Transactions of Nanjing University of Aeronautics & Astronautics
Issue 3,2025 Table of Contents
2025(3):275-286.
DOI: 10.16356/j.1005-1120.2025.03.001
Abstract:
Obtaining residual stress is crucial for controlling the machining deformation in annular parts， and can directly influence the performance and stability of key components in advanced equipment. Since existing research has achieved global residual stress field inference for components by using the deformation force-based method where the deformation force is monitored during the machining process， reliable acquisition of deformation force still remains a significant challenge under complex machining conditions. This paper proposes a hierarchical optimization method for the layout of deformation force monitoring of annular parts. The proposed method establishes two optimization objectives by analyzing the relationship between the deformation force and the residual stress in annular parts， i.e.， equivalence and ill-conditioning of solving process. Specifically， the equivalence of the monitored deformation force and residual stress in terms of effect on caused machining deformation is evaluated by local deformation， and the ill-conditioning is also optimized to enhance the stability of residual stress inference. Verification is implemented in both simulation and actual machining experiments， demonstrating effectiveness of the proposed layout optimization method in inferring residual stress field of annular parts with deformation force.
Obtaining residual stress is crucial for controlling the machining deformation in annular parts， and can directly influence the performance and stability of key components in advanced equipment. Since existing research has achieved global residual stress field inference for components by using the deformation force-based method where the deformation force is monitored during the machining process， reliable acquisition of deformation force still remains a significant challenge under complex machining conditions. This paper proposes a hierarchical optimization method for the layout of deformation force monitoring of annular parts. The proposed method establishes two optimization objectives by analyzing the relationship between the deformation force and the residual stress in annular parts， i.e.， equivalence and ill-conditioning of solving process. Specifically， the equivalence of the monitored deformation force and residual stress in terms of effect on caused machining deformation is evaluated by local deformation， and the ill-conditioning is also optimized to enhance the stability of residual stress inference. Verification is implemented in both simulation and actual machining experiments， demonstrating effectiveness of the proposed layout optimization method in inferring residual stress field of annular parts with deformation force.
2025(3):287-296.
DOI: 10.16356/j.1005-1120.2025.03.002
Abstract:
To investigate the residual stress distribution and its influence on machining deformation in 6061-T651 aluminum alloy plates， this paper uses the crack compliance method to study the residual stress characteristics of 6061-T651 aluminum alloy plates with a thickness of 75 mm produced by two domestic manufacturers in China. The results indicate that both types of plates exhibit highly consistent and symmetrical M-shaped residual stress profile along the thickness direction， manifested as surface layer compression and core tension. The strain energy density across all specimens ranges from 1.27 kJ/m3 to 1.43 kJ/m3. Machining deformation simulations of an aerospace component incorporating these measured stresses showed minimal final deformation difference between the material sources， with a maximum deviation of only 0.009 mm across specimens. These findings provide critical data for material selection and deformation control in aerospace manufacturing.
To investigate the residual stress distribution and its influence on machining deformation in 6061-T651 aluminum alloy plates， this paper uses the crack compliance method to study the residual stress characteristics of 6061-T651 aluminum alloy plates with a thickness of 75 mm produced by two domestic manufacturers in China. The results indicate that both types of plates exhibit highly consistent and symmetrical M-shaped residual stress profile along the thickness direction， manifested as surface layer compression and core tension. The strain energy density across all specimens ranges from 1.27 kJ/m3 to 1.43 kJ/m3. Machining deformation simulations of an aerospace component incorporating these measured stresses showed minimal final deformation difference between the material sources， with a maximum deviation of only 0.009 mm across specimens. These findings provide critical data for material selection and deformation control in aerospace manufacturing.
2025(3):297-309.
DOI: 10.16356/j.1005-1120.2025.03.003
Abstract:
The production mode of manufacturing industry presents characteristics of multiple varieties， small-batch and personalization， leading to frequent disturbances in workshop. Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance. In order to improve the intelligence and adaptability of production scheduler， a novel distributed scheduling architecture is proposed， which has the ability to autonomously allocate tasks and handle disturbances. All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines. Firstly， the multi-agent technology is applied to build a self-organizing manufacturing system， enabling each machine to be equipped with the ability of active information interaction and joint-action execution. Secondly， various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents， thereby flexibly achieving global perception of environmental state. To ensure the adaptability and superiority of production decisions in dynamic environment， deep reinforcement learning is applied to build a smart production scheduler. Based on the perceived environment state， the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration. The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed. Compared to heuristic dispatching rules， the proposed method achieves an average performance improvement of 34.0% in three scenarios in terms of order tardiness. The proposed system can provide a new reference for the design of smart manufacturing systems.
The production mode of manufacturing industry presents characteristics of multiple varieties， small-batch and personalization， leading to frequent disturbances in workshop. Traditional centralized scheduling methods are difficult to achieve efficient and real-time production management under dynamic disturbance. In order to improve the intelligence and adaptability of production scheduler， a novel distributed scheduling architecture is proposed， which has the ability to autonomously allocate tasks and handle disturbances. All production tasks are scheduled through autonomous collaboration and decision-making between intelligent machines. Firstly， the multi-agent technology is applied to build a self-organizing manufacturing system， enabling each machine to be equipped with the ability of active information interaction and joint-action execution. Secondly， various self-organizing collaboration strategies are designed to effectively facilitate cooperation and competition among multiple agents， thereby flexibly achieving global perception of environmental state. To ensure the adaptability and superiority of production decisions in dynamic environment， deep reinforcement learning is applied to build a smart production scheduler. Based on the perceived environment state， the scheduler intelligently generates the optimal production strategy to guide the task allocation and resource configuration. The feasibility and effectiveness of the proposed method are verified through three experimental scenarios using a discrete manufacturing workshop as the test bed. Compared to heuristic dispatching rules， the proposed method achieves an average performance improvement of 34.0% in three scenarios in terms of order tardiness. The proposed system can provide a new reference for the design of smart manufacturing systems.
2025(3):310-321.
DOI: 10.16356/j.1005-1120.2025.03.004
Abstract:
Zirconia （ZrO?） ceramic material has been widely applied to various fields due to its unique properties of high strength， high hardness， and excessive temperature resistance. However， the high-quality micro-hole machining of zirconia ceramic material remains a significant challenge at present. In this study， experiments on peck drilling of ?0.2 mm and ?0.5 mm micro-holes in zirconia ceramics using diamond-coated drills are conducted. The characteristics of the force signal during the drilling process， the influence of drilling parameters on the drilling force and the chipping size at the hole exit， and features of the tool wear stages of the diamond coated drill are analyzed. Experimental results suggest that when machining micro-holes in zirconia ceramics， there is a positive correlation between the axial force and the size of the chipping at the exit. The axial force increases with the increase of the feed rate and the step distance， and it shows a trend of first increasing and next decreasing with the increase of the spindle speed. The wear of the drill bit has a significant impact on the quality of the hole exit. It is found that with the continuous drilling of seven holes， the axial force increases by 144.2%， and the size of edge chipping at the exit increases from about 20 μm to more than 130 μm. This study can provide some valuable references for improving the micro-hole processing quality of material.
Zirconia （ZrO?） ceramic material has been widely applied to various fields due to its unique properties of high strength， high hardness， and excessive temperature resistance. However， the high-quality micro-hole machining of zirconia ceramic material remains a significant challenge at present. In this study， experiments on peck drilling of ?0.2 mm and ?0.5 mm micro-holes in zirconia ceramics using diamond-coated drills are conducted. The characteristics of the force signal during the drilling process， the influence of drilling parameters on the drilling force and the chipping size at the hole exit， and features of the tool wear stages of the diamond coated drill are analyzed. Experimental results suggest that when machining micro-holes in zirconia ceramics， there is a positive correlation between the axial force and the size of the chipping at the exit. The axial force increases with the increase of the feed rate and the step distance， and it shows a trend of first increasing and next decreasing with the increase of the spindle speed. The wear of the drill bit has a significant impact on the quality of the hole exit. It is found that with the continuous drilling of seven holes， the axial force increases by 144.2%， and the size of edge chipping at the exit increases from about 20 μm to more than 130 μm. This study can provide some valuable references for improving the micro-hole processing quality of material.
2025(3):322-336.
DOI: 10.16356/j.1005-1120.2025.03.005
Abstract:
This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control （CNC） machining processes and improve tool utilization efficiency. Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments. To address this， we implement real-time tool condition recognition by introducing deep learning technology. Aiming to the insufficient recognition accuracy， we propose a pyramid pooling-based vision Transformer network （P2ViT-Net） method for tool condition recognition. Using images as input effectively mitigates the issue of low-dimensional signal features. We enhance the vision Transformer （ViT） framework for image classification by developing the P2ViT model and adapt it to tool condition recognition. Experimental results demonstrate that our improved P2ViT model achieves 94.4% recognition accuracy， showing a 10% improvement over conventional ViT and outperforming all comparative convolutional neural network models.
This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control （CNC） machining processes and improve tool utilization efficiency. Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments. To address this， we implement real-time tool condition recognition by introducing deep learning technology. Aiming to the insufficient recognition accuracy， we propose a pyramid pooling-based vision Transformer network （P2ViT-Net） method for tool condition recognition. Using images as input effectively mitigates the issue of low-dimensional signal features. We enhance the vision Transformer （ViT） framework for image classification by developing the P2ViT model and adapt it to tool condition recognition. Experimental results demonstrate that our improved P2ViT model achieves 94.4% recognition accuracy， showing a 10% improvement over conventional ViT and outperforming all comparative convolutional neural network models.
2025(3):337-353.
DOI: 10.16356/j.1005-1120.2025.03.006
Abstract:
Deep hole gun drilling is in a closed and semi-closed state， and the machining process is complex. The unstable drilling force， severe tool wear， and poor processing quality have always been difficulties in deep hole gun drilling. 304 stainless steel has good corrosion and heat resistance， and is widely used in various industries. However， high hardness， poor plasticity， and characteristics of sticking knives have always restricted its development in engineering applications. Therefore， this paper uses 304 stainless steel as the research object and performs process parameter optimization and tool wear experiments. Firstly， based on the optimization experiment of process parameters， the influence of cutting speed and feed rate on drilling force and hole wall roughness is analyzed. The process parameters of the subsequent experiment are optimized as follows： spindle speed is 1 270 r / mm， feed rate is 0.02 mm/r， and oil pressure is 3 MPa. Secondly， based on the tool wear experiment， the variation law of tool wear and tool wear form is studied. With the help of scanning electron microscopy （SEM） and energy dispersive spectroscopy （EDS）， the tool wear mechanism of deep hole gun drilling 304 stainless steel is expounded. Finally， the influence of tool wear on the processing quality is revealed， and the suggestion of tool regrinding is put forward.
Deep hole gun drilling is in a closed and semi-closed state， and the machining process is complex. The unstable drilling force， severe tool wear， and poor processing quality have always been difficulties in deep hole gun drilling. 304 stainless steel has good corrosion and heat resistance， and is widely used in various industries. However， high hardness， poor plasticity， and characteristics of sticking knives have always restricted its development in engineering applications. Therefore， this paper uses 304 stainless steel as the research object and performs process parameter optimization and tool wear experiments. Firstly， based on the optimization experiment of process parameters， the influence of cutting speed and feed rate on drilling force and hole wall roughness is analyzed. The process parameters of the subsequent experiment are optimized as follows： spindle speed is 1 270 r / mm， feed rate is 0.02 mm/r， and oil pressure is 3 MPa. Secondly， based on the tool wear experiment， the variation law of tool wear and tool wear form is studied. With the help of scanning electron microscopy （SEM） and energy dispersive spectroscopy （EDS）， the tool wear mechanism of deep hole gun drilling 304 stainless steel is expounded. Finally， the influence of tool wear on the processing quality is revealed， and the suggestion of tool regrinding is put forward.
2025(3):354-367.
DOI: 10.16356/j.1005-1120.2025.03.007
Abstract:
The accuracy of the full-scale aircraft static tests is greatly influenced by the aircraft attitude. This paper proposes an aircraft attitude optimization method based on the characteristics of the test. The aim is to address three typical problems of attitude control in the full-scale aircraft static tests： （1） The coupling of rigid-body displacement and elastic deformation after large deformation， （2） the difficulty of characterizing the aircraft attitude by measurable structure， and （3） the insufficient adaptability of the center of gravity reference to complex loading conditions. The methodology involves the establishment of two observation coordinate systems， a ground coordinate system and an airframe coordinate system， and two deformation states， before and after airframe deformation. A subsequent analysis of the parameter changes of these two states under different coordinate systems is then undertaken， with the objective being to identify the key parameters affecting the attitude control accuracy of large deformation aircraft. Three optimization objective functions are established according to the test loading characteristics and the purpose of the test： （1） To minimize the full-scale aircraft loading angle error， （2） to minimize the full-scale aircraft loading additional load， and （3） to minimize the full-scale aircraft loading wing root additional bending moment. The optimization calculation results are obtained by using the particle swarm optimization algorithm， and the typical full-scale aircraft static test load condition of large passenger aircraft is taken as an example. The analysis of the results demonstrates that by customizing the measurable structure of the aircraft as the observation point for the aircraft attitude， and by obtaining the translational and rotational control parameters of the observation point during the test based on the optimization objective function， the results are reasonable， and the project can be implemented and used to control the aircraft’s attitude more accurately in complex force test conditions.
The accuracy of the full-scale aircraft static tests is greatly influenced by the aircraft attitude. This paper proposes an aircraft attitude optimization method based on the characteristics of the test. The aim is to address three typical problems of attitude control in the full-scale aircraft static tests： （1） The coupling of rigid-body displacement and elastic deformation after large deformation， （2） the difficulty of characterizing the aircraft attitude by measurable structure， and （3） the insufficient adaptability of the center of gravity reference to complex loading conditions. The methodology involves the establishment of two observation coordinate systems， a ground coordinate system and an airframe coordinate system， and two deformation states， before and after airframe deformation. A subsequent analysis of the parameter changes of these two states under different coordinate systems is then undertaken， with the objective being to identify the key parameters affecting the attitude control accuracy of large deformation aircraft. Three optimization objective functions are established according to the test loading characteristics and the purpose of the test： （1） To minimize the full-scale aircraft loading angle error， （2） to minimize the full-scale aircraft loading additional load， and （3） to minimize the full-scale aircraft loading wing root additional bending moment. The optimization calculation results are obtained by using the particle swarm optimization algorithm， and the typical full-scale aircraft static test load condition of large passenger aircraft is taken as an example. The analysis of the results demonstrates that by customizing the measurable structure of the aircraft as the observation point for the aircraft attitude， and by obtaining the translational and rotational control parameters of the observation point during the test based on the optimization objective function， the results are reasonable， and the project can be implemented and used to control the aircraft’s attitude more accurately in complex force test conditions.
2025(3):368-384.
DOI: 10.16356/j.1005-1120.2025.03.008
Abstract:
To tackle the instability fault diagnosis challenges in wide-speed-range supersonic inlets， this study proposes an inlet fault decision fusion diagnosis algorithm based on attention mechanism feature fusion， achieving efficient diagnosis of instability faults across wide-speed regimes. First， considering the requirement for wall pressure data extraction in mathematical modeling of wide-speed-range inlets， a supersonic inlet reference model is established for computational fluid dynamics （CFD） simulations. Second， leveraging data-driven modeling techniques and support vector machine （SVM） algorithms， a high-precision mathematical model covering wide-speed domains and incorporating instability mechanisms is rapidly developed using CFD-derived inlet wall pressure data. Subsequently， an inlet fault decision fusion diagnosis method is proposed. Pressure features are fused via attention mechanisms， followed by Dempster-Shafer （D-S） evidence theory-based decision fusion， which integrates advantages of multiple intelligent algorithms to overcome the limitations of single-signal diagnosis methods （low accuracy and constrained optimization potential）. The simulation results demonstrate the effectiveness of the data-driven wide-speed-range inlet model in achieving high precision and rapid convergence. In addition， the fusion diagnosis algorithm has been shown to attain over 95% accuracy in the detection of instability， indicating an improvement of more than 5% compared to the accuracy of other single fault diagnosis algorithms. This enhancement effectively eliminates the occurrence of missed or false diagnoses， while demonstrates robust performance under operational uncertainties.
To tackle the instability fault diagnosis challenges in wide-speed-range supersonic inlets， this study proposes an inlet fault decision fusion diagnosis algorithm based on attention mechanism feature fusion， achieving efficient diagnosis of instability faults across wide-speed regimes. First， considering the requirement for wall pressure data extraction in mathematical modeling of wide-speed-range inlets， a supersonic inlet reference model is established for computational fluid dynamics （CFD） simulations. Second， leveraging data-driven modeling techniques and support vector machine （SVM） algorithms， a high-precision mathematical model covering wide-speed domains and incorporating instability mechanisms is rapidly developed using CFD-derived inlet wall pressure data. Subsequently， an inlet fault decision fusion diagnosis method is proposed. Pressure features are fused via attention mechanisms， followed by Dempster-Shafer （D-S） evidence theory-based decision fusion， which integrates advantages of multiple intelligent algorithms to overcome the limitations of single-signal diagnosis methods （low accuracy and constrained optimization potential）. The simulation results demonstrate the effectiveness of the data-driven wide-speed-range inlet model in achieving high precision and rapid convergence. In addition， the fusion diagnosis algorithm has been shown to attain over 95% accuracy in the detection of instability， indicating an improvement of more than 5% compared to the accuracy of other single fault diagnosis algorithms. This enhancement effectively eliminates the occurrence of missed or false diagnoses， while demonstrates robust performance under operational uncertainties.
2025(3):385-394.
DOI: 10.16356/j.1005-1120.2025.03.009
Abstract:
The Vienna rectifier is a widely adopted solution for high-power rectification due to its efficiency and straightforward design. However， its performance can degrade under unbalanced three-phase voltage conditions， leading to current zero-crossing distortion and compromised dynamic response. This paper investigates the causes of these distortions， identifying a phase shift between the input current and the grid voltage as a primary factor， and proposes an effective distortion phase identification strategy. Furthermore， the dynamic performance is enhanced through improved current reference calculations and a refined power feedforward strategy. This approach optimizes the system’s response to load changes and maintains output voltage stability under unbalanced conditions. Simulation results validate the effectiveness of the proposed methods in reducing current distortion and improving overall performance.
The Vienna rectifier is a widely adopted solution for high-power rectification due to its efficiency and straightforward design. However， its performance can degrade under unbalanced three-phase voltage conditions， leading to current zero-crossing distortion and compromised dynamic response. This paper investigates the causes of these distortions， identifying a phase shift between the input current and the grid voltage as a primary factor， and proposes an effective distortion phase identification strategy. Furthermore， the dynamic performance is enhanced through improved current reference calculations and a refined power feedforward strategy. This approach optimizes the system’s response to load changes and maintains output voltage stability under unbalanced conditions. Simulation results validate the effectiveness of the proposed methods in reducing current distortion and improving overall performance.
2025(3):395-410.
DOI: 10.16356/j.1005-1120.2025.03.010
Abstract:
This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing （LSI） and the vector space model （VSM）. It integrates the theory of technological ecological niches and evaluates four key dimensions： patent quality， energy efficiency engineering， technological modules， and intelligent computing power. The findings reveal that USA has established strong technological barriers through standard-essential patents （SEPs） in wireless communication and integrated circuits. In contrast， Chinese mainland firms rely heavily on fundamental technologies. Qualcomm Inc. in USA and Taiwan Semiconductor Manufacturing Company （TSMC） in Chineses Taiwan have built a comprehensive patent portfolio in energy efficiency engineering. While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes. Huawei Inc. in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration. Semiconductor manufacturing and radio frequency （RF） components still rely on external suppliers. This highlights the urgent need for innovation in new materials and open-source architectures. To enhance intelligent computing power， Chinese mainland firms must address coordination challenges. They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware， operating systems， and developer networks.
This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing （LSI） and the vector space model （VSM）. It integrates the theory of technological ecological niches and evaluates four key dimensions： patent quality， energy efficiency engineering， technological modules， and intelligent computing power. The findings reveal that USA has established strong technological barriers through standard-essential patents （SEPs） in wireless communication and integrated circuits. In contrast， Chinese mainland firms rely heavily on fundamental technologies. Qualcomm Inc. in USA and Taiwan Semiconductor Manufacturing Company （TSMC） in Chineses Taiwan have built a comprehensive patent portfolio in energy efficiency engineering. While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes. Huawei Inc. in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration. Semiconductor manufacturing and radio frequency （RF） components still rely on external suppliers. This highlights the urgent need for innovation in new materials and open-source architectures. To enhance intelligent computing power， Chinese mainland firms must address coordination challenges. They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware， operating systems， and developer networks.
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