Detection method for cutter state of numerical control machine tool, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a method for detecting the cutter state of a numerical control machine tool, electronic equipment and a storage medium, relates to the technical field of machine tool machining, and can realize real-time detection of the cutter state under the condition that material machining is not affected, so that sudden cutter breaking events can be found in time, and the yield of machined materials is ensured. The method for detecting the cutter state of the numerical control machine tool comprises the steps that previous vibration time sequence data and current vibration time sequence data generated by a current cutter of the numerical control machine tool in the machining process are obtained; according to the previous vibration time sequence data and the current vibration time sequence data, respectively obtaining vibration characteristic matrixes corresponding to the previous vibration time sequence data and the current vibration time sequence data; according to the previous vibration time sequence data and the vibration characteristic matrix corresponding to the current vibration time sequence data, the characteristic difference degree of the current tool between the previous machining process and the current machining process is obtained; and determining the current state of the current cutter based on a comparison result of the feature difference degree and the current difference degree threshold value.

Description

Technical Field

[0001] The present application relates to the technical field of machine tool processing, and in particular to a method for detecting a tool state of a numerically controlled machine tool, an electronic device, and a storage medium. Background Art

[0002] CNC machine tools process materials through the tools installed on them. The status of the tools determines the quality and accuracy of the materials processed by the CNC machine tools. Therefore, it is necessary to detect the status of the tools to ensure the quality and accuracy of the processed materials.

[0003] At present, tool setting instruments are often embedded in CNC machine tools. After the tool processes a certain amount of material, the processing is suspended, and then the tool status is detected by the tool setting instrument. The above method has the following two problems: First, the tool setting instrument takes a long time to detect a single detection, which affects the material processing efficiency; second, the tool setting instrument detects the tool breakage at a certain interval, which may result in failure to detect the broken tool in time, thus affecting the quality and accuracy of the processed material. Summary of the invention

[0004] The embodiments of the present application provide a method, electronic device and storage medium for detecting the tool status of a CNC machine tool, which can realize real-time detection of the tool status without affecting material processing, so as to timely discover sudden tool breakage events and ensure the yield of the processed material.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting a tool state of a CNC machine tool, the method comprising:

[0006] Obtain the previous vibration time series data and the current vibration time series data generated by the current tool of the CNC machine tool during the machining process;

[0007] According to the previous vibration time series data and the current vibration time series data, respectively obtain the vibration feature matrix corresponding to the previous vibration time series data and the current vibration time series data; wherein the vibration feature matrix includes vibration time domain features and vibration frequency domain features;

[0008] According to the vibration feature matrix corresponding to the previous vibration time series data and the current vibration time series data, obtaining the current feature difference between the previous machining process and the current machining process of the current tool;

[0009] Based on the comparison result of the current feature difference and the current difference threshold, the current state of the current tool is determined.

[0010] In the embodiment of the present application, by obtaining the previous vibration time series data and the current vibration time series data generated by a certain tool in the CNC machine tool during the last two processing processes, and then respectively obtaining the time-frequency domain features corresponding to the previous vibration time series data and the current vibration time series data, and then through the time-frequency domain features corresponding to the previous vibration time series data and the current vibration time series data, the characteristic difference between the current vibration time series data and the previous vibration time series data can be more accurately determined, that is, the characteristic difference reflects the degree of change of the current vibration time series data compared to the previous vibration time series data. The smaller the characteristic difference, the smaller the degree of change of the current vibration time series data compared to the previous vibration time series data. On the contrary, the larger the characteristic difference, the larger the degree of change of the current vibration time series data compared to the previous vibration time series data. Finally, the above-mentioned characteristic difference is compared with the current difference threshold, so as to determine the current tool state corresponding to the above-mentioned tool when generating the current vibration data. In this method, the tool status can be detected in real time without affecting the processing of the CNC machine tool, so that sudden tool breakage events can be discovered in time, thereby ensuring the quality and accuracy of the processed materials.

[0011] Optionally, the obtaining, according to the previous vibration time series data and the current vibration time series data, vibration characteristic matrices corresponding to the previous vibration time series data and the current vibration time series data respectively includes:

[0012] Based on the first target frequency band, the previous vibration time series data and the current vibration time series data are filtered respectively;

[0013] Based on the filtered previous vibration time series data and the current vibration time series data, the vibration time domain features and the vibration frequency domain features corresponding to the previous vibration time series data and the current vibration time series data are extracted respectively.

[0014] In the embodiment of the present application, the first target frequency band can be considered as the working frequency band of the current tool in the processing stage. Then, the previous vibration timing data and the current vibration timing data generated in the last two processing processes are filtered respectively through the first target frequency band, which is beneficial to screen out other frequency bands irrelevant to the processing stage, so that the time-frequency domain features extracted for the processing stage based on the previous vibration timing data and the current vibration timing are more accurate.

[0015] Optionally, based on the filtered previous vibration time series data and the current vibration time series data, respectively extracting the vibration time domain features and the vibration frequency domain features corresponding to the previous vibration time series data and the current vibration time series data, including:

[0016] Determining the fundamental frequency of the time-frequency domain conversion according to the set rotation speed of the current tool;

[0017] Determining the maximum frequency multiple of the time-frequency domain conversion according to the number of blades of the current tool and the base frequency;

[0018] Determine at least two second target frequency bands for time-frequency domain conversion according to the base frequency and the maximum multiple frequency;

[0019] The filtered previous vibration time series data and the current vibration time series data are respectively converted into time-frequency domains, and the vibration frequency domain class features corresponding to each second target frequency band in the previous vibration time series data and the current vibration time series data are respectively obtained.

[0020] In an embodiment of the present application, based on the set rotation speed and number of blades of the current tool, multiple second target frequency bands containing effective signals of tool vibration can be determined, and then the frequency domain features in each second target frequency band can be obtained respectively, thereby improving the accuracy of the extracted frequency domain features.

[0021] Optionally, the determining the current state of the current tool based on the comparison result of the current feature difference and the current difference threshold includes:

[0022] Determine whether the current feature difference is greater than the current difference threshold;

[0023] If it is greater than the current difference threshold, it is determined that the current tool is in an abnormal state.

[0024] In the embodiment of the present application, if the difference between the current vibration time series data and the previous vibration time series data is large, it indicates that the current vibration time series data has a large change compared to the previous vibration time series data. If the tool state corresponding to the previous vibration time series data is normal, then it can be inferred that the tool state of the current tool when generating the current vibration time series data is abnormal. If the tool state corresponding to the previous vibration time series data is abnormal, if the current feature difference of the current tool is greater than the current difference threshold, then the tool state of the current tool when generating the current vibration time series data is still abnormal.

[0025] Optionally, after determining whether the current feature difference is greater than the current difference threshold, the method further includes:

[0026] If it is not greater than the current difference threshold, it is determined that the current tool is in a normal state.

[0027] In the embodiment of the present application, if the difference between the current vibration time series data and the previous vibration time series data is small, it indicates that the current vibration time series data has a small change compared to the previous vibration time series data, and if the tool state corresponding to the previous vibration time series data is normal, then it can be inferred that the tool state of the current tool when generating the current vibration time series data is normal. If the tool state corresponding to the previous vibration time series data is abnormal, if the current feature difference of the current tool is not greater than the current difference threshold, then the tool state of the current tool when generating the current vibration time series data is still normal.

[0028] Optionally, before determining the current state of the current tool based on the comparison result of the current feature difference and the current difference threshold, the method further includes:

[0029] Acquire multiple vibration time series data of the machining process of a previously set number of times;

[0030] Acquire a multiple vibration feature matrix corresponding to the multiple vibration time series data;

[0031] According to the vibration feature matrix corresponding to the multiple vibration time series data, obtaining the feature difference between two adjacent machining processes of the CNC machine tool during the previously set number of machining processes;

[0032] The current difference threshold is updated according to the feature difference between two adjacent times in the previously set number of processing steps.

[0033] In an embodiment of the present application, due to the differences in workpieces during the processing of the current tool, the current difference threshold may not be able to distinguish the state of the current tool well. Therefore, after each set number of processing times, the current difference threshold will be updated to improve the accuracy of tool state judgment.

[0034] Optionally, updating the current difference threshold according to the feature differences between two adjacent processings in previous processings includes:

[0035] Obtain the feature differences between two adjacent processings in the previous several times and construct a difference set;

[0036] Obtaining a threshold to be updated according to a mean and a variance of the feature differences corresponding to the difference set and a size parameter of the current tool;

[0037] Determine whether the threshold to be updated is greater than the initial difference threshold;

[0038] If it is greater than, the threshold to be updated is used as the current difference threshold;

[0039] If not, the initial difference threshold is used as the current difference threshold.

[0040] In an embodiment of the present application, for several previous machining processes, the overall similarity mean and overall similarity variance corresponding to multiple feature differences can be determined based on the feature differences between the vibration time series data generated by two adjacent machining processes. Then, the threshold to be updated is determined based on the overall similarity mean, the overall similarity variance, and the size parameters of the current tool. If the threshold to be updated is greater than the initial difference threshold obtained by previous training, the threshold to be updated is used as the new current difference threshold, and the detection adaptability is changed with a larger current difference threshold to better detect the tool state; if the threshold to be updated is not greater than the initial difference threshold obtained by previous training, the initial difference threshold is used as the new current difference threshold, that is, the lower limit value of the threshold is used to detect the tool state.

[0041] Optionally, before determining the current state of the current tool based on the comparison result of the current feature difference and the current difference threshold, the method further includes:

[0042] Acquire the time series data of all vibrations and tool status data generated by the tool during the historical machining process of the CNC machine tool;

[0043] Obtaining a vibration characteristic matrix corresponding to the previous vibration time series data;

[0044] According to the vibration feature matrix corresponding to the previous vibration time series data, the feature difference between two adjacent times in the historical processing of the CNC machine tool is obtained, and a set of previous feature difference is constructed;

[0045] With the purpose that the initial difference threshold can distinguish between an abnormal state and a normal state of a tool, the initial difference threshold is iteratively adjusted according to the previous feature difference sets and the tool state until the iteration ends, thereby obtaining the initial difference threshold;

[0046] The initial difference threshold is used as the initial value of the current difference threshold.

[0047] In an embodiment of the present application, an initial difference threshold that can effectively distinguish between abnormal and normal states of the tool can be learned based on the vibration time series data and tool status data generated by the tool during the historical processing of the CNC machine tool, and used as the initial value of the current difference threshold.

[0048] In a second aspect, an embodiment of the present application provides an electronic device, comprising a memory for storing a computer program and a processor for executing the computer program, wherein when the computer program is executed by the processor, the electronic device is triggered to execute the steps of the method described in any embodiment of the first aspect.

[0049] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the processor executes the steps of the method described in any embodiment of the first aspect.

[0050] It should be understood that the second to fourth aspects of the embodiments of the present application are consistent with the technical solutions of the first aspect of the embodiments of the present application, and the beneficial effects achieved by each aspect and the corresponding feasible implementation methods are similar and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A schematic flow chart of a method for detecting tool status of a CNC machine tool provided in an embodiment of the present application;

[0053] Figure 2 A schematic diagram of a flow chart of a method for extracting a vibration feature matrix provided in an embodiment of the present application;

[0054] Figure 3 A flowchart of a method for extracting frequency domain features provided in an embodiment of the present application;

[0055] Figure 4 A schematic flow chart of a method for determining a tool state of a current tool provided in an embodiment of the present application;

[0056] Figure 5 A flowchart of a method for determining a current difference threshold provided in an embodiment of the present application;

[0057] Figure 6 A flowchart of a method for updating a current difference threshold provided in an embodiment of the present application;

[0058] Figure 7 A flowchart of a method for updating a current difference threshold provided in an embodiment of the present application;

[0059] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to better understand the technical solution of this specification, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0061] It should be clear that the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0062] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit this specification. The singular forms of "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0063] At present, the tool setting instrument is often embedded in the CNC machine tool, and then the tool will stop processing after processing a certain amount of materials, and then use the tool setting instrument to detect the tool status. The above detection method has the following two problems: First, when using the tool setting instrument to detect the tool status, it is necessary to stop processing, and a single detection takes a long time, which affects the material processing efficiency; second, the tool setting instrument detects the tool status at a certain interval. If the tool breaks suddenly, it may not be detected in time, affecting the quality and accuracy of the processed materials, and increasing the defective rate of the materials.

[0064] In view of this, an embodiment of the present application provides a method for detecting the tool state of a CNC machine tool. In this method, vibration time series data of the tool in the CNC machine tool during the last two processing processes are acquired in real time, and then the characteristic difference between the time-frequency domain characteristics of the above vibration time series data is calculated, and the characteristic difference is compared with the current difference threshold to obtain the tool state detection result, that is, real-time detection of the tool state is achieved without affecting material processing, so as to timely discover sudden tool breakage events and ensure the yield of the processed material.

[0065] The technical solution protected by the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0066] See also Figure 1 , is a flow chart of a method for detecting tool status of a CNC machine tool provided in an embodiment of the present application. The method is applied to electronic devices, for example, the electronic devices may be personal computers (PCs), workstations, servers, etc., wherein the server may be a general-purpose computer, a dedicated computer, or a server cluster, and the present application does not impose any particular restrictions on this. The flow of the method is described as follows:

[0067] Step 101: Acquire the previous vibration time series data and the current vibration time series data generated by the current tool of the CNC machine tool during the machining process.

[0068] In the embodiment of the present application, a vibration sensor can be installed on the spindle of the CNC machine tool, and then the vibration sensor can collect the vibration time series data generated by the tool after the CNC machine tool is started. It should be understood that the vibration sensor collects three-axis vibration time series data, namely, the vibration time series data of the tool on the X-axis, the vibration time series data of the Y-axis, and the vibration time series data of the Z-axis.

[0069] The vibration timing data collected by the vibration sensor and the corresponding processing attribute information will be transmitted to the electronic device. For example, the processing attribute information may include the machine number, material serial number, tool number, program segment, timestamp, machine status (whether it is waiting for material, alarm, processing), machine operation task (whether it is inspection, material processing), etc. The electronic device can filter the received vibration timing data according to the above-mentioned processing attribute information, for example, filter out the vibration timing data of the program segment in processing, and then filter out the vibration timing data of the specific machine and specific tool number to be detected on this basis. Furthermore, the material serial number is used to filter out the previous vibration timing data and the current vibration timing data generated in the last two processing processes. Of course, it can also be combined with other attribute information for screening, and this application does not impose any special restrictions on this.

[0070] Step 102: According to the previous vibration time series data and the current vibration time series data, respectively obtain the vibration characteristic matrix corresponding to the previous vibration time series data and the current vibration time series data.

[0071] In the embodiment of the present application, the vibration feature matrix includes vibration time domain features and vibration frequency domain features. The vibration time domain features and vibration frequency domain features are described in detail below.

[0072] The vibration time domain features include: amplitude feature index, statistical feature index, waveform feature index and impact feature index. Among them, the amplitude feature index includes: peak value, valley value, peak-to-peak value; the statistical feature index includes: mean value, variance, standard deviation, root mean square value; the waveform feature index includes: kurtosis, skewness and waveform index; the impact feature index includes: peak index, pulse index and marginal coefficient. The following is a detailed introduction to the above thirteen vibration time domain features.

[0073] Vibration time domain feature 1:

[0074] Peak value: The maximum value of the signal, reflecting the maximum amplitude of tool vibration. The formula for the peak value is shown in (1):

[0075] f td1 =max(s(t))(1)

[0076] Among them, f td1 represents the peak value, and s(t) represents the vibration amplitude of any axis at the tth time stamp.

[0077] Vibration time domain feature 2:

[0078] Valley value: The minimum value of the signal, reflecting the minimum amplitude of tool vibration. The formula for the valley value is shown in (2):

[0079] f td2 =min(s(t))(2)

[0080] Among them, f td2 Indicates valley value.

[0081] Vibration time domain feature three:

[0082] Peak-to-peak value: The difference between the peak value and the valley value, which reflects the amplitude range of tool vibration.

[0083] The formula for the peak-to-peak value is shown in (3):

[0084] f td3 =f td1 -f td2 (3)

[0085] Among them, f td3 Indicates peak-to-peak value.

[0086] Vibration time domain feature 4:

[0087] Mean: represents the average amplitude of the signal, reflecting the average level of tool vibration. The formula for the mean is shown in (4):

[0088]

[0089] Among them, f td4 represents the mean, and T represents the total number of timestamps.

[0090] Vibration time domain feature 5:

[0091] Variance: measures the degree to which the signal deviates from the mean, reflecting the stability of tool vibration. The calculation formula of variance is shown in (5):

[0092]

[0093] Among them, f td5 Represents variance.

[0094] Vibration time domain feature six:

[0095] Standard deviation: The square root of the variance, which is also an indicator of tool vibration stability. The calculation formula of standard deviation is shown in (6):

[0096]

[0097] Among them, f td6Represents standard deviation.

[0098] Vibration time domain feature seven:

[0099] RMS value: The RMS value of a signal is often used to measure the energy of tool vibration. The RMS value calculation formula is shown in (7):

[0100]

[0101] Among them, f td7 Represents the root mean square value.

[0102] Vibration time domain feature eight:

[0103] Kurtosis: The concentration of signal distribution, describing the concentration near the tool vibration peak. The calculation formula of Kurtosis is shown in (8):

[0104]

[0105] Among them, f td8 Represents kurtosis.

[0106] Vibration time domain feature nine:

[0107] Skewness: The asymmetry of the signal distribution, describing the degree of deviation of the tool vibration. The calculation formula of skewness is shown in (9):

[0108]

[0109] Among them, f td9 Represents skewness.

[0110] Vibration time domain feature 10:

[0111] Waveform index: The ratio between the root mean square value of the signal and the absolute value of the average value of the signal. The waveform index can reflect the characteristics of the tool during processing as follows:

[0112] First, the asymmetry and nonlinearity of the signal waveform: The waveform index can reveal the symmetry and nonlinear characteristics of the signal waveform. If the signal is a completely symmetrical sine wave, then its waveform factor is close to 1. For the actual tool vibration signal, due to the possible asymmetric impact and irregular fluctuations, the waveform index will be greater than 1, and the larger the ratio, the more irregular the signal waveform.

[0113] Second, the complexity of tool vibration: A larger value of the waveform index usually means that the tool vibration signal contains more complex components, such as impact, noise or non-periodic components. This may be related to tool wear, uneven load during cutting or the dynamic characteristics of the machine tool.

[0114] Third, signal energy distribution: The waveform factor is also related to the distribution of signal energy. A higher waveform factor may indicate that the signal energy is concentrated at certain moments or frequencies, which may affect the cutting efficiency and processing quality of the tool.

[0115] The calculation formula of the waveform indicator is shown in formula (10):

[0116]

[0117] Among them, f td10 Indicates a waveform indicator.

[0118] Vibration time domain feature 11:

[0119] Peak index: reflects the impact component in tool vibration. If the peak index is large, it means that there is a large impact component in tool vibration, which may be related to tool wear, breakage or instability during processing. The calculation formula of the peak index is shown in (11):

[0120]

[0121] Among them, f td11 Indicates peak index.

[0122] Vibration time domain feature 12:

[0123] Pulse index: A relative measure of the maximum amplitude or intensity of a signal and the overall energy level or intensity. It reflects the relationship between the extreme value and the average value of the signal in amplitude or intensity. The pulse index can reflect the following characteristics of the tool during processing:

[0124] Tool wear status: As the tool gradually wears, parameters such as the maximum cutting force and maximum vibration amplitude during cutting may change. At the same time, the overall vibration or cutting force level may also be affected. Therefore, this ratio can be used as a sensitive indicator to monitor the wear status of the tool. When the ratio increases, it may indicate that tool wear has caused the maximum amplitude or intensity of the cutting process to increase relative to the overall energy level.

[0125] Stability of the cutting process: The stability of the cutting process is critical to the quality of tool processing. The RMS value of the signal reflects the overall vibration or cutting force level during the cutting process, while the maximum value of the absolute value of the signal reflects the extreme conditions during the cutting process. Therefore, this ratio can reflect the stability of the cutting process. If the ratio fluctuates greatly, it means that there are large amplitude or intensity changes in the cutting process, which may indicate that the cutting process is not stable enough.

[0126] Changes in cutting conditions: Changes in cutting conditions such as cutting speed, feed rate, cutting depth, etc. will affect the maximum cutting force, maximum vibration amplitude, and overall vibration or cutting force level during the cutting process. Therefore, by monitoring the changes in this ratio, the impact of changes in cutting conditions on the tool processing process can be discovered in a timely manner.

[0127] The calculation formula of the pulse index is shown in (12):

[0128]

[0129] Among them, f td12 Indicates the pulse indicator.

[0130] Vibration time domain feature 13:

[0131] Marginal coefficient: In the context of tool processing, the marginal coefficient reflects the relationship between the maximum amplitude in the tool vibration signal and the discrete degree of vibration. A higher marginal coefficient indicates that there are larger peaks in the tool vibration, which may mean that there are larger shocks or instabilities in the processing process relative to the average level of vibration. This indicator can be used to evaluate the wear state of the tool or abnormal conditions in the processing process because it can reveal the relationship between the extreme values ​​in the vibration signal and the overall vibration level. The calculation formula of the marginal coefficient is shown in (13):

[0132]

[0133] Among them, f td13 represents the marginal coefficient.

[0134] The vibration frequency domain features include: power spectrum feature indicators, statistical feature indicators, and waveform feature indicators. Among them, the power spectrum feature indicators include: power spectrum density peak, peak frequency; statistical feature indicators include: average power spectrum density, power spectrum density variance, frequency standard deviation, frequency root mean square; waveform feature indicators include: spectrum kurtosis, spectrum skewness, frequency skewness, frequency kurtosis, center of gravity frequency, D factor, E factor, G factor. The following is a detailed introduction to the above fourteen vibration frequency domain features.

[0135] Vibration frequency domain feature 1:

[0136] Average power spectral density: The average value of the power spectral density in the entire frequency domain reflects the overall energy distribution of tool vibration. The calculation formula of the average power spectral density is shown in (14):

[0137]

[0138] Among them, f tfd1represents the average power spectral density, p(k) represents the power spectral density of the kth frequency point, and K represents the total number of frequency points.

[0139] Vibration frequency domain feature 2:

[0140] Power spectral density variance: The degree of dispersion of power spectral density reflects the difference in energy distribution of different frequency components. The calculation formula of power spectral density variance is shown in (15):

[0141]

[0142] Among them, f tfd2 represents the power spectral density variance.

[0143] Vibration frequency domain feature three:

[0144] Spectral skewness: The asymmetry of the power spectrum density distribution reflects the degree of skewness of the tool vibration spectrum. The calculation formula of frequency deviation is shown in (16):

[0145]

[0146] Among them, f tfd3 Represents the spectral skewness.

[0147] Vibration frequency domain feature 4:

[0148] Spectral kurtosis: The sharpness of the power spectrum density distribution reflects the peak concentration of the tool vibration spectrum. The calculation formula of the spectrum skewness is shown in (17):

[0149]

[0150] Among them, f tfd4 represents the spectral kurtosis.

[0151] Vibration frequency domain feature 5:

[0152] Center of gravity frequency: The geometric center of the power spectrum, reflecting the main concentration area of ​​the tool vibration energy. The calculation formula of the center of gravity frequency is shown in (18):

[0153]

[0154] Among them, f tfd5 represents the center frequency, and f(k) represents the frequency of the kth frequency point.

[0155] Vibration frequency domain feature six:

[0156] Frequency standard deviation: The weighted standard deviation of the spectrum, which describes the width of the spectrum distribution of the signal. The calculation formula of the frequency standard deviation is shown in (19):

[0157]

[0158] Among them, f tfd6 Represents the frequency standard deviation.

[0159] Vibration frequency domain feature seven:

[0160] Frequency root mean square: The weighted root mean square value of the spectrum, which describes the energy center of the signal's spectrum distribution. The calculation formula for frequency root mean square is shown in (20):

[0161]

[0162] Among them, f tfd7 Represents the root mean square frequency.

[0163] Vibration frequency domain feature eight:

[0164] D factor: It is a dimensionless number used to describe the shape of the spectrum. It describes the distribution characteristics of the signal spectrum, especially the sharpness of the spectrum and the influence of high-frequency components. The calculation formula of D factor is shown in (21):

[0165]

[0166] Among them, f tfd8 Denotes the D factor.

[0167] Vibration frequency domain feature nine:

[0168] E factor: It is a dimensionless number used to describe the shape of the spectrum. It reflects the influence of the energy center and high-frequency components of the spectrum. The calculation formula of E factor is shown in (22):

[0169]

[0170] Among them, f tfd9 Indicates the E factor.

[0171] Vibration frequency domain feature 10:

[0172] G factor: It is a dimensionless number that can be used to standardize the spectral width of the signal so that it is independent of the center of gravity frequency. It reflects the relative width or variability of the spectral distribution. The calculation formula of G factor is shown in (23):

[0173]

[0174] Among them, f tfd10 Indicates the G factor.

[0175] Vibration frequency domain feature 11:

[0176] Frequency skewness: It represents the weighted skewness of the signal spectrum and describes the asymmetry of the spectrum distribution. The calculation formula of frequency skewness is shown in (24):

[0177]

[0178] Among them, f tfd11 Represents frequency skewness.

[0179] Vibration frequency domain feature 12:

[0180] Frequency kurtosis: describes the sharpness of the signal spectrum, that is, whether the spectrum distribution has a high peak. High kurtosis indicates that there are significant sharp components in the spectrum, which may correspond to shocks or transient events in the signal. The calculation formula of frequency kurtosis is shown in (25):

[0181]

[0182] Among them, f tfd12 Represents frequency kurtosis.

[0183] Vibration frequency domain feature 13:

[0184] Power spectral density peak: The maximum value of the power spectral density reflects the power of the most significant frequency component. The calculation formula for the power spectral density peak is as shown in (26):

[0185] f tfd13 =max(p(k))(26)

[0186] Among them, f tfd13 Represents the peak value of the power spectral density.

[0187] Vibration frequency domain feature 14:

[0188] Peak frequency: The frequency point with the largest power spectrum density indicates the most significant frequency component in the tool vibration. The calculation formula for peak frequency is shown in (27):

[0189] f tfd14 =argmax(p(k))(27)

[0190] Among them, f tfd14 Indicates the peak frequency.

[0191] In some embodiments, considering that the vibration frequency of the tool during the current machining process may be mainly concentrated in a specific frequency band, therefore, in an embodiment of the present application, a vibration feature matrix can be extracted only for the vibration timing data within the machining frequency band, thereby improving the accuracy of the extracted vibration feature matrix.

[0192] See also Figure 2, which is a flow chart of a method for extracting a vibration feature matrix provided in an embodiment of the present application. Step 102 can be implemented by executing sub-steps 1021 to 1022:

[0193] Step 1021: filtering the previous vibration time series data and the current vibration time series data respectively based on the first target frequency band.

[0194] Step 1022: Based on the filtered previous vibration time series data and current vibration time series data, respectively extract the vibration time domain features and vibration frequency domain features corresponding to the previous vibration time series data and the current vibration time series data.

[0195] In the embodiment of the present application, the first target frequency band can be considered as the working frequency band of the current tool in the processing stage. For example, the first target frequency band can be [50Hz, 1950Hz]. Then, the previous vibration timing data and the current vibration timing data generated in the last two processing processes are filtered respectively by the first target frequency band, which is beneficial to screen out other frequency bands that are not related to the processing stage, so that the time-frequency domain features extracted for the processing stage based on the previous vibration timing data and the current vibration timing are more accurate.

[0196] In some embodiments, considering that the rotation speed and number of blades of the tool itself during operation may also affect the distribution of its effective vibration signal, therefore, in an embodiment of the present application, frequency domain features can be extracted within the frequency band where the effective signals of tool vibration are concentrated, thereby improving the accuracy of the extracted frequency domain features.

[0197] See also Figure 3 , which is a flow chart of a method for extracting frequency domain features provided in an embodiment of the present application. Step 1022 can be implemented by executing sub-steps 10221 to 10224:

[0198] Step 10221: Determine the fundamental frequency of the time-frequency domain conversion according to the current set rotation speed of the tool.

[0199] In the embodiment of the present application, taking the current set speed of the tool as 6000 r / min as an example, the base frequency of the time-frequency domain conversion is 6000 / 60=100 Hz.

[0200] Step 10222: Determine the maximum multiple of the time-frequency domain conversion based on the number of cutting edges and the fundamental frequency of the current tool.

[0201] In the embodiment of the present application, taking the current tool having four blades as an example, if the base frequency is 100 Hz, then the maximum multiple of the time-frequency domain conversion is 100*4=400 Hz.

[0202] Step 10223: Determine at least two second target frequency bands for time-frequency domain conversion according to the base frequency and the maximum multiple frequency.

[0203] In the embodiment of the present application, if the base frequency is 100 Hz and the maximum multiplier is 400 Hz, then the second target frequency bands can be directly determined to be [50 Hz, 250 Hz] and [250 Hz, 450 Hz] respectively.

[0204] Of course, in another possible embodiment, a plurality of intermediate multiples can be determined between the base frequency and the maximum multiple, and then at least two second target frequencies for time-frequency domain conversion can be determined based on the base frequency, the plurality of intermediate multiples and the maximum multiple. Continuing with the example of a base frequency of 100 Hz and a maximum multiple of 400 Hz, the plurality of intermediate multiples that can be determined are 200 Hz and 300 Hz, respectively. On this basis, the second target frequency bands determined are [50 Hz, 150 Hz], [150 Hz, 250 Hz], [250 Hz, 350 Hz], and [350 Hz, 450 Hz].

[0205] It is worth noting that each second target frequency band needs to cover the base frequency, the middle multiple frequency or the maximum multiple frequency.

[0206] Step 10224: Perform time-frequency domain conversion on the filtered previous vibration time series data and the current vibration time series data, respectively, to obtain vibration frequency domain class features corresponding to each second target frequency band in the previous vibration time series data and the current vibration time series data.

[0207] In an embodiment of the present application, after determining multiple second target frequency bands in which effective signals of tool vibration are concentrated based on the set rotation speed and number of blades of the current tool, when performing time-frequency domain conversion, the vibration frequency characteristics of the previous vibration time series data and the current vibration time series data in each second target frequency band can be obtained respectively, without the need to obtain the vibration frequency domain characteristics of other frequency bands except the second target frequency band, thereby improving the accuracy of the extracted vibration frequency domain characteristics.

[0208] Step 103: Obtain the current feature difference between the previous machining process and the current machining process of the current tool according to the vibration feature matrix corresponding to the previous vibration time series data and the current vibration time series data.

[0209] In the embodiment of the present application, the Euclidean distance between the vibration feature matrix corresponding to the previous vibration time series data and the current vibration time series data can be calculated to obtain the current feature difference between the two recent processing processes of the current tool. Of course, the above-mentioned current feature difference can also be obtained by other methods, and the present application does not impose any special restrictions on this.

[0210] It is worth noting that before calculating the characteristic similarity, the vibration time domain features and the vibration frequency domain features in the vibration feature matrix can be standardized respectively.

[0211] Step 104: Based on the comparison result of the current feature difference and the current difference threshold, determine the current state of the current tool.

[0212] In the embodiment of the present application, the current characteristic difference between the current vibration time series data and the previous vibration time series data reflects the degree of change of the current vibration time series data compared with the previous vibration time series data. The smaller the current characteristic difference, the smaller the degree of change of the current vibration time series data compared with the previous vibration time series data. On the contrary, the larger the current characteristic difference, the larger the degree of change of the current vibration time series data compared with the previous vibration time series data. By comparing the above-mentioned current characteristic difference with the current difference threshold, the current tool state corresponding to the above-mentioned tool when generating the current vibration data is determined.

[0213] See also Figure 4 , is a flowchart of a method for determining the tool state of a current tool provided in an embodiment of the present application. Step 104 can be specifically implemented by executing sub-steps 1041 to 1043:

[0214] Step 1041: Determine whether the current feature difference is greater than the current difference threshold, wherein the current feature difference is the Euclidean distance between the vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data.

[0215] In the embodiment of the present application, the Euclidean distance is continued to be used to evaluate the current feature difference between the vibration feature matrix corresponding to the current vibration time series data and the previous vibration time series data. Therefore, the Euclidean distance between the vibration feature matrix corresponding to the current vibration time series data and the previous vibration time series data can be used to compare the set difference threshold.

[0216] Step 1042: If it is greater than the current difference threshold, it is determined that the current tool is in an abnormal state.

[0217] Step 1043: If it is not greater than the current difference threshold, it is determined that the current tool is in a normal state.

[0218] In the embodiment of the present application, if the Euclidean distance between the current vibration time series data and the previous vibration time series data is large, it indicates that the current vibration time series data has a large degree of change compared to the previous vibration time series data. If the tool state corresponding to the previous vibration time series data is normal, it can be inferred that the tool state of the current tool when generating the current vibration time series data is abnormal. If the tool state corresponding to the previous vibration time series data is abnormal, if the Euclidean distance of the current tool is greater than the current difference threshold, it can be inferred that the tool state of the current tool when generating the current vibration time series data is still abnormal.

[0219] If the Euclidean distance between the current vibration time series data and the previous vibration time series data is small, it means that the current vibration time series data has a smaller change degree than the previous vibration time series data. If the tool state corresponding to the previous vibration time series data is normal, then it can be inferred that the tool state of the current tool when generating the current vibration time series data is normal. If the tool state corresponding to the previous vibration time series data is abnormal, if the Euclidean distance of the current tool is not greater than the current difference threshold, then it can be inferred that the tool state of the current tool when generating the current vibration time series data is still normal.

[0220] How to determine the current difference threshold and the initial difference threshold is described in detail below.

[0221] See also Figure 5 , is a flow chart of a method for determining a current difference threshold provided in an embodiment of the present application. Before executing step 104, steps 201 to 204 may also be executed:

[0222] Step 201: Acquire the time series data of all vibrations generated by the tool during the historical machining process of the CNC machine tool and the tool status data.

[0223] In the embodiment of the present application, the vibration time series data generated by the tool within the preset time period and the corresponding processing attribute information can be obtained. The specific processing attribute information can be found above and will not be repeated here. Then, the vibration time series data is screened based on the processing attribute information, for example, the vibration time series data in the non-processing program segment is screened out, the vibration time series data with too long or too short processing time is screened out, the vibration time series data of the machine state is not processing, and the vibration time series data of the machine operation task is not material processing, so as to obtain the vibration time series data generated by the tool during the processing.

[0224] Then, the detection frequency, tool parameters and alarm information of the tool setting instrument in history can be obtained. According to the number of vibration time series data generated by the tool during the processing and the detection frequency of the tool setting instrument, the vibration time series data are grouped. For example, the detection frequency is 5, that is, a tool inspection is performed every time 5 materials are processed. If the number of vibration time series data obtained is 10, then the 10 vibration time series data can be divided into 2 groups. For each group of vibration time series data, the tool setting instrument will perform a tool inspection after generating the last vibration data of the group. If a broken tool is detected, an alarm message will be output.

[0225] For the vibration time series data of the nth group, if there is no corresponding alarm information after the last vibration time series data, the tool state corresponding to the vibration time series data of the nth group is determined to be normal. If there is an alarm information after the last vibration time series data, and the tool parameters indicate that the tool has not been replaced, the tool state corresponding to the vibration time series data of the nth group is determined to be suspected abnormal; if there is an alarm information after the last vibration time series data, and the tool parameters indicate that the tool has been replaced, the tool state corresponding to the last vibration time series data is determined to be abnormal, and the tool states corresponding to the other vibration time series data in the vibration time series data of the nth group except the last vibration time series data are determined to be suspected abnormal.

[0226] Step 202: Obtain the vibration characteristic matrix corresponding to each vibration time series data.

[0227] In an embodiment of the present application, characteristics are extracted from all vibration time series data to obtain a corresponding vibration feature matrix, wherein the vibration feature matrix includes vibration time domain features and vibration frequency domain features. The specific features of the vibration time domain features and vibration frequency domain features can be found in the above description and will not be repeated here.

[0228] It is worth noting that the status of the tool in the vibration time series data can be a normal state, an abnormal state, or a suspected abnormal state, which can be selected according to actual needs.

[0229] Step 203: According to the vibration feature matrix corresponding to the previous vibration time series data, the feature difference between two adjacent times in the historical processing of the CNC machine tool is obtained, and a set of previous feature difference is constructed.

[0230] In the embodiment of the present application, the characteristic difference can be obtained by calculating the Euclidean distance between the vibration characteristic matrices corresponding to the adjacent vibration time series data, thereby constructing a set of characteristic difference degrees of all times. Of course, the characteristic difference can also be obtained by other methods, and the present application does not impose any special restrictions on this.

[0231] It is worth noting that before calculating the feature difference, the vibration time domain features and vibration frequency domain features in the vibration feature matrix can be standardized separately. Since the main purpose of this application is to detect abnormal tool status, during the standardization process, only the vibration feature matrix corresponding to the normal tool status is standardized.

[0232] Step 204: With the purpose of enabling the initial difference threshold to distinguish between the abnormal state and the normal state of the tool, the initial difference threshold is iteratively adjusted according to the previous feature difference sets and the tool state until the iteration is completed to obtain the initial difference threshold; the initial difference threshold is used as the initial value of the current difference threshold.

[0233] Two possible threshold iterative adjustment implementation schemes are provided here.

[0234] For the first feature difference in the previous feature difference sets, if the first feature difference is less than the initial value of the initial difference threshold, the predicted tool state is determined to be normal; since the real tool state is normal at this time, and the predicted tool state is also normal, it indicates that the initial value of the initial difference threshold is set appropriately, and the initial difference threshold should be controlled to remain unchanged or updated in a decreasing direction. If the first feature difference is greater than the initial value of the initial difference threshold, the predicted tool state is determined to be abnormal; since the real tool state is normal at this time, and the predicted tool state is abnormal, it indicates that the initial value of the initial difference threshold is set too small, and the initial difference threshold should be controlled to be updated in an increasing direction. The subsequent iterative process is analogous to this until the latest initial difference threshold is obtained.

[0235] The second solution is to use the current initial difference threshold to judge the tool state for n groups of vibration time series data, obtain the judgment index (such as accuracy, false positive rate, etc.) corresponding to the n groups of vibration time series data, and then iteratively adjust the initial difference threshold according to the judgment index.

[0236] In some embodiments, considering that the tool itself will be constantly worn during the machining process, the current difference threshold used to determine the tool state should also be adaptively updated to improve the accuracy of the tool state determination.

[0237] See also Figure 6 , is a flow chart of a method for updating the current difference threshold provided in an embodiment of the present application. Before executing step 104, steps 301 to 303 may also be executed:

[0238] Step 301: Acquire multiple vibration time series data of a previously set number of machining processes.

[0239] Step 302: Obtain multiple vibration feature matrices corresponding to multiple vibration time series data.

[0240] Step 303: According to the multiple vibration feature matrix corresponding to the multiple vibration time series data, the feature difference corresponding to two adjacent times in the previously set number of machining processes of the CNC machine tool is obtained.

[0241] Step 304: Update the current difference threshold according to the feature difference between two adjacent processings of the previously set number of times.

[0242] In an embodiment of the present application, due to the differences in workpieces during the processing of the current tool, the current difference threshold may not be able to distinguish the state of the current tool well. Therefore, after each set number of processing times, the current difference threshold will be updated to improve the accuracy of tool state judgment.

[0243] How to update the current difference threshold is described in detail below.

[0244] See also Figure 7 , which is a flow chart of a method for updating the current difference threshold provided in an embodiment of the present application. Step 304 can be implemented by executing sub-steps 3041 to 3044:

[0245] Step 3041: Obtain the feature differences corresponding to two adjacent processings in previous processing processes and construct a difference set.

[0246] Step 3042: Obtain the threshold to be updated according to the mean and variance of the feature difference corresponding to the difference set and the size parameters of the current tool.

[0247] Step 3043: Determine whether the threshold to be updated is greater than the initial difference threshold.

[0248] Step 3044: If it is greater than, the threshold to be updated is used as the current difference threshold; if it is less than, the initial difference threshold is used as the current difference threshold.

[0249] In the embodiment of the present application, for several previous machining processes, the overall similarity mean and overall similarity variance corresponding to multiple feature differences can be determined based on the feature difference between the vibration time series data generated by two adjacent machining processes. Then, the threshold to be updated is determined based on the overall similarity mean, the overall similarity variance and the size parameters of the current tool. The calculation formula for the threshold to be updated is shown in (28):

[0250] y=μ+cl*σ(28)

[0251] Wherein, y represents the threshold to be updated, μ represents the mean of the characteristic difference corresponding to the difference set, σ represents the variance of the characteristic difference corresponding to the difference set, and cl represents the size parameter of the tool. Of course, in the above embodiment, the standard deviation can also be used to replace the variance, and this application does not impose any special restrictions on this.

[0252] If the threshold to be updated is greater than the initial difference threshold obtained by previous training, the threshold to be updated is used as the new current difference threshold. It should be understood that the judgment mechanism for tool abnormality in this application is that the feature difference of this time is greater than the current difference threshold. In this case, when the threshold to be updated is greater than the initial difference threshold obtained by previous training, the threshold to be updated is used as the new current difference threshold. This means that the feature difference of this time needs to be greater than the threshold to be updated to be judged as a tool abnormality. Here, a larger current difference threshold is used as the standard for judging tool abnormality, changing the detection adaptability to better detect the tool status.

[0253] If the threshold to be updated is not greater than the initial difference threshold obtained by previous training, the initial difference threshold is used as the new current difference threshold, that is, the lower limit value of the threshold is used to detect the tool state.

[0254] See also Figure 8 , is an electronic device provided in an embodiment of the present application, the electronic device includes at least one processor 401, the processor 401 is used to execute a computer program stored in a memory, and implement the embodiment of the present application as provided in the following Figure 1-7 The flowchart diagram steps of the method for detecting the tool status of a CNC machine tool are shown.

[0255] Optionally, the processor 401 may specifically be a central processing unit, a specific ASIC, or one or more integrated circuits for controlling program execution.

[0256] Optionally, the electronic device may further include a memory 402 connected to the at least one processor 401, and the memory 402 may include a ROM, a RAM, and a disk memory. The memory 402 is used to store data required by the processor 401 when it is running, that is, it stores instructions that can be executed by the at least one processor 401. The at least one processor 401 executes the instructions stored in the memory 402 to execute the following steps: Figure 1-7 The method shown. Wherein, the number of memories 402 is one or more. Wherein, the number of memories 402 is one or more.

[0257] The present application also provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is run on a processor, the processor executes Figure 1-7 method.

[0258] The above description is only a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this specification should be included in the scope of protection of this specification.

Claims

1. A method for detecting the tool status of a CNC machine tool, characterized in that: The method comprises: Obtain the previous vibration time series data and the current vibration time series data generated by the current tool of the CNC machine tool during the machining process; According to the previous vibration time series data and the current vibration time series data, respectively obtain the vibration feature matrix corresponding to the previous vibration time series data and the current vibration time series data; wherein the vibration feature matrix includes vibration time domain features and vibration frequency domain features; According to the vibration feature matrix corresponding to the previous vibration time series data and the current vibration time series data, obtaining the current feature difference between the previous machining process and the current machining process of the current tool; Based on the comparison result of the current feature difference and the current difference threshold, the current state of the current tool is determined.

2. The detection method according to claim 1, characterized in that: The step of obtaining the vibration characteristic matrices corresponding to the previous vibration time series data and the current vibration time series data respectively according to the previous vibration time series data and the current vibration time series data comprises: Based on the first target frequency band, the previous vibration time series data and the current vibration time series data are filtered respectively; Based on the filtered previous vibration time series data and the current vibration time series data, the vibration time domain features and the vibration frequency domain features corresponding to the previous vibration time series data and the current vibration time series data are extracted respectively.

3. The detection method according to claim 2, characterized in that: The method of extracting the vibration time domain features and the vibration frequency domain features corresponding to the previous vibration time series data and the current vibration time series data based on the filtered previous vibration time series data and the current vibration time series data respectively includes: Determining the fundamental frequency of the time-frequency domain conversion according to the set rotation speed of the current tool; Determining the maximum frequency multiple of the time-frequency domain conversion according to the number of blades of the current tool and the fundamental frequency; Determine at least two second target frequency bands for time-frequency domain conversion according to the base frequency and the maximum multiple frequency; The filtered previous vibration time series data and the current vibration time series data are respectively converted into time-frequency domains, and the vibration frequency domain class features corresponding to each second target frequency band in the previous vibration time series data and the current vibration time series data are respectively obtained.

4. The detection method according to claim 1, characterized in that: The determining the current state of the current tool based on the comparison result of the current feature difference and the current difference threshold comprises: Determine whether the current feature difference is greater than the current difference threshold; wherein the current feature difference is the Euclidean distance between the vibration feature matrices corresponding to the previous vibration time series data and the current vibration time series data; If it is greater than the current difference threshold, it is determined that the current tool is in an abnormal state.

5. The detection method according to claim 4, characterized in that: After determining whether the current feature difference is greater than the current difference threshold, the method further includes: If it is not greater than the current difference threshold, it is determined that the current tool is in a normal state.

6. The detection method according to claim 1, characterized in that: Before determining the current state of the current tool based on the comparison result of the current feature difference and the current difference threshold, the method further includes: Acquire multiple vibration time series data of the machining process of a previously set number of times; Acquire a multiple vibration feature matrix corresponding to the multiple vibration time series data; According to the vibration feature matrix corresponding to the multiple vibration time series data, obtaining the feature difference between two adjacent machining processes of the CNC machine tool during the previously set number of machining processes; The current difference threshold is updated according to the feature difference between two adjacent times in the previously set number of processing steps.

7. The detection method according to claim 6, characterized in that: The updating of the current difference threshold according to the comparison result of the feature difference between two adjacent features in the previous several processing processes and the initial threshold comprises: Obtain the feature differences between two adjacent processings in the previous several times and construct a difference set; Obtaining a threshold to be updated according to a mean and a variance of the feature differences corresponding to the difference set and a size parameter of the current tool; Determine whether the threshold to be updated is greater than the initial difference threshold; If it is greater than, the threshold to be updated is used as the current difference threshold; If not, the initial difference threshold is used as the current difference threshold.

8. The detection method according to claim 7, characterized in that: Before determining the current state of the current tool based on the comparison result of the current feature difference and the current difference threshold, the method further includes: Acquire the time series data of all vibrations and tool status data generated by the tool during the historical machining process of the CNC machine tool; Obtaining a vibration characteristic matrix corresponding to the previous vibration time series data; According to the vibration feature matrix corresponding to the previous vibration time series data, the feature difference between two adjacent times in the historical processing of the CNC machine tool is obtained, and a set of previous feature difference is constructed; With the purpose that the initial difference threshold can distinguish between an abnormal state and a normal state of a tool, the initial difference threshold is iteratively adjusted according to the previous feature difference sets and the tool state until the iteration ends, thereby obtaining the initial difference threshold; The initial difference threshold is used as the initial value of the current difference threshold.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program to implement the detection method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the detection method according to any one of claims 1 to 8 is implemented.

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