Embedded numerical control machining simulation method

Abstract

The invention provides an embedded numerical control machining simulation method, which is applied to an embedded processor and comprises the following steps of: S1, initializing cutter feature information of numerical control equipment; s2, receiving the cutter state information transmitted by the sensor, and performing digital processing on the received cutter state information to obtain cutter state data; s3, feature extraction is carried out according to the obtained cutter state data, and cutter state feature data are obtained; s4, impact force simulation analysis is conducted according to the obtained cutter state characteristic data, and an impact force simulation analysis result is obtained; s5, calling a corresponding tool wear state simulation model to perform tool wear simulation analysis according to the impact force simulation analysis result to obtain a tool wear state simulation analysis result; and S6, when the cutter wear state simulation analysis result is abnormal, corresponding early warning information is sent to an upper computer. According to the invention, the real-time performance and the reliability of the processing state analog simulation of the numerical control equipment can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of embedded system auxiliary manufacturing of numerical control processing equipment, in particular to an embedded-based numerical control processing simulation method. 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 precision, efficiency and stability in CNC machining processes are also increasing. In order to ensure production quality and reduce unplanned downtime of equipment, how to effectively monitor the tools during the machining process has become one of the research focuses that has attracted widespread attention in the industry.

[0003] At present, most simulation technologies for CNC machining usually have the problem of insufficient real-time performance, that is, they are usually based on offline data (status data collected by CNC equipment in a static state) to realize equipment status analysis, and cannot accurately simulate the status of CNC equipment (such as the real-time wear status of machining tools) during machining. There is a lag in real-time monitoring, which is not conducive to effective early warning of the status of CNC machining equipment in the first time, and cannot respond in time to the decline in machining quality and equipment damage caused by abnormalities in CNC equipment tools and other components, affecting the reliability of CNC machining management. Summary of the invention

[0004] In view of the above-mentioned technical problem that it is impossible to provide a real-time and accurate early warning of the status of a CNC machining device, the present invention aims to provide an embedded-based CNC machining simulation method.

[0005] The purpose of the present invention is achieved by the following technical solutions: The present invention proposes an embedded-based numerical control machining simulation method, which is applied to an embedded processor and includes the following steps: S1, initializing the tool feature information of the CNC equipment; S2, during the execution of the machining task, receiving tool status information transmitted by the sensor, and digitally processing the received tool status information to obtain tool status data, wherein the tool status data includes tool vibration signal data and AE signal data; S3, filtering and standardizing the acquired tool state data, and further extracting features to obtain tool state feature data; S4, performing impact force simulation analysis according to the obtained tool state characteristic data to obtain an impact force simulation analysis result, wherein the impact force simulation analysis result includes normal impact and abnormal impact; S5, according to the impact force simulation analysis result, calling the corresponding tool wear state simulation model, combining the currently acquired state feature data to perform tool wear simulation analysis, and obtaining the tool wear state simulation analysis result; S6, when the simulation analysis result of tool wear status is abnormal, a corresponding warning message is sent to the upper computer.

[0006] Preferably, step S2 comprises: S21, establishing a communication connection with a vibration sensor disposed on the tool, and receiving in real time tool vibration signal data collected by the vibration sensor in real time; S22, establishing a communication connection with an acoustic emission sensor disposed on the tool, and receiving AE signal data collected by the acoustic emission sensor.

[0007] Preferably, the state data also includes position data, cutting force data, cutting speed data and temperature data; Step S2 also includes: S23, establishing a communication connection with a position sensor provided on the CNC machining table, and receiving tool position change data collected by the position sensor; S24, receiving cutting force data generated by the tool during the grinding process collected by the piezoelectric force sensor; S25, receiving the temperature change data of the tool grinding area collected by the thermocouple sensor.

[0008] Preferably, step S3 comprises: S31, performing filtering and correction processing according to the obtained state data to obtain pre-processed state data; S32, extracting feature points and feature parameters based on the preprocessed state data to obtain state feature data; wherein the feature parameters include the short-term change trend, change rate, and short-term maximum / minimum of the state data.

[0009] Preferably, step S4 comprises: 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: ; 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, oh m and oh x Indicates the preset sensitivity adjustment factor, s 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, simulating characteristic parameters according to the obtained impact force SHCK And the standard impact force set SHth For comparative analysis: when SHCK≥SHth When , the current impact force simulation analysis result is abnormal impact; Otherwise when SHCK <SHth , the current impact force simulation analysis result is normal impact.

[0010] Preferably, step S5 comprises: 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: ; In the formula, WT Indicates the simulated wear of the tool in the current time period. mc1 Indicates the wear resistance coefficient of the tool material, F c Indicates the cutting force applied to the tool during 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, oh Xm Indicates the frequency of the vibration signal in the current time period; new zealand represents the cutting wear index, mz represents the heat loss wear index, pz Indicates the impact wear index; S52, when the impact force simulation analysis result is abnormal impact, calling the second tool wear state simulation model to perform tool wear simulation, wherein the second tool wear state simulation model used is: ; In the formula, WT Indicates the simulated wear of the tool in the current time period. mc1 Indicates the wear resistance coefficient of the tool material, F c Indicates the cutting force applied to the tool during 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) Indicates the first AE signal in the current time period. i The amplitude of the sampling points, i=1,2,…NI, NI Indicates the total number of AE signal sampling points in the current time period. oh AE Indicates the sampling frequency of the AE signal in the current time period, new zealand represents the cutting wear index, mz represents the heat loss wear index, pz Indicates the impact wear index; qz represents the impact conversion factor; oh q and oh p represents the preset weight factor, where oh q +oh p =1 ; S53, based on the obtained simulated wear amount of the tool in the current time period WT Compared with the preset standard cycle wear With For comparison, WT>Wth ,The simulation analysis result of the periodic tool wear state is abnormal; S54, accumulating the simulated wear amount of the tool in each time period to obtain the accumulated simulated wear amount of the tool ∑ WT Compared with the preset standard cumulative wear EWth For comparison, ∑WT>EWth , the simulation analysis result of the cumulative tool wear state is abnormal.

[0011] Preferably, step S5 further includes: S501, further obtaining the wear amount of the tool based on a single indicator in the current time period SWT 1 and SWT 2 ;in ; ; S502, the acquired single index wear amount and the preset single cycle wear rate are combined SWTth For comparison, SWT 1 >SWTth or SWT 2 >SWTth When , the simulation analysis result of the single indicator wear state corresponding to the output is abnormal.

[0012] Preferably, step S6 comprises: 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 cumulative tool wear state simulation analysis result is abnormal, output a second warning information to the upper computer.

[0013] The beneficial effects of the present invention are: 1) The heterogeneous multi-core embedded system on the CNC equipment can realize high-speed data acquisition and processing, realize the synchronous equipment status simulation analysis during the processing, ensure the synchronization of the simulation process with the actual processing process, and respond to abnormal conditions of the CNC equipment tool during the processing in a timely manner; 2) Based on the proposed tool wear state simulation model, the impact force effect can be further introduced to truly simulate the wear of the tool during the machining process based on the traditional "force-heat" wear analysis, which improves the accuracy of the simulation of the tool state during the CNC equipment machining process, can adapt to the application in different application scenarios, and improve the reliability of CNC machining management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0015] Figure 1 The present invention is a flowchart of an embedded-based numerical control machining simulation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described in conjunction with the following application scenarios.

[0017] See also Figure 1 As shown in the embodiment, a numerical control machining simulation method based on embedded system is shown, which is applied to an embedded processor and includes the following steps: S1, initializing the tool feature information of the CNC equipment; S2, during the execution of the machining task, receiving tool status information transmitted by the sensor, and digitally processing the received tool status information to obtain tool status data, wherein the tool status data includes tool vibration signal data and AE signal data; S3, filtering and standardizing the acquired tool state data, and further extracting features to obtain tool state feature data; S4, performing impact force simulation analysis according to the obtained tool state characteristic data to obtain an impact force simulation analysis result, wherein the impact force simulation analysis result includes normal impact and abnormal impact; S5, according to the impact force simulation analysis result, calling the corresponding tool wear state simulation model, combining the currently acquired state feature data to perform tool wear simulation analysis, and obtaining the tool wear state simulation analysis result; S6, when the simulation analysis result of tool wear status is abnormal, a corresponding warning message is sent to the upper computer.

[0018] The above-mentioned implementation mode of the present invention 1) realizes high-speed data collection and processing through the heterogeneous multi-core embedded system installed on the CNC equipment, can realize synchronous equipment status simulation analysis during the processing process, ensure the synchronization of the simulation process with the actual processing process, and promptly respond to abnormal conditions of the CNC equipment tool during the processing process.

[0019] 2) Based on the proposed tool wear state simulation model, the impact force effect can be further introduced to truly simulate the wear of the tool during the machining process based on the traditional "force-heat" wear analysis, which improves the accuracy of the simulation of the tool state during the CNC equipment machining process, can adapt to the application in different application scenarios, and improve the reliability of CNC machining management.

[0020] In an optional implementation scenario, the embedded processor uses ARM Cortex-A53, which has a high main frequency and multi-core architecture to support complex real-time data processing requirements. The embedded processor uses a heterogeneous multi-core architecture to allocate real-time data acquisition tasks to low-power cores and simulation computing tasks to high-performance cores to improve the overall efficiency of the system.

[0021] Preferably, in step S1, the CNC equipment tool feature information includes basic parameters and processing parameters of the tool, including tool material (including material wear resistance coefficient, thermal sensitivity coefficient, etc.), size parameters (length, diameter, rotation speed, mass, etc.), processing physical model parameters (tool cutting wear index, heat loss index, impact wear index, etc.), and basic parameters of the tool required in the traditional "thermal-mechanical" wear analysis model.

[0022] By obtaining the tool feature information of the CNC equipment, it serves as the basic parameter for tool state simulation in the subsequent processing process.

[0023] Preferably, step S2 comprises: S21, establishing a communication connection with a vibration sensor disposed on the tool, and receiving in real time tool vibration signal data collected by the vibration sensor in real time; S22, establishing a communication connection with an acoustic emission sensor disposed on the tool, and receiving AE signal data collected by the acoustic emission sensor.

[0024] Taking into account the traditional tool-based "thermal-mechanical" wear analysis process, the current tool wear state is usually analyzed based on the cutting force and heat effects on the tool. However, the traditional analysis method considers relatively single factors and cannot adapt to the situation in which the tool is affected by the complex conditions of the processing site during the actual processing. Therefore, it is easy to cause the simulation results to be over-idealized, which makes the simulation results out of touch with the actual processing conditions, affecting the accuracy and adaptability of the simulation analysis.

[0025] In the implementation manner of the present invention, by arranging sensors at corresponding positions of the CNC machining equipment, and the embedded system receives the vibration signals and AE (acoustic emission) signals of the tool collected by the sensors during the machining process as the basis for tool state simulation, it is possible to further introduce the vibration characteristics of the tool and the feedback characteristics in the high-frequency sound field as a basis in addition to the traditional cutting force analysis and heat analysis, further simulate the impact force characteristics of the tool, and further perform wear analysis based on the impact force characteristics, which helps to improve the accuracy and adaptability of tool wear simulation.

[0026] Among them, the AE signal can provide accurate feedback on cracks, brittleness, etc. of the internal material of the tool, thereby realizing the characterization of the impact force characteristics inside the tool.

[0027] Preferably, the state data also includes position data, cutting force data, cutting speed data and temperature data; Step S2 also includes: S23, establishing a communication connection with a position sensor provided on the CNC machining table, and receiving tool position change data collected by the position sensor; S24, receiving cutting force data generated by the tool during the grinding process collected by the piezoelectric force sensor; S25, receiving the temperature change data of the tool grinding area collected by the thermocouple sensor.

[0028] In the above-mentioned embodiment of the present invention, in addition to vibration signal data and AE signal data, relevant status data is also needed for cutting force analysis and heat analysis. Therefore, by setting sensors at corresponding positions of CNC machining equipment to further obtain status data required for simulation analysis, the data collected in real time can be used as the basis of simulation analysis, thereby improving the real-time level of simulation analysis.

[0029] Preferably, step S3 comprises: S31, performing filtering and correction processing according to the obtained state data to obtain pre-processed state data; S32, extracting feature points and feature parameters based on the preprocessed state data to obtain state feature data; wherein the feature parameters include the short-term change trend, change rate, and short-term maximum / minimum of the state data.

[0030] According to the acquired tool vibration signal and AE signal data, etc., the acquired data is first subjected to standardization processing such as filtering, so that the received discrete point data can be organized into continuous digital signal data, and the noise points contained therein can be eliminated by 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 characteristic parameters are extracted based on the signal data change characteristics within a period of time (such as the data of the most recent 1000 sampling points, or the data collected within the most recent 5 minutes) to serve as the basis for subsequent simulation analysis.

[0031] In a real processing environment, electromagnetic interference in the field environment of CNC equipment and dynamic factors such as coolant during processing will produce noise on the vibration signal data collected by the sensor. Therefore, filtering is first performed on the acquired status data, especially the vibration signal data, to eliminate the noise effect in the data and improve the data quality.

[0032] Preferably, in step S31, filtering is performed according to the obtained state data, specifically including: To obtain vibration signal data within a period of time X(t) Perform EMD empirical mode decomposition to obtain multiple IMF components of vibration signal data IMF 1 (t) , IMF 2 (t) … IMFN (t) and the residual r(t) ; For each IMF component, the partitioning factor of each IMF component is calculated respectively, and the partitioning factor calculation function used is: ; In the formula, HS (i) represents the partitioning factor of the ith IMF component, where i=1,2,…N , N Indicates the total number of IMF components, AIMF i (t) indicates the i IMF component t The amplitude at the sampling moment, where t=1,2,…T , T represents the total duration of the signal, f i (t) Indicates i IMF component t The instantaneous frequency at the sampling moment, Indicates i The average amplitude of each IMF component at each sampling time, β1 Represents the preset high frequency sensitivity factor, where 1<β1<2 , β2 represents the preset pulse sensitivity factor, where 0<β2<3 , β3 represents the preset weight factor, where 1<β3<2 ; According to the division factor of each IMF component, the division judgment threshold HST is obtained, where HST= , represents the average value of the partitioning factors of each IMF component; According to the partitioning factors of each IMF component, when the partitioning factor HS(i)>HST , then mark the i IMF components are high-frequency IMFs, otherwise when the partition factor HS (i) ≤ HST , then mark the i The IMF components are low-frequency IMFs; Adaptive filtering is performed on the high-frequency IMF component, and the filtering function used is: ; In the formula, Represents the first j IMF component t The amplitude at the sampling moment, where j The IMF components belong to high-frequency components. IMFj (t) Indicates j IMF component t The amplitude at each sampling moment, TBS (IMF j (t)) represents the morphological top-hat operation, , , P Indicates the set top hat operation scale, 2<P<50 , m Indicates the set local window size, where 2<m<5 ; m Indicates the set adjustment factor, 1<μ<3 , c Represents the set filter factor, where 0.5<γ< 3 ; For the low-frequency IMF component, a detection window is used to traverse the low-frequency IMF component to detect whether there is low-frequency noise interference. The detection function used is: ; in, Sim k (t) Indicates the corresponding k IMF component t The detection value at the sampling moment, where k The IMF components belong to low-frequency components. |IMF k (q)| Indicates j IMF component t The absolute value of the amplitude at each sampling moment, Indicates j The average value of the square of the amplitude at each sampling time in the IMF component, D Indicates the preset detection window size, STL Indicates the set standard energy value; When there is Sim k (t)>0 When , it is judged that the current low-frequency IMF component has low-frequency noise interference; otherwise, when there is no Sim k (t)>0 When , it is determined that the current low-frequency IMF component does not have low-frequency noise interference; Low-frequency filtering is performed on the IMF component with low-frequency noise interference, and the low-frequency filtering function used is: ; In the formula, Represents the first k IMF component t The amplitude at each sampling moment, IMF k (t) Indicates k IMF component t The amplitude at each sampling moment, IMF k (r) Indicates k IMF component r The amplitude at each sampling moment, Indicates k The average amplitude of each sampling moment in the IMF component, a k (t) Indicates k IMF component t The slope at the sampling moment, ω1 Represents the set waveform change factor, where 0.05<ω1<0.2 , ω2 Represents the set low-frequency noise sensitivity factor, where 0.1<ω2<0.5 , ω3 represents the set attenuation factor, where 0.01<ω3<0.1 ; For low-frequency IMF components without low-frequency noise interference, no filtering is performed; Based on the IMF components with or without filtering , and the residual r(t) Reconstruction is performed to obtain the vibration signal data after filtering.

[0033] The vibration signal data after filtering is further corrected and standardized to obtain preprocessed vibration signal data, and feature point extraction and feature parameter extraction are further performed to obtain feature data related to the vibration signal data.

[0034] 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.

[0035] Preferably, step S4 comprises: 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: ; 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, oh m and oh x Indicates the preset sensitivity adjustment factor, s 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, simulating characteristic parameters according to the obtained impact force SHCK And the standard impact force set SHth For comparative analysis: when SHCK≥SHth When , the current impact force simulation analysis result is abnormal impact; Otherwise when SHCK <SHth , the current impact force simulation analysis result is normal impact.

[0036] 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.

[0037] 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.

[0038] Preferably, step S5 comprises: 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: ; In the formula, WT Indicates the simulated wear of the tool in the current time period. mc1 Indicates the wear resistance coefficient of the tool material, F cIndicates the cutting force applied to the tool during 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, oh Xm Indicates the frequency of the vibration signal in the current time period; new zealand represents the cutting wear index, mz represents the heat loss wear index, pz Indicates the impact wear index; S52, when the impact force simulation analysis result is abnormal impact, calling the second tool wear state simulation model to perform tool wear simulation, wherein the second tool wear state simulation model used is: ; In the formula, WT Indicates the simulated wear of the tool in the current time period. mc1 Indicates the wear resistance coefficient of the tool material, F c Indicates the cutting force applied to the tool during 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 Indicates the total number of AE signal sampling points in the current time period. oh AE Indicates the sampling frequency of the AE signal in the current time period, new zealand represents the cutting wear index, mz represents the heat loss wear index, pz Indicates the impact wear index; qzrepresents the impact conversion factor; oh q and oh p represents the preset weight factor, where oh q +oh p =1 ; S53, based on the obtained simulated wear amount of the tool in the current time period WT Compared with the preset standard cycle wear With For comparison, WT>Wth ,The simulation analysis result of the periodic tool wear state is abnormal; S54, accumulating the simulated wear amount of the tool in each time period to obtain the accumulated simulated wear amount of the tool ∑ WT Compared with the preset standard cumulative wear EWth For comparison, ∑WT>EWth , the simulation analysis result of the cumulative tool wear state is abnormal.

[0039] In an optional implementation scenario, the cutting wear index new zealand , Heat loss wear index mz and impact wear index pz It is used to adjust the size of the three parts in the tool wear state simulation model. For the acquisition of the above parameters, the tool wear state can be characterized by a single indicator. Based on the preset cumulative wear amount and the wear cycle under a single indicator, the index of the corresponding indicator can be adjusted. For example, when only considering the cutting loss, the cutting force data of the tool is recorded, and according to a single indicator: WT=mc1×F c To accumulate the total wear of the tool until wear occurs, adjust the cutting wear index based on the accumulated wear obtained from experience new zealand , so that the cumulative wear amount obtained based on the same group of cutting force data The value of is the preset cumulative wear amount. Similarly, using the same method, continue to use the preset cumulative wear amount and single indicator WT= mc2×(F c ×v c ) as well as The actual accumulated wear amount is counted, and the corresponding heat loss wear index is adjusted based on the difference between the actual accumulated wear amount and the preset accumulated wear amount when the wear state occurs. mz and impact wear index pz .

[0040] Considering that the impact force characteristics of the tool during the processing of CNC equipment have a direct impact on its wear state, when the tool is under the influence of normal impact force, it is necessary to consider the influence of the general vibration of the tool on the 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 non-predicted impact on the tool can bring about sudden consumption of the tool. In the above-mentioned embodiment of the present invention, when performing simulation analysis of the tool wear state, based on the current impact state identification result of the tool, the corresponding wear state simulation model is called to simulate the current wear state of the tool, thereby improving the accuracy of the tool wear simulation analysis. Among them, for the normal impact state, the tool is in the conventional impact force consumption at this time. 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, thereby feeding back the current wear state of the tool. In the abnormal impact state, the tool may be subjected to abnormal impact from outside or inside the tool. Therefore, the vibration signal characteristics are used as feedback of the external impact of the tool (expressed as a sudden change in vibration signal), and the AE signal characteristics are used as feedback of the internal impact (expressed as a sudden change in AE signal) to further characterize the impact wear of 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 to conduct comprehensive statistics to simulate the current wear state of the tool. It can especially comprehensively quantify the characteristics when abnormal impact occurs (for example, abnormal impact caused by cracks inside the tool, or external violent impact, etc.), thereby improving the accuracy of tool wear simulation. Based on the wear amount obtained by the simulation model, further real-time wear and cumulative wear analysis can be performed, which can accurately warn and identify real-time abnormal situations or cumulative abnormal situations, which helps to improve the reliability of CNC machine tool state simulation analysis.

[0041] Preferably, step S5 further includes: S501, further obtaining the wear amount of the tool based on a single indicator in the current time period SWT 1 and SWT 2 ;in ; ; S502, the acquired single index wear amount and the preset single cycle wear rate are combined SWTth For comparison, SWT 1 >SWTth or SWT 2 >SWTthWhen , the simulation analysis result of the single indicator wear state corresponding to the output is abnormal.

[0042] In addition to comprehensive wear analysis, the embedded processor can also perform simulation analysis based on the wear state of the tool at a single indicator, thereby obtaining the corresponding single indicator wear state simulation analysis results.

[0043] Similar to traditional cutting force analysis or heat analysis, wear analysis based on a single indicator can also provide similar analysis results to traditional tool wear analysis as a further reference.

[0044] Preferably, step S6 comprises: 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 cumulative tool wear state simulation analysis result is abnormal, output a second warning information to the upper computer.

[0045] When the simulation analysis result of the periodic tool wear state is abnormal, it indicates that the current real-time processing state of the current CNC machining equipment is abnormal, and a real-time early warning is required to avoid damage to the CNC equipment and affect the processing quality due to the further continuation of the abnormal state. When the simulation analysis result of the cumulative tool wear state is abnormal, it indicates that the current tool is in a wear period prone to abnormal problems, and the tool of the CNC equipment needs to be replaced in time to avoid abnormal situations.

[0046] It should be noted that each functional unit / module in each embodiment of the present invention may be integrated into one processing unit / module, or each unit / module may exist physically separately, or two or more units / modules may be integrated into one unit / module. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of software functional unit / module.

[0047] Through the description of the above implementation modes, it can be clearly understood by those skilled in the art 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: an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a 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 embodiment 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. Computer-readable media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a computer. Computer-readable media can include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, 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.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should analyze that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. An embedded CNC machining simulation method, applied to an embedded processor, characterized in that: The steps include: S1, initializing the tool feature information of the CNC equipment; S2, during the execution of the machining task, receiving tool status information transmitted by the sensor, and digitally processing the received tool status information to obtain tool status data, wherein the tool status data includes tool vibration signal data and AE signal data; S3, filtering and standardizing the acquired tool state data, and further extracting features to obtain tool state feature data; S4, performing impact force simulation analysis according to the obtained tool state characteristic data to obtain an impact force simulation analysis result, wherein the impact force simulation analysis result includes normal impact and abnormal impact; S5, according to the impact force simulation analysis result, calling the corresponding tool wear state simulation model, combining the currently acquired state feature data to perform tool wear simulation analysis, and obtaining the tool wear state simulation analysis result; S6, when the simulation analysis result of tool wear status is abnormal, a corresponding warning message is sent to the upper computer.

2. The embedded CNC machining simulation method according to claim 1, characterized in that: Step S2 includes: S21, establishing a communication connection with a vibration sensor disposed on the tool, and receiving in real time tool vibration signal data collected by the vibration sensor in real time; S22, establishing a communication connection with an acoustic emission sensor disposed on the tool, and receiving 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 state data also includes position data, cutting force data, cutting speed data and temperature data; Step S2 also includes: S23, establishing a communication connection with a position sensor provided on the CNC machining table, and receiving tool position change data collected by the position sensor; S24, receiving cutting force data generated by the tool during the grinding process collected by the piezoelectric force sensor; S25, receiving the temperature change data of the tool grinding area collected by the thermocouple sensor.

4. The embedded CNC machining simulation method according to claim 2 is characterized in that: Step S3 includes: S31, performing filtering and correction processing according to the obtained state data to obtain pre-processed state data; S32, extracting feature points and feature parameters based on the preprocessed state data to obtain state feature data; wherein the feature parameters include the short-term change trend, change rate, and short-term maximum / minimum of the state data.

5. The embedded CNC machining simulation method according to claim 4 is characterized in that: Step S4 includes: 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: ； 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; S42, simulating characteristic parameters according to the obtained impact force SHCK And the standard impact force set SH For comparative analysis: when SHCK≥SHth When , the current impact force simulation analysis result is abnormal impact; Otherwise when SHCK <SHth , the current impact force simulation analysis result is normal impact.

6. The embedded-based numerical control machining simulation method according to claim 5 is characterized in that: Step S5 includes: 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: ； In the formula, WT Indicates the simulated wear of the tool in the current time period. mc1 Indicates the wear resistance coefficient of the tool material, F c Indicates the cutting force applied to the tool during 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 represents the cutting wear index, mz represents the heat loss wear index, pz Indicates the impact wear index; S52, when the impact force simulation analysis result is abnormal impact, calling the second tool wear state simulation model to perform tool wear simulation, wherein the second tool wear state simulation model used is: ； In the formula, WT Indicates the simulated wear of the tool in the current time period. mc1 Indicates the wear resistance coefficient of the tool material, F c Indicates the cutting force applied to the tool during 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 Indicates the total number of AE signal sampling points in the current time period. ωq AE Indicates 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 Indicates the impact wear index; qz represents the impact conversion factor; ω q and ω p represents the preset weight factor, where ω q +ω p =1 ; S53, based on the obtained simulated wear amount of the tool in the current time period WT Compared with the preset standard cycle wear Wt For comparison, WT>Wth ,The simulation analysis result of the periodic tool wear state is abnormal; S54, accumulating the simulated wear amount of the tool in each time period to obtain the accumulated simulated wear amount of the tool ∑WT Compared with the preset standard cumulative wear E For comparison, ∑WT>EWth , the simulation analysis result of the cumulative tool wear state is abnormal.

7. The embedded CNC machining simulation method according to claim 6 is characterized in that: Step S5 also includes: S501, further obtaining the wear amount of the tool based on a single indicator in the current time period SWT 1 and SWT 2 ;in ; ; S502, the acquired single index wear amount and the preset single cycle wear rate are combined SWT For comparison, SWT 1 > SWT or SWT 2 >SWTth When , the simulation analysis result of the single indicator wear state corresponding to the output is abnormal.

8. The embedded CNC machining simulation method according to claim 6 is 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 cumulative tool wear state simulation analysis result is abnormal, output a second warning information to the upper computer.

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