An Embedded-Based Numerical Control Machining Simulation Method

Abstract

The present invention provides an embedded-based numerical control machining simulation method, which is applied to an embedded processor and includes the following steps: S1 Initialize the tool feature information of the numerical control device; S2 Receive the tool status information transmitted by the sensor, and digitally process the received tool status information to obtain tool status data; S3 Extract features based on the obtained tool status data to obtain tool status feature data; S4 Perform impact force simulation analysis based on the obtained tool status feature data to obtain an impact force simulation analysis result; S5 According to the impact force simulation analysis result, call the corresponding tool wear status simulation model to perform tool wear simulation analysis to obtain a tool wear status simulation analysis result; S6 When the tool wear status simulation analysis result is abnormal, send a corresponding warning message to the host computer. The present invention helps to improve the real-time performance and reliability of the machining status simulation of numerical control devices.

Description

Technical Field

[0001] The present invention relates to the technical field of embedded system assisted manufacturing for numerical control processing equipment, and particularly to a numerical control processing simulation method based on embedded systems. Background Art

[0002] Computer Numerical Control (CNC) technology is an important support for modern manufacturing and is widely used in various fields. With the continuous improvement of industrial automation, the requirements for the accuracy, efficiency, and stability of the CNC machining process are also increasing. In order to ensure production quality and reduce the unplanned downtime of equipment, how to effectively monitor the cutting tools during the machining process has become one of the key research focuses in the industry.

[0003] Currently, in most of the simulation technologies for CNC machining, there is usually a problem of insufficient real-time performance, that is, the analysis of equipment status is usually based on offline data (status data collected when the numerical control equipment is in a static state), and it is impossible to accurately simulate the status of the numerical control equipment (such as the real-time wear status of the cutting tool) during machining. There is a situation of lag in real-time monitoring, which is not conducive to effectively warning the status of the CNC machining equipment in a timely manner, and it is impossible to respond in a timely manner to the decline in machining quality and equipment damage caused by abnormalities in components such as the cutting tool of the numerical control equipment, affecting the reliability of CNC machining management. Summary of the Invention

[0004] In view of the above technical problem that the status of the CNC machining equipment cannot be warned in real time and accurately, the present invention aims to provide a numerical control processing simulation method based on embedded systems.

[0005] The object of the present invention is achieved by the following technical solutions:

[0006] The present invention proposes a numerical control processing simulation method based on embedded systems, which is applied to an embedded processor and includes the following steps:

[0007] S1, initialize the tool feature information of the numerical control equipment;

[0008] S2, during the execution of the machining task, receive the tool status information transmitted by the sensor, and digitally process the received tool status information to obtain tool status data, where the tool status data includes tool vibration signal data and AE signal data;

[0009] S3, perform filtering and normalization processing on the obtained tool status data, and further perform feature extraction to obtain tool status feature data;

[0010] S4. Conduct an impact force simulation analysis based on the obtained tool state characteristic data to obtain the impact force simulation analysis results, where the impact force simulation analysis results include normal impacts and abnormal impacts;

[0011] S5. According to the impact force simulation analysis results, call the corresponding tool wear state simulation model, and combine with the currently obtained state characteristic data to conduct a tool wear simulation analysis to obtain the tool wear state simulation analysis results;

[0012] S6. When the tool wear state simulation analysis results are abnormal, send corresponding warning information to the host computer.

[0013] Preferably, step S2 includes:

[0014] S21. Establish a communication connection with the vibration sensor set on the tool, and receive the tool vibration signal data collected by the vibration sensor in real time;

[0015] S22. Establish a communication connection with the acoustic emission sensor set on the tool, and receive the AE signal data collected by the acoustic emission sensor.

[0016] Preferably, the state data further includes position data, cutting force data, cutting speed data, and temperature data;

[0017] Step S2 further includes:

[0018] S23. Establish a communication connection with the position sensor set on the numerical control workbench, and receive the tool position change data collected by the position sensor;

[0019] S24. Receive the cutting force data generated by the tool during the grinding process collected by the piezoelectric force sensor;

[0020] S25. Receive the tool grinding area temperature change data collected by the thermocouple sensor.

[0021] Preferably, step S3 includes:

[0022] S31. Conduct filtering and calibration processing based on the obtained state data to obtain the preprocessed state data;

[0023] S32. Extract feature points and feature parameters from the preprocessed state data to obtain state characteristic data; where the feature parameters include the short-term change trend, change rate, and short-term maximum / minimum value of the state data.

[0024] Preferably, step S4 includes:

[0025] S41. Calculate the impact force simulation characteristic parameters of the current tool according to the state characteristic data of the obtained vibration signal data. The impact force simulation characteristic parameter calculation function used is as follows:

[0026] ;

[0027] In the formula, SHCK represents the impact force simulation characteristic parameter of the current time period, Z m represents the zero-crossing rate of the vibration signal within the current time period, a m-peak represents the maximum value of the absolute value of the vibration signal acceleration within the current time period, a m-T represents the preset impact force threshold, ω m and ω x represent the preset sensitivity adjustment factor, σ Xm represents the standard deviation of the vibration signal amplitude within the current time period; mean |Xm| represents the average amplitude of the vibration signal within the current time period;

[0028] S42. Compare and analyze according to the obtained impact force simulation characteristic parameter SHCK and the set standard impact force SHth :

[0029] When SHCK ≥ SHth , the obtained current impact force simulation analysis result is abnormal impact;

[0030] Otherwise, when SHCK < SHth , the obtained current impact force simulation analysis result is normal impact.

[0031] Preferably, step S5 includes:

[0032] S51. When the impact force simulation analysis result is normal impact, call the first tool wear state simulation model to perform tool wear simulation. The first tool wear state simulation model used is as follows:

[0033] ;

[0034] In the formula, WT represents the simulated wear amount of the tool in the current time period, mc1 represents the wear resistance coefficient of the tool material, F c represents the cutting force received by the tool in the current time period; mc2 represents the thermal sensitivity coefficient of the tool material, vc Represents the grinding speed of the tool in the current time period, mc3 Represents the collision coefficient of the tool in the current time period, M eff Represents the weight of the tool, a m-peak Represents the maximum value of the absolute acceleration of the vibration signal in the current time period, Xm max Represents the maximum amplitude of the vibration signal in the current time period, ω Xm Represents the frequency of the vibration signal in the current time period; nz Represents the cutting wear index, mz Represents the heat loss wear index, pz Represents the impact wear index;

[0035] S52. When the impact force simulation analysis result is an abnormal impact, call the second tool wear state simulation model to conduct tool wear simulation. The second tool wear state simulation model used is:

[0036] ;

[0037] In the formula, WT Represents the simulated wear amount of the tool in the current time period, mc1 Represents the wear resistance coefficient of the tool material, F c Represents the cutting force received by the tool in the current time period; mc2 Represents the thermal sensitivity coefficient of the tool material, v c Represents the grinding speed of the tool in the current time period, mc3 Represents the collision coefficient of the tool in the current time period, M eff Represents the weight of the tool, a m-peak Represents the maximum value of the absolute acceleration of the vibration signal in the current time period, β AE Represents the calibrated acoustic emission parameter, Y(i) Represents the amplitude of the i th sampling point of the AE signal in the current time period, where i = 1, 2, … NI, NI Represents the total number of sampling points of the AE signal in the current time period, ωq AE Represents the sampling frequency of the AE signal in the current time period, nz Represents the cutting wear index, mz Represents the heat loss wear index, pz Represents the impact wear index; qz Represents the impact conversion factor;ω q and ω p represent preset weight factors, where ω q +ω p =1 ;

[0038] S53. Compare the obtained simulated wear amount of the tool in the current time period WT with the preset standard periodic wear amount Wth When WT > Wth , the simulation analysis result of the periodic tool wear state is abnormal;

[0039] S54. Accumulate the simulated wear amounts of the tool in each time period to obtain the accumulated simulated wear amount of the tool ∑ WT Compare it with the preset standard accumulated wear amount EWth When ∑WT > EWth , the simulation analysis result of the accumulated tool wear state is abnormal.

[0040] Preferably, step S5 further includes:

[0041] S501. Further obtain the wear amount of the tool based on a single index in the current time period SWT 1 and SWT 2 ; where ; ;

[0042] S502. Compare the obtained wear amount of a single index with the preset single-period wear rate SWTth When SWT 1 > SWTth or SWT 2 > SWTth Output the simulation analysis result of the corresponding single-index wear state as abnormal.

[0043] Preferably, step S6 includes:

[0044] S61. When the simulation analysis result of the periodic tool wear state is abnormal, output a first warning message to the host computer;

[0045] S62. When the simulation analysis result of the accumulated tool wear state is abnormal, output a second warning message to the host computer.

[0046] The beneficial effects of the present invention are:

[0047] 1) High-speed data acquisition and processing are achieved through the heterogeneous multi-core embedded system installed on the numerical control equipment, enabling synchronous equipment status simulation and analysis during the machining process, ensuring that the simulation process is synchronized with the actual machining process, and promptly responding to abnormal conditions that occur to the cutting tool of the numerical control equipment during the machining process;

[0048] 2) Based on the proposed cutting tool wear state simulation model, on the basis of traditional "force-thermal" wear analysis, the influence of impact force is further introduced to truly simulate the wear condition of the cutting tool during the machining process, improving the accuracy of the simulation of the cutting tool state during the machining process of the numerical control equipment, being able to adapt to applications in different scenarios, and improving the reliability of numerical control machining management. Description of the Drawings

[0049] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0050] Figure 1 It is a method flow chart of a numerical control machining simulation method based on embedded for the embodiments of the present invention. Detailed Embodiments

[0051] The present invention is further described in combination with the following application scenarios.

[0052] See Figure 1 As shown in the embodiments, it shows a numerical control machining simulation method based on embedded, which is applied to an embedded processor and includes the following steps:

[0053] S1, Initialize the cutting tool feature information of the numerical control equipment;

[0054] S2, During the execution of the machining task, receive the cutting tool state information transmitted by the sensor, and digitally process the received cutting tool state information to obtain cutting tool state data, where the cutting tool state data includes cutting tool vibration signal data and AE signal data;

[0055] S3, Perform filtering and standardization processing on the obtained cutting tool state data, and further perform feature extraction to obtain cutting tool state feature data;

[0056] S4, Perform impact force simulation analysis according to the obtained cutting tool state feature data to obtain an impact force simulation analysis result, where the impact force simulation analysis result includes normal impact and abnormal impact;

[0057] S5, According to the impact force simulation analysis result, call the corresponding cutting tool wear state simulation model, and combine the currently obtained state feature data to perform cutting tool wear simulation analysis to obtain a cutting tool wear state simulation analysis result;

[0058] S6. When the simulation analysis result of the tool wear state shows an abnormality, send corresponding warning information to the host computer.

[0059] In the above embodiments of the present invention, 1) the high-speed acquisition and processing of data are realized through the heterogeneous multi-core embedded system installed on the numerical control equipment, and the synchronous simulation analysis of the equipment state during the machining process can be realized, ensuring that the simulation process is synchronized with the actual machining process and promptly responding to the abnormal conditions of the numerical control equipment tool during the machining process.

[0060] 2) Based on the proposed tool wear state simulation model, on the basis of the traditional "force-thermal" wear analysis, the impact force influence can be further introduced to truly simulate the wear situation of the tool during the machining process, improving the accuracy of the simulation and emulation of the tool state during the machining process of the numerical control equipment, being able to adapt to the applications in different application scenarios, and improving the reliability of the numerical control machining management.

[0061] In an optional implementation scenario, the embedded processor uses ARM Cortex-A53, which has a high main frequency and a multi-core architecture to support complex real-time data processing requirements. Among them, the embedded processor uses a heterogeneous multi-core architecture, allocating the real-time data acquisition task to the low-power core and the simulation calculation task to the high-performance core to improve the overall efficiency of the system.

[0062] Preferably, in step S1, the tool characteristic information of the numerical control equipment includes the basic parameters and machining parameters of the tool, including the tool material (including the wear resistance coefficient, thermal sensitivity coefficient, etc. of the material), dimensional parameters (length, diameter, rotation speed, mass, etc.), machining physical model parameters (tool cutting wear index, heat loss index, impact wear index, etc.), and the basic parameters of the tool required in the traditional "thermal-force" wear analysis model.

[0063] By obtaining the tool characteristic information of the numerical control equipment, it is used as the basic parameter for the tool state simulation in the subsequent machining process.

[0064] Preferably, step S2 includes:

[0065] S21. Establish a communication connection with the vibration sensor set on the tool and receive the tool vibration signal data collected by the vibration sensor in real time;

[0066] S22. Establish a communication connection with the acoustic emission sensor set on the tool and receive the AE signal data collected by the acoustic emission sensor.

[0067] In the process of traditional "thermal - force" wear analysis based on cutting tools, the wear state of the current cutting tool is usually analyzed based on the influence of cutting force and heat on the cutting tool. However, in the traditional analysis method, the considered factors are relatively single and cannot adapt to the complex situation of the cutting tool affected by the actual processing site during the actual machining process. Therefore, it is easy to cause the simulation results to be overly idealized in some areas, resulting in the disconnection between the simulation results and the actual machining situation, which affects the accuracy and adaptability of the simulation analysis.

[0068] In the embodiment of the present invention, by setting sensors at corresponding positions of the numerical control machining equipment, and the embedded system receives the vibration signal and AE (acoustic emission) signal of the cutting tool collected by the sensors during the machining process as the basis for tool state simulation. On the basis of the traditional cutting force analysis and heat analysis, the vibration characteristics of the cutting tool and the feedback characteristics under the high - frequency sound field can be further introduced as the basis to further simulate and analyze the impact force characteristics of the cutting tool, and further conduct wear analysis based on the impact force characteristics, which helps to improve the accuracy and adaptability of the cutting tool wear simulation.

[0069] Among them, through the AE signal, the cracks, embrittlement, etc. of the internal material of the cutting tool can be accurately feedback, so as to realize the characterization of the impact force characteristics inside the cutting tool.

[0070] Preferably, the state data further includes position data, cutting force data, cutting speed data and temperature data;

[0071] Step S2 further includes:

[0072] S23, establish a communication connection with the position sensor set on the numerical control machining table, and receive the tool position change data collected by the position sensor;

[0073] S24, receive the cutting force data generated by the cutting tool during the grinding process collected by the piezoelectric force sensor;

[0074] S25, receive the tool grinding area temperature change data collected by the thermocouple sensor.

[0075] In the above - mentioned embodiment of the present invention, in addition to the vibration signal data and AE signal data, relevant state data is also required for cutting force analysis and heat analysis. Therefore, by setting sensors at corresponding positions of the numerical control machining equipment to further obtain the state data required for simulation analysis, the real - time collected data can be used as the basis for simulation analysis, thereby improving the real - time level of simulation analysis.

[0076] Preferably, step S3 includes:

[0077] S31, perform filtering and calibration processing on the obtained state data to obtain the pre - processed state data;

[0078] S32. Extract feature points and feature parameters from the preprocessed state data to obtain state feature data; the feature parameters include the short-term change trend, change rate, and short-term maximum / minimum value of the state data.

[0079] Based on the acquired tool vibration signal and AE signal data, etc., first perform standardization processing such as filtering on the acquired data, which can organize the received discrete point data into continuous digital signal data, and eliminate the noise points contained therein through filtering to improve the quality of the state data. At the same time, based on the obtained state data (such as digital signal data, etc.), the corresponding feature parameters are extracted based on the signal data change characteristics within a certain time period (for example, the data of the last 1000 sampling points, or the data collected within the last 5-minute time period) for use as the basis for subsequent simulation analysis.

[0080] In the actual machining environment, electromagnetic interference in the on-site environment of the numerical control equipment and dynamic factors such as coolant during the machining process will have a noise impact on the vibration signal data collected by the sensor. Therefore, for the acquired state data, especially the vibration signal data, filtering processing is first performed to eliminate the noise impact in the data and improve the data quality.

[0081] Preferably, in step S31, the filtering process based on the obtained state data specifically includes:

[0082] For the vibration signal data obtained within a certain time period X(t) Perform EMD empirical mode decomposition to obtain multiple IMF components of the vibration signal data IMF 1 (t) , IMF 2 (t) … IMF N (t) and the residual r(t) ;

[0083] For each IMF component, calculate the division factor of each IMF component respectively, and the division factor calculation function used is:

[0084] ;

[0085] In the formula, HS(i) represents the division factor of the i-th IMF component, where i = 1, 2, … N , N represents the total number of IMF components, AIMF i (t) represents the i th tThe amplitude at a sampling moment, where t = 1, 2, … T , T represents the total duration of the signal, f i (t) represents the i th instantaneous frequency at the t th sampling moment in the th IMF component, i represents the average amplitude of each sampling moment of the β1 th IMF component, where 1<β1<2 , β2 represents a preset high-frequency sensitivity factor, where 0<β2<3 , β3 represents a preset pulse sensitivity factor, where 1<β3<2 ;

[0086] According to the division factor of each IMF component, obtain the division judgment threshold HST, where HST = , represents the average value of the division factors of each IMF component;

[0087] According to the division factor of each IMF component, when the division factor HS(i)> HST , then mark the i th IMF component as a high-frequency IMF, otherwise when the division factor HS(i)≤ HST , then mark the i th IMF component as a low-frequency IMF;

[0088] Perform adaptive filtering processing on the high-frequency IMF components, where the filtering processing function used is:

[0089] ;

[0090] In the formula, represents the amplitude at the j th sampling moment in the t th IMF component after filtering processing, where the j th IMF component belongs to the high-frequency component, IMF j (t) represents the amplitude at the j th sampling moment in the t th IMF component, TBS(IMF j (t)) represents morphological top-hat operation, , , P represents the set scale of the top-hat operation, 2<P<50 ,m represents the set local window size, where 2<m<5 ; μ represents the set adjustment factor, 1 < μ < 3 , γ represents the set filtering factor, where 0.5 < γ < 3 ;

[0091] For the low-frequency IMF components, a detection window is used to traverse the low-frequency IMF components to detect whether there is low-frequency noise interference. The detection function used is:

[0092] ;

[0093] where, Sim k (t) represents the detection value corresponding to the k th sampling moment in the t th IMF component, where the k th IMF component belongs to the low-frequency component, |IMF k (q)| represents the absolute value of the amplitude at the j th sampling moment in the t th IMF component, represents the average value of the squared amplitudes of each sampling moment in the j th IMF component, D represents the preset detection window size, STL represents the set standard energy value;

[0094] When there is Sim k (t)>0 , it is determined that the current low-frequency IMF component has low-frequency noise interference; otherwise, when there is no Sim k (t)>0 , it is determined that the current low-frequency IMF component has no low-frequency noise interference;

[0095] For the IMF components with low-frequency noise interference, low-frequency filtering processing is performed. The low-frequency filtering processing function used is:

[0096] ;

[0097] In the formula, represents the amplitude at the k th sampling moment in the t th IMF component after filtering processing, IMF k (t) represents the k th IMF component, and the tThe amplitude at a sampling moment IMF k (r) Indicates the k th IMF component and the r amplitude at the th sampling moment. k Indicates the average amplitude at each sampling moment in the a k (t) th IMF component. k Indicates the t th sampling moment and the ω1 slope at the 0.05 < ω1 < 0.2 th sampling moment in the ω2 th IMF component. 0.1 < ω2 < 0.5 Indicates the set waveform change factor, where ω3 Indicates the set low-frequency noise sensitivity factor, where 0.01 < ω3 < 0.1 ;

[0098] For low-frequency IMF components without low-frequency noise interference, no filtering is performed.

[0099] Based on the IMF components after filtering or without filtering and the residual r (t) perform reconstruction to obtain the vibration signal data after filtering.

[0100] Based on the vibration signal data after filtering, further perform calibration and standardization processing to obtain the preprocessed vibration signal data, and further perform feature point extraction and feature parameter extraction to obtain the feature data related to the vibration signal data.

[0101] In the processing environment of numerical control equipment, it is easy to be affected by the electromagnetic interference of the environment and the splashing of the coolant, so that the vibration signal data collected by the sensor is subject to noise interference. The above-mentioned embodiment of the present invention proposes a technical solution specifically for preprocessing the acquired vibration signal data, wherein EMD empirical mode decomposition is first performed based on the acquired vibration signal data to obtain the IMF component of the vibration signal. Based on the obtained multiple IMF components, based on the signal energy and time domain pulse characteristics, combined with the instantaneous frequency characteristics, the high and low frequency characteristics of the IMF components are comprehensively evaluated, and accurate high and low frequency division is performed on this basis; wherein, for the electromagnetic interference that the high-frequency IMF components may be subjected to, an adaptive filtering processing method is proposed to filter the high-frequency IMF components, wherein based on the morphological theory, the top hat characteristics of the high-frequency components are introduced, which can adaptively and effectively remove the pulse noise existing in the high-frequency IMF components, while retaining the characteristic information of the vibration signal feedback to the greatest extent. As for the low-frequency IMF component, the detection window is first used to detect the low-frequency noise (such as low-frequency noise caused by coolant splashing / impact, which usually produces 8-12Hz low-frequency interference signals) in the low-frequency IMF component based on the morphological characteristics of the low-frequency waveform. The noise interference in the low-frequency component can be accurately identified. For the part with interference, low-frequency noise filtering is further performed, which can adaptively eliminate the low-frequency noise interference and restore the characteristic signal, and can specifically remove the noise interference caused by the coolant environment. Finally, the high-frequency IMF component and the low-frequency IMF component after filtering are reconstructed to obtain the pre-processed vibration signal for subsequent further feature extraction processing as the basis for subsequent tool state simulation. By filtering the acquired vibration signal in the above manner, the noise generated by the two main interference sources in the CNC machining scene can be specifically eliminated, the data quality can be improved, and the adaptability and reliability of the subsequent tool state simulation can be indirectly improved.

[0102] Preferably, step S4 comprises:

[0103] S41, calculating the impact force simulation characteristic parameter of the current tool according to the state characteristic data of the obtained vibration signal data, wherein the impact force simulation characteristic parameter calculation function used is:

[0104] ;

[0105] In the formula, SHCK Indicates the impact force simulation characteristic parameter of the current time period, Z m Indicates the zero-crossing rate of the vibration signal in the current time period, a m-peak Indicates the maximum absolute value of the acceleration of the vibration signal in the current time period.a m-T Indicates the preset impact force threshold, ω m and ω x Indicates the preset sensitivity adjustment factor, σ Xm Indicates the standard deviation of the vibration signal amplitude in the current time period; mean |Xm| Indicates the average amplitude of the vibration signal in the current time period;

[0106] S42, simulating characteristic parameters according to the obtained impact force SHCK And the standard impact force set SHth For comparative analysis:

[0107] when SHCK ≥ SHth When , the current impact force simulation analysis result is abnormal impact;

[0108] Otherwise when SHCK < SHth , the current impact force simulation analysis result is normal impact.

[0109] Considering that the impact force characteristics of the tool during the CNC equipment processing have a direct impact on its wear state, when the tool is under normal impact force, it is necessary to consider the impact of the general vibration of the tool on tool wear (for example). At this time, the impact force of the tool is a relatively stable process, that is, there is a stable consumption. However, when the tool is affected by abnormal impact, the unpredictable impact on the tool can cause sudden consumption of the tool. Therefore, when performing real-time simulation analysis on the tool state, it is necessary to first perform impact force simulation analysis of the tool to quantify the impact force on the tool, and identify the type of impact the tool is currently subjected to based on the impact force simulation analysis results.

[0110] The above-mentioned embodiment of the present invention analyzes the impact force characteristics currently applied to the tool based on the vibration signal data of the tool. Through the proposed impact force simulation characteristic parameter calculation function, it is possible to quantify the impact force characteristics currently applied to the tool based on the vibration characteristics of the tool, accurately feedback the internal or external impact force applied to the tool, and identify the current impact state of the tool based on the obtained impact force simulation characteristic parameters.

[0111] Preferably, step S5 comprises:

[0112] S51, when the impact force simulation analysis result is a normal impact, calling the first tool wear state simulation model to perform tool wear simulation, wherein the first tool wear state simulation model used is:

[0113] ;

[0114] In the formula, WT represents the simulated wear amount of the tool in the current time period, mc1 represents the wear resistance coefficient of the tool material, F c represents the cutting force applied to the tool in the current time period; mc2 represents the thermal sensitivity coefficient of the tool material, v c represents the grinding speed of the tool in the current time period, mc3 represents the collision coefficient of the tool in the current time period, M eff represents the weight of the tool, a m-peak represents the maximum value of the absolute value of the vibration signal acceleration in the current time period, Xm max represents the maximum amplitude of the vibration signal in the current time period, ω Xm represents the frequency of the vibration signal in the current time period; nz represents the cutting wear index, mz represents the thermal loss wear index, pz represents the impact wear index;

[0115] S52. When the impact force simulation analysis result is an abnormal impact, call the second tool wear state simulation model to perform tool wear simulation. The second tool wear state simulation model used is:

[0116] ;

[0117] In the formula, WT represents the simulated wear amount of the tool in the current time period, mc1 represents the wear resistance coefficient of the tool material, F c represents the cutting force applied to the tool in the current time period; mc2 represents the thermal sensitivity coefficient of the tool material, v c represents the grinding speed of the tool in the current time period, mc3 represents the collision coefficient of the tool in the current time period, M eff represents the weight of the tool, a m-peak represents the maximum value of the absolute value of the vibration signal acceleration in the current time period, β AE represents the calibrated acoustic emission parameter, Y(i) represents the amplitude of the i-th sampling point of the AE signal in the current time period, where i = 1, 2, … NI,NI represents the total number of AE signal sampling points within the current time period, ωq AE represents the sampling frequency of the AE signal within the current time period, nz represents the cutting wear index, mz represents the thermal loss wear index, pz represents the impact wear index; qz represents the impact conversion factor; ω q and ω p represent the preset weight factors, where ω q +ω p =1 ;

[0118] S53, according to the obtained simulated wear amount of the tool in the current time period WT and the preset standard periodic wear amount Wth are compared. When WT > Wth , the simulation analysis result of the periodic tool wear state is abnormal;

[0119] S54, accumulate the simulated wear amounts of the tool in each time period to obtain the accumulated simulated wear amount of the tool ∑ WT and the preset standard accumulated wear amount EWth are compared. When ∑WT > EWth , the simulation analysis result of the accumulated tool wear state is abnormal.

[0120] In an alternative implementation scenario, the cutting wear index nz , the thermal loss wear index mz and the impact wear index pz are used to adjust the sizes of three parts in the tool wear state simulation model. For the acquisition of the above parameters, the wear state of the tool can be characterized first according to the single - index method. Based on the preset accumulated wear amount and combined with the wear cycle under the single index, the index corresponding to the index is adjusted. For example, when only considering cutting loss, record the cutting force data received by the tool, and according to the single index: WT = mc1 × F c accumulate the total wear amount of the tool until wear occurs, and adjust the cutting wear index nz according to the accumulated wear amount obtained from this experience, so that the value of the accumulated wear amount obtained from the same set of cutting force data is the preset accumulated wear amount. Similarly, using the same method, continue to adjust based on this preset accumulated wear amount and the single index WT = mc2 × (F c ×vc ) and Statistically analyze the actual cumulative wear amount, and adjust the corresponding heat loss wear index based on the difference between the actual cumulative wear amount at the time of wear occurrence and the preset cumulative wear amount mz and the impact wear index pz .

[0121] Considering that during the machining process of numerical control equipment, the impact force characteristics received by the tool have a direct impact on its wear state. When the tool is under the influence of normal impact force, the general vibration situation of the tool needs to be considered for its impact on tool wear (for example). At this time, the impact force received by the tool is a relatively stable process, that is, there is a stable consumption. However, when the tool is under the influence of abnormal impact, the non-predicted impact received by the tool at this time can bring a sudden change in consumption to the tool. In the above-mentioned embodiment of the present invention, when performing the simulation analysis of the tool wear state, based on the current impact state recognition result of the tool, the corresponding tool wear state simulation model is called to simulate the current wear state of the tool, improving the accuracy of the tool wear simulation analysis. Among them, for the normal impact state, at this time the tool is in the conventional impact force consumption. Therefore, based on the obtained signal characteristics added to the simulation model, the consumption of the tool in the three dimensions of cutting force, heat, and impact force can be statistically analyzed, so as to feedback the current wear state of the tool. For the abnormal impact state, at this time the tool may be affected by abnormal impacts from outside or inside the tool. Therefore, the vibration signal characteristics are used as the feedback of the external impact of the tool (manifested as a drastic vibration signal), and the AE signal characteristics are used as the internal impact feedback (manifested as a drastic AE signal) to further characterize the impact force wear received by the tool. The proposed simulation model can accurately feedback the abnormal impact based on the vibration signal data characteristics and AE signal data characteristics, and combine the cutting force and heat wear amounts for comprehensive statistics to simulate the current wear state of the tool, and can specifically quantify the characteristics when an abnormal impact occurs (such as an abnormal impact caused by a crack inside the tool or an external intense impact, etc.), improving the accuracy of the tool wear simulation. Based on the wear amount obtained from the simulation model as the basis, further perform real-time wear and cumulative wear analysis, which can accurately warn and identify real-time abnormal situations or cumulative abnormal situations, and help improve the reliability of the numerical control machine tool state simulation analysis.

[0122] Preferably, step S5 further includes:

[0123] S501, further obtain the wear amount of the tool based on a single index within the current time period SWT 1 and SWT 2; among which ; ;

[0124] S502, compare the obtained single - index wear amount with the preset single - cycle wear rate SWTth and when SWT 1 > SWTth or SWT 2 > SWTth output the corresponding single - index wear - state simulation analysis result as abnormal.

[0125] In addition to the comprehensive wear analysis, the embedded processor can also perform simulation analysis based on the wear state of the tool in a single index, so as to obtain the corresponding single - index wear - state simulation analysis result.

[0126] Similar to the traditional cutting - force analysis or heat analysis, through the wear analysis of a single index, it can also play a role similar to the traditional tool - wear analysis as a further reference.

[0127] Preferably, step S6 includes:

[0128] S61, when the cycle tool wear - state simulation analysis result is abnormal, output the first warning message to the host computer;

[0129] S62, when the cumulative tool wear - state simulation analysis result is abnormal, output the second warning message to the host computer.

[0130] When the cycle tool wear - state simulation analysis result is abnormal, it indicates that the current real - time machining state of the current CNC machining equipment has an abnormality, and real - time warning is required to avoid damage to the CNC equipment and affect the machining quality due to the further continuation of the abnormal state. When the cumulative tool wear - state simulation analysis result is abnormal, it indicates that the current tool is in the wear - out period prone to abnormal problems, and the tool of the CNC equipment needs to be replaced in time to avoid the occurrence of abnormal situations.

[0131] It should be noted that in each embodiment of the present invention, each functional unit / module can be integrated in a processing unit / module, or each unit / module can exist physically alone, or two or more units / module can be integrated in one unit / module. The above - mentioned integrated unit / module can be implemented in the form of hardware or in the form of software functional unit / module.

[0132] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiments can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. The computer-readable medium includes computer storage media and communication media, where the communication media includes any medium that facilitates the transfer of a computer program from one place to another. The storage media can be any available medium that can be accessed by a computer. The computer-readable medium can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the protection scope of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An embedded CNC machining simulation method, applied to an embedded processor, characterized in that: It includes the following steps: S1 Initialize the tool feature information of the numerical control equipment; S2 During the execution of the machining task, receive the tool status information transmitted by the sensor, and digitally process the received tool status information to obtain tool status data, where the tool status data includes tool vibration signal data and AE signal data; S3 Perform filtering and normalization processing on the obtained tool status data, and further perform feature extraction to obtain tool status feature data; S4 Perform impact force simulation analysis based on the obtained tool status feature data to obtain the impact force simulation analysis result, where the impact force simulation analysis result includes normal impact and abnormal impact; S5 According to the impact force simulation analysis result, call the corresponding tool wear status simulation model, and combine the currently obtained status feature data to perform tool wear simulation analysis to obtain the tool wear status simulation analysis result; S6 When the tool wear status simulation analysis result is abnormal, send a corresponding warning message to the host computer; Among them, step S4 includes: S41 Calculate the impact force simulation characteristic parameter of the current tool according to the status characteristic data of the obtained vibration signal data, where the calculation function of the impact force simulation characteristic parameter used is: Where SHCK represents the impact force simulation characteristic parameter of the current time period, Z m Indicates the zero-crossing rate of the vibration signal in the current time period, a m-peak Indicates the maximum absolute value of the acceleration of the vibration signal in the current time period, a m-T Represents the preset impact force threshold, ω m and ω x Represents the preset sensitivity adjustment factor, σ Xm Indicates the standard deviation of the vibration signal amplitude in the current time period; mean |Xm| Indicates the average amplitude of the vibration signal in the current time period; S42 Compare and analyze the obtained impact force simulation characteristic parameter SHCK with the set standard impact force SHth: When SHCK≥SHth, the current impact force simulation analysis result is an abnormal impact; Otherwise, when SHCK<SHth, the current impact force simulation analysis result is a normal impact.

2. The embedded CNC machining simulation method according to claim 1, characterized in that: Step S2 includes: S21 Establish a communication connection with the vibration sensor set on the tool, and receive the tool vibration signal data collected by the vibration sensor in real time; S22 Establish a communication connection with the acoustic emission sensor set on the tool, and receive the AE signal data collected by the acoustic emission sensor.

3. The embedded CNC machining simulation method according to claim 2 is characterized in that: The status data also includes position data, cutting force data, cutting speed data, and temperature data; Step S2 also includes: S23 Establish a communication connection with the position sensor set on the numerical control workbench, and receive the tool position change data collected by the position sensor; S24 Receive the cutting force data generated by the tool during the grinding process collected by the piezoelectric force sensor; S25 Receive the tool grinding area temperature change data collected by the thermocouple sensor.

4. The embedded CNC machining simulation method according to claim 2 is characterized in that: Step S3 includes: S31 Perform filtering and calibration processing on the obtained status data to obtain the preprocessed status data; S32 Perform feature point extraction and feature parameter extraction on the preprocessed status data to obtain status feature data; where the feature parameters include the short-term change trend, change rate, and short-term maximum / minimum value of the status data.

5. The embedded CNC machining simulation method according to claim 1, characterized in that: Step S5 includes: S51 When the impact force simulation analysis result is a normal impact, call the first tool wear status simulation model to perform tool wear simulation, where the first tool wear status simulation model used is: Where WT represents the simulated wear of the tool in the current time period, mc1 represents the wear resistance coefficient of the tool material, and F c Indicates the cutting force on the tool in the current time period; mc2 indicates the thermal sensitivity coefficient of the tool material, v c Indicates the grinding speed of the tool in the current time period, mc3 indicates the collision coefficient of the tool in the current time period, M eff Indicates the weight of the tool, a m-peak Indicates the maximum absolute value of the acceleration of the vibration signal in the current time period, Xm max Indicates the maximum amplitude of the vibration signal in the current time period, ω Xm Indicates the frequency of the vibration signal in the current time period; nz indicates the cutting wear index, mz indicates the heat loss wear index, and pz indicates the impact wear index; S52 When the impact force simulation analysis result is an abnormal impact, call the second tool wear status simulation model to perform tool wear simulation, where the second tool wear status simulation model used is: Where WT represents the simulated wear of the tool in the current time period, mc1 represents the wear resistance coefficient of the tool material, and F c Indicates the cutting force on the tool in the current time period; mc2 indicates the thermal sensitivity coefficient of the tool material, v c Indicates the grinding speed of the tool in the current time period, mc3 indicates the collision coefficient of the tool in the current time period, M eff Indicates the weight of the tool, a m-peak Indicates the maximum absolute value of the acceleration of the vibration signal in the current time period, β AE represents the calibrated acoustic emission parameters, Y(i) represents the amplitude of the i-th sampling point of the AE signal in the current time period, where i = 1, 2, ... NI, NI represents the total number of AE signal sampling points in the current time period, ωq AE represents the sampling frequency of the AE signal in the current time period, nz represents the cutting wear index, mz represents the heat loss wear index, pz represents the impact wear index; qz represents the impact conversion factor; Xm max Indicates the maximum amplitude of the vibration signal in the current time period, ω Xm Indicates the frequency of the vibration signal in the current time period; ω q and ω p Represents the preset weight factor, where ω q +ω p =1; S53 compares the simulated wear amount WT of the tool in the current time period with the preset standard cycle wear amount Wth, and when WT>Wth, the simulation analysis result of the cycle tool wear state is abnormal; S54 accumulates the simulated wear of the tool in each time period, obtains the accumulated simulated wear ∑WT of the tool and compares it with the preset standard accumulated wear EWth. When ∑WT>EWth, the accumulated tool wear state simulation analysis result is abnormal.

6. The embedded-based numerical control machining simulation method according to claim 5 is characterized in that: Step S5 also includes: S501 further obtains the wear amount SWT1 and SWT2 of the tool based on a single indicator in the current time period; SWT2=mc2×(F c ×v c ) mz ; mc1 represents the wear resistance coefficient of the tool material, mc2 represents the thermal sensitivity coefficient of the tool material, F c Indicates the cutting force on the tool in the current time period, nz indicates the cutting wear index, v c It represents the grinding speed of the tool in the current time period, and mz represents the heat loss wear index; S502 compares the acquired single indicator wear amount with the preset single cycle wear rate SWTth. When SWT1>SWTth or SWT2>SWTth, the corresponding single indicator wear state simulation analysis result is output as abnormal.

7. The embedded-based numerical control machining simulation method according to claim 5, characterized in that: Step S6 includes: S61: when the simulation analysis result of the periodic tool wear state is abnormal, outputting the first warning information to the upper computer; S62: When the simulation analysis result of the accumulated tool wear state is abnormal, a second warning message is output to the upper computer.

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