Tool anomaly monitoring method, device and machine tool

Abstract

The application discloses a tool abnormality monitoring method, device and machine tool, and belongs to the technical field of machine tools. The tool abnormality monitoring method comprises the following steps: acquiring spindle running information of a machine tool in a machining process, wherein the spindle running information comprises load information, speed information and speed mode information of N sampling points, N is an integer greater than 2; determining M target sampling points meeting a preset condition based on the speed information and the speed mode information of each sampling point in the N sampling points, M is a positive integer smaller than N; acquiring corresponding load information of each target sampling point in the M target sampling points in K machining samples, obtaining a first load matrix for representing M*K load information, K is a positive integer; determining a monitoring boundary corresponding to the M target sampling points based on the first load matrix, and determining a tool state of the machine tool based on the monitoring boundary. In this way, the accuracy of the monitoring result of the tool state is improved.

Description

Technical Field

[0001] This application belongs to the field of machine tool technology, specifically relating to a method, device and machine tool for monitoring tool anomalies. Background Technology

[0002] With technological advancements, the demand for abnormal tool load monitoring in high-precision machining fields such as drilling centers, Swiss-type machining centers, and mold processing is constantly emerging. This makes conventional data feature values ​​for tool load monitoring insufficient to meet increasingly complex requirements. Moreover, because the calculation methods for conventional data feature values ​​for tool load monitoring are routine and fixed, when faced with more complex, multi-dimensional, and uncertain high-precision monitoring needs, the data feature values ​​obtained based on these routine and fixed calculation methods are insufficient to accurately characterize the health status of the tool.

[0003] It is evident that the monitoring schemes for monitoring tool status in related technologies suffer from poor accuracy. Summary of the Invention

[0004] The purpose of this application is to provide a tool anomaly monitoring method, device, and machine tool that can solve the problem of poor accuracy in related art tool status monitoring schemes.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a method for monitoring the status of machine tool cutting tools, the method comprising:

[0007] The spindle operation information of the machine tool during the machining process is acquired. The spindle operation information includes load information, speed information and speed mode information of N sampling points, where N is an integer greater than 2.

[0008] Based on the velocity information and velocity mode information of each of the N sampling points, M target sampling points that meet the preset conditions are determined, where M is a positive integer less than N;

[0009] Obtain the load information corresponding to each of the M target sampling points in the K processed samples to obtain the first load matrix used to characterize the M*K load information, where K is a positive integer;

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

[0011] Secondly, embodiments of this application provide a machine tool cutting tool status monitoring device, the device comprising:

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

[0013] The first determining module is used to determine M target sampling points that meet preset conditions based on the velocity information and velocity mode information of each of the N sampling points, where M is a positive integer less than N;

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

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

[0016] Thirdly, embodiments of this application provide a machine tool including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0017] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0018] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0019] In a sixth aspect, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the method described in the first aspect.

[0020] In this embodiment, M target sampling points that meet the conditions are determined based on spindle running information. Then, based on the load information of each of the M target sampling points in K machining samples, a first load matrix is ​​obtained to characterize the M*K load information. Since the first load matrix is ​​determined based on K machining samples, it can accurately characterize the load information of the machine tool, improving the accuracy of the monitoring boundary obtained based on the first load matrix, and thus improving the accuracy of the monitoring results of the tool status. Attached Figure Description

[0021] Figure 1This is a flowchart of the machine tool tool status monitoring method provided in the embodiments of this application;

[0022] Figure 2 This is a structural diagram of the machine tool tool status monitoring device provided in the embodiments of this application;

[0023] Figure 3 This is a structural diagram of the machine tool provided in the embodiments of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0026] The machine tool status monitoring method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0027] See Figure 1 , Figure 1 This is a flowchart of the machine tool status monitoring method provided in the embodiments of this application.

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

[0029] Step 101: Obtain the spindle operation information of the machine tool during the machining process. The spindle operation information includes load information, speed information and speed mode information of N sampling points.

[0030] Where N is an integer greater than 2.

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

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

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

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

[0035] For example, in a dot-mapping scenario, the absolute value of each sampling point can be calculated based on the formula speed_b - speed_mode_b, and sampling points whose absolute values ​​are less than a preset value (speed_threshold) can be identified 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 velocity information is Si and its corresponding velocity 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|. Then, it is determined whether the 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 of the M target sampling points in the K processed samples, and obtain the first load matrix used to characterize the M*K load information.

[0038] Where K is a positive integer.

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

[0040] In this step, the load information of each target sampling point in each processing sample can be obtained by acquiring the load information of each target sampling point in each of the M target sampling points in the K processing samples, thereby obtaining an M*K load matrix, which includes M*K load information.

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

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

[0043]

[0044] Where k represents the number of training samples, m represents the number of sampling points for each sample, and dij represents the load value at the j-th 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 status of the machine tool based on the monitoring boundaries.

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

[0047] Specifically, the mean and standard deviation of the first load matrix can be calculated to determine the monitoring boundaries corresponding to the M target sampling points based on the mean and standard deviation.

[0048] In this embodiment, M target sampling points that meet the conditions are determined based on the spindle running information. Then, based on the load information of each of the M target sampling points in K machining samples, a first load matrix is ​​obtained to characterize the M*K load information. Since the first load matrix is ​​determined based on K machining samples, it can accurately characterize the machine tool load information, improving the accuracy of the monitoring boundary obtained based on the first load matrix, and thus improving the accuracy of the monitoring results 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] Obtain the median load information from the first load matrix, which is used to characterize the load of each of the M target sampling points;

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

[0052] Based on the second load matrix, calculate the mean and standard deviation of each of the M target sampling points;

[0053] 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 is determined.

[0054] In this embodiment, the first load matrix is ​​updated based on the load median information to obtain the second load matrix. This allows the average value and standard deviation of each of the M target sampling points determined based on the second load moment to more accurately characterize the monitoring boundary 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 and standard deviation of each of the M target sampling points to obtain M sum coordinates, and the line connecting the M sum coordinates is used to form the first boundary;

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

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

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

[0060] In some embodiments, the median of the first load matrix can be calculated column-wise to obtain the 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, which can be expressed as:

[0062]

[0063] The mean and standard deviation of d_new can be calculated column-wise, thus obtaining the mean 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 boundary and the difference coordinates used to form the second boundary, where border can be understood as coordinate values, and the sum coordinate border = μ. j +Zσ j The 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 can be adjusted based on actual needs, and no specific limitation is made on the value of Z here.

[0066] In some embodiments, determining M target sampling points that satisfy preset conditions based on the velocity information and velocity mode information of each of the N sampling points includes:

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

[0068] When the processing scenario is the target processing scenario, based on the load information, speed information and speed mode information of each of the N sampling points, M target sampling points that meet the preset conditions are determined;

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

[0070] In this embodiment, since the parameters required to determine the M target sampling points vary depending on the different processing scenarios, the parameters required for that processing scenario can be determined by defining the processing scenario of the target object. Compared to using a fixed algorithm to calculate the corresponding target sampling points, this effectively reduces the computational load required to determine the M target sampling points that meet the preset conditions. Moreover, it can also adapt to different tool processing scenarios.

[0071] For milling and drilling 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 target sampling points that meet the preset conditions. Alternatively, the first derivative of spindle_b can be calculated, and the result can be represented by spindle_b_d. The sampling points that meet the condition |spindle_b_d|<1 can be determined as target sampling points that meet the preset conditions.

[0072] For tapping scenarios, the absolute value of each sampling point can be calculated based on the formula speed_b - speed_mode_b, and sampling points whose absolute values ​​are less than a preset value (speed_threshold) can be identified as target sampling points that meet the preset conditions. Alternatively, the first derivative of spindle_b can be calculated, and the result can be represented by spindle_b_d. Sampling points that meet the condition |spindle_b_d| < 0.5 can be identified as target sampling points that meet the preset conditions.

[0073] For turning machining scenarios, the absolute value of each sampling point can be calculated based on the formula speed_b - speed_mode_b, and sampling points whose absolute values ​​are less than a preset value (speed_threshold) can be identified as target sampling points that meet the preset conditions. Alternatively, the first derivative of spindle_b can be calculated, and the result can be represented by spindle_b_d, with sampling points that satisfy the condition |spindle_b_d| < 0.5 identified as target sampling points that meet the preset conditions.

[0074] In some embodiments, multiple sets of target sampling points that meet preset conditions can be obtained. Each set of target sampling points can be understood as a continuous data segment. The starting positions of these continuous data segments are represented by the sequence start:[s1,s2,…,sl], and the ending 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 can also be operated on 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 following operations can also be performed on the sequence end:

[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, and 3 can be set based on actual needs.

[0087] The machine tool tool status monitoring method of this application embodiment acquires the spindle operation information of the machine tool during the machining process. The spindle operation information includes load information, speed information, and speed mode information of N sampling points, where N is an integer greater than 2. Based on the speed information and speed mode information of each of the N sampling points, M target sampling points that meet preset conditions are determined, where M is a positive integer less than N. The load information corresponding to each of the M target sampling points in K machining samples is acquired 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 boundary corresponding to the M target sampling points is determined, and the tool status of the machine tool is determined based on the monitoring boundary. This improves the accuracy of the tool status monitoring results.

[0088] See Figure 2 , Figure 2 This is a structural diagram of the machine tool tool status monitoring device provided in the embodiments of this application, as shown below. Figure 2 As shown, the machine tool tool status 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. The spindle operation information includes load information, speed information and speed mode information of N sampling points, where N is an integer greater than 2.

[0090] The first determining module 202 is used to determine M target sampling points that meet preset conditions based on the velocity information and velocity mode information of each of 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 of the M target sampling points in the K processed samples, and obtain a first load matrix for characterizing the M*K load information, where K is a positive integer.

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

[0093] Optionally, the second determining module 204 is specifically used for:

[0094] Obtain the median load information from the first load matrix, which is used to characterize the load of each of the M target sampling points;

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

[0096] Based on the second load matrix, calculate the mean and standard deviation of each of the M target sampling points;

[0097] 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 is determined.

[0098] Optionally, the second determining module 204 is specifically used for:

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

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

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

[0102] Optionally, the first determining module 205 is specifically used for:

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

[0104] When the processing scenario is the target processing scenario, based on the load information, speed information and speed mode information of each of the N sampling points, M target sampling points that meet the preset conditions are determined;

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

[0106] The machine tool tool status monitoring device provided in this application embodiment can achieve... Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0107] Optionally, such as Figure 3 As shown, this application embodiment also provides a machine tool 300, including a processor 301 and a memory 302. The memory 302 stores a program or instructions that can be executed on the processor 301. When the program or instructions are executed by the processor 301, they implement the various steps of the above-described machine tool tool status monitoring method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0108] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described machine tool tool status monitoring method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

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

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

[0111] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0112] This application provides a computer program product, including computer instructions. When these computer instructions are executed by a processor, they implement the various processes of the above-described machine tool tool status monitoring method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0113] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0115] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for monitoring the status of machine tool cutting tools, characterized in that, The method includes: The spindle operation information of the machine tool during the machining process is acquired. The spindle operation information includes load information, speed information and speed mode information of N sampling points, where N is an integer greater than 2. Based on the velocity information and velocity mode information of each of the N sampling points, M target sampling points that meet the preset conditions are determined, where M is a positive integer less than N; Obtain the load information corresponding to each of the M target sampling points in the K processed samples to obtain the first load matrix used to characterize the 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 status of the machine tool is determined based on the monitoring boundaries; The step of determining the monitoring boundaries corresponding to the M target sampling points based on the first load matrix includes: Obtain the median load information from the first load matrix, which is used to characterize the load of each of the M target sampling points; Based on the median load information and the first load matrix, a second load matrix is ​​obtained; Based on the second load matrix, calculate the mean and standard deviation of each of the M target sampling points; Calculate the sum of the mean and standard deviation of each of the M target sampling points to obtain M sum coordinates, and the line connecting the M sum coordinates is used to form the first boundary; Calculate the difference between the mean and standard deviation of each of the M target sampling points to obtain M difference coordinates, and the line connecting the M difference coordinates is used to form the second boundary; The first boundary and the second boundary are determined as the monitoring boundaries corresponding to the M target sampling points.

2. The method according to any one of claims 1, characterized in that, The step of determining M target sampling points that meet preset conditions based on the velocity information and velocity mode information of each of the N sampling points includes: Obtain the processing scenario of the target object; When the processing scenario is the target processing scenario, based on the load information, speed information and speed mode information of each of the N sampling points, M target sampling points that meet the preset conditions are determined; The target machining scenarios include milling, drilling, tapping, and turning.

3. A machine tool cutting tool status monitoring device, characterized in that, The apparatus, used in the method of any one of claims 1 or 2, comprises: The first acquisition module is used to acquire the spindle operation information of the machine tool during the machining process. The spindle operation information includes load information, speed information and speed mode information of N sampling points, where N is an integer greater than 2. The first determining module is used to determine M target sampling points that meet preset conditions based on the velocity information and velocity mode information of each of the N sampling points, where M is a positive integer less than N; The second acquisition module is used to acquire the load information corresponding to each of the M target sampling points in the K processed samples, and obtain a first load matrix for characterizing the M*K load information, where K is a positive integer. The second determining module is used to determine the monitoring boundaries corresponding to the M target sampling points based on the first load matrix, and to determine the tool state of the machine tool based on the monitoring boundaries.

4. The apparatus according to claim 3, characterized in that, The second determining module is specifically used for: Obtain the median load information from the first load matrix, which is used to characterize the load of each of the M target sampling points; Based on the median load information and the first load matrix, a second load matrix is ​​obtained; Based on the second load matrix, calculate the mean and standard deviation of each of the M target sampling points; 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 is determined.

5. The apparatus according to claim 4, characterized in that, The second determining module is specifically used for: Calculate the sum of the mean and standard deviation of each of the M target sampling points to obtain M sum coordinates, and the line connecting the M sum coordinates is used to form the first boundary; Calculate the difference between the mean and standard deviation of each of the M target sampling points to obtain M difference coordinates, and the line connecting the M difference coordinates is used to form the second boundary; The first boundary and the second boundary are determined as the monitoring boundaries corresponding to the M target sampling points.

6. The apparatus according to any one of claims 3 to 5, characterized in that, The first determining module is specifically used for: Obtain the processing scene of the target object; if the processing scene is the target processing scene, determine M target sampling points that meet the preset conditions based on the load information, speed information and speed mode information of each of the N sampling points; The target machining scenarios include milling, drilling, tapping, and turning.

7. A machine tool, characterized in that, It includes a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and the program or instructions, when executed by the processor, implement the steps of the machine tool condition monitoring method as described in any one of claims 1 or 2.

8. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the machine tool status monitoring method as described in any one of claims 1 or 2.

9. A computer program product, characterized in that, It includes computer instructions, which, when executed by a processor, implement the steps of the machine tool condition monitoring method as described in any one of claims 1 or 2.

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