Cutter state indicating method and indicating device for variable-pitch screw multi-cutter machining

Abstract

The invention relates to the technical field of tool state detection, in particular to a tool state indicating method and device for variable-pitch screw multi-tool machining. Determining a vibration fluctuation coefficient according to the vibration data of each cutter in any direction and the difference of all vibration data of each cutter in any direction between each time period and the adjacent previous time period; for a vertical direction in all directions of each cutter, determining a frequency domain fluctuation coefficient according to a difference and a distribution condition of frequency domain fluctuation conditions of all vibration data between each time period and the first time period; and based on the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in all time periods, determining the weight of the tool, and monitoring the state of the tool in combination with the vibration data. The fluctuation conditions of the vibration data in the time domain and the frequency domain are comprehensively analyzed, and the accuracy of cutter state detection is improved.

Description

Technical Field

[0001] The present application relates to the technical field of tool status detection, and in particular to a tool status indication method and an indication device for variable pitch screw multi-tool machining. Background Art

[0002] With the continuous advancement of manufacturing technology, the processing requirements of precision mechanical parts are getting higher and higher. As a special mechanical component, variable pitch screw plays an important role in many application fields due to its non-uniform pitch design, such as in vacuum pumps and other equipment. These equipments have extremely high requirements for the processing accuracy of the screw. Therefore, in the processing of variable pitch screw, ensuring the good condition of the tool is crucial to ensure the quality of the final product.

[0003] The existing technology uses vibration data to establish a model between the tool state and monitoring data, thereby realizing the monitoring of the tool state. However, the model between the tool state and monitoring data in the existing technology is established under the condition of fixed tool and fixed pitch. When the tool is replaced or the parameters are modified, tool compensation is required, which introduces new errors. Therefore, this method is relatively complex and has poor flexibility. When the processing conditions or tools need to be changed frequently, the accuracy is also poor. And as the processing proceeds, the tool state is getting worse and worse. In addition to the sudden change, the vibration data of the tool also has certain gradual characteristics as a whole. In the traditional anomaly detection algorithm, the gradual characteristics of this data will reduce the accuracy of tool state detection. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a tool status indication method and indication device for variable pitch screw multi-tool machining. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a tool status indication method for variable pitch screw multi-tool machining, the method comprising the following steps: During variable pitch screw multi-tool machining, the vibration data of each tool in all directions at the current moment and at each acquisition moment within a preset time period before it is obtained; The preset time is divided into multiple time periods, and the vibration characteristic value of each tool in each time period is determined according to the discrete degree of all vibration data of each tool in any direction in each time period, and the vibration fluctuation coefficient of each tool in each time period is determined by combining the difference of all vibration data of each tool in any direction between each time period and its adjacent previous time period; For the vertical direction in all directions of each tool, the frequency domain vibration value of each tool in each time period is determined according to the difference in the frequency domain fluctuation of all vibration data between each time period and the first time period, and the frequency domain fluctuation coefficient of each tool in each time period is determined in combination with the distribution of all vibration data in the frequency domain in any direction of each tool in each time period; Based on the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in all time periods, and combining the vibration fluctuation coefficient and the frequency domain fluctuation coefficient, determine the tool weight of each tool in each time period; Based on the vibration data of each tool in all directions at each acquisition moment and the tool weight, the characteristic vector of each tool at each acquisition moment is determined. Based on the distance between the characteristic vectors of each tool at any two acquisition moments, the distance formula between the characteristic vectors of each tool at any two acquisition moments is determined. Combined with the characteristic vectors of each tool at all acquisition moments and the abnormality detection algorithm, the tool status of variable pitch screw multi-tool machining is monitored.

[0005] Preferably, the vibration characteristic value of each tool in each time period is the cumulative sum of the discrete degrees of vibration data of each tool in all directions in each time period.

[0006] Preferably, the expression of the vibration fluctuation coefficient of each tool in each time period is: ; In the formula, represents the vibration fluctuation coefficient of tool j in time period i; represents the vibration characteristic value of tool j in time period i; , , Respectively represent the differences of all vibration data of tool j in the horizontal, vertical and longitudinal directions in all directions between time period i and its adjacent previous time period; Indicates a preset constant greater than 0.

[0007] Preferably, the method for determining the frequency domain vibration value of each tool in each time period is: All vibration data in the vertical direction of each tool in each time period are used as the input of the time-frequency conversion algorithm, and the frequency spectrum of each tool in the vertical direction in each time period is output, and it is used as the frequency spectrum of each tool in each time period; All peaks in the spectrum diagram of each tool in each time period are extracted, and all peaks are fitted to obtain the peak fitting curve. The difference between the integral area of ​​the peak fitting curve of each tool in each time period and the integral area of ​​the peak fitting curve of the first time period is taken as the frequency domain vibration value of each tool in each time period.

[0008] Preferably, the expression of the frequency domain fluctuation coefficient of each tool in each time period is: ; In the formula, represents the frequency domain fluctuation coefficient of tool j in time period i; , represent the main frequencies in the spectrum of tool j in time period i and time period ik respectively; Represents the frequency domain vibration value of tool j in time period i.

[0009] Preferably, the method for determining the tool weight of each tool in each time period is: Calculate the normalized value of the product of the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in each time period; Analyze the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool at all time periods; The product of the normalized value and the correlation of each tool in each time period is used as the tool weight of each tool in each time period.

[0010] Preferably, the method for determining the feature vector of each tool at each acquisition time is: The vibration data of each tool in all directions at each acquisition moment are multiplied by the tool weight corresponding to the time period of the acquisition moment to form the feature vector of each tool at each acquisition moment.

[0011] Preferably, the distance formula between the feature vectors of each tool at any two acquisition moments is: ; In the formula, is the time when tool j is collected and collection time The distance between the feature vectors; is the tool j at the time of collection and collection time The Euclidean distance between the feature vectors, , They are respectively the collection time of tool j and collection time Tool weight.

[0012] Preferably, the monitoring of the tool status of the variable pitch screw multi-tool machining includes: The feature vector of each tool at the current moment and all previous acquisition moments is used as the input of the LOF anomaly detection algorithm, where the distance formula between the feature vectors of each tool at any two acquisition moments is used as the distance formula in the LOF anomaly detection algorithm, and the anomaly score value of each tool at the current moment and each previous acquisition moment is output; The abnormal score value of each tool at the current moment and all previous acquisition moments is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output; If the abnormal score value at the current moment is greater than the segmentation threshold, the tool state is abnormal, otherwise, the tool state is normal.

[0013] In the second aspect, an embodiment of the present application also provides a tool status indication device for variable pitch screw multi-tool machining, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned tool status indication methods for variable pitch screw multi-tool machining when executing the computer program.

[0014] This application has at least the following beneficial effects: This application constructs a vibration fluctuation coefficient based on the time series changes of the vibration data of the tool in different directions. Its beneficial effect is that the vibration fluctuation coefficient reflects the vibration changes of the tool in the time domain, enhances the sensitivity of capturing the trend of vibration data changes, and thus enhances the sensitivity of identifying abnormal tool status; this application then constructs a frequency domain fluctuation coefficient based on the fluctuation of the vibration data in the vertical direction of the tool in the frequency domain. Its beneficial effect is that the frequency domain fluctuation coefficient reflects the vibration changes of the tool in the frequency domain, further enhances the accuracy of identifying tool abnormalities, and can more accurately reflect the changes in tool status; this application constructs a tool weight by combining the vibration fluctuation coefficient and the frequency domain fluctuation coefficient. Its beneficial effect is that the tool weight reflects the degree of wear of the tool in each period, which can reduce the influence of the gradual characteristics of the vibration data on the accuracy of abnormal monitoring and improve the accuracy of abnormal detection. This application weights the vibration data at each acquisition moment by comprehensively analyzing the fluctuation of vibration data in the time domain and frequency domain, and monitors the wear status of the tool in combination with the abnormal detection algorithm, which reduces the influence of the normal gradual characteristics of the vibration data of the tool on abnormal detection and improves the accuracy of tool status detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of the steps of a tool status indication method for variable pitch screw multi-tool machining provided by an embodiment of the present application; Figure 2 A schematic diagram of a vibration fluctuation coefficient extraction process provided by an embodiment of the present application; Figure 3 A schematic diagram of a tool weight acquisition process provided by an embodiment of the present application; Figure 4 A schematic diagram of a tool status detection process provided for an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the tool status indication method and indication device for variable pitch screw multi-tool machining proposed by the present application, its specific implementation method, structure, features and effects are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the tool status indication method and indication device for variable pitch screw multi-tool machining provided by the present application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of the steps of a tool state indication method for variable pitch screw multi-tool machining provided by an embodiment of the present application, the method comprising the following steps: Step S1: During variable pitch screw multi-tool machining, vibration data of each tool in all directions at the current moment and at each acquisition moment within a preset time period before the current moment are obtained.

[0021] During the variable pitch screw multi-tool machining process, an acceleration sensor is installed on the rear half of the tool just above each turning tool. During the working period of each tool, vibration data of each tool in all directions at the current moment and at each collection moment within a preset time period before it, i.e., acceleration data, is collected. The data collection frequency is set to f, and all directions include horizontal, vertical and longitudinal directions. The horizontal direction is the direction in which the tool moves left and right on the machine tool with the machine tool spindle as the center, the vertical direction is the direction in which the tool moves forward and backward along the machine tool spindle on the machine tool, and the longitudinal direction indicates the direction in which the tool moves up and down on the machine tool perpendicular to the machine tool processing table.

[0022] It should be noted that the values ​​of the preset time length and the data acquisition frequency f are manually set. In this embodiment, the preset time length is 1s, and the data acquisition frequency f is 1kHz. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0023] Step S2: Divide the preset time length into multiple time periods, and determine the vibration characteristic value of each tool in each time period according to the discrete degree of all vibration data of each tool in any direction in each time period, and determine the vibration fluctuation coefficient of each tool in each time period by combining the difference of all vibration data of each tool in any direction between each time period and its adjacent previous time period.

[0024] The existing technology is to establish a tool status monitoring model under fixed processing conditions and fixed tools to indicate the tool status. However, due to the complexity of actual operation, the processing conditions or tools are subject to various changes. Therefore, it is impossible to conduct experiments to establish models in each mode. Usually, a tool compensation amount is constructed to achieve adaptability to changes in processing conditions or tools, and new errors are inevitably introduced. However, in variable pitch screw multi-tool processing, since multiple tools need to be used simultaneously when processing the same component, different pitches need to be changed. Therefore, the status monitoring of each tool must introduce a tool compensation amount, which is equivalent to introducing multiple different error amounts when processing the same component, which has a greater impact on the quality of the component.

[0025] In order to avoid the problem of constructing a fixed tool state monitoring model and introducing multiple error quantities, the tool state monitoring is achieved by detecting abnormal values ​​in the vibration data based on the phenomenon that changes in the tool state will cause vibration during variable pitch screw multi-tool processing. However, due to the wear of the tool during operation, the tool state shows a trend of getting worse and worse, which leads to certain gradual characteristics of the vibration. In traditional anomaly detection algorithms, the fluctuation trend of the data is not taken into account, so this gradual feature will interfere with the determination of abnormal values, which will lead to the inability to accurately determine the state of the tool.

[0026] Therefore, in order to accurately monitor the status of the tool, the sensitivity of identifying abnormal status of the tool is enhanced by analyzing the time series changes of the vibration data. Specifically: During variable pitch screw multi-tool processing, when the tool is in good condition, the tool surface is smooth and the friction is relatively small, so that the vibration of the tool is relatively small; as the tool is used, there is slight wear, at this time the friction of the tool increases relatively, and the vibration also increases relatively, but the amplitude of the change is small; as the tool wear increases, the defects on the tool surface become larger and larger, thereby increasing the friction of the tool and making the vibration of the tool larger and larger.

[0027] When processing the workpiece, the change trends of the tool in each movement direction are not exactly the same. The vertical direction is the forward direction of the tool and is also the main direction of the cutting force. As the tool wears, the cutting force will gradually increase, resulting in drastic changes in the vibration data in the forward direction; the longitudinal direction is the direction perpendicular to the workpiece surface. As the tool wear increases, the contact area and pressure between the tool and the workpiece will change, resulting in relatively drastic changes in the vibration in the longitudinal direction; and in the transverse direction, due to the relatively small cutting force, the vibration changes are relatively gentle.

[0028] Therefore, by analyzing the fluctuation of all vibration data in the same direction of the tool and the difference in vibration data in different directions, the wear of the tool can be judged, specifically: (1) In order to analyze the changes in tool vibration data within a local time and better understand the wear of the tool, the preset time is evenly divided into multiple time periods.

[0029] (2) Further, according to the discrete degree of all vibration data of each tool in any direction in each time period, the vibration characteristic value of each tool in each time period is determined to judge the comprehensive fluctuation of vibration data in different directions, so as to identify whether the tool state is abnormal, specifically: The discreteness of all vibration data in any direction of each tool in each time period is analyzed, and the cumulative sum of the discreteness in all directions is used as the vibration characteristic value of each tool in each time period, which is used to characterize the comprehensive fluctuation of the tool vibration data in each time period. The greater the discreteness of the vibration data, the greater the vibration characteristic value, indicating that the more intense the tool vibration is and the more serious the tool wear may be.

[0030] It should be noted that there are many methods for measuring the degree of discreteness of a set of data. In this embodiment, the degree of discreteness of all vibration data in any direction of each tool in each time period is measured by calculating the variance of all vibration data in any direction of each tool in each time period. The implementer may also use other methods for measuring the degree of discreteness of data, such as dispersion coefficient or standard deviation. Regarding the selection of the method for measuring the degree of discreteness of a set of data, this embodiment does not impose any special restrictions.

[0031] (3) Further, according to the vibration characteristic value of each tool in each time period, and combined with the difference of all vibration data of each tool in any direction between each time period and its adjacent previous time period, the vibration fluctuation coefficient of each tool in each time period is determined to judge the difference degree of vibration between different directions, and thus judge the wear condition of the tool, specifically: Vibration fluctuation coefficient of tool j in time period i The expression is: ; In the formula, represents the vibration fluctuation coefficient of tool j in time period i; represents the vibration characteristic value of tool j in time period i; , , Respectively represent the differences of all vibration data of tool j in the horizontal, vertical and longitudinal directions in all directions between time period i and its adjacent previous time period; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is artificially set. The value of is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation result, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0032] It should be noted that there are many methods for measuring the differences between data groups. In this embodiment, the Euclidean distance of all vibration data of tool j in the horizontal, vertical and longitudinal directions between time period i and its adjacent previous time period is calculated to measure the difference in vibration data in the same direction between different time periods. The implementer may also use other methods for measuring the differences between data groups, such as Mahalanobis distance and Manhattan distance. This embodiment does not impose any special restrictions on the selection of methods for measuring the differences between data groups.

[0033] The calculation steps of the Euclidean distance are well-known techniques, and the specific calculation process will not be described in detail.

[0034] Furthermore, according to the vibration fluctuation coefficient of each tool in each time period, it can be understood that when the i-th time period is in the working state of tool wear, the vibration fluctuation in each direction in the i-th time period is large, and the more serious the tool wear, the more serious the vibration fluctuation, and the greater the fluctuation difference between the i-th time period and the previous time period, that is, , , The larger the value, the more serious the tool wear. At the same time, due to the change in vibration caused by tool wear, the degree of vibration change in the horizontal and longitudinal directions is smaller than the degree of vibration change in the vertical direction of the tool. Therefore The larger the tool wear, the greater the vibration characteristic value, and the vibration fluctuation coefficient On the contrary, when the tool has no wear or little wear, the vibration fluctuations in all directions in the i-th period are smaller, and the fluctuation difference between the i-th period and the previous period is smaller, that is, , , The smaller the values ​​are and the smaller the vibration characteristic value is, the smaller the vibration fluctuation coefficient is, indicating that the tool wear is lighter.

[0035] Preferably, the schematic diagram of the vibration fluctuation coefficient extraction process provided in this embodiment is as follows: Figure 2 shown.

[0036] Step S3: For each tool in the vertical direction in all directions, determine the frequency domain vibration value of each tool in each time period according to the difference in the frequency domain fluctuation of all vibration data between each time period and the first time period, and determine the frequency domain fluctuation coefficient of each tool in each time period in combination with the distribution of all vibration data in the frequency domain in any direction of each tool in each time period.

[0037] The vibration fluctuation coefficient can identify the time series changes of vibration data and enhance the ability to identify tool abnormalities. However, due to the complexity of actual operation, in addition to tool abnormalities that can cause vibration fluctuations, there are other factors that can affect vibration data, such as changes in production parameters or production environment. If the tool status is identified only by relying on the vibration fluctuation coefficient, it will cause certain misjudgments. Therefore, it is necessary to combine the frequency domain information of the vibration for further judgment.

[0038] In the processing of variable pitch screws, when the tool is not worn or the wear is relatively light, the tool surface is relatively smooth, the friction is small, and the blade is relatively sharp, so the main frequency of the vibration data in the frequency domain is small, and the amplitude fluctuation in the entire frequency domain is relatively slight. As the degree of tool wear increases, the friction between the tool surface and the workpiece becomes greater and greater, causing the vibration data to have a main frequency shift in the frequency domain, and the amplitude fluctuation in the entire frequency domain is more obvious, that is, the spectrum fluctuation is more chaotic.

[0039] When the tool is not worn or the wear is light, the tool state difference is small, so the fluctuation of tool vibration is also relatively small. Therefore, the main frequency of each segment will not strictly follow the trend of the main frequency backward shift, and there will be certain fluctuations. However, as the tool wear increases, the fluctuation of tool vibration becomes more and more obvious, resulting in the main frequency change in the corresponding frequency domain increasingly strictly following the characteristics of the main frequency backward shift. Therefore, the more serious the tool wear, the greater the average difference in the main frequency change.

[0040] Therefore, by analyzing the fluctuation and distribution of vibration data in the frequency domain, the wear state of the tool can be further accurately determined. Specifically: (1) For each tool in the vertical direction in all directions, the frequency domain vibration value of each tool in each time period is determined according to the difference in the frequency domain fluctuation of all vibration data between each time period and the first time period, so as to judge the fluctuation of vibration data in the frequency domain, and thus accurately judge the tool wear state, specifically: In the variable pitch screw machining process, since the vertical direction of the tool is most sensitive to tool wear, all vibration data in the vertical direction of each tool in each time period is used as the input of the time-frequency conversion algorithm, and the frequency spectrum of each tool in the vertical direction in each time period is output and used as the frequency spectrum of each tool in each time period; It should be noted that there are many commonly used time-frequency conversion algorithms. In this embodiment, discrete Fourier transform is used to convert time domain data into frequency domain. Implementers can also use other time-frequency conversion algorithms such as wavelet transform. Regarding the selection of time-frequency conversion algorithm, this embodiment does not impose any special restrictions.

[0041] Furthermore, all peaks in the spectrum diagram of each tool in each time period are extracted, and all peaks are fitted to obtain the peak fitting curve. The difference between the integral area of ​​the peak fitting curve of each tool in each time period and the integral area of ​​the peak fitting curve of the first time period is taken as the frequency domain vibration value of each tool in each time period.

[0042] The first time period indicates the time period that is first in chronological order after the preset time period is divided.

[0043] It should be understood that there are many methods for measuring the difference between data. In this embodiment, the difference between the integral area of ​​the peak fitting curve of each tool in each time period and the integral area of ​​the peak fitting curve of the first time period is calculated by taking the absolute value of the integral area of ​​the peak fitting curve of each tool in each time period and the integral area of ​​the peak fitting curve of the first time period to measure the difference in the integral area of ​​the peak fitting curve between each time period and the first time period. The implementer may also use other methods for measuring the difference between data, such as ratio. This embodiment does not impose any special restrictions on the selection of methods for measuring the difference between data.

[0044] In addition, it should be understood that there are many commonly used fitting methods. In this embodiment, nonlinear least squares fitting method is used to fit all peak data. The implementer can also use other fitting methods such as polynomial function data fitting. Regarding the selection of fitting method, this embodiment does not impose any special restrictions.

[0045] Among them, discrete Fourier transform is a well-known technology in the field of signal processing, and its specific process of converting time domain signals to frequency domain will not be repeated; and the nonlinear least squares fitting method, the peak extraction process and the process of calculating the integral area based on the fitting curve are all well-known technologies, and the specific implementation process will not be repeated.

[0046] According to the frequency domain vibration value of each tool in each time period, it can be understood that if the tool wear is more serious in the current time period, the difference between the fluctuation in the frequency domain of this time period and the fluctuation in the first time period will be greater, so the difference between the integral area of ​​the peak fitting curve of the tool in this time period and the integral area of ​​the peak fitting curve of the first time period will be greater, that is, the frequency domain vibration value of the tool will be greater; conversely, if the tool wear is more serious in the current time period, the difference between the fluctuation in the frequency domain of this time period and the fluctuation in the first time period will be greater, so the difference between the integral area of ​​the peak fitting curve of the tool in this time period and the integral area of ​​the peak fitting curve of the first time period will be greater, that is, the frequency domain vibration value of the tool will be greater.

[0047] (2) Based on the frequency domain vibration value of each tool in each time period and combined with the distribution of all vibration data in any direction of each tool in each time period in the frequency domain, the frequency domain fluctuation coefficient of each tool in each time period is determined to further determine the tool wear state, specifically: Frequency domain fluctuation coefficient of tool j in time period i The expression is: ; In the formula, , represent the main frequencies in the spectrum of tool j in time period i and time period ik respectively; Represents the frequency domain vibration value of tool j in time period i.

[0048] According to the frequency domain fluctuation coefficient of each tool in each period, it can be understood that if the tool wear in the current period is more serious, the main frequency in the frequency domain of this period will be larger, and the more serious the tool wear is, the more strictly it follows the main frequency backward shift characteristics, and the more the main frequency is backward shifted, that is, ,and The smaller the frequency domain fluctuation coefficient is, the more serious the tool wear is, the larger the frequency domain vibration value is, and the larger the frequency domain fluctuation coefficient is; on the contrary, if the tool wear in the current period is relatively light, the main frequency in the frequency domain of this period is smaller, and the tool wear is light, the main frequency The smaller, Relatively large, the smaller the frequency domain vibration value, the smaller the frequency domain fluctuation coefficient.

[0049] Step S4: Based on the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in all time periods, and combining the vibration fluctuation coefficient and the frequency domain fluctuation coefficient, determine the tool weight of each tool in each time period.

[0050] In the original anomaly detection algorithm, the changing trend of the data is not considered, the weight of each data point is the same, and the anomaly is detected only based on the value of the data point itself, which makes it difficult to accurately detect anomalies when facing a data set with gradual characteristics. Therefore, in order to avoid this problem, the changing trend of the tool vibration data is considered, and the vibration fluctuation coefficient and frequency domain fluctuation coefficient are calculated based on the changing characteristics of the tool vibration data in the time domain and frequency domain. When performing anomaly detection, tool weights are assigned to each time period based on the time domain and frequency domain changes of the vibration data in each vibration segment, and then outlier detection is performed in combination with the tool weights. Specifically: By analyzing the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in all time periods, and combining the vibration fluctuation coefficient and the frequency domain fluctuation coefficient, the tool weight of each tool in each time period is determined to reduce the influence of the gradual change characteristics of the vibration data on the abnormal detection of the vibration data, thereby improving the accuracy of abnormal detection and more accurately monitoring the wear state of the tool, specifically: Calculate the normalized value of the product of the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in each time period; Furthermore, the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient at all time periods of each tool is analyzed; It should be noted that there are many methods for measuring the correlation between data groups. In this embodiment, the Pearson correlation coefficient between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in all time periods is calculated to measure the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient. The implementer may also use other methods for measuring the correlation between data groups, such as the Spearman correlation coefficient or the Kendall rank correlation coefficient. This embodiment does not impose any special restrictions on the selection of methods for measuring the correlation of data groups.

[0051] The calculation steps of the Pearson correlation coefficient are well-known techniques, and the specific calculation process will not be described in detail.

[0052] Furthermore, the product of the normalized value and the correlation of each tool in each time period is used as the tool weight of each tool in each time period.

[0053] Furthermore, according to the tool weight of each tool in each time period, it can be understood that when the vibration change is caused by tool wear, the vibration data will maintain a consistent change trend in the time domain and frequency domain, that is, the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of the tool is large, and the more serious the tool wear, the larger the vibration fluctuation coefficient and the frequency domain fluctuation coefficient. Therefore, the more serious the tool wear, the greater the tool weight; conversely, when the vibration change is not caused by tool wear, or the degree of tool wear is relatively light, the corresponding tool weight is smaller.

[0054] Preferably, the schematic diagram of the tool weight acquisition process provided in this embodiment is as follows Figure 3 shown.

[0055] Step S5: The vibration data of each tool in all directions at each acquisition moment are multiplied by the tool weight corresponding to the time period of the acquisition moment to form the feature vector of each tool at each acquisition moment. Based on the distance between the feature vectors of each tool at any two acquisition moments, the distance formula between the feature vectors of each tool at any two acquisition moments is determined. In combination with the feature vectors of each tool at all acquisition moments and the abnormality detection algorithm, the tool status of variable pitch screw multi-tool machining is monitored.

[0056] When monitoring the tool status during variable pitch screw multi-tool machining, the vibration data of each tool in all directions at each acquisition time is combined into the vibration vector of each tool at each acquisition time. Each vibration vector must belong to one of all time periods, and the vibration vector of each tool at each acquisition time is multiplied by the tool weight of the time period in which the acquisition time is located, which is used as the feature vector of each tool at each acquisition time.

[0057] Furthermore, based on the distance between the feature vectors of each tool at any two acquisition moments, the distance formula between the feature vectors of each tool at any two acquisition moments is determined, specifically: The distance formula between the feature vectors of each tool at any two acquisition moments is: ; In the formula, is the time when tool j is collected and collection time The distance between the feature vectors; is the tool j at the time of collection and collection time The Euclidean distance between the feature vectors, , They are respectively the collection time of tool j and collection time Tool weight.

[0058] The feature vector of each tool at the current moment and all previous acquisition moments is used as the input of the LOF anomaly detection algorithm, where the distance formula between the feature vectors of each tool at any two acquisition moments is used as the distance formula in the LOF anomaly detection algorithm, and the anomaly score value of each tool at the current moment and each previous acquisition moment is output; The abnormal score value of each tool at the current moment and all previous acquisition moments is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output; If the abnormal score value at the current moment is greater than the segmentation threshold, the tool state is abnormal, and a tool state prompt is given, indicating that the tool wear is more serious; conversely, if the abnormal score value at the current moment is less than or equal to the segmentation threshold, the tool state is normal.

[0059] Among them, the LOF anomaly detection algorithm is a well-known technology, and its specific principle will not be repeated here.

[0060] Preferably, the tool state detection process schematic diagram provided in this embodiment is as follows Figure 4 shown.

[0061] Based on the same inventive concept as the above method, an embodiment of the present application also provides a tool status indication device for variable pitch screw multi-tool machining, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the tool status indication method for variable pitch screw multi-tool machining described above are implemented.

[0062] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0064] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A tool status indication method for variable pitch screw multi-tool machining, characterized in that: The method comprises the following steps: During variable pitch screw multi-tool machining, the vibration data of each tool in all directions at the current moment and at each acquisition moment within a preset time period before it is obtained; The preset time is divided into multiple time periods, and the vibration characteristic value of each tool in each time period is determined according to the discrete degree of all vibration data of each tool in any direction in each time period, and the vibration fluctuation coefficient of each tool in each time period is determined by combining the difference of all vibration data of each tool in any direction between each time period and its adjacent previous time period; For the vertical direction in all directions of each tool, the frequency domain vibration value of each tool in each time period is determined according to the difference in the frequency domain fluctuation of all vibration data between each time period and the first time period, and the frequency domain fluctuation coefficient of each tool in each time period is determined in combination with the distribution of all vibration data in the frequency domain in any direction of each tool in each time period; Based on the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in all time periods, and combining the vibration fluctuation coefficient and the frequency domain fluctuation coefficient, determine the tool weight of each tool in each time period; Based on the vibration data of each tool in all directions at each acquisition moment and the tool weight, the characteristic vector of each tool at each acquisition moment is determined. Based on the distance between the characteristic vectors of each tool at any two acquisition moments, the distance formula between the characteristic vectors of each tool at any two acquisition moments is determined. Combined with the characteristic vectors of each tool at all acquisition moments and the abnormality detection algorithm, the tool status of variable pitch screw multi-tool machining is monitored.

2. The tool status indication method for variable pitch screw multi-tool machining according to claim 1, characterized in that: The vibration characteristic value of each tool in each time period is the cumulative sum of the discrete degrees of vibration data of each tool in all directions in each time period.

3. The tool status indication method for variable pitch screw multi-tool machining according to claim 1, characterized in that: The expression of the vibration fluctuation coefficient of each tool in each time period is: ; In the formula, represents the vibration fluctuation coefficient of tool j in time period i; represents the vibration characteristic value of tool j in time period i; , , Respectively represent the differences of all vibration data of tool j in the horizontal, vertical and longitudinal directions in all directions between time period i and its adjacent previous time period; Indicates a preset constant greater than 0.

4. The tool status indication method for variable pitch screw multi-tool machining according to claim 1, characterized in that: The method for determining the frequency domain vibration value of each tool in each time period is as follows: All vibration data in the vertical direction of each tool in each time period are used as the input of the time-frequency conversion algorithm, and the frequency spectrum of each tool in the vertical direction in each time period is output, and it is used as the frequency spectrum of each tool in each time period; All peaks in the spectrum diagram of each tool in each time period are extracted, and all peaks are fitted to obtain the peak fitting curve. The difference between the integral area of ​​the peak fitting curve of each tool in each time period and the integral area of ​​the peak fitting curve of the first time period is taken as the frequency domain vibration value of each tool in each time period.

5. The tool status indication method for variable pitch screw multi-tool machining according to claim 4, characterized in that: The expression of the frequency domain fluctuation coefficient of each tool in each time period is: ; In the formula, represents the frequency domain fluctuation coefficient of tool j in time period i; , represent the main frequencies in the spectrum of tool j in time period i and time period ik respectively; Represents the frequency domain vibration value of tool j in time period i.

6. The tool status indication method for variable pitch screw multi-tool machining according to claim 1, characterized in that: The method for determining the tool weight of each tool in each time period is as follows: Calculate the normalized value of the product of the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool in each time period; Analyze the correlation between the vibration fluctuation coefficient and the frequency domain fluctuation coefficient of each tool at all time periods; The product of the normalized value and the correlation of each tool in each time period is used as the tool weight of each tool in each time period.

7. The tool status indication method for variable pitch screw multi-tool machining according to claim 1, characterized in that: The method for determining the feature vector of each tool at each acquisition time is: The vibration data of each tool in all directions at each acquisition moment are multiplied by the tool weight corresponding to the time period of the acquisition moment to form the feature vector of each tool at each acquisition moment.

8. The tool status indication method for variable pitch screw multi-tool machining according to claim 1, characterized in that: The distance formula between the feature vectors of each tool at any two acquisition moments is: ; In the formula, is the time when tool j is collected and collection time The distance between the feature vectors; is the tool j at the time of collection and collection time The Euclidean distance between the feature vectors, , They are respectively the collection time of tool j and collection time Tool weight.

9. The tool status indication method for variable pitch screw multi-tool machining according to claim 1, characterized in that: The monitoring of the tool status of variable pitch screw multi-tool machining includes: The feature vector of each tool at the current moment and all previous acquisition moments is used as the input of the LOF anomaly detection algorithm, where the distance formula between the feature vectors of each tool at any two acquisition moments is used as the distance formula in the LOF anomaly detection algorithm, and the anomaly score value of each tool at the current moment and each previous acquisition moment is output; The abnormal score value of each tool at the current moment and all previous acquisition moments is used as the input of the threshold segmentation algorithm, and the segmentation threshold is output; If the abnormal score value at the current moment is greater than the segmentation threshold, the tool state is abnormal, otherwise, the tool state is normal.

10. A tool status indication device for variable pitch screw multi-tool machining, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the tool status indication method for variable pitch screw multi-tool machining as described in any one of claims 1-9 are implemented.

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