Founded in 1951 monthly
ISSN 1005-2402
CN 11-3398/TH
Responsible Department：China Association for Science andTechnology
Sponsors：China Mechanical Engineering Society
Beijing Machine Tool Research Institute Co., Ltd.
Chief Editor： Huang Zhenghua
Deputy Managing Editor: TAN Hongying
Director ofMachine Tool Magazine Agency: HUANG Zuguang
Post Distribution: 2-636
External Code: M397
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Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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2023, (9): 9-16.
doi: 10.19287/j.mtmt.1005-2402.2023.09.001
Abstract:
Electrostatic monitoring is a highly sensitive monitoring method, which can monitor the occurrence of system performance degradation earlier, but the electrostatic signal is weak, and in the actual complex environment is easily disturbed by changes in working conditions and reduces its monitoring capability. In the process of rolling bearing electrostatic monitoring, the effective electrostatic signal is easily drowned by noise, and the fault characteristics are difficult to extract. In order to solve the above problems, a method based on the sparse representation of the clustering shrinkage segmental orthogonal matching tracking algorithm (CcStOMP) is proposed for rolling bearing electrostatic signal fault feature extraction. The method adds a clustering shrinkage mechanism to the segmented orthogonal matching tracking algorithm (StOMP), performs secondary filtering on the selected atoms during the atomic search, updates the support set, and finally solves for the weights and updates the residuals to reconstruct the original electrostatic signal to extract the rolling bearing fault feature components, maintaining fast convergence while improving the accuracy of sparse recovery. By monitoring the measured signals of rolling bearing outer ring and bearing roller faults, a comparison with the StOMP algorithm shows that the CcStOMP algorithm has the advantage of accurately extracting the fault characteristic components of the rolling bearing electrostatic monitoring signals.
Electrostatic monitoring is a highly sensitive monitoring method, which can monitor the occurrence of system performance degradation earlier, but the electrostatic signal is weak, and in the actual complex environment is easily disturbed by changes in working conditions and reduces its monitoring capability. In the process of rolling bearing electrostatic monitoring, the effective electrostatic signal is easily drowned by noise, and the fault characteristics are difficult to extract. In order to solve the above problems, a method based on the sparse representation of the clustering shrinkage segmental orthogonal matching tracking algorithm (CcStOMP) is proposed for rolling bearing electrostatic signal fault feature extraction. The method adds a clustering shrinkage mechanism to the segmented orthogonal matching tracking algorithm (StOMP), performs secondary filtering on the selected atoms during the atomic search, updates the support set, and finally solves for the weights and updates the residuals to reconstruct the original electrostatic signal to extract the rolling bearing fault feature components, maintaining fast convergence while improving the accuracy of sparse recovery. By monitoring the measured signals of rolling bearing outer ring and bearing roller faults, a comparison with the StOMP algorithm shows that the CcStOMP algorithm has the advantage of accurately extracting the fault characteristic components of the rolling bearing electrostatic monitoring signals.
2023, (9): 17-24.
doi: 10.19287/j.mtmt.1005-2402.2023.09.002
Abstract:
In this paper, an improved northern goshawk optimization algorithm (IMNGO) is proposed to optimize the variational mode decomposition (VMD) and support vector machine (SVM) for small-sample bearing fault identification. This algorithm can effectively solve the problems of insufficient information extraction and few available fault samples when the rolling bearing fails. The experimental data is collected through the cloud platform, and the VMD parameters are optimized using the IMNGO algorithm to find the best intrinsic mode component (IMF), construct the eigenvector energy spectrum and the principal component contribution map, and screen the best IMF component. Finally, the extracted feature information is imported into the SVM optimized by IMNGO for small sample detection and recognition of bearings. After IMNGO optimization, the recognition accuracy rate under single working conditions reached 99.20%, and the recognition accuracy rate under complex working conditions reached 94.45%. Under the small sample data, the method proposed in this paper has greatly improved the recognition accuracy compared with the traditional detection method.
In this paper, an improved northern goshawk optimization algorithm (IMNGO) is proposed to optimize the variational mode decomposition (VMD) and support vector machine (SVM) for small-sample bearing fault identification. This algorithm can effectively solve the problems of insufficient information extraction and few available fault samples when the rolling bearing fails. The experimental data is collected through the cloud platform, and the VMD parameters are optimized using the IMNGO algorithm to find the best intrinsic mode component (IMF), construct the eigenvector energy spectrum and the principal component contribution map, and screen the best IMF component. Finally, the extracted feature information is imported into the SVM optimized by IMNGO for small sample detection and recognition of bearings. After IMNGO optimization, the recognition accuracy rate under single working conditions reached 99.20%, and the recognition accuracy rate under complex working conditions reached 94.45%. Under the small sample data, the method proposed in this paper has greatly improved the recognition accuracy compared with the traditional detection method.
2023, (9): 25-33.
doi: 10.19287/j.mtmt.1005-2402.2023.09.003
Abstract:
An broad transfer learning algorithm based on angular domain resampling(ADR-BTL) is proposed for bearing fault diagnosis in response to the fact that the signals collected in variable operating conditions are usually non-stationary signals and the distribution of the collected vibration data is inconsistent. Firstly, the non-stationary time domain vibration signal is converted into an angular domain stationary signal, and the smoothed signal is feature extracted to construct a multi domain feature dataset. Then, the source domain data and target domain data under different operating conditions are domain adapted using the balanced distribution adaptation (BDA) method to reduce the distribution differences between domains. Finally, a broad transfer learning model is constructed. The experiment first verified that the angular domain resampling method can smooth the vibration signal, and then through simulation sample analysis, it was found that the BDA method can solve the problem of inconsistent data distribution. Finally, the experimental results showed that the proposed ADR-BTL recognition rate reached 98.9%, and the recognition effect was the best, proving that the proposed method is effective in bearing fault diagnosis.
An broad transfer learning algorithm based on angular domain resampling(ADR-BTL) is proposed for bearing fault diagnosis in response to the fact that the signals collected in variable operating conditions are usually non-stationary signals and the distribution of the collected vibration data is inconsistent. Firstly, the non-stationary time domain vibration signal is converted into an angular domain stationary signal, and the smoothed signal is feature extracted to construct a multi domain feature dataset. Then, the source domain data and target domain data under different operating conditions are domain adapted using the balanced distribution adaptation (BDA) method to reduce the distribution differences between domains. Finally, a broad transfer learning model is constructed. The experiment first verified that the angular domain resampling method can smooth the vibration signal, and then through simulation sample analysis, it was found that the BDA method can solve the problem of inconsistent data distribution. Finally, the experimental results showed that the proposed ADR-BTL recognition rate reached 98.9%, and the recognition effect was the best, proving that the proposed method is effective in bearing fault diagnosis.
2023, (9): 34-38, 51.
doi: 10.19287/j.mtmt.1005-2402.2023.09.004
Abstract:
Gears and their transmission devices are the core basic components of modern mechanical equipment, and the residual stress distribution state of gears is an important factor affecting their service life. Taking 18CrNiMo7-6 carburized steel as the research object, the influence of ultrasonic rolling strengthening processing parameters such as static pressure, ultrasonic amplitude and ultrasonic frequency on the residual stress of the specimen surface was studied by combining numerical simulation and experimental research. The results show that ultrasonic rolling strengthening effectively improves the residual stress on the surface of 18CrNiMo7-6 carburized steel, and with the increase of static pressure and ultrasonic amplitude, the residual compressive stress, maximum residual compressive stress and residual compressive stress layer thickness of 18CrNiMo7-6 carburized steel specimen are significantly improved.
Gears and their transmission devices are the core basic components of modern mechanical equipment, and the residual stress distribution state of gears is an important factor affecting their service life. Taking 18CrNiMo7-6 carburized steel as the research object, the influence of ultrasonic rolling strengthening processing parameters such as static pressure, ultrasonic amplitude and ultrasonic frequency on the residual stress of the specimen surface was studied by combining numerical simulation and experimental research. The results show that ultrasonic rolling strengthening effectively improves the residual stress on the surface of 18CrNiMo7-6 carburized steel, and with the increase of static pressure and ultrasonic amplitude, the residual compressive stress, maximum residual compressive stress and residual compressive stress layer thickness of 18CrNiMo7-6 carburized steel specimen are significantly improved.
2023, (9): 39-43.
doi: 10.19287/j.mtmt.1005-2402.2023.09.005
Abstract:
Ti2AlNb, as a key material of a new type of aero-engine, has become a difficult material to machine due to its high strength, stiffness and creep resistance. Short electric arc milling (SEAM) is a new and efficient electrical discharge machining method, which is independent of the hardness, strength and toughness of materials, and has become an important method for difficult to machine materials. Taking Ti2AlNb as the experimental object, using tungsten copper and 304 stainless steel as the electrode materials, the processing mechanism of SEAM under different voltage was studied using DC power supply, and the material removal rate (MRR), surface roughness (Sa) and relative tool electrode wear rate (RTWR) of the workpiece were analyzed. In addition, the surface morphology, cross section morphology, element distribution and microhardness of processed Ti2AlNb were also studied. The experimental results show that under 24V voltage, compared with stainless steel electrode, tungsten copper electrode has the highest MRR, the highest is 1981m3/min, the lowest RTWR of the electrode, the relative electrode loss is only 0.82%. The absolute difference of the width of the cross-section of the workpiece is the smallest, and the hardness of the recast layer of the workpiece is close to the hardness of the base metal, which is conducive to further processing.
Ti2AlNb, as a key material of a new type of aero-engine, has become a difficult material to machine due to its high strength, stiffness and creep resistance. Short electric arc milling (SEAM) is a new and efficient electrical discharge machining method, which is independent of the hardness, strength and toughness of materials, and has become an important method for difficult to machine materials. Taking Ti2AlNb as the experimental object, using tungsten copper and 304 stainless steel as the electrode materials, the processing mechanism of SEAM under different voltage was studied using DC power supply, and the material removal rate (MRR), surface roughness (Sa) and relative tool electrode wear rate (RTWR) of the workpiece were analyzed. In addition, the surface morphology, cross section morphology, element distribution and microhardness of processed Ti2AlNb were also studied. The experimental results show that under 24V voltage, compared with stainless steel electrode, tungsten copper electrode has the highest MRR, the highest is 1981m3/min, the lowest RTWR of the electrode, the relative electrode loss is only 0.82%. The absolute difference of the width of the cross-section of the workpiece is the smallest, and the hardness of the recast layer of the workpiece is close to the hardness of the base metal, which is conducive to further processing.
2023, (9): 44-51.
doi: 10.19287/j.mtmt.1005-2402.2023.09.006
Abstract:
As a brittle material, glass is prone to crack and edge collapse during grinding. This paper takes quartz glass and glass-ceramics as the research objects, carries out the multi-factor experiment of ultrasonic/non-ultrasonic grinding, analyzes the primary and secondary order of the factors affecting the grinding force and surface roughness and the optimal processing parameters, and studies the influence of the processing methods, parameters and material differences on the grinding force and surface roughness of the two kinds of glass. The results show that the grinding force and roughness of quartz glass and glass-ceramics basically decrease with the increase of spindle speed, increase with the increase of grinding depth, and increase with the increase of feed speed; Compared with quartz glass, the grinding force and surface roughness of glass-ceramics are larger under the same processing parameters and processing methods; Compared with non-ultrasonic working conditions, ultrasonic can significantly reduce the grinding force of two kinds of glass and play a certain role in reducing the surface roughness; However, ultrasonic machining can restrain the grinding force and reduce the surface roughness of glass-ceramics more obviously. The research can provide data support for efficient processing of glass materials.
As a brittle material, glass is prone to crack and edge collapse during grinding. This paper takes quartz glass and glass-ceramics as the research objects, carries out the multi-factor experiment of ultrasonic/non-ultrasonic grinding, analyzes the primary and secondary order of the factors affecting the grinding force and surface roughness and the optimal processing parameters, and studies the influence of the processing methods, parameters and material differences on the grinding force and surface roughness of the two kinds of glass. The results show that the grinding force and roughness of quartz glass and glass-ceramics basically decrease with the increase of spindle speed, increase with the increase of grinding depth, and increase with the increase of feed speed; Compared with quartz glass, the grinding force and surface roughness of glass-ceramics are larger under the same processing parameters and processing methods; Compared with non-ultrasonic working conditions, ultrasonic can significantly reduce the grinding force of two kinds of glass and play a certain role in reducing the surface roughness; However, ultrasonic machining can restrain the grinding force and reduce the surface roughness of glass-ceramics more obviously. The research can provide data support for efficient processing of glass materials.
Research of health monitoring technology for CNC machine tools based on ICEEMDAN-RCMWPE and WOA-KELM
2023, (9): 52-59.
doi: 10.19287/j.mtmt.1005-2402.2023.09.007
Abstract:
Aiming at the problems of insufficient extraction of tool health status features and low recognition accuracy of CNC machine tools, this paper proposes a tool health status monitoring method. Firstly, the improved complete set empirical mode decomposition (ICEEMDAN) and the fine composite multi-scale weighted permutation entropy (RCMWPE) are used for signal processing and feature extraction, and the feature information that can characterize the health state of the tool is extracted. Secondly, by t-SNE processing, the dimensionality reduction and fusion of feature information are realized. Finally, the obtained low-dimensional features are input into the constructed whale-optimized kernel extreme learning machine (WOA-KELM) health recognition model, so as to classify and identify the tool health state. Experimental verification shows that the proposed signal processing, feature extraction and state recognition model have achieved good results in tool health monitoring, and its state recognition accuracy is as high as 99.76%, which can efficiently and accurately classify and identify tool wear status.
Aiming at the problems of insufficient extraction of tool health status features and low recognition accuracy of CNC machine tools, this paper proposes a tool health status monitoring method. Firstly, the improved complete set empirical mode decomposition (ICEEMDAN) and the fine composite multi-scale weighted permutation entropy (RCMWPE) are used for signal processing and feature extraction, and the feature information that can characterize the health state of the tool is extracted. Secondly, by t-SNE processing, the dimensionality reduction and fusion of feature information are realized. Finally, the obtained low-dimensional features are input into the constructed whale-optimized kernel extreme learning machine (WOA-KELM) health recognition model, so as to classify and identify the tool health state. Experimental verification shows that the proposed signal processing, feature extraction and state recognition model have achieved good results in tool health monitoring, and its state recognition accuracy is as high as 99.76%, which can efficiently and accurately classify and identify tool wear status.
2023, (9): 60-65, 73.
doi: 10.19287/j.mtmt.1005-2402.2023.09.008
Abstract:
The glass fiber reinforced composite plastic (GFRP) was used as the test material, according to the experimental design of the cutting speed (251.2, 301.44, 351.68, 401.92, 452.16 m/min) and feed rate (0.1, 0.2, 0.3, 0.4, 0.5 m/min), a carbide tool was selected to carry out milling test on GFRP, and the effects of cutting speed and feed rate on milling force, milling temperature and surface roughness were studied, and the surface damage mechanism of GFRP was investigated by scanning electron microscopy. The results show that the milling force and surface roughness decrease when the cutting speed increases, while the milling temperature increases gradually; the milling force, milling temperature and surface roughness increase gradually when the feed rate increases. The mean value analysis concludes that the influence of feed rate on milling force, milling temperature and surface roughness is greater than the spindle speed. The surface damage of GFRP is mainly in the form of resin damage and fiber damage.
The glass fiber reinforced composite plastic (GFRP) was used as the test material, according to the experimental design of the cutting speed (251.2, 301.44, 351.68, 401.92, 452.16 m/min) and feed rate (0.1, 0.2, 0.3, 0.4, 0.5 m/min), a carbide tool was selected to carry out milling test on GFRP, and the effects of cutting speed and feed rate on milling force, milling temperature and surface roughness were studied, and the surface damage mechanism of GFRP was investigated by scanning electron microscopy. The results show that the milling force and surface roughness decrease when the cutting speed increases, while the milling temperature increases gradually; the milling force, milling temperature and surface roughness increase gradually when the feed rate increases. The mean value analysis concludes that the influence of feed rate on milling force, milling temperature and surface roughness is greater than the spindle speed. The surface damage of GFRP is mainly in the form of resin damage and fiber damage.
2023, (9): 66-73.
doi: 10.19287/j.mtmt.1005-2402.2023.09.009
Abstract:
To solve the problem of tool wear monitoring in the machining process of titanium alloy structural parts under complex operational conditions, a novel tool wear monitoring method based on the power spectrum energy of spindle vibration was proposed. Firstly, the energy indicators of the 0~7f0 frequency band reflecting the tool wear degradation process, as well as the tooth passing frequency f0 and the frequency multiplier 3f0 amplitude indicators are extracted through power spectrum analysis as sensitive information for monitoring the tool degradation process. Secondly, a method is proposed to determine the tool wear fault threshold based on the machining quality constraints of structural parts. Finally, the feasibility of this method is verified on the cavity structure. The results show that compared with the tool wear monitoring methods based on FFT spectrum, time-domain statistical, and wavelet packet frequency band energy, the proposed tool wear monitoring method is more effective and can accurately identify the early tool wear.
To solve the problem of tool wear monitoring in the machining process of titanium alloy structural parts under complex operational conditions, a novel tool wear monitoring method based on the power spectrum energy of spindle vibration was proposed. Firstly, the energy indicators of the 0~7f0 frequency band reflecting the tool wear degradation process, as well as the tooth passing frequency f0 and the frequency multiplier 3f0 amplitude indicators are extracted through power spectrum analysis as sensitive information for monitoring the tool degradation process. Secondly, a method is proposed to determine the tool wear fault threshold based on the machining quality constraints of structural parts. Finally, the feasibility of this method is verified on the cavity structure. The results show that compared with the tool wear monitoring methods based on FFT spectrum, time-domain statistical, and wavelet packet frequency band energy, the proposed tool wear monitoring method is more effective and can accurately identify the early tool wear.
2023, (9): 74-79.
doi: 10.19287/j.mtmt.1005-2402.2023.09.010
Abstract:
At present, the roughness of cutting surface needs to be combined with manual experience and multiple testing methods, and the machining quality is difficult to be guaranteed. On the basis of giving full play to the role of historical parameters in machining stage, a wear monitoring model was established. At the same time, in order to meet the requirements of the algorithm accuracy and response rate, we introduced the adaptive generalized regression neural network (AGRNN) for roughness prediction. The results show that the correlation coefficient between the calculated roughness prediction data and the actual value reaches R2=0.988, the prediction model reaches the ideal control state, the prediction accuracy meets the control standard, and the response time can be further shortened after the equipment adjustment. Spindle speed 1000～2000 r/min, feed 0.2～0.3 mm/r, axial cutting depth 0.2～0.4 mm, radial cutting depth 1～5 mm range, AGRNN corresponding wear and roughness MAPE of 3.685 and 2.236 in turn, It is lower than the four algorithms of convolutional neural network (CNN), Gaussian process regression (GPR), support vector machine (SVM) and multiple linear regression (MLR), achieving the ideal prediction effect and significantly shortening the control decision time.
At present, the roughness of cutting surface needs to be combined with manual experience and multiple testing methods, and the machining quality is difficult to be guaranteed. On the basis of giving full play to the role of historical parameters in machining stage, a wear monitoring model was established. At the same time, in order to meet the requirements of the algorithm accuracy and response rate, we introduced the adaptive generalized regression neural network (AGRNN) for roughness prediction. The results show that the correlation coefficient between the calculated roughness prediction data and the actual value reaches R2=0.988, the prediction model reaches the ideal control state, the prediction accuracy meets the control standard, and the response time can be further shortened after the equipment adjustment. Spindle speed 1000～2000 r/min, feed 0.2～0.3 mm/r, axial cutting depth 0.2～0.4 mm, radial cutting depth 1～5 mm range, AGRNN corresponding wear and roughness MAPE of 3.685 and 2.236 in turn, It is lower than the four algorithms of convolutional neural network (CNN), Gaussian process regression (GPR), support vector machine (SVM) and multiple linear regression (MLR), achieving the ideal prediction effect and significantly shortening the control decision time.
2023, (9): 80-87.
doi: 10.19287/j.mtmt.1005-2402.2023.09.011
Abstract:
An energy consumption monitoring and prediction system based on Lora transmission is designed for the energy consumption management of high energy consumption enterprises. The system as a whole is divided into two pieces: software and hardware. The hardware mainly consists of smart meter, Lora module and LoraWAN gateway. The smart meter collects the main energy consumption of the enterprise, and the Lora module accepts and transmits the collected data. Also, the platform server is mapped using a star topology network with the Lora-WAN gateway as the central node. The software uses a B/S architecture that is more suitable for enterprises. The landing system as a whole uses SpringBoot and Vue2 as the main framework, with Mybatis plus as the ORM framework, and uses Shiro for access control. The database selected for the system is MYSQL. In addition, a prediction model based on the APSO-LSTM algorithm was designed to predict the energy consumption value of the workshop for the large amount of energy consumption data generated in the workshop, which can provide a reference for enterprises to achieve cost saving and low carbon production. The test results show that the designed system has good visualization effects and high accuracy of energy consumption prediction, which has certain application value.
An energy consumption monitoring and prediction system based on Lora transmission is designed for the energy consumption management of high energy consumption enterprises. The system as a whole is divided into two pieces: software and hardware. The hardware mainly consists of smart meter, Lora module and LoraWAN gateway. The smart meter collects the main energy consumption of the enterprise, and the Lora module accepts and transmits the collected data. Also, the platform server is mapped using a star topology network with the Lora-WAN gateway as the central node. The software uses a B/S architecture that is more suitable for enterprises. The landing system as a whole uses SpringBoot and Vue2 as the main framework, with Mybatis plus as the ORM framework, and uses Shiro for access control. The database selected for the system is MYSQL. In addition, a prediction model based on the APSO-LSTM algorithm was designed to predict the energy consumption value of the workshop for the large amount of energy consumption data generated in the workshop, which can provide a reference for enterprises to achieve cost saving and low carbon production. The test results show that the designed system has good visualization effects and high accuracy of energy consumption prediction, which has certain application value.
2023, (9): 88-94.
doi: 10.19287/j.mtmt.1005-2402.2023.09.012
Abstract:
A data acquisition architecture that integrates unit devices into a unified OPC UA architecture for communication is designed in order to address the problem of data acquisition difficulties and response time extension, etc. during a large number of acquired communication protocols data for smart made units of dropper pre-assembly of high-speed railway is combined, then to be centralized and transferred to the cloud for analysis. An end-to-end motor fault real-time diagnosis method applied to the edge field has been proposed by combining the characteristics of motor vibration data in the cloud after the collected data is preprocessed at the edge nearby and then coordinated to the cloud. The application of the proposed method in the cantilever pre-assembly production line has been verified, and the results show that the data acquisition and fault analysis method is universal and efficient.
A data acquisition architecture that integrates unit devices into a unified OPC UA architecture for communication is designed in order to address the problem of data acquisition difficulties and response time extension, etc. during a large number of acquired communication protocols data for smart made units of dropper pre-assembly of high-speed railway is combined, then to be centralized and transferred to the cloud for analysis. An end-to-end motor fault real-time diagnosis method applied to the edge field has been proposed by combining the characteristics of motor vibration data in the cloud after the collected data is preprocessed at the edge nearby and then coordinated to the cloud. The application of the proposed method in the cantilever pre-assembly production line has been verified, and the results show that the data acquisition and fault analysis method is universal and efficient.
2023, (9): 95-101.
doi: 10.19287/j.mtmt.1005-2402.2023.09.013
Abstract:
In traditional monolithic architecture, the functions of the manufacturing execution system are coupled to each other. In order to improve the reconfigurable and maintainability of manufacturing execution system, Research on key technologies of microservice based manufacturing execution system is carried out, a multi-layer system architecture based on microservice is established to decouple system processes from business logic. A domain scenario-driven microservice decomposition method is proposed, the correlation between tables is calculated by analyzing the relationship between use cases and data, Girvan-Newman (G-N) algorithm is used to decompose the microservices with maximum modularity. Finally, the manufacturing execution system of a bearing enterprise is taken as an application example, the microservice decomposition scheme of manufacturing execution system with the maximum modularity can be obtained by using case diagram and entity relationship diagram.
In traditional monolithic architecture, the functions of the manufacturing execution system are coupled to each other. In order to improve the reconfigurable and maintainability of manufacturing execution system, Research on key technologies of microservice based manufacturing execution system is carried out, a multi-layer system architecture based on microservice is established to decouple system processes from business logic. A domain scenario-driven microservice decomposition method is proposed, the correlation between tables is calculated by analyzing the relationship between use cases and data, Girvan-Newman (G-N) algorithm is used to decompose the microservices with maximum modularity. Finally, the manufacturing execution system of a bearing enterprise is taken as an application example, the microservice decomposition scheme of manufacturing execution system with the maximum modularity can be obtained by using case diagram and entity relationship diagram.
2023, (9): 102-108, 137.
doi: 10.19287/j.mtmt.1005-2402.2023.09.014
Abstract:
In order to meet the requirement of rapid response of resource rescheduling in intelligent manufacturing shop and the characteristics of invisible disturbance difficult to measure and capture, a decision-making method of resource monitoring and rescheduling in intelligent manufacturing shop was proposed. Firstly, a resource abnormal state monitoring model is established based on the good continuous monitoring performance of support vector machine. Secondly, the accuracy of SVM model was improved by combining Lasso regression algorithm and K-nearest neighbor value classification algorithm, and the fault-tolerant mechanism was constructed by data substitution method to ensure the transient smooth operation of the system in case of anomalies. Then, the workshop resource rescheduling method is designed, and the classifier is trained for rescheduling scheme selection by historical case data to guide the efficient production of intelligent manufacturing workshop under the condition of invisible disturbance. Finally, the effectiveness of the proposed rescheduling decision method is verified by an example of actual workshop invisible disturbance.
In order to meet the requirement of rapid response of resource rescheduling in intelligent manufacturing shop and the characteristics of invisible disturbance difficult to measure and capture, a decision-making method of resource monitoring and rescheduling in intelligent manufacturing shop was proposed. Firstly, a resource abnormal state monitoring model is established based on the good continuous monitoring performance of support vector machine. Secondly, the accuracy of SVM model was improved by combining Lasso regression algorithm and K-nearest neighbor value classification algorithm, and the fault-tolerant mechanism was constructed by data substitution method to ensure the transient smooth operation of the system in case of anomalies. Then, the workshop resource rescheduling method is designed, and the classifier is trained for rescheduling scheme selection by historical case data to guide the efficient production of intelligent manufacturing workshop under the condition of invisible disturbance. Finally, the effectiveness of the proposed rescheduling decision method is verified by an example of actual workshop invisible disturbance.
2023, (9): 109-114.
doi: 10.19287/j.mtmt.1005-2402.2023.09.015
Abstract:
The traditional casting sorting detection is carried out manually, which is labor-intensive and prone to visual errors due to fatigue, as well as some castings with small defects that are difficult to identify by the human eye. Therefore, machine vision technology has shown a good application prospect in the field of casting sorting. Based on this, the composition and key technologies of the sorting detection system of machine vision technology and the application status of machine vision in defect detection and sorting robots are analyzed and discussed in detail. Finally, the future development trend of casting sorting is prospected.
The traditional casting sorting detection is carried out manually, which is labor-intensive and prone to visual errors due to fatigue, as well as some castings with small defects that are difficult to identify by the human eye. Therefore, machine vision technology has shown a good application prospect in the field of casting sorting. Based on this, the composition and key technologies of the sorting detection system of machine vision technology and the application status of machine vision in defect detection and sorting robots are analyzed and discussed in detail. Finally, the future development trend of casting sorting is prospected.
2023, (9): 115-121.
doi: 10.19287/j.mtmt.1005-2402.2023.09.016
Abstract:
Automatic extraction of weld feature points is the key to autonomous weld paths planning for robot. Conventional welding usually requires teaching welding intermediate points, which usually needs to be re-teached when the workpiece is changed or there is an error in the assembly, resulting in low welding efficiency. In this paper, we propose a method for automatic extraction of welding feature points based on a monocular camera, bilateral filtering is used to smooth the image; then Sobel operator is applied to calculate the gradient of the pixels and the average gray value of the image is used as the threshold to binary pixels; then a closed operation is used to fill the fine holes and connects the intermittent fine contour lines;then an improved contour tracking operator is used to eliminate residual noise on the image;then the feature points are taken from image and converted from the pixel coordinate system to the user coordinate system by camera calibration.Different shapes of flat sheet butt weld workpieces are chosen for feature point extraction experiments, by comparing the difference between the feature points and the weld bevel center points obtained by laser sensor in the y-axis direction of the user coordinate system,the average errors are 0.45 mm, 0.23 mm, 0.42 mm for hybrid, linear and curve welds respectively,which indicating that the method can automatically identify weld from the image and extract the weld feature points, furthermore, eliminate teaching of the welding system.
Automatic extraction of weld feature points is the key to autonomous weld paths planning for robot. Conventional welding usually requires teaching welding intermediate points, which usually needs to be re-teached when the workpiece is changed or there is an error in the assembly, resulting in low welding efficiency. In this paper, we propose a method for automatic extraction of welding feature points based on a monocular camera, bilateral filtering is used to smooth the image; then Sobel operator is applied to calculate the gradient of the pixels and the average gray value of the image is used as the threshold to binary pixels; then a closed operation is used to fill the fine holes and connects the intermittent fine contour lines;then an improved contour tracking operator is used to eliminate residual noise on the image;then the feature points are taken from image and converted from the pixel coordinate system to the user coordinate system by camera calibration.Different shapes of flat sheet butt weld workpieces are chosen for feature point extraction experiments, by comparing the difference between the feature points and the weld bevel center points obtained by laser sensor in the y-axis direction of the user coordinate system,the average errors are 0.45 mm, 0.23 mm, 0.42 mm for hybrid, linear and curve welds respectively,which indicating that the method can automatically identify weld from the image and extract the weld feature points, furthermore, eliminate teaching of the welding system.
2023, (9): 122-130.
doi: 10.19287/j.mtmt.1005-2402.2023.09.017
Abstract:
Aiming at the problem that the grinding wheel dresser can’t detect the contour shape in real time in harsh environment, an inspection system of grinding wheel contour shape based on machine vision is built. The background cut up algorithm based on margin detection is used to set the ROI area, the contour extraction and cubic spline interpolation are carried out in the subsequent images to get a complete contour image, and then the improved sub-pixel edge extraction method is used to get a higher precision contour image. The system can totally compensate the contour curve with illumination variation range from −200 Lux to 150 Lux and contour missing ratio less than 25%, and the time for single image processing is within 160 ms. The average image quality index, square root error of pixel value, structural similarity and image peak signal-to-noise ratio of accuracy detection are 0.550 0, 7 803, 7.24 and 0.037 0, which can realize the detection of grinding wheel contour shape during machining.
Aiming at the problem that the grinding wheel dresser can’t detect the contour shape in real time in harsh environment, an inspection system of grinding wheel contour shape based on machine vision is built. The background cut up algorithm based on margin detection is used to set the ROI area, the contour extraction and cubic spline interpolation are carried out in the subsequent images to get a complete contour image, and then the improved sub-pixel edge extraction method is used to get a higher precision contour image. The system can totally compensate the contour curve with illumination variation range from −200 Lux to 150 Lux and contour missing ratio less than 25%, and the time for single image processing is within 160 ms. The average image quality index, square root error of pixel value, structural similarity and image peak signal-to-noise ratio of accuracy detection are 0.550 0, 7 803, 7.24 and 0.037 0, which can realize the detection of grinding wheel contour shape during machining.
2023, (9): 131-137.
doi: 10.19287/j.mtmt.1005-2402.2023.09.018
Abstract:
Three-dimensional woven carbon fiber-reinforced aluminum matrix composites (abbreviated as 3D-CF/Al composites) have higher specific strength, specific stiffness, impact damage resistance and crack expansion resistance due to the presence of a spatial mesh structure composed of continuous carbon fiber bundles inside. This paper reviews the effects of weave structure and preparation process parameters on the mechanical properties of 3D-CF/Al composites and the progress of research on meso-scale structure modeling and mechanical analysis, and proposes a theoretical concept of macro-meso-scale structure and mechanical synergy for the design, preparation and application of 3D-CF/Al composites in order to realize the digital design, manufacturing and performance of composites.
Three-dimensional woven carbon fiber-reinforced aluminum matrix composites (abbreviated as 3D-CF/Al composites) have higher specific strength, specific stiffness, impact damage resistance and crack expansion resistance due to the presence of a spatial mesh structure composed of continuous carbon fiber bundles inside. This paper reviews the effects of weave structure and preparation process parameters on the mechanical properties of 3D-CF/Al composites and the progress of research on meso-scale structure modeling and mechanical analysis, and proposes a theoretical concept of macro-meso-scale structure and mechanical synergy for the design, preparation and application of 3D-CF/Al composites in order to realize the digital design, manufacturing and performance of composites.
2023, (9): 138-145.
doi: 10.19287/j.mtmt.1005-2402.2023.09.019
Abstract:
With the digital development of modern industry, robotics and other control technologies are valued and developed rapidly, of which the robot arm as the core component of the trajectory tracking control is particularly important, permanent magnet synchronous motor (PMSM) with fast, accurate and stable servo positioning function, gradually become the carrier of the robot arm.Since the traditional servo position positioning control is difficult to meet the requirements of high position accuracy and strong anti-disturbance ability of the manipulator, there are also many adjustment parameters and the disadvantages of uncontrollable speed. To solve these problems, this paper proposes a self-anti-disturbance backstepping control method for PMSM specified position-velocity tracking with load estimation. The method reduces the number of control parameters, increases the amount of speed control, enhances the adaptability of the control system to changes in the internal parameters of the motor, and at the same time allows for rapid adjustment according to changes in the mass of the robot arm, improving the flexibility and reliability of position control. Through simulation and experimental results, it is verified that the proposed method has good position positioning ability and anti-interference ability under the condition that the motion trajectory of the manipulator is fixed or fixed, which verifies the effectiveness and feasibility of the proposed strategy.
With the digital development of modern industry, robotics and other control technologies are valued and developed rapidly, of which the robot arm as the core component of the trajectory tracking control is particularly important, permanent magnet synchronous motor (PMSM) with fast, accurate and stable servo positioning function, gradually become the carrier of the robot arm.Since the traditional servo position positioning control is difficult to meet the requirements of high position accuracy and strong anti-disturbance ability of the manipulator, there are also many adjustment parameters and the disadvantages of uncontrollable speed. To solve these problems, this paper proposes a self-anti-disturbance backstepping control method for PMSM specified position-velocity tracking with load estimation. The method reduces the number of control parameters, increases the amount of speed control, enhances the adaptability of the control system to changes in the internal parameters of the motor, and at the same time allows for rapid adjustment according to changes in the mass of the robot arm, improving the flexibility and reliability of position control. Through simulation and experimental results, it is verified that the proposed method has good position positioning ability and anti-interference ability under the condition that the motion trajectory of the manipulator is fixed or fixed, which verifies the effectiveness and feasibility of the proposed strategy.
2023, (9): 146-153.
doi: 10.19287/j.mtmt.1005-2402.2023.09.020
Abstract:
In order to explore a new way to improve the comprehensive performance of machine tool, a new method of using steel-polypropylene fiber reinforced artificial granite composite (SPFRAG) instead of cast iron to make lathe bed is put forward. The SPFRAG bed structure was redesigned on the basis of a lathe bed, and the response surface model was established by using Kriging interpolation method, and the weight proportion was determined by sensitivity analysis of each design variable, based on the multi-objective genetic algorithm (MOGA) , the optimization of the bed structure is carried out with the constraints of the bed mass and the first natural frequency, and with the maximum total displacement and the maximum equivalent stress as the optimization objectives. The results show that the static and dynamic characteristics of SPFRAG bed are better than those of cast iron bed, which proves the feasibility of the scheme.
In order to explore a new way to improve the comprehensive performance of machine tool, a new method of using steel-polypropylene fiber reinforced artificial granite composite (SPFRAG) instead of cast iron to make lathe bed is put forward. The SPFRAG bed structure was redesigned on the basis of a lathe bed, and the response surface model was established by using Kriging interpolation method, and the weight proportion was determined by sensitivity analysis of each design variable, based on the multi-objective genetic algorithm (MOGA) , the optimization of the bed structure is carried out with the constraints of the bed mass and the first natural frequency, and with the maximum total displacement and the maximum equivalent stress as the optimization objectives. The results show that the static and dynamic characteristics of SPFRAG bed are better than those of cast iron bed, which proves the feasibility of the scheme.
2023, (9): 154-161.
doi: 10.19287/j.mtmt.1005-2402.2023.09.021
Abstract:
The geometric assembly model of multi-axis CNC abrasive belt grinder is preprocessed, the auxiliary coordinate system and the initial position of the machine tool motion axis are defined, the machine tool workbench group and tool group are analyzed, and the motion (translation and rotation) of each motion axis are sequentially connected to get the kinematic chain of the grinder. The coordinate transformation matrix is derived based on the kinematic chain of the machine tool, the post-processing algorithm of the grinder is obtained, the post-processing coordinate transformation function is constructed, and the development of the post-processor is realized based on MATLAB. Finally, taking the wide-chord hollow fan blade as the verification object, the correctness of the post-processing algorithm is verified by VERICUT virtual simulation environment and real machine environment.
The geometric assembly model of multi-axis CNC abrasive belt grinder is preprocessed, the auxiliary coordinate system and the initial position of the machine tool motion axis are defined, the machine tool workbench group and tool group are analyzed, and the motion (translation and rotation) of each motion axis are sequentially connected to get the kinematic chain of the grinder. The coordinate transformation matrix is derived based on the kinematic chain of the machine tool, the post-processing algorithm of the grinder is obtained, the post-processing coordinate transformation function is constructed, and the development of the post-processor is realized based on MATLAB. Finally, taking the wide-chord hollow fan blade as the verification object, the correctness of the post-processing algorithm is verified by VERICUT virtual simulation environment and real machine environment.
2023, (9): 162-166.
doi: 10.19287/j.mtmt.1005-2402.2023.09.022
Abstract:
In this paper, the transmission characteristics of permanent magnet magnetorheological transmission are analyzed. The transmission device adopts cylindric working structure and the excitation source is set as the permanent magnet built-in type, which interferes with the flow field at the working area through the special magnetic column structure, and enhances the magnetic field strength at the working area. Under the combined action of shear and extrusion, the output torque capacity of magnetorheological transmission device can be developed to a greater extent. The model was established to analyze the influencing factors of the output torque value of the transmission device, and the prototype of the magnetorheological transmission device was developed, and the static load characteristics, speed regulation characteristics and torque characteristics of the prototype were tested. The results show that the design of the transmission device can be continuous and stable transfer torque.The increase of the magnetic field strength will effectively adjust the output speed. When the magnetic field strength is constant, the output torque of the whole transmission device has a certain linear relationship with the input speed of the motor, showing that the output torque is continuous and stable.
In this paper, the transmission characteristics of permanent magnet magnetorheological transmission are analyzed. The transmission device adopts cylindric working structure and the excitation source is set as the permanent magnet built-in type, which interferes with the flow field at the working area through the special magnetic column structure, and enhances the magnetic field strength at the working area. Under the combined action of shear and extrusion, the output torque capacity of magnetorheological transmission device can be developed to a greater extent. The model was established to analyze the influencing factors of the output torque value of the transmission device, and the prototype of the magnetorheological transmission device was developed, and the static load characteristics, speed regulation characteristics and torque characteristics of the prototype were tested. The results show that the design of the transmission device can be continuous and stable transfer torque.The increase of the magnetic field strength will effectively adjust the output speed. When the magnetic field strength is constant, the output torque of the whole transmission device has a certain linear relationship with the input speed of the motor, showing that the output torque is continuous and stable.
2023, (9): 167-174.
doi: 10.19287/j.mtmt.1005-2402.2023.09.023
Abstract:
The energy absorbing box needs multi-pass stretch forming due to its large ratio of height to diameter. The stretching coefficient has great influence on the forming quality of the energy absorbing box. In order to determine the stretching coefficient of each pass objectively and reasonably, a optimization method of multi-pass stretch coefficient of the energy absorbing box based on BSO-BP neural network was proposed. Taking a certain type of car energy absorbing box as the research object, the maximum thinning rate and FLD forming field safe ratio of the box were used as the forming quality evaluation criteria. Firstly, with the tensile coefficient of each pass as the experimental factor, the Latin hypercube experimental design method combined with the finite element analysis technique was used to establish experimental samples. Secondly, the weight and threshold of BP neural network were optimized based on the brain-storming algorithm (BSO), and the BSO-BP neural network model of multi-pass stretch coefficient was established. Thirdly, multi-objective particle swarm optimization algorithm (MOPSO) was used in the established BSO-BP neural network model to obtain the optimal multi-pass tensile coefficient that met the forming quality evaluation criteria. Finally, the effectiveness of the proposed method is verified by actual production, which provides a new method for objective and reasonable determination of tensile coefficient of deep drawing products in engineering.
The energy absorbing box needs multi-pass stretch forming due to its large ratio of height to diameter. The stretching coefficient has great influence on the forming quality of the energy absorbing box. In order to determine the stretching coefficient of each pass objectively and reasonably, a optimization method of multi-pass stretch coefficient of the energy absorbing box based on BSO-BP neural network was proposed. Taking a certain type of car energy absorbing box as the research object, the maximum thinning rate and FLD forming field safe ratio of the box were used as the forming quality evaluation criteria. Firstly, with the tensile coefficient of each pass as the experimental factor, the Latin hypercube experimental design method combined with the finite element analysis technique was used to establish experimental samples. Secondly, the weight and threshold of BP neural network were optimized based on the brain-storming algorithm (BSO), and the BSO-BP neural network model of multi-pass stretch coefficient was established. Thirdly, multi-objective particle swarm optimization algorithm (MOPSO) was used in the established BSO-BP neural network model to obtain the optimal multi-pass tensile coefficient that met the forming quality evaluation criteria. Finally, the effectiveness of the proposed method is verified by actual production, which provides a new method for objective and reasonable determination of tensile coefficient of deep drawing products in engineering.
2023, (9): 175-180.
doi: 10.19287/j.mtmt.1005-2402.2023.09.024
Abstract:
Aiming at the problems of the difficulty in ensuring the accuracy of the thickness of the spacecraft's thin-walled cabin and the low processing efficiency, the on-machine measurement and compensation method of the wall thickness of the thin-walled parts are studied based on the ultrasonic touch probe. First, the ultrasonic touch probe is used to measure the thickness of the thin-walled parts and the external profile, and the external profile deviation and thickness deviation are compensated at the same time. The compensation value calculation and optimization method is designed to calculate the corresponding lightening groove compensation value. Then, develop the post-processing of NX programming software, the compensation value is automatically compensated to the NC machining program,realize the on-machine measurement and compensation processing of the wall thickness of the thin-walled parts automatically, and improve the processing efficiency and quality stability.
Aiming at the problems of the difficulty in ensuring the accuracy of the thickness of the spacecraft's thin-walled cabin and the low processing efficiency, the on-machine measurement and compensation method of the wall thickness of the thin-walled parts are studied based on the ultrasonic touch probe. First, the ultrasonic touch probe is used to measure the thickness of the thin-walled parts and the external profile, and the external profile deviation and thickness deviation are compensated at the same time. The compensation value calculation and optimization method is designed to calculate the corresponding lightening groove compensation value. Then, develop the post-processing of NX programming software, the compensation value is automatically compensated to the NC machining program,realize the on-machine measurement and compensation processing of the wall thickness of the thin-walled parts automatically, and improve the processing efficiency and quality stability.
2023, (9): 181-188.
doi: 10.19287/j.mtmt.1005-2402.2023.09.025
Abstract:
The sand molding machine is used for processing complex sand mold parts. The geometric error of the molding machine has 21 items, and each error item has a significant impact on different elements of the part processing. Therefore, this article proposes to identify the key geometric errors and related compensations for typical part processing elements, and to improve the accuracy of sand mold parts processing through compensation. Taking the typical processing elements of sand mold parts processed by the sand molding machine as an example, firstly, the typical processing elements include four typical elements: cylindrical platform, conical platform, diamond platform, and inclined quadrangular platform. Secondly, based on the sensitivity analysis of errors, the key geometric errors corresponding to the typical processing elements are identified. Finally, targeted compensation is carried out according to the typical part processing elements. The experimental verification is conducted on the CAMTC-SMM2000 sand molding machine, and the sum of the sensitivity influence factors of the identified key geometric errors is all greater than 90%. After implementing error compensation, the processing accuracy of the four typical part processing elements is improved by 55%～70%. The results show that this method can effectively identify the key geometric errors under different part processing elements and improve the processing accuracy of sand mold parts.
The sand molding machine is used for processing complex sand mold parts. The geometric error of the molding machine has 21 items, and each error item has a significant impact on different elements of the part processing. Therefore, this article proposes to identify the key geometric errors and related compensations for typical part processing elements, and to improve the accuracy of sand mold parts processing through compensation. Taking the typical processing elements of sand mold parts processed by the sand molding machine as an example, firstly, the typical processing elements include four typical elements: cylindrical platform, conical platform, diamond platform, and inclined quadrangular platform. Secondly, based on the sensitivity analysis of errors, the key geometric errors corresponding to the typical processing elements are identified. Finally, targeted compensation is carried out according to the typical part processing elements. The experimental verification is conducted on the CAMTC-SMM2000 sand molding machine, and the sum of the sensitivity influence factors of the identified key geometric errors is all greater than 90%. After implementing error compensation, the processing accuracy of the four typical part processing elements is improved by 55%～70%. The results show that this method can effectively identify the key geometric errors under different part processing elements and improve the processing accuracy of sand mold parts.
2023, (9): 196-200.
doi: 10.19287/j.mtmt.1005-2402.2023.09.027
Abstract:
Aiming at the problems of high grinding temperature, poor surface quality and low service life of grinding wheel in grinding process, this paper analyzed the mechanism of tangential ultrasonic assisted grinding from two aspects of kinematics and cutting force. Taking Ti6Al4V as workpiece material, the influences of ultrasonic amplitude, grinding depth, feed speed and spindle speed on material surface roughness were studied through comparative experiments. The experimental results show that, compared with the single grinding process, the surface roughness Ra is reduced by 25%～40% after the introduction of ultrasonic vibration, the surface pits are reduced significantly, and the surface topography changes significantly. With the increase of amplitude, the surface roughness of the material decreases by 40%. The roughness decreases with the increase of rotational speed by 25%. It is proved that the introduction of ultrasonic vibration can reduce the roughness and improve the surface quality of grinding workpiece. The results provide guidance for the selection of grinding parameters.
Aiming at the problems of high grinding temperature, poor surface quality and low service life of grinding wheel in grinding process, this paper analyzed the mechanism of tangential ultrasonic assisted grinding from two aspects of kinematics and cutting force. Taking Ti6Al4V as workpiece material, the influences of ultrasonic amplitude, grinding depth, feed speed and spindle speed on material surface roughness were studied through comparative experiments. The experimental results show that, compared with the single grinding process, the surface roughness Ra is reduced by 25%～40% after the introduction of ultrasonic vibration, the surface pits are reduced significantly, and the surface topography changes significantly. With the increase of amplitude, the surface roughness of the material decreases by 40%. The roughness decreases with the increase of rotational speed by 25%. It is proved that the introduction of ultrasonic vibration can reduce the roughness and improve the surface quality of grinding workpiece. The results provide guidance for the selection of grinding parameters.
2023, (9): 189-195.
doi: 10.19287/j.mtmt.1005-2402.2023.09.026
Abstract:
In order to investigate the effect of VC as a grain growth inhibitor on the in-situ generation of WC-reinforced nickel-based coatings, WC-reinforced nickel-based coatings with VC contents of 0.1%, 1.5% and 2% were prepared on the surface of H13 steel. After cutting, grinding and polishing, and corrosion treatment, the molten specimens were analysed and tested by scanning electron microscopy (SEM), X-ray diffractometer (XRD), energy spectrometer (EDS), micro hardness tester, and friction wear tester for the analysis of tissue, physical phase, hardness, and friction wear properties, respectively. The results show that VC inhibits the growth of WC particles significantly. As the VC content increased, the WC particles were gradually refined and the coating was homogeneous and dense; the VC inhibitor slowed down the dissolution-precipitation process of WC and reduced the surface energy of WC, which were important reasons for the inhibition of WC grain growth. With the addition of VC, the production of WC hard phases is inhibited and the vanadium containing material increases. The hardness of WC-enhanced nickel-based coatings increases with the increase in VC content, but when the VC content is greater than 1.5%, the impurity phase of the coating increases and the WC content decreases, resulting in a decrease in the hardness of the coating. When the VC content is 1.5%, it can effectively refine the grain and improve the hardness of the coating. The friction wear performance of WC-enhanced nickel-based coatings with VC inhibitor is better, and the wear mechanism is mainly adhesive wear, brittle spalling and abrasive wear.
In order to investigate the effect of VC as a grain growth inhibitor on the in-situ generation of WC-reinforced nickel-based coatings, WC-reinforced nickel-based coatings with VC contents of 0.1%, 1.5% and 2% were prepared on the surface of H13 steel. After cutting, grinding and polishing, and corrosion treatment, the molten specimens were analysed and tested by scanning electron microscopy (SEM), X-ray diffractometer (XRD), energy spectrometer (EDS), micro hardness tester, and friction wear tester for the analysis of tissue, physical phase, hardness, and friction wear properties, respectively. The results show that VC inhibits the growth of WC particles significantly. As the VC content increased, the WC particles were gradually refined and the coating was homogeneous and dense; the VC inhibitor slowed down the dissolution-precipitation process of WC and reduced the surface energy of WC, which were important reasons for the inhibition of WC grain growth. With the addition of VC, the production of WC hard phases is inhibited and the vanadium containing material increases. The hardness of WC-enhanced nickel-based coatings increases with the increase in VC content, but when the VC content is greater than 1.5%, the impurity phase of the coating increases and the WC content decreases, resulting in a decrease in the hardness of the coating. When the VC content is 1.5%, it can effectively refine the grain and improve the hardness of the coating. The friction wear performance of WC-enhanced nickel-based coatings with VC inhibitor is better, and the wear mechanism is mainly adhesive wear, brittle spalling and abrasive wear.
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