficult to solve the contradiction between response speed and overshoot, and its anti-interference ability is limited. To solve the problem, an integrated control strategy of bearingless switched reluctance motor based on active disturbance rejection control (ADRC) is proposed. A first-order speed ADRC controller and a second-order displacement ADRC controller are designed for direct instantaneous torque control and direct instantaneous force control system, respectively. With the integral time-weighted absolute error (ITAE) as the optimization objective, the particle swarm optimization algorithm is used to tune the main parameters of the ADRC controllers, overcoming the drawbacks of adjusting parameters manually, such as complex operation and time consumption. Simulations and experiments are carried out. The results demonstrate that the control strategy has superior dynamic and static response performance in both speed and displacement control and has good application prospects.

关键词： Control systems Rotors Torque Windings Force Motors Centralized control Switched reluctance motors Stator windings particle swarm optimization Active disturbance rejection control (ADRC) bearingless switched reluctance motor (BSRM) particle swarm optimization algorithm sharing suspension windings

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Design of impact ultrasonic penetrator and optimization of impact efficiency

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ADVANCES IN SPACE RESEARCH 2025年第8期75卷 6070-6088页

作者： Wang, Yinchao Yu, Ziming Yin, Zihao Zhang, Weiwei Zu, Lin Tao, Guanghong Yu, Suyang Shenyang Aerosp Univ Sch Mechatron Engn Shenyang 110136 Peoples R China Shenyang Aerosp Univ Key Lab Rapid Dev & Mfg Technol Aircraft Minist Educ Shenyang 110136 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China Deep Space Explorat Lab Hefei 230026 Peoples R China

This paper proposes, optimizes, and experimentally investigates a thread-clamped ultrasonic penetrator that uses a spring to induce forced vibration in the transducer, thereby enhancing drilling efficiency. The penetrator consists mainly of a transducer, a free mass, an elastic energy storage unit, a drilling tool, and a housing. The elastic energy storage unit is attached to the transducer's flange, permitting limited axial movement. When excited by a sinusoidal signal at a specific frequency, the transducer's front end generates high-frequency longitudinal vibrations that impact the free mass. Upon colliding with the drilling tool and the transducer subsequently, the elastic energy storage unit absorbs and utilizes this energy, optimizing the energy transfer process. This study designs the penetrator's structure, analyzes the motion curves of each component, and derives the kinetic energy curve of the drilling tool. A novel particle swarm optimization algorithm is employed to optimize the key parameters of the penetrator, verifying the optimization effect. The prototype was fabricated, and its vibration and output characteristics were tested. The results from rigorous testing clearly demonstrate a significant improvement in the penetrator's drilling efficiency after meticulous structural and parameter optimization. Both simulation and experimental results confirm the feasibility of the penetrator. (c) 2025 Published by Elsevier B.V. on behalf of COSPAR.

关键词： Piezoelectric actuator Kinetic optimization Energy amplification particle swarm optimization algorithm

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A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

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INFORMATION SCIENCES 2025年 686卷

作者： Karahasan, Ozlem Bas, Eren Egrioglu, Erol Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkiye

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

关键词： Simple recurrent artificial neural network Exponential smoothing Sine cosine optimization algorithm particle swarm optimization algorithm Stock exchange Forecasting

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