Table of Content

25 January 2026, Volume 54 Issue 1

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Energy，Power & Electrical Engineering

Study on Low Nitrogen Combustion Characteristics of Metal Fiber Burner in Limited Space

GAN Yunhua, CHEN Kui, CHEN Ningguang, CHENG Jing, XU Yan

2026, 54(1):  1-9.  doi:10.12141/j.issn.1000-565X.250027

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Metal fiber surface combustion technology has become one of the most promising low nitrogen combustion technologies for gas boilers because of its advantages of stable combustion process and uniform temperature distribution. Based on the low-nitrogen combustion experimental data of metal fiber surface in limited space, this study established a three-dimensional physical model of metal fiber surface combustion and carried out the full-premixed combustion numerical solution based on the porous medium resistance model, metal fiber turbulence model and EDC combustion model. It obtained the flame combustion conditions and the velocity characteristics of hot smoke flow field of metal fiber surface combustion in limited space, as well as the temperature field distribution and fuel distribution in furnace. Considering the influence of different excess air coefficient, it studied the emission characte-ristics and average generation rate distribution of prompt NO _x and thermal NO _x on metal fiber surface. The results show that under different excess air coefficients, the generation rate of prompt NO _x is faster than that of thermal NO _x . However, limited by the effective reaction space and reaction duration, the volume occupied by prompt NO _x in the furnace is much smaller than that of thermal NO _x .With the increase of excess air coefficient, the generation rate and emission of thermal NO _x gradually decrease. When α=1.6, the emission of NO _x from flue gas outlet is 22.55 mg/m³, which meets the standard of low nitrogen combustion. Therefore, in addition to changing the flame temperature, oxygen concentration and combustion mode, the generation of thermal NO _x can be suppressed by controlling the excessive air coefficient, and ultra-low nitrogen combustion of industrial gas boilers can also be effectively achieved.

Numerical Simulation on Characteristics of Coaxial Dual-Chamber Plasma Generator

LIU Dingping, PAN Shuhuan, WU Chaochao

2026, 54(1):  10-18.  doi:10.12141/j.issn.1000-565X.250040

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To reveal the distribution characteristics of the arc-plasma and the behavior of the flow field and further analyze the relationship between external parameters and arc behavior, this study conducts a numerical simulation of a coaxial dual-chamber plasma generator based on Magneto Hydro Dynamics (MHD) theory. The research investigates the correlations among arc voltage, cathode spot distribution, and airflow parameters with the axisymmetric model by incorporating coupled calculations of the flow field and electromagnetic field. The simulation results indicate that arc voltage is relatively insensitive to radial airflow; within the studied parameter range, fluctuations in radial airflow pressure have a maximum impact of only 4.2% on arc voltage. In contrast, arc voltage exhibits a strong positive correlation with axial airflow velocity, and a fitted correlation equation has been obtained. Temperature and velocity distribution analyses show that the maximum nozzle temperature exceeds 3 500 K, ensuring sufficient ignition capability for low-quality coal and stable combustion performance. Moreover, the results confirm that, under proper airflow configuration, the coaxial dual-chamber structure enables plasma igniters to achieve both high power and extended electrode lifespan. It reveals the anti-ablation mechanism of this structure, namely, the alternating sweeping effect of the two gas flows—the axial flow primarily controls the output power, while the radial flow regulates the arc root position through periodic fluctuations, thereby preventing single-point erosion and extending the electrode lifespan. Furthermore, the study further identifies the balance point between the two gas flows through optimized design experiments, providing theoretical support for future research on the long-term durability of plasma generators.

Coordinated Reactive Power Control Strategy for Hybrid VSG and SVG Systems Considering Low-Voltage Ride-Through Current Limiting

LIN Hai, WANG Jiarui, ZHANG Yanning, GU Shenhui

2026, 54(1):  19-29.  doi:10.12141/j.issn.1000-565X.250093

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To address the challenges of current over-limitation and limited voltage support capability in grid-forming energy storage converters during low-voltage ride-through (LVRT) under grid fault-induced voltage sags, this study proposes a coordinated reactive power control strategy for a hybrid Virtual Synchronous Generator (VSG) and Static Var Generator (SVG) system incorporating an LVRT current limiting strategy. Firstly, it introduces a typical control methodology of VSG, and conducted an analysis on the transient characteristic power-angle curves under different levels of grid voltage sags. Subsequently, it developed an improved q-axis priority current limiting strategy by modifying the existing approach, where the active power reference value is proportionally reduced according to the diminished d-axis current, resulting in an adaptive active power reference q-axis priority current limiting approach. Furthermore, to overcome the restricted voltage support capacity of standalone VSG during severe grid voltage sags, a collaborative control framework integrating SVG with VSG is established, accompanied by a reactive power allocation strategy. The strategy redesigns the reactive power control loop of VSG with SVG priority for reactive power compensation as a prerequisite, while introducing the current limiting mechanism of SVG and designing the specific cooperative control process for the hybrid system. The effectiveness of the proposed methodology is conclusively validated through Simulink simulation results. The experimental results demonstrate that the proposed current limiting strategy effectively restricts the output current, while the hybrid system control methodology significantly enhances voltage recovery performance during grid voltage sags.

Performance Analysis of FCI-DCSK-NR over Power Line Channel

CHENG Guixian, LING Qin, ZHENG Cai, CHEN Fang, ZHANG Quanling, CHEN Pingping

2026, 54(1):  30-41.  doi:10.12141/j.issn.1000-565X.250233

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A differential chaos shift keying scheme that fuses noise reduction with full carrier indexing (FCI-DCSK-NR) is proposed for power-line channels. The scheme integrates noise reduction and full-carrier index techniques into differential chaos shift keying (DCSK) modulation to effectively tackle the complex power line channel environment, especially the interference caused by impulsive noise. Firstly, in this scheme, all subcarriers except the refe-rence carrier are divided into two categories, namely Type-1 active carriers and Type-2 active carriers. The two types of carriers are distinguished via index bits; the carriers selected by the index bits serve as Type-1 active ca-rriers, while the remaining subcarriers function as Type-2 active carriers. Subsequently, Type-1 and Type-2 active carriers transmit Type-1 and Type-2 modulation bits using chaotic signals and their Hilbert-transformed signals, respectively. Moreover, identical information is transmitted in multiple distinct time slots at the transmitter, and the received replicas are averaged at the receiver to mitigate impulsive noise. A closed-form bit-error-rate (Bit error rate, BER) expression for FCI-DCSK-NR over power line channels is derived and verified by Monte-Carlo simulations. Analytical and numerical results show that the proposed scheme effectively mitigates impulse noise in power line channels. Compared to traditional DCSK and Generalized Carrier Index DCSK (GCI-DCSK) schemes, the proposed method exhibits improvements in both BER performance and data rate. Additionally, this experimental scheme is well-suited for integration into training programs of power grid companies, universities, and research institutes, contributing to the cultivation of relevant technical talent.

Short-Term Power Load Multi-Step Forecasting for Commercial Building Based on Improved Informer

ZHOU Xuan, LI Kexin, GUO Zixuan, YU Zhuliang, YAN Junwei, CAI Panpan

2026, 54(1):  42-52.  doi:10.12141/j.issn.1000-565X.250024

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Short-term power load multi-step forecasting for commercial buildings plays a pivotal role in urban orderly power consumption and virtual power plant scheduling. The power load time series in commercial buildings is characterized by strong stochasticity, non-stationarity, and nonlinearity, and traditional iterative multi-step power load forecasting strategy suffers from error accumulation effects that degrade prediction accuracy, a short-term power load multi-step forecasting method based on Frequency Enhanced Channel Attention Mechanism (FECAM)-Sparrow Search Algorithm (SSA)-Informer is proposed. Based on the time-domain features output by the Informer encoder, the method uses FECAM to adaptively model the frequency dependence between feature channels, and further extractings the frequency-domain features of multi-dimensional input sequences. The decoder then integrates both time-frequency domain information to directly generate future multi-step load sequences. Furthermore, due to the lack of theoretical basis for the improved Informer hyperparameter settings, the SSA is used to optimize model hyperparameters such as learning rate, batch size, fully connected dimensions, and dropout rate. Experimental validation using annual load data from a commercial building in Guangzhou demonstrates that, compared with other deep learning models, the proposed model significantly improved prediction accuracy across varying forecast horizons (steps of 48, 96, 288, 480 and 672), exhibiting superior performance in short-term power load multi-step forecasting.

Electronics, Communication & Automation Technology

Channel Estimation Method Based on Channel Enhanced Deep Horblock Network

LIU Jiaojiao, WANG Ruochen, MA Biyun

2026, 54(1):  53-59.  doi:10.12141/j.issn.1000-565X.240594

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In high-mobility scenarios, wireless communications undergo time and frequency doubly selective fa-ding, making channel estimation essential for accurately obtaining channel state information (CSI), which in turn enhances the perfor-mance of communication systems. The Time-Frequency Doubly Selective Channel is a channel model that characterizes signal fading with selective properties in both time and frequency dimensions. To address the challenges of channel estimation in such environments, deep learning methods have been widely adopted in recent years. Networks that originally excelled in computer vision and natural language processing, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), have been applied to channel estimation techniques. However, due to significant differences in data characteristics and task objectives between channel estimation and image processing, these approaches still face numerous challenges. This study introduced a novel channel estimation deep learning algorithm based on a Channel Enhanced Deep Horblock Network (CEHNet). The proposed algorithm treats the time-frequency grid of the doubly selective channel as a two-dimensional image and employs a Super-Resolution (SR) network to reconstruct the CSI. Additionally, a preprocessing method that increases amplitude features is utilized to expand the dataset, and Lasso regression is incorporated as a constraint to accelerate the network convergence speed. Experimental results demonstrate that, across various channel models, the proposed CEHNet algorithm outperforms traditional channel estimation methods such as Super-Resolution Convolutional Neural Networks (SRCNN) when the number of pilots is limited. Furthermore, CEHNet exhibits significantly faster convergence rates, achieving a fourfold performance improvement over SRCNN at a signal-to-noise ratio (SNR) of 22 dB.

A Lightweight Multivariate Time Series Prediction Method for Wastewater Treatment

TANG Lili, LIU Yiqi

2026, 54(1):  60-69.  doi:10.12141/j.issn.1000-565X.250132

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In wastewater treatment processes, the efficient modeling of key water quality parameters is crucial for achieving process control optimization, anomaly detection, and decision support. However, the process data generally exhibits characteristics such as temporal dependence, multivariable coupling, and non-stationarity under varying operating conditions, posing significant challenges to accurate modeling. To address these issues, this paper proposes a Lightweight Multivariate Time Series Prediction Method for Wastewater Treatment based on the Stationary Wavelet Transform (SWT) and Collaborative Attention (CA) mechanism. This model first performed multi-scale decomposition on wastewater data and used the stationary wavelet transform to extract data features from sequences at different scales. Subsequently, a collaborative attention mechanism based on geometric attention and sparse attention was constructed to effectively capture the complex coupling relationships and temporal features among key water quality parameters. Finally, the features reconstructed via inverse wavelet transform were mapped to the final prediction results through a dual-prediction layer. The model was trained and validated on a measured dataset from a wastewater treatment plant in Dongguan, with multi-step prediction tasks and partial data visualization analyses conducted. Experimental results show that, in the 24-step multi-output prediction tasks, the proposed model achieves a reduction of 9.15% to 37.70% in multi-output root mean square deviation (RMSSD) compared to benchmark models. In other prediction tasks, its accuracy ranks second only to TimesNet, which has a significantly larger parameter scale. These results demonstrate an effective balance between lightweight design and high accuracy, thereby validating the efficacy of the proposed model for time-series prediction in wastewater treatment.

A Single-Channel Speech Separation Model Based on Time-Domain Comprehensive Attention Mechanism

YANG Junmei, ZHANG Bangcheng, YANG Lu, ZENG Delu

2026, 54(1):  70-82.  doi:10.12141/j.issn.1000-565X.250054

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Single-channel speech separation aims to extract clean target speaker speech from a mixed audio signal recorded by a single microphone, with significant application value in scenarios such as smart homes, conference systems, and hearing aids. With the rapid development of deep learning technology, self-attention network-based approaches to single-channel speech separation have achieved remarkable progress. While self-attention networks excel at capturing contextual information in long sequence, they still exhibit limitations in capturing detailed features such as temporal/spectral continuity, spectral structure, and timbre in real-world speech scenarios. Moreover, existing separation architectures based on a single attention paradigm struggle to achieve effective multi-scale feature fusion. To address these challenges, this paper proposed a Temporal Comprehensive Attention Network (TCANet), which addresses the aforementioned issues through a synergistic design of local and global attention modules. Local modeling employs an S&C-SENet-enhanced Conformer structure to capture short-term features such as spectral structure and timbre in detail, while global modeling incorporates a modified Transformer module with relative position embedding to explicitly learn long-term speech dependencies in speech. Furthermore, TCANet achieves cross-scale fusion of intra-block local features and inter-block global correlations through a dimension transformation mechanism. Experimental results on three benchmark datasets—LRS2-2Mix, Libri2Mix, and EchoSet—demonstrate that the proposed method outperforms existing end-to-end speech separation approaches in terms of scale-invariant signal-to-noise ratio improvement (SI-SNRi) and signal-to-distortion ratio improvement (SDRi).

UWB/INS Indoor Positioning Method Based on Self-Resetting Genetic Particle Filtering

YANG Yonghui, LI Zhixian, WANG Minhui, XU Hanming, CHEN Yingcong, WEN Shangsheng

2026, 54(1):  83-93.  doi:10.12141/j.issn.1000-565X.250011

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As a paradigm of the new-generation indoor positioning technology, ultra-wideband (UWB) technology is often combined with the inertial navigation system (INS) in practical applications to solve the non-line-of-sight (NLOS) error issue in positioning. However, the centralized information processing method fails to effectively distinguish the sources of NLOS errors. To ensure positioning accuracy, additional anchor nodes need to be deployed, which leads to redundancy of positioning anchor nodes, and further results in information waste and increased costs. Aiming at the problems of NLOS error identification and elimination in indoor positioning, this paper proposed a UWB/INS indoor positioning method based on self-reset genetic particle filtering (SGPF). With the SGPF algorithm as its core, this method traces the source of NLOS errors in measured values using the estimated values of the INS system, so as to improve the tracking stability under NLOS environments. The method first groups physical anchor nodes and divides likelihood regions in combination with virtual anchor nodes. Then, based on the preliminary estimation of the INS, it identifies high-probability regions through an NLOS error identification strategy, while eliminating NLOS anchor node groups and their corresponding measured values. Finally, it judges the state of the particle set by combining the number of effective particles, determines whether to enable genetic resampling to optimize particle diversity, and ultimately improves the robustness of the algorithm. The SGPF algorithm integrates the structural advantages of the standard particle filter (PF) and genetic algorithms, and can effectively alleviate the problems of particle degradation and impoverishment and achieve higher robustness with a smaller number of particles and lower time consumption. Experimental results show that: under line-of-sight environments, the SGPF algorithm requires only 30% of the number of particles used in the PF algorithm to achieve the same positioning effect, and its calculation time is much lower than that of the traditional genetic particle filter algorithm; under NLOS environments, the SGPF algorithm has an average positioning error of 0.055 2 m. Compared to traditional particle filter and traditional genetic particle filter algorithms, the localization error is reduced by 56.98% and 48.94% respectively.

Mechanical Engineering

A Collaborative Control Method for Inkjet Printing Accuracy of Electronic Additive Manufacturing

LIU Qingtao, YU Panyu, GUO Jiongqi, YIN Enhuai, YANG Pengtao, LÜ Jingxiang

2026, 54(1):  94-103.  doi:10.12141/j.issn.1000-565X.250084

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Electronic additive manufacturing technology possesses significant application value in high-precision microelectronics manufacturing. However, the improvement of printing quality is always restricted by the droplet placement inaccuracies caused by speed fluctuations. To address this issue, a collaborative control strategy based on LinuxCNC, termed S-shaped speed planning + fixed-distance injection (SSP-FDI), was proposed. By optimizing the traditional trapezoidal speed algorithm in numerical control systems into an S-shaped speed algorithm, mechanical shock can be effectively reduced. Simultaneously, by adopting a fixed-distance triggering mode, the droplet spacing can be accurately controlled, thus mitigating the impact of speed fluctuations on placement accuracy. Moreover, an experimental platform integrating five-axis motion control and electronic inkjet printing technology was independently developed, and the corresponding control system was developed. Finally, comparative experiments involving multi-angle polylines and electrode printing were designed. The results demonstrate that, as compared with the traditional trapezoidal speed planning + fixed frequency injection (TSP-FFI) strategy, SSP-FDI strategy significantly reduces droplet placement errors. In a 20 mm × 20 mm rectangular electrode printing experiment with a substrate temperature of 100 ℃, the maximum surface roughness of compensated electrodes decreases to 6 μm. Across five substrate temperature groups, the surface roughness of printed samples shows an average reduction of 18.79% and an average resistivity reduction of 18.70%. These findings indicate that the proposed LinuxCNC-based colla-borative control strategy effectively improves the printing quality for complex trajectories, offering a novel technical solution to high-precision additive manufacturing of electronic devices.

Vibration Control of Three-Flexible Beam Coupling System Based on Fuzzy Algorithm

QIU Zhicheng, LI Meng, LI Min

2026, 54(1):  104-114.  doi:10.12141/j.issn.1000-565X.250060

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In the aerospace field, the rigid-flexible coupled structure is widely used due to its high structural efficiency. However, the existence of the rigid-flexible coupling effect poses a significant challenge to active vibration control. To address this issue, this paper takes the three-flexible beam coupling system as the research object and conducts active vibration control research. During the investigation, first, a vibration measurement and control platform for a three-flexible beam coupling system was established, and piezoelectric sensors and actuators were used to detect and suppress the vibration signals. Based on this, vibration measurement and control algorithm design were carried out. Subsequently, the system dynamics model was established by combining the finite element method with the Hamilton variational principle. The main modal shapes of the system’s free vibration in the simulation environment were determined, and the modal coordinates were introduced to obtain the state space equations of the system. At the same time, considering the uncertainty of model parameters, wavelet analysis and jump spider optimization algorithm were used to accurately identify the parameters of the system state space equations. In addition, considering the nonlinearity and parameter uncertainty of the system, a fuzzy logic controller based on Gaussian membership function was designed to suppress the vibration of the flexible beams. Simulated and experimental results show that, within the same control saturation voltage period, the fuzzy logic controller performs better than the large-gain PD (Proportional and Derivative) control in suppressing the vibration of the three-flexible beam coupling system. It can suppress the large-amplitude vibration quickly while suppressing the small-amplitude vibration at a faster speed, effectively shortening the time for the system to reach a stable state and significantly improving the vibration control effect. In summary, the fuzzy logic controller based on Gaussian membership function designed in this paper overcomes the nonlinearity and parameter uncertainty in the vibration control of rigid-flexible coupling structures, and shows stronger adaptability and higher control efficiency than the traditional large-gain PD control.

Brake Noise Reduction of Elevator Traction Machine Based on Particle Dampers

CHEN Zhong, LIU Qi, WU Hongbing, XIAO Jiafeng, XU Yang, ZHAN Xiaoyu, SHEN Shuang

2026, 54(1):  115-123.  doi:10.12141/j.issn.1000-565X.250124

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To overcome the relatively high sound pressure level noise generated by the elevator traction machine during the braking process, this study proposes a vibration and noise reduction solution based on particle dampers. Firstly, the vibration characteristics of the brake wheel and brake pads are investigated through finite element analysis, and the key vibration modes are identified by correlating the principal vibration frequencies obtained from whole-machine vibration and noise tests. On the basis of these findings and by considering the symmetry and spatial layout of the brake wheel structure, an innovative cavity design is introduced in the brake wheel to accommodate particle dampers. Then, coupled EDEM-ADAMS simulation technology is employed to optimize the parameters of solid particles, with a focus on addressing three critical technical issues: (1) the selection of damping particle material (to avoid the interference from magnetic fields, pure aluminum is ultimately selected); (2) the analysis of energy dissipation process of the particle damper within the cavity (with the adoption of discrete element analysis); and (3) the optimization of particle radius and filling ratio of the damper (by integrating the discrete element analysis with the multi-body dynamics simulation). Finally, an experimental validation is conducted in a semi-anechoic chamber, by setting a timed braking control strategy with a 5-second cycle, using an acceleration sensor array to collect vibration signals, and synchronously recording sound pressure level data. The results indicate that, the installation of particle dampers helps to reduce the average sound pressure level during the braking process by 20.7%, thus confirming the effectiveness of the proposed solution. This research provides a novel technical approach to noise control of electromagnetic braking systems and is of significant engineering application value.

DSBM of Multi-Nozzle Scanning for Spray Forming and Its Intelligent Optimization Approach

LENG Sheng, HUANG Haize, JIANG Zenghua, LU Huarui, MA Wantai

2026, 54(1):  124-133.  doi:10.12141/j.issn.1000-565X.250059

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Lightweight and high-performance aluminum alloys are crucial for the weight reduction design of aerospace equipment, thus spray forming with rapid solidification technology has garnered increasing attention for the fabrication of high-strength aluminum alloys. To meet the demands of large-scale aerospace components, a multi-nozzle collaborative system is required to achieve larger billet diameters. During the scanning and deposition process of atomization cones formed by multiple nozzles at a certain inclination angle on the deposition interface, ensuring uniform distribution of the molten material, a flat deposition interface at the top of the billet, and stable growth are key to obtaining high-quality, dense, and uniform deposited billet structures. These factors are key to producing high-quality billets with dense and uniform microstructures. The process parameters associated with multi-nozzle configurations directly influence the scanning trajectories of atomized droplets and the material deposition state at the interface, playing a decisive role in billet growth. Accordingly, by targeting the fabrication of large-size billets with consistent surface morphology and uniform deposition quality, a multi-nozzle deposition surface behavior model (DSBM) at the micro-scale was established based on the scanned deposition height, taking into account the overlap and intersection of deposition regions that arise during scanning of the multi-nozzle atomization cones. The initial nozzle tilt angle, nozzle eccentric offset, and melt mass flow rate were selected as adjustable parameters; constraints were set according to the actual operating conditions to construct a DSBM-based control model. Using the height difference H of the billet’s deposition-surface unevenness as the optimization objective, the GA-DSBM intelligent control method for the deposition interface was employed to simulate and optimize the relevant process para-meters during deposition. A four-nozzle spray-forming experiment was conducted to verify the optimized parameters. The resulting billet, with a diameter of 600 mm, exhibited a surface unevenness height difference of 7.52 mm, meeting the process design requirements. Meanwhile, the top-surface unevenness of the billet was markedly reduced, interfacial material uniformity was improved, and the billet porosity was effectively lowered—thereby validating the feasibility of the proposed intelligent control and optimization method.

Modeling of Milling Force for Ball End Milling Cutter Based on Oblique Cutting

JIN Qichao, LI Jun, YE Ziyin, YU Hongyu, GUO Lei

2026, 54(1):  134-148.  doi:10.12141/j.issn.1000-565X.250053

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In order address the complex three-dimensional force-field distribution and the significant dynamic variation of undeformed chip thickness during cutting with a ball end mill under varying helix-angle conditions, this paper proposed a milling force modeling method that integrated oblique cutting theory with dynamic kinematic simulation, enabling high-accuracy modeling and prediction of cutting forces in multi-axis ball end milling. An analytical framework of oblique cutting mechanics was established based on the equivalent planar method, where three-dimensional cutting was converted into two-dimensional planar cutting through spatial coordinate transformation. A composite mechanical model that simultaneously incorporated both shear and ploughing effects was derived. This model reveals the control mechanism of the tool inclination angle on the material flow direction in the cutting zone, the morphology of the shear deformation zone, and the stress distribution. Subsequently, the geometric characteristics of the cutting-edge profile of the ball end mill were constructed.Combined with the tool-workpiece kinematic coupling model, the differential equations of the tool tooth motion were solved, and dynamic machining surface topography was simulated using an improved Z-MAP algorithm, enabling the extraction of time-varying undeformed chip thickness distribution. Furthermore, a multi-scale mechanical mapping strategy was proposed, where the cutting edge was discretized into micro-cutting units along the curved direction. Based on the analytical oblique-cutting model, iterative integration of tangential, radial, and axial forces for each micro-unit was performed, ultimately superimposing them to obtain complete three-dimensional milling force time-domain signals. Finally, an experiment was carried out for verification, the results indicate that the maximum prediction errors of milling forces in the axial, feed, and width directions are 18.3%, 10.8%, and 22.4%, respectively, verifying the accuracy and applicability of the model in force analysis of complex tool geometries. This research method combined macroscopic kinematic simulation with microscopic mechanical analysis and provided theoretical support for process parameter optimization, tool structure design, and machining stability enhancement of ball end milling.

Characteristics and High-Precision Positioning Analysis of Fluid-Solid Noise Sources in Inclined Axis Piston Motor

CHEN Fulong, HUANG Hui, DU Heng, SU Junshou, LI Yuzheng, LI Fuqi

2026, 54(1):  149-160.  doi:10.12141/j.issn.1000-565X.250044

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As the main actuator in hydraulic systems, motors generate significant noise radiation that increasingly fails to meet low-noise requirements. Due to unresolved issues such as unclear primary noise sources and low loca-lization accuracy, noise reduction in motors remains challenging. Therefore, to identify the main noise sources and improve localization accuracy, this study employed multiple approaches: A fluid simulation model of the motor was established using Pumplinx software to obtain the variation of fluid excitation forces at the motor’s port plate. A co-simulation using ADAMS and AMESim was conducted to acquire the variation of excitation forces caused by pistons impacting the cylinder block during motor operation. Combined with transient finite element analysis, the transient analysis method in ANSYS was used to obtain the vibration displacement response on the surfaces of the motor housing and rear end cover. Using this vibration data from ANSYS as acoustic boundary conditions, a boundary element analysis was performed in LMS Virtual Lab to simulate the motor’s acoustic field, thereby identifying the main noise sources and primary noise generation areas. Subsequently, a motor sound intensity noise test bench was designed to obtain sound intensity variation cloud maps, verifying the accuracy of the multi-physics simulation results. Then, considering the relationships among the observation matrix, sparse representation, and reconstruction algorithm, the regularized orthogonal matching pursuit reconstruction algorithm was adopted to determine the loca-lization areas of the main motor noise. Finally, the feasibility of the optimized reconstruction algorithm in improving loca-lization accuracy was verified with the sound intensity test bench. The results show that the multi-physics field simulation of the motor model is correct, the main noise sources are the pressure impact at the valve plate and piston collisions, the main noise area is distributed around the valve plate, and the new localization accuracy reaches 25 mm, achieving the determination of the main motor noise sources and an improvement in localization accuracy.