Cutting tool abnormality detection device and abnormality detection method

Abstract

To provide an abnormality sensing device and an abnormality sensing method for a cutting tool which can stably sense an abnormality of the cutting tool regardless of a progress of wear of the cutting tool.SOLUTION: An abnormality sensing method of a cutting tool detects vibrations in constituent elements of a machine tool which is machining; calculates frequency spectrum of the vibrations; extracts amplitude of all frequency components that are defined by frequencies corresponding to a spindle rotation speed of the machine tool×n (provided, 1≤n≤the number of blades of a tool, n is an integer); and determines presence or absence of blade edge damage, on the basis of a Mahalanobis distance calculated by MT (Mahalanobis and Taguchi) method in which data on the extracted amplitude are defined as feature amounts, and a predetermined threshold value.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an abnormality detection device and an abnormality detection method for a cutting tool. [Background technology]

[0002] Patent Document 1 describes a cutting tool anomaly detection device and method that, in a machine tool equipped with a cutting tool having multiple blades, detects the load on the spindle during cutting as a current value or power value of the spindle motor, calculates the frequency spectrum of the time-series data of the detected current value or power value, and determines whether the cutting tool is damaged based on the amplitude of a predetermined frequency component and a predetermined threshold value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-104257 Summary of the Invention [Problem to be solved by the invention]

[0004] The cutting tool anomaly detection device and anomaly detection method described in Patent Document 1 are particularly effective in multi-blade tools with a large number of blades. When wear progresses, the wear progression of each blade is often not uniform, resulting in large variations in the cutting load on each blade, and the frequency spectrum reflects periodic fluctuations caused by the variations in cutting load between each blade. Therefore, when a single variable such as amplitude at a predetermined frequency is used to determine whether or not there is chipping at the cutting edge, as in the cutting tool abnormality detection device and abnormality detection method described in Patent Document 1, it may be difficult to detect an abnormality in the cutting tool when chipping at the cutting edge occurs at different stages of tool wear progression. An object of the present invention is to provide an apparatus and method for detecting an abnormality in a cutting tool that enable stable detection of an abnormality in the cutting tool regardless of the progress of tool wear. [Means for solving the problem]

[0005] In one embodiment of the present invention, the anomaly detection method detects vibrations in components of a machine tool during machining, calculates the frequency spectrum of the vibrations, extracts amplitudes from the frequency spectrum for all frequency components defined as the frequency corresponding to the spindle speed of the machine tool x n (where 1≦n≦number of tool teeth, n is an integer), and determines whether or not the cutting edge of the cutting tool is chipped based on the Mahalanobis distance calculated using the MT (Mahalanobis-Taguchi) method, which uses the extracted amplitude data as a feature, and a predetermined threshold value. [Effects of the Invention]

[0006] Therefore, in the present invention, it is possible to detect the occurrence of chipping of the cutting tool's cutting edge and the number of chipped cutting edges regardless of the progress of tool wear due to the cumulative number of processed pieces, and it is possible to suppress the occurrence of continuous machining defects due to deterioration in machining quality and machining accuracy as chipping of the cutting edge increases, and to prevent the production of a large number of defective products. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is an overall view of a cutting tool abnormality detection device according to a first embodiment. [Figure 2] 1 is a flowchart 1 showing the flow of an anomaly detection method according to the first embodiment. [Figure 3] 10 is a flowchart 2 showing the flow of the anomaly detection method in the first embodiment. [Figure 4] FIG. 10 is a diagram showing the amplitude intensity distribution under normal conditions when the cutting tool of the first embodiment has machined 300 workpieces in total since the start of use. [Figure 5] FIG. 5 is a diagram showing the amplitude intensity distribution when machining is performed using a cutting tool having 42 blades, one of which has been artificially damaged, that has been used to machine up to 300 workpieces shown in FIG. 4 according to the first embodiment. [Figure 6] FIG. 10 is a diagram showing the amplitude intensity distribution under normal conditions when the cutting tool of the first embodiment has been used to machine 1,500 workpieces in total. [Figure 7] FIG. 7 is a diagram showing the amplitude intensity distribution when machining is performed using a cutting tool having 42 blades, one of which has been artificially damaged, that has been used to machine up to 1,500 workpieces shown in FIG. 6 according to the first embodiment. [Figure 8] FIG. 10 is a diagram showing the amplitude intensity distribution when the cutting tool of the first embodiment has been used to machine 3,300 workpieces in total. [Figure 9] FIG. 10 is a diagram showing the amplitude intensity distribution when machining is performed using a cutting tool having 42 blades, one of which has been artificially damaged, that has been used to machine up to 3,300 workpieces shown in FIG. 8 according to the first embodiment. [Figure 10] 10 is a graph showing the relationship between the cumulative number of processes and the Mahalanobis distance in the first embodiment. [Figure 11] 10 is a graph showing the relationship between the first cumulative number of processes and the Mahalanobis distance in the first embodiment. [Figure 12] 10 is a graph showing the relationship between the second cumulative number of processes and the Mahalanobis distance in the first embodiment. [Figure 13] 10 is a graph showing the relationship between the third cumulative number of processes and the Mahalanobis distance in the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] [Embodiment 1] FIG. 1 is an overall view of a cutting tool abnormality detection device according to a first embodiment.

[0009] (Machine tool configuration) The machine tool 1 has a cutting tool 5 equipped with a plurality of blades 6, a spindle 9 driven by a drive motor (not shown) for axially moving and rotating the cutting tool 5, a spindle holding member 10 for holding the spindle 9, a work chuck 8 for gripping and synchronously rotating a workpiece 7 to be internally geared by skiving with the cutting tool 5, an NC control unit 2 for controlling the axial movement and rotation of the spindle 9 and the synchronous rotation of the work chuck 8, and an alarm unit 4.

[0010] (Configuration of anomaly detection device) The abnormality detection device 3 includes a vibration sensor 11 attached to a spindle holding member 10, a signal amplifier 12 that amplifies the vibration signal measured by the vibration sensor 11, a signal memory 13 that stores the vibration signal amplified by the signal amplifier 12, a frequency analysis unit 14 that calculates the frequency spectrum of the vibration signal stored in the signal memory 13, a multivariate analysis unit 15 that extracts amplitudes for all frequency components defined as the frequency corresponding to the rotation speed of the spindle 9 × n (where 1 ≦ n ≦ the number of tool teeth, n is an integer) from the frequency spectrum calculated by the frequency analysis unit 14 and calculates the Mahalanobis distance by the Mahalanobis-Taguchi (MT) method using the extracted amplitude data as a feature, a frequency amplitude data memory 16 that stores the extracted amplitude data for each machining cycle, and a judgment unit 17 that judges whether or not the cutting edge of the cutting tool is chipped by comparing the Mahalanobis distance calculated by the multivariate analysis unit 15 with a predetermined threshold value.

[0011] FIG. 2 is a flowchart 1 showing the flow of the anomaly detection method in the first embodiment, and FIG. 3 is a flowchart 2 showing the flow of the anomaly detection method in the first embodiment.

[0012] (Operation of the abnormality detection device) In step S1, when machining of the workpiece 7 is started by the machine tool 1, a machining start signal is generated from the NC control unit 2 as a trigger to start an abnormality detection process. In step S2, the vibration sensor 11 starts measuring the vibration caused by the cutting process, and the signal amplifier 12 amplifies the vibration signal measured by the vibration sensor 11, and outputs the amplified vibration signal to the signal memory unit 13 (vibration detection step). In step S3, the signal storage unit 13 stores the input amplified vibration signal as time-series data up to a predetermined number of samples (signal storage step). In step S4, the frequency analysis unit 14 calculates the frequency spectrum of the amplified vibration signal from the time series data of the amplified vibration signal stored in the signal storage unit 13 (frequency analysis step). In step S5, the multivariate analysis unit 15 extracts amplitude data for all frequency components defined by the frequency corresponding to the spindle rotation speed × n (where 1≦n≦number of tool blades, n is an integer) from the frequency spectrum calculated by the frequency analysis unit 14 (amplitude extraction step). In step S6, the extracted amplitude data is stored in the frequency amplitude data storage unit 16 for each machining cycle (amplitude data storage step). The amplitude data stored for each machining cycle is used to construct the unit space (normal population) of the MT (Mahalanobis-Taguchi) method. In step S7, the multivariate analysis unit 15 calculates the Mahalanobis distance by the MT (Mahalanobis-Taguchi) method using the amplitude data stored in the frequency amplitude data storage unit 16 as a feature (analysis step). In calculating the Mahalanobis distance, the unit space (normal group) of the MT (Mahalanobis-Taguchi) method is set using a group of amplitude data at the beginning of use of the cutting tool. The reason for this is that when the cutting tool is first used, the cutting tool is in a brand new state immediately after replacement, and therefore a group of amplitude data in a normal cutting state can be obtained. In setting the unit space (normal data group) in the first embodiment, amplitude data for machining 20 (any number) workpieces at the beginning of use of each cutting tool was collected for a plurality of cutting tools of the same type other than the machining examples in each of Figs. 11, 12, and 13 described later, and used as the amplitude data group. This allows the amplitude data group across multiple cutting tools to be used to set the unit space (normal data group), thereby averaging out the variation in the performance of the cutting tools, and by setting a unit space (normal data group) that serves as a common standard, it is possible to avoid the complication of setting a unit space (normal data group) each time a tool is changed. In addition, the sensitivity of detecting chipped cutting edges can be improved by dynamically setting the unit space (normal data group) of the MT (Mahalanobis-Taguchi) method using the amplitude data group from the previous machining cycle to any number of machining cycles going back for the currently executing machining cycle. In step S8, the determination unit 17 compares the calculated Mahalanobis distance with a predetermined threshold value (comparison step). If the calculated Mahalanobis distance is greater than the predetermined threshold value, the process proceeds to step S9, and if the calculated Mahalanobis distance is less than the predetermined threshold value, the process proceeds to step S10, where the abnormality detection process ends. In step S9, the judgment unit 17 outputs an abnormality signal to the NC control unit 2 and the alarm unit 4, the NC control unit 2 stops the machine tool 1, the alarm unit 4 issues an alarm indicating that an abnormality has occurred, and the process proceeds to step S10, where the abnormality detection process is terminated (output step).

[0013] (Amplitude extraction step) FIG. 4 is a diagram showing the amplitude intensity distribution under normal conditions when the cutting tool of embodiment 1 has been used to machine up to a cumulative number of 300 workpieces, and FIG. 5 is a diagram showing the amplitude intensity distribution when machining is performed using a cutting tool of embodiment 1 shown in FIG. 4 with an artificial defect in one of the 42 cutting edges that has been used to machine up to 300 workpieces. 4 and 5 show the amplitude intensity distribution extracted for all frequency components defined as the frequency corresponding to the rotation speed of the spindle 9 x n (where 1≦n≦number of tool blades, n is an integer) from the frequency spectrum of vibration detected by the vibration sensor 11 when machining was performed using a 42-blade cutting tool under the following machining conditions: spindle rotation speed 3676 min-1, tool feed rate per spindle rotation 0.1 mm / rev, and machining allowance 0.2 mm. Under the above conditions, the frequency when n = 1 is 61.3 Hz, and the maximum frequency is 2573 Hz when n = 42 (hereinafter, the frequency corresponding to the rotation speed of the spindle 9 x n is referred to as the nth frequency, and the amplitude of the nth frequency is referred to as the nth frequency component).

[0014] 4 and 5, the primary frequency component 22 after the cutting edge breakage increases significantly compared to the primary frequency component 21 before the cutting edge breakage. In this case, by setting the threshold value 23 so that the primary frequency component 22 after the cutting edge breakage exceeds it, it is possible to detect an abnormality in which one cutting edge is broken.

[0015] FIG. 6 is a diagram showing the amplitude intensity distribution under normal conditions when the cutting tool of embodiment 1 has been used to machine up to a cumulative number of 1,500 workpieces, and FIG. 7 is a diagram showing the amplitude intensity distribution when machining is performed using a cutting tool of embodiment 1 shown in FIG. 6 with an artificial defect in one of the 42 cutting edges that has been used to machine up to 1,500 workpieces. The processing conditions are the same as those in Figs.

[0016] 6 and 7, it is clear that the 10th-order frequency component 25 after chipping of the cutting edge is significantly increased compared to the 10th-order frequency component 24 before chipping of the cutting edge. In this case, by setting the threshold value 26 so that the 10th-order frequency component 25 after chipping of the cutting edge exceeds the threshold value 26, it is possible to detect an abnormality in which one cutting edge is chipped.

[0017] FIG. 8 is a diagram showing the amplitude intensity distribution when the cutting tool of embodiment 1 has been used to machine up to a cumulative number of 3,300 workpieces, and FIG. 9 is a diagram showing the amplitude intensity distribution when machining is performed using the cutting tool of embodiment 1 shown in FIG. 7 with an artificial defect in one of the 42 blades that has been used to machine up to 3,300 workpieces. The processing conditions are the same as those in Figs.

[0018] Comparing Figures 8 and 9, the fourth-order frequency component 28 after the cutting edge breakage increases significantly compared to the fourth-order frequency component 27 before the cutting edge breakage. In this case, by setting the threshold value 29 so that the fourth-order frequency component 28 after the cutting edge breakage exceeds it, it is possible to detect an abnormality in which one cutting edge is broken.

[0019] As described above, it is clear that it is possible to detect chipping of the cutting tool's cutting edge by comparing a predetermined frequency component with a threshold value for each cumulative number of machining operations. However, since the frequency component suitable for determining an abnormality differs depending on the cumulative number of machining operations, it is difficult to determine chipping of the cutting edge using a single variable such as the amplitude of a predetermined frequency component. The reason for this is that vibration during cutting is caused by fluctuations in cutting load. With a cutting tool that has multiple blades, the cutting load from each blade is generated repeatedly with each rotation of the tool when cutting the workpiece, resulting in vibration with a frequency component equal to the spindle rotation speed x the number of tool blades. In principle, if there is no difference in cutting load between each blade, vibration with a frequency lower than the frequency of the spindle rotation speed x the number of tool blades will not occur. On the other hand, if there are differences in cutting loads between the teeth, a repeating pattern of cutting load fluctuations based on the differences in cutting loads on each tooth occurs with each rotation of the tool, and vibrations with a frequency spectrum that reflects this repeating pattern of cutting load fluctuations are generated.For example, if the cutting load on one tooth is significantly greater than the other teeth, a large cutting load fluctuation occurs once with each rotation of the tool, and vibrations with a frequency equivalent to 1 x spindle rotation speed, i.e., a primary frequency component, appear in the frequency spectrum. The causes of differences in cutting load between each blade include differences in the state of wear of the blades and damage to the cutting edge. In this invention, abnormalities are detected by detecting changes in the repeating pattern of cutting load fluctuations before and after cutting edge damage as changes in the frequency spectrum of vibration during cutting. The repetitive pattern of cutting load fluctuations when cutting edge fracture occurs is a pattern in which the repetitive pattern of cutting load fluctuations before the cutting edge fracture is superimposed with the cutting load fluctuations due to the cutting edge fracture. Therefore, the change in the frequency spectrum of vibration before and after cutting edge fracture depends on the frequency spectrum of vibration before the cutting edge fracture. In a cutting tool with multiple cutting edges, if the wear state of each edge changes differently as the cumulative number of cuts increases, the repetitive pattern of cutting load fluctuations will change depending on the cumulative number of cuts. In other words, since the frequency spectrum before cutting edge breakage differs depending on the cumulative number of cuts when cutting edge breakage occurs, the change in the frequency spectrum of vibration after cutting edge breakage depends on the cumulative number of cuts. Therefore, the frequency components suitable for detecting abnormalities also change depending on the cumulative number of cuts.

[0020] For this reason, the anomaly detection method in one embodiment of the present invention further determines whether or not the cutting tool has a chipped cutting edge based on the Mahalanobis distance calculated by the MT (Mahalanobis-Taguchi) method using the extracted amplitude data as a feature and a predetermined threshold value.

[0021] (Analysis step) FIG. 10 is a graph showing the relationship between the cumulative number of processes and the Mahalanobis distance in the first embodiment. That is, this is the transition of the Mahalanobis distance with respect to the cumulative number of machining operations when machining a workpiece using a cutting tool with an artificial defect in the cutting edge of one of the 42 cutting edges, after machining a cumulative number of 300 pieces under the machining conditions (spindle speed 3676 min-1, tool feed per spindle revolution 0.1 mm / rev, machining allowance 0.1 mm).

[0022] The unit space (normal data group) S of the MT (Mahalanobis-Taguchi) method was set using amplitude data from the machining cycles one to ten times before the current machining cycle. As the cumulative number of machining operations increases, the unit space (normal data group) S, which is the basis for calculating the Mahalanobis distance, also changes dynamically, so the Mahalanobis distance becomes smaller unless a sudden phenomenon such as cutting edge chipping occurs. For this reason, while the Mahalanobis distance of the unit space (normal data group) S before the cutting edge chipping is approximately 1, the Mahalanobis distance 46 after the cutting edge chipping is approximately 400. This shows that the detection sensitivity for cutting edge chipping is higher than the average difference of approximately 20 between the case where there is no cutting edge chipping and the case where there is one cutting edge chipping, as shown in Figures 11, 12, and 13 described below.

[0023] FIG. 11 is a graph showing the relationship between the first cumulative number of processes and the Mahalanobis distance in the first embodiment. In other words, this is the transition of the Mahalanobis distance with respect to the cumulative number of machining operations when machining one workpiece using an artificially damaged cutting edge of one of the 42 cutting edges of a cutting tool that has been used to machine up to 300 workpieces, and then machining a total of 10 workpieces using this tool, and then machining 10 workpieces using a cutting edge that is different from the cutting edge that initially caused the damage, and so on, and so on until a total of four cutting edges have been damaged.

[0024] The Mahalanobis distances obtained after machining 10 workpieces with one chipped cutting edge form a data group 30 of Mahalanobis distances with large values ​​compared to the unit space (normal data group) S of the Mahalanobis distances before the chipped cutting edge, i.e., in machining up to a cumulative number of 300 pieces. For this reason, the chipped cutting edge in one location can be detected by setting the first threshold value 42. In addition, data group 31 of Mahalanobis distances in the state where there are two chipped edges consists of Mahalanobis distances that are larger than data group 30 of Mahalanobis distances, and data group 32 of Mahalanobis distances in the state where there are three chipped edges and data group 33 of Mahalanobis distances in the state where there are four chipped edges also form groups consisting of Mahalanobis distances whose size corresponds to the increase in the number of chipped edges. Therefore, the number of cutting edge chips can be detected by setting a second threshold value 43 between the Mahalanobis distance data group 30 and the Mahalanobis distance data group 31, setting a third threshold value 44 between the Mahalanobis distance data group 31 and the Mahalanobis distance data group 32, and further setting a fourth threshold value 45 between the Mahalanobis distance data group 32 and the Mahalanobis distance data group 33.

[0025] FIG. 12 is a graph showing the relationship between the second cumulative number of processes and the Mahalanobis distance in the first embodiment. In other words, after machining a cumulative number of 1,500 workpieces, a total of 10 workpieces are machined continuously, and then the process is repeated to machine 10 workpieces each time a chip is added to the cutting edge, until a total of four chips are left on the cutting edge. This shows the change in Mahalanobis distance versus the cumulative number of workpieces machined.

[0026] Using the same first threshold value 42, second threshold value 43, third threshold value 44, and fourth threshold value 45 as in Figure 11, it is possible to distinguish between the unit space (normal data group) S of Mahalanobis distance in machining up to a cumulative machining count of 1,500 pieces with no cutting edge chipping, the Mahalanobis distance data group 34 in a state where there is one cutting edge chipping, the Mahalanobis distance data group 35 in a state where there is two cutting edge chipping, the Mahalanobis distance data group 36 in a state where there are three cutting edge chipping, and the Mahalanobis distance data group 37 in a state where there are four cutting edge chippings, and it is clear that it is possible to detect the occurrence of cutting edge chipping and the number of cutting edge chippings.

[0027] FIG. 13 is a graph showing the relationship between the third cumulative number of processes and the Mahalanobis distance in the first embodiment. In other words, after machining a cumulative number of 3,300 workpieces, a total of 10 workpieces are machined continuously, and then the process is repeated to machine 10 workpieces each time a chip is added to the cutting edge, until a total of four chips are left on the cutting edge. This shows the change in Mahalanobis distance versus the cumulative number of workpieces machined.

[0028] Using the same first threshold value 42, second threshold value 43, third threshold value 44, and fourth threshold value 45 as in Figure 10, it is possible to distinguish between the unit space (normal data group) S of Mahalanobis distance in machining up to a cumulative machining count of 3,300 pieces with no cutting edge chipping, the Mahalanobis distance data group 38 in a state where there is one cutting edge chipping, the Mahalanobis distance data group 39 in a state where there is two cutting edge chipping, the Mahalanobis distance data group 40 in a state where there are three cutting edge chippings, and the Mahalanobis distance data group 41 in a state where there are four cutting edge chippings, and it can be seen that it is possible to similarly detect the occurrence of cutting edge chipping and the number of cutting edge chippings.

[0029] Next, the effects of the first embodiment will be described.

[0030] (1) Vibrations in the components of the machine tool during machining are detected, the frequency spectrum of the vibrations is calculated, and from the frequency spectrum, the amplitudes are extracted for all frequency components defined as the frequency corresponding to the spindle speed of the machine tool x n (where 1≦n≦number of tool teeth, n is an integer).The extracted amplitude data is used as a feature, and the Mahalanobis distance calculated using the MT (Mahalanobis-Taguchi) method is used to calculate the Mahalanobis distance.The presence or absence of chipping on the cutting tool cutting edge is determined based on a predetermined threshold value. Therefore, it is possible to detect the occurrence of chipping of the cutting tool's cutting edge and the number of chipped cutting edges regardless of the progress of tool wear due to the cumulative number of processed pieces, and it is possible to suppress the occurrence of continuous machining defects due to deterioration in machining quality and machining accuracy as chipping of the cutting edge increases, thereby preventing the production of a large number of defective products.

[0031] (2) In calculating the Mahalanobis distance, the amplitude data set at the beginning of use of the cutting tool was used to set the unit space (normal data set) of the MT (Mahalanobis-Taguchi) method. Therefore, when the cutting tool is first used, the cutting tool is in a brand new state immediately after replacement, so a group of amplitude data in a normal cutting state can be obtained.

[0032] (3) In calculating the Mahalanobis distance, amplitude data for 20 (any number) workpieces processed at the beginning of use of each cutting tool was collected for multiple cutting tools of the same type, and used as an amplitude data group to set the unit space (normal data group) for the MT (Mahalanobis-Taguchi) method. Therefore, by using a group of amplitude data across multiple cutting tools to set a unit space (a group of normal data), the variation in the performance of the cutting tools can be averaged out, and by setting a unit space that serves as a common standard, the complication of setting a unit space each time a tool is changed can be avoided.

[0033] (4) In calculating the Mahalanobis distance, the unit space (normal data group) of the MT (Mahalanobis-Taguchi) method is dynamically set using the amplitude data group from the previous machining cycle to an arbitrary number of machining cycles going back for the currently executing machining cycle. Therefore, the sensitivity for detecting chipped cutting edges can be improved.

[0034] Other Embodiments The above describes an embodiment for carrying out the present invention, but the specific configuration of the present invention is not limited to the configuration of the embodiment, and design changes and the like that do not deviate from the gist of the invention are also included in the present invention. [Explanation of symbols]

[0035] 1 machine tool, 2 NC control unit, 3 abnormality detection device, 4 alarm unit, 5 cutting tool, 11 vibration sensor, 12 signal amplifier unit, 13 signal storage unit, 14 frequency analysis unit, 15 multivariate analysis unit, 16 frequency amplitude data storage unit, 17 judgment unit, 41 first threshold value (predetermined threshold value), 42 second threshold value (predetermined threshold value), 43 third threshold value (predetermined threshold value), 44 fourth threshold value (predetermined threshold value)

Claims

1. An abnormality detection device for detecting chipping of a cutting tool having a plurality of blades, a vibration sensor that detects vibrations of the cutting tool during machining; a signal amplifier that amplifies the vibration signal detected by the vibration sensor; a signal storage unit that stores the vibration signal amplified by the signal amplifier unit; a frequency analysis unit that calculates a frequency spectrum of the vibration signal stored in the signal storage unit; a multivariate analysis unit that extracts amplitude data for all frequency components defined by a frequency corresponding to the spindle rotation speed of the cutting tool x n (where 1≦n≦the number of tool teeth, n is an integer) from the frequency spectrum calculated by the frequency analysis unit, and calculates a Mahalanobis distance by an MT (Mahalanobis-Taguchi) method using the extracted amplitude data as a feature quantity; a frequency amplitude data storage unit that stores the amplitude data extracted by the multivariate analysis unit for each processing cycle; a determination unit that determines whether the cutting tool has a chipped cutting edge based on the Mahalanobis distance calculated by the multivariate analysis unit and a predetermined threshold value; Equipped with In calculating the Mahalanobis distance, a unit space is set by a data group consisting of amplitude data from the machining cycle immediately preceding the currently executed machining cycle to an arbitrary number of machining cycles back from the amplitude data stored in the frequency amplitude data storage unit. A cutting tool abnormality detection device characterized by:

2. An abnormality detection device for detecting chipping of a cutting tool having a plurality of blades, a vibration sensor that detects vibrations of the cutting tool during machining; a signal amplifier that amplifies the vibration signal detected by the vibration sensor; a signal storage unit that stores the vibration signal amplified by the signal amplifier unit; a frequency analysis unit that calculates a frequency spectrum of the vibration signal stored in the signal storage unit; a multivariate analysis unit that extracts amplitude data for all frequency components defined by a frequency corresponding to the spindle rotation speed of the cutting tool x n (where 1≦n≦the number of tool teeth, n is an integer) from the frequency spectrum calculated by the frequency analysis unit, and calculates a Mahalanobis distance by an MT (Mahalanobis-Taguchi) method using the extracted amplitude data as a feature quantity; a frequency amplitude data storage unit that stores the amplitude data extracted by the multivariate analysis unit for each processing cycle; a determination unit that determines whether the cutting tool has a chipped cutting edge based on the Mahalanobis distance calculated by the multivariate analysis unit and a predetermined threshold value; Equipped with In the calculation of the Mahalanobis distance, a unit space is set by a data group selected from the amplitude data stored in the frequency amplitude data storage unit, an arbitrary number of amplitude data at the initial stage of use of a plurality of cutting tools of the same type. A cutting tool abnormality detection device characterized by:

3. An anomaly detection method executed by an anomaly detection device, The anomaly detection device a vibration detection step of detecting vibrations of a component of the machine tool during cutting using a cutting tool having a plurality of blades; a signal storage step of recording the detected vibration signal as time-series data up to a predetermined number of samples; a frequency analysis step of calculating a frequency spectrum of the time series data of the vibration signal recorded in the signal storage step; an amplitude extraction step of extracting amplitudes for all frequency components defined by a frequency corresponding to the spindle rotation speed of the machine tool x n (where 1≦n≦number of tool teeth, n is an integer) from the frequency spectrum; an amplitude data storage step of storing the amplitude data extracted in the amplitude extraction step for each machining cycle; an analysis step of calculating a Mahalanobis distance by multivariate analysis using the extracted amplitude data as a feature value by the Mahalanobis-Taguchi (MT) method; a comparison step of comparing the calculated Mahalanobis distance with a predetermined threshold value; an output step of outputting an abnormality signal when the Mahalanobis distance exceeds the predetermined threshold value in the comparison step; Equipped with In the calculation of the Mahalanobis distance in the analyzing step, a unit space is set by a data group consisting of amplitude data from the machining cycle immediately before the currently executed machining cycle to a machining cycle counted back by an arbitrary number from the amplitude data stored in the amplitude data storing step. A method for detecting an abnormality in a cutting tool.

4. An anomaly detection method executed by an anomaly detection device, The anomaly detection device a vibration detection step of detecting vibrations of a component of the machine tool during cutting using a cutting tool having a plurality of blades; a signal storage step of recording the detected vibration signal as time-series data up to a predetermined number of samples; a frequency analysis step of calculating a frequency spectrum of the time series data of the vibration signal recorded in the signal storage step; an amplitude extraction step of extracting amplitudes for all frequency components defined by a frequency corresponding to the spindle rotation speed of the machine tool x n (where 1≦n≦number of tool teeth, n is an integer) from the frequency spectrum; an amplitude data storage step of storing the amplitude data extracted in the amplitude extraction step for each machining cycle; an analysis step of calculating a Mahalanobis distance by multivariate analysis using the extracted amplitude data as a feature value by the Mahalanobis-Taguchi (MT) method; a comparison step of comparing the calculated Mahalanobis distance with a predetermined threshold value; an output step of outputting an abnormality signal when the Mahalanobis distance exceeds the predetermined threshold value in the comparison step; Equipped with In the calculation of the Mahalanobis distance, a unit space is set by a data group selected from the amplitude data stored in the amplitude data storage step, an arbitrary number of amplitude data at an initial stage of use of a plurality of cutting tools of the same type. A method for detecting an abnormality in a cutting tool.

Images