Tool abnormity monitoring method and device and machine tool

Abstract

The invention discloses a tool abnormity monitoring method and device and a machine tool, and belongs to the technical field of machine tools. The tool abnormity monitoring method comprises the steps that main shaft operation information of a machine tool in the machining process is obtained, the main shaft operation information comprises load information, speed information and speed modal information of N sampling points, and N is an integer larger than 2; based on the speed information and the speed modal information of each sampling point in the N sampling points, M target sampling points meeting a preset condition are determined, and M is a positive integer smaller than N; corresponding load information of each target sampling point in the M target sampling points in K processing samples is obtained, a first load matrix used for representing M * K pieces of load information is obtained, and K is a positive integer; and based on the first load matrix, monitoring boundaries corresponding to the M target sampling points are determined, and the cutter state of the machine tool is determined based on the monitoring boundaries. Therefore, the accuracy of the monitoring result of the cutter state is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of machine tools, and specifically relates to a tool abnormality monitoring method, device and machine tool. Background Art

[0002] With the development of technology, the demand for abnormal tool load monitoring in high-precision machining fields such as drilling centers, core machines, and mold processing continues to emerge, making it difficult for conventional data feature values ​​that can be used for tool load monitoring to cope with increasingly complex needs. Moreover, since the calculation method of conventional data feature values ​​that can be used for tool load monitoring is conventional and fixed, when faced with more complex, multi-dimensional, and more uncertain high-precision monitoring needs, the data feature values ​​for tool load monitoring obtained based on conventional and fixed calculation methods are difficult to accurately characterize the health status of the tool.

[0003] It can be seen that the monitoring scheme for monitoring the tool status in the related art has the problem of poor accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a tool abnormality monitoring method, device and machine tool, which can solve the problem of poor accuracy of the monitoring scheme for monitoring the tool status in the related art.

[0005] In order to solve the above technical problems, this application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring the state of a machine tool tool, the method comprising:

[0007] Obtaining spindle operation information of the machine tool during the machining process, wherein the spindle operation information includes load information, speed information, and speed modal information of N sampling points, where N is an integer greater than 2;

[0008] Based on the speed information and speed modal information of each sampling point in the N sampling points, determining M target sampling points that meet the preset conditions, where M is a positive integer less than N;

[0009] Obtaining the load information corresponding to each target sampling point in the M target sampling points in the K processing samples, and obtaining a first load matrix for representing the M*K load information, where K is a positive integer;

[0010] Based on the first load matrix, monitoring boundaries corresponding to the M target sampling points are determined, and based on the monitoring boundaries, a tool state of the machine tool is determined.

[0011] In a second aspect, an embodiment of the present application provides a machine tool tool state monitoring device, the device comprising:

[0012] A first acquisition module is used to acquire spindle operation information of the machine tool during the machining process, wherein the spindle operation information includes load information, speed information and speed modal information of N sampling points, where N is an integer greater than 2;

[0013] A first determination module is used to determine M target sampling points that meet preset conditions based on the speed information and speed modal information of each sampling point in the N sampling points, where M is a positive integer less than N;

[0014] A second acquisition module is used to acquire the load information corresponding to each of the M target sampling points in the K processing samples, and obtain a first load matrix for representing M*K load information, where K is a positive integer;

[0015] The second determination module is used to determine the monitoring boundaries corresponding to the M target sampling points based on the first load matrix, and determine the tool state of the machine tool based on the monitoring boundaries.

[0016] In a third aspect, an embodiment of the present application provides a machine tool, comprising a processor and a memory, wherein the memory stores programs or instructions that can be executed on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0019] In a sixth aspect, an embodiment of the present application provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the method described in the first aspect.

[0020] In an embodiment of the present application, M target sampling points that meet the conditions are determined based on the spindle operation information, and then the load information of each target sampling point in the M target sampling points in the K processing samples is used to obtain a first load matrix for characterizing M*K load information. Since the first load matrix is ​​determined based on the K processing samples, the first load matrix can accurately characterize the load information of the machine tool, thereby improving the accuracy of the monitoring boundary obtained based on the first load matrix, and thereby improving the accuracy of the monitoring results of the tool status. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1is a flow chart of a machine tool tool status monitoring method provided by an embodiment of the present application;

[0022] Figure 2 is a structural diagram of a machine tool tool status monitoring device provided in an embodiment of the present application;

[0023] Figure 3 It is a structural diagram of the machine tool provided in the embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0026] The following is a detailed description of the machine tool tool status monitoring method provided by the embodiment of the present application through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0027] See also Figure 1 , Figure 1 It is a flow chart of a machine tool tool status monitoring method provided in an embodiment of the present application.

[0028] like Figure 1 As shown, the machine tool tool status monitoring method can be applied to machine tools, including the following steps:

[0029] Step 101 : obtaining spindle operation information of a machine tool during a machining process, wherein the spindle operation information includes load information, speed information, and speed modal information of N sampling points.

[0030] Wherein, N is an integer greater than 2.

[0031] In some embodiments, the load information of N sampling points can be expressed as spindle_b:[x1,x2,x3,…,xN], the speed information of N sampling points can be expressed as speed_b:[x1,x2,x3,…,xN], and the speed mode information of N sampling points can be expressed as speed_mode_b:[x1,x2,x3,…,xn].

[0032] Step 102: Based on the speed information and speed modal information of each of the N sampling points, determine M target sampling points that meet preset conditions.

[0033] Wherein, M is a positive integer less than N.

[0034] In this step, M target sampling points that meet preset conditions may be determined based on the speed information and speed modal information of each of the N sampling points.

[0035] For example, for the dotting processing scenario, the absolute value of each sampling point can be calculated based on the formula speed_b-speed_mode_b, and the sampling points whose absolute values ​​are less than the preset value (speed_threshold) are determined as sampling points that meet the preset conditions, so as to determine M target sampling points based on the sampling points that meet the preset conditions.

[0036] Specifically, for the i-th sampling point, if its corresponding speed information is Si and its corresponding speed mode information is SMi, the absolute value of the sampling point can be calculated based on the formula speed_b-speed_mode_b = |Si-SMi|, and then it is determined whether its absolute value is greater than the preset value (speed_threshold) to determine whether the sampling point meets the preset conditions.

[0037] Step 103: Obtain the load information corresponding to each target sampling point in the M target sampling points in the K processing samples, and obtain a first load matrix for representing M*K load information.

[0038] Wherein, K is a positive integer.

[0039] The K processing samples mentioned above can be understood as processing parameters of the machine tool for K target objects, and the target objects can be understood as parts or components machined on the machine tool.

[0040] In this step, the load information corresponding to each target sampling point in K processing samples can be obtained to obtain the load information of each target sampling point in each processing sample, thereby obtaining an M*K load matrix including M*K load information.

[0041] In some embodiments, the value of K may be determined based on an empirical value. For example, K may be 200, which means that 200 processing samples for the target object need to be obtained before the calculation of the monitoring boundary can be started.

[0042] The first load matrix d can be expressed as:

[0043]

[0044] Where k represents the number of training samples, m represents the number of sampling points of each sample, and dij represents the load value at the jth sampling point of the i-th sample.

[0045] Step 104: Based on the first load matrix, determine the monitoring boundaries corresponding to the M target sampling points, and determine the tool state of the machine tool based on the monitoring boundaries.

[0046] In this step, the monitoring boundaries corresponding to the M target sampling points may be calculated based on the first load matrix, so as to determine the tool state of the machine tool based on the calculated monitoring boundaries.

[0047] Specifically, the average value and the standard deviation of the first load matrix may be calculated, so as to determine the monitoring boundaries corresponding to the M target sampling points based on the average value and the standard deviation.

[0048] In this embodiment, M target sampling points that meet the conditions are determined based on the spindle operation information, and then based on the load information of each target sampling point in the M target sampling points in the K processing samples, a first load matrix for characterizing M*K load information is obtained. Since the first load matrix is ​​determined based on the K processing samples, the first load matrix can accurately characterize the load information of the machine tool, thereby improving the accuracy of the monitoring boundary obtained based on the first load matrix, and further improving the accuracy of the monitoring result of the tool status.

[0049] In some embodiments, determining the monitoring boundaries corresponding to the M target sampling points based on the first load matrix includes:

[0050] Obtaining load median information for characterizing each target sampling point among the M target sampling points in the first load matrix;

[0051] Based on the load median information and the first load matrix, a second load matrix is ​​obtained;

[0052] Based on the second load matrix, calculating the average value and standard deviation of each target sampling point in the M target sampling points;

[0053] Based on the average value and standard deviation of each of the M target sampling points, the monitoring boundaries corresponding to the M target sampling points are determined.

[0054] In this embodiment, the second load matrix is ​​obtained by calculating the median load information and updating the first load matrix based on the median load information; the mean value and standard deviation of each target sampling point in the M target sampling points determined based on the second load matrix can more accurately characterize the monitoring boundaries corresponding to the M target sampling points, thereby further improving the accuracy of the monitoring results of the tool status.

[0055] In some embodiments, determining the monitoring boundary corresponding to the M target sampling points based on the average value and standard deviation of each of the M target sampling points includes:

[0056] Calculate the sum of the mean value and the standard deviation of each target sampling point in the M target sampling points to obtain M sum coordinates, and the connecting line of the M sum coordinates is used to form a first boundary;

[0057] Calculate the difference between the mean value and the standard deviation of each of the M target sampling points to obtain M difference coordinates, and the connecting line of the M difference coordinates is used to form a second boundary;

[0058] The first boundary and the second boundary are determined as monitoring boundaries corresponding to the M target sampling points.

[0059] In this embodiment, the sum and difference of the mean value and the standard deviation of each target sampling point among the M target sampling points can be used to obtain M sum coordinates and M difference coordinates, and then the first boundary formed by the connection line of the M sum coordinates and the second boundary formed by the connection line of the M difference coordinates are determined as the monitoring boundaries corresponding to the M target sampling points, thereby improving the accuracy of the monitoring boundaries.

[0060] In some embodiments, the median of the first load matrix may be calculated column by column to obtain load median information d_median: [dm_1, dm_2, ... dm_j, ... dm_m];

[0061] Then, d-d_median is calculated to obtain the second load matrix d_new, and the second load matrix d_new can be expressed as:

[0062]

[0063] Among them, the mean and standard deviation of d_new can be calculated by column, that is, the mean value μ:[μ1,μ2,…,μ j ,…,μ m ], standard deviationσ:[σ1,σ2,…,σ j ,…,σ m ].

[0064] Finally, we can use the formula border = μ j ±Zσ j , calculate the sum coordinates used to form the first border, and the difference coordinates used to form the second border, where border can be understood as a coordinate value, and the sum coordinate border = μ j +Zσ j , difference coordinate border = μ j -Zσ j , j∈[1,m], Z can be understood as a weight parameter, and its value can be 3.5.

[0065] In some embodiments, the value of Z may also be adjusted based on actual needs, and the value of Z is not specifically limited here.

[0066] In some embodiments, determining M target sampling points that meet preset conditions based on the speed information and speed modal information of each sampling point in the N sampling points includes:

[0067] Obtain the processing scene of the target object;

[0068] In a case where the processing scene is a target processing scene, based on the load information, speed information, and speed modal information of each of the N sampling points, determining M target sampling points that meet preset conditions;

[0069] The target processing scenarios include milling processing scenarios, drilling processing scenarios, tapping processing scenarios and turning processing scenarios.

[0070] In this embodiment, due to different processing scenarios, the parameters required to determine the M target sampling points are also different; therefore, the processing scenario of the target object can be determined to determine the parameters required for the processing scenario, which effectively reduces the amount of calculation required to determine the M target sampling points that meet the preset conditions compared to using a fixed algorithm to calculate the corresponding target sampling points. Moreover, it can also adapt to different tool processing scenarios.

[0071] For milling and drilling processing scenarios, the absolute value of each sampling point can be calculated based on the formula speed_b-speed_mode_b, and the sampling points whose absolute values ​​are less than the preset value (speed_threshold) can be determined as the target sampling points that meet the preset conditions; the first-order derivative of spindle_b can also be calculated, and the derivative result is represented by spindle_b_d, and the sampling points that meet the condition |spindle_b_d|<1 can be determined as the target sampling points that meet the preset conditions.

[0072] For the tapping processing scenario, the absolute value of each sampling point can be calculated based on the formula speed_b-speed_mode_b, and the sampling points with absolute values ​​less than the preset value (speed_threshold) can be determined as the target sampling points that meet the preset conditions; the first-order derivative of spindle_b can also be calculated, and the derivative result is represented by spindle_b_d, and the sampling points that meet the condition |spindle_b_d|<0.5 can be determined as the target sampling points that meet the preset conditions.

[0073] For the turning processing scenario, the absolute value of each sampling point can be calculated based on the formula speed_b-speed_mode_b, and the sampling point with an absolute value less than the preset value (speed_threshold) can be determined as the target sampling point that meets the preset conditions; the first-order derivative of spindle_b can be calculated, and the derivative result is represented by spindle_b_d, and the sampling point that meets the condition |spindle_b_d|<0.5 is determined as the target sampling point that meets the preset conditions; and the first-order derivative of speed_b can be calculated, and the derivative result is represented by speed_b_d, and the sampling point that meets the condition |speed_b_d|<0.5 is determined as the target sampling point that meets the preset conditions.

[0074] In some embodiments, multiple target sampling point sets that meet preset conditions can be obtained, each target sampling point set can be understood as a continuous data segment, and the starting positions of these continuous data segments are represented by the sequence start: [s1, s2, ..., sl], and the end positions are represented by the sequence end: [e1, e2, ..., el], where l represents the number of continuous data segments.

[0075] In some embodiments, the sequence start may also be operated as follows:

[0076] If (ei-si)>1000: si=si+50;

[0077] If 200<(ei-si)<=1000: si=si+20;

[0078] If 50<(ei-si)<=200: si=si+10;

[0079] If(ei-si)<=50: si=si+5.

[0080] Among them, parameters such as 50, 20, 10, and 5 can be set based on actual needs.

[0081] In some embodiments, the sequence end may also be operated as follows:

[0082] If(ei-si)>1000:ei=ei–30;

[0083] If 200<(ei-si)<=1000: ei=ei–10;

[0084] If 50<(ei-si)<=200: ei=ei–5;

[0085] If (ei-si)<=50: ei=ei–3.

[0086] Among them, parameters such as 30, 10, 5, 3, etc. can be set based on actual needs.

[0087] The machine tool tool state monitoring method of the embodiment of the present application obtains the spindle operation information of the machine tool during the processing, and the spindle operation information includes the load information, speed information and speed modal information of N sampling points, where N is an integer greater than 2; based on the speed information and speed modal information of each sampling point in the N sampling points, M target sampling points that meet the preset conditions are determined, where M is a positive integer less than N; the load information corresponding to each target sampling point in the M target sampling points in K processing samples is obtained to obtain a first load matrix for characterizing M*K load information, where K is a positive integer; based on the first load matrix, the monitoring boundaries corresponding to the M target sampling points are determined, and the tool state of the machine tool is determined based on the monitoring boundaries. This improves the accuracy of the tool state monitoring results.

[0088] See also Figure 2 , Figure 2 is a structural diagram of a machine tool tool status monitoring device provided in an embodiment of the present application, such as Figure 2 As shown, the machine tool tool state monitoring device 200 includes:

[0089] The first acquisition module 201 is used to acquire the spindle operation information of the machine tool during the machining process, wherein the spindle operation information includes load information, speed information and speed modal information of N sampling points, where N is an integer greater than 2;

[0090] A first determination module 202 is used to determine M target sampling points that meet a preset condition based on the speed information and speed modal information of each sampling point in the N sampling points, where M is a positive integer less than N;

[0091] The second acquisition module 203 is used to acquire the load information corresponding to each target sampling point in the M target sampling points in the K processing samples, and obtain a first load matrix for representing M*K load information, where K is a positive integer;

[0092] The second determination module 204 is used to determine the monitoring boundaries corresponding to the M target sampling points based on the first load matrix, and determine the tool state of the machine tool based on the monitoring boundaries.

[0093] Optionally, the second determining module 204 is specifically configured to:

[0094] Obtaining load median information for characterizing each target sampling point among the M target sampling points in the first load matrix;

[0095] Based on the load median information and the first load matrix, a second load matrix is ​​obtained;

[0096] Based on the second load matrix, calculating the average value and standard deviation of each target sampling point in the M target sampling points;

[0097] Based on the average value and standard deviation of each of the M target sampling points, the monitoring boundaries corresponding to the M target sampling points are determined.

[0098] Optionally, the second determining module 204 is specifically configured to:

[0099] Calculate the sum of the mean value and the standard deviation of each target sampling point in the M target sampling points to obtain M sum coordinates, and the connecting line of the M sum coordinates is used to form a first boundary;

[0100] Calculate the difference between the mean value and the standard deviation of each of the M target sampling points to obtain M difference coordinates, and the connecting line of the M difference coordinates is used to form a second boundary;

[0101] The first boundary and the second boundary are determined as monitoring boundaries corresponding to the M target sampling points.

[0102] Optionally, the first determining module 205 is specifically configured to:

[0103] Obtain the processing scene of the target object;

[0104] In a case where the processing scene is a target processing scene, based on the load information, speed information, and speed modal information of each of the N sampling points, determining M target sampling points that meet preset conditions;

[0105] The target processing scenarios include milling processing scenarios, drilling processing scenarios, tapping processing scenarios and turning processing scenarios.

[0106] The machine tool tool status monitoring device provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0107] Alternatively, if Figure 3 As shown, the embodiment of the present application also provides a machine tool 300, including a processor 301 and a memory 302, and the memory 302 stores programs or instructions that can be executed on the processor 301. When the program or instructions are executed by the processor 301, the various steps of the above-mentioned machine tool tool state monitoring method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0108] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned machine tool tool status monitoring method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0109] The processor is the processor in the machine tool described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0110] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned machine tool tool status monitoring method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0111] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0112] An embodiment of the present application provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the above-mentioned machine tool tool status monitoring method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0113] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0114] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0115] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A machine tool tool status monitoring method, characterized in that: The method comprises: Obtaining spindle operation information of the machine tool during the machining process, wherein the spindle operation information includes load information, speed information, and speed modal information of N sampling points, where N is an integer greater than 2; Based on the speed information and speed modal information of each sampling point in the N sampling points, determining M target sampling points that meet the preset conditions, where M is a positive integer less than N; Obtaining the load information corresponding to each target sampling point in the M target sampling points in the K processing samples, and obtaining a first load matrix for representing the M*K load information, where K is a positive integer; Based on the first load matrix, monitoring boundaries corresponding to the M target sampling points are determined, and based on the monitoring boundaries, a tool state of the machine tool is determined.

2. The method according to claim 1, characterized in that The determining, based on the first load matrix, monitoring boundaries corresponding to the M target sampling points includes: Obtaining load median information for characterizing each target sampling point among the M target sampling points in the first load matrix; Based on the load median information and the first load matrix, a second load matrix is ​​obtained; Based on the second load matrix, calculating the average value and standard deviation of each target sampling point in the M target sampling points; Based on the average value and standard deviation of each of the M target sampling points, the monitoring boundaries corresponding to the M target sampling points are determined.

3. The method according to claim 2, characterized in that The determining, based on the average value and standard deviation of each of the M target sampling points, the monitoring boundary corresponding to the M target sampling points includes: Calculate the sum of the mean value and the standard deviation of each target sampling point in the M target sampling points to obtain M sum coordinates, and the connecting line of the M sum coordinates is used to form a first boundary; Calculate the difference between the mean value and the standard deviation of each of the M target sampling points to obtain M difference coordinates, and the connecting line of the M difference coordinates is used to form a second boundary; The first boundary and the second boundary are determined as monitoring boundaries corresponding to the M target sampling points.

4. The method according to any one of claims 1 to 3, characterized in that The determining of M target sampling points that meet preset conditions based on the speed information and speed modal information of each sampling point in the N sampling points includes: Obtain the processing scene of the target object; In a case where the processing scene is a target processing scene, based on the load information, speed information, and speed modal information of each of the N sampling points, determining M target sampling points that meet preset conditions; The target processing scenarios include milling processing scenarios, drilling processing scenarios, tapping processing scenarios and turning processing scenarios.

5. A machine tool tool status monitoring device, characterized in that: The device comprises: A first acquisition module is used to acquire spindle operation information of the machine tool during the machining process, wherein the spindle operation information includes load information, speed information and speed modal information of N sampling points, where N is an integer greater than 2; A first determination module is used to determine M target sampling points that meet preset conditions based on the speed information and speed modal information of each sampling point in the N sampling points, where M is a positive integer less than N; A second acquisition module is used to acquire the load information corresponding to each of the M target sampling points in the K processing samples, and obtain a first load matrix for representing M*K load information, where K is a positive integer; The second determination module is used to determine the monitoring boundaries corresponding to the M target sampling points based on the first load matrix, and determine the tool state of the machine tool based on the monitoring boundaries.

6. The device according to claim 5, characterized in that The second determining module is specifically used to: Obtaining load median information for characterizing each target sampling point among the M target sampling points in the first load matrix; Based on the load median information and the first load matrix, a second load matrix is ​​obtained; Based on the second load matrix, calculating the average value and standard deviation of each target sampling point in the M target sampling points; Based on the average value and standard deviation of each of the M target sampling points, the monitoring boundaries corresponding to the M target sampling points are determined.

7. The device according to claim 6, characterized in that The second determining module is specifically used to: Calculate the sum of the mean value and the standard deviation of each target sampling point in the M target sampling points to obtain M sum coordinates, and the connecting line of the M sum coordinates is used to form a first boundary; Calculate the difference between the mean value and the standard deviation of each of the M target sampling points to obtain M difference coordinates, and the connecting line of the M difference coordinates is used to form a second boundary; The first boundary and the second boundary are determined as monitoring boundaries corresponding to the M target sampling points.

8. The device according to any one of claims 5 to 7, characterized in that The first determining module is specifically configured to: Acquire a processing scene of a target object; when the processing scene is a target processing scene, determine M target sampling points that meet preset conditions based on load information, speed information, and speed modal information of each sampling point in the N sampling points; The target processing scenarios include milling processing scenarios, drilling processing scenarios, tapping processing scenarios and turning processing scenarios.

9. A machine tool, characterized in that: It comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the machine tool tool status monitoring method as described in any one of claims 1 to 4 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the machine tool tool status monitoring method according to any one of claims 1 to 4 are implemented.

11. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the machine tool tool status monitoring method according to any one of claims 1 to 4.

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