Cutter monitoring method and device, machine tool and storage medium

Abstract

The invention discloses a tool monitoring method and device, a machine tool and a readable storage medium, and relates to the technical field of machine tools, the method comprises the steps that preset parameters of a tool in a current cutting state are obtained, and the preset parameters at least comprise a main shaft rotation parameter, a cutting feeding parameter and a tool temperature change parameter; acquiring a corresponding standard parameter based on the cutting state, wherein the standard parameter has a mapping relation corresponding to the preset parameter and tool wear information; and tool wear information is output according to the standard parameters and the preset parameters. The tool monitoring method provided by the invention is helpful for operators or maintainers to obtain the current wear information of the tool more timely, and is convenient for processing and maintenance.

Description

Technical Field

[0001] This invention relates to the field of machine tool technology, and more specifically to a tool monitoring method, device, machine tool, and readable storage medium. Background Technology

[0002] In high-precision machine tools, the stability of the machining effect is required to a high extent, and the service life of the cutting tool can affect the machining effect to a certain extent.

[0003] In the prior art, such as application number CN201010611057.7 entitled "A High-Speed ​​Electric Spindle with Ball Bearing," an internal structure scheme for an electric spindle is disclosed, which can achieve high-speed machining. However, during high-speed machining, as the degree of tool wear changes, the machining effect will gradually deteriorate, and existing monitoring methods are difficult to detect in a timely manner whether the tool meets the expected machining effect. Summary of the Invention

[0004] The main objective of this invention is to provide a tool monitoring method, device, machine tool, and readable storage medium that helps operators or maintenance personnel obtain current tool wear information more promptly.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a tool monitoring method, applied in a machine tool, the method comprising:

[0007] Obtain preset parameters of the tool under the current cutting state, the preset parameters including at least spindle rotation parameters, cutting feed parameters and tool temperature change parameters;

[0008] Based on the cutting state, corresponding standard parameters are obtained, and the standard parameters have a mapping relationship between the corresponding preset parameters and tool wear information.

[0009] The tool wear information is output based on the standard parameters and the preset parameters.

[0010] In one embodiment of the tool monitoring method, before obtaining the corresponding standard parameters based on the cutting state, the method includes:

[0011] The machine tool's cutting big data is acquired and processed to obtain a data chain corresponding to each machine tool under different cutting conditions. Each data chain has corresponding tool wear parameters, spindle rotation parameters, cutting feed parameters, and tool temperature change parameters.

[0012] Based on the cutting state corresponding to the current cutting state, a matching data chain is determined as the standard data chain;

[0013] The standard parameters are determined based on the standard data link.

[0014] In one embodiment of the tool monitoring method, determining the matching data chain as a standard data chain based on the cutting state corresponding to the current cutting state includes:

[0015] Obtain the data chains corresponding to the cutting state that is consistent with the current cutting state as the first data chain group;

[0016] Select data chains with the same spindle rotation parameters and the same cutting feed parameters from the first data chain group to form the second data chain group;

[0017] Obtain the frequency of tool temperature change parameters in the second data chain group;

[0018] The data chains corresponding to the most frequent tool temperature change parameters are used as the third data chain group, and the average value of tool wear parameters is calculated based on the tool wear parameters in all data chains in the third data chain group.

[0019] The data chain corresponding to the tool wear parameter that is closest to the average value of the tool wear parameters is determined as the matching data chain.

[0020] In one embodiment of the tool monitoring method, the data chain further includes environmental parameters. Before acquiring the data chains corresponding to the cutting state consistent with the current cutting state as a first data chain group, the method further includes:

[0021] Acquire data chains that are consistent with the environmental parameters under the current cutting state to serve as a pre-screened data chain group, the environmental parameters including ambient temperature parameters and / or humidity parameters.

[0022] In one embodiment of the tool monitoring method, the cutting state includes any one of the following: climb, reverse, and side cutting.

[0023] In one embodiment of the tool monitoring method, the method further includes:

[0024] Obtain the current vibration parameters of the tool;

[0025] If the vibration parameters include characteristic parameters, then the tool wear information is corrected; wherein, the characteristic parameters include parameters characterizing brittle fracture of the tool.

[0026] Secondly, the present invention provides a tool monitoring device for use in machine tools, the device comprising:

[0027] The acquisition module is used to acquire preset parameters of the tool under the current cutting state. The preset parameters include at least spindle rotation parameters, cutting feed parameters, and tool temperature change parameters. Based on the cutting state, the module acquires corresponding standard parameters, which have a mapping relationship between the preset parameters and tool wear information.

[0028] The output module is used to output tool wear information based on the standard parameters and the preset parameters.

[0029] In one embodiment of the tool monitoring device, the acquisition module is further configured to acquire and process large cutting data of the machine tool to obtain data chains corresponding to each machine tool under different cutting states. Each data chain has corresponding tool wear parameters, spindle rotation parameters, cutting feed parameters, and tool temperature change parameters. The module also acquires data chains corresponding to cutting states consistent with the current cutting state as a first data chain group and acquires the frequency of tool temperature change parameters in the second data chain group.

[0030] The filtering module is used to filter out data chains with the same spindle rotation parameters and the same cutting feed parameters from the first data chain group to form a second data chain group.

[0031] The device also includes a calculation module, which is used to calculate the average value of tool wear parameters based on the tool wear parameters in all data chains in the third data chain group, according to each data chain corresponding to the most frequent tool temperature change parameters.

[0032] The determination module is used to determine the data chain corresponding to the tool wear parameter that is closest to the average value of the tool wear parameters as the matching data chain.

[0033] Thirdly, the present invention also provides a machine tool, including a spindle and a cutting tool mounted on the spindle, and further comprising:

[0034] Machine tool control system, wherein the machine tool control system is used to apply the steps in the tool monitoring method as described above; or

[0035] The tool monitoring device described above.

[0036] Fourthly, the present invention also provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the tool monitoring method described above.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] The tool monitoring method provided by this invention uses pre-constructed standard parameters and matches them with preset parameters under the current cutting state to obtain tool wear information corresponding to the matched standard parameters. This enables real-time monitoring of the tool, providing operators and maintenance personnel with maintenance references as early as possible, timely detection of whether the tool meets the expected processing effect, reducing scrap costs caused by tool processing not meeting expectations, improving production efficiency, and ensuring the stability of processing effect. Attached Figure Description

[0039] Figure 1 This is a flowchart of one embodiment of the tool monitoring method provided by the present invention;

[0040] Figure 2 This is a flowchart of another embodiment of the tool monitoring method provided by the present invention;

[0041] Figure 3 This is a functional block diagram of the tool monitoring device provided by the present invention.

[0042] Explanation of reference numerals in the attached figures:

[0043] Tool monitoring device 100; acquisition module 101; output module 102. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] It should be noted that when a component is referred to as being "set on" another component, it can be directly set on the other component or there may be an intervening component. When a component is referred to as being "connected to" another component, it can be directly connected to the other component or there may be an intervening component. When a component is referred to as being "mounted on" another component, it can be directly mounted on the other component or there may be an intervening component.

[0046] Furthermore, it should be understood that all directional indications in the embodiments (such as up, down, left, right, center, etc.) are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the figure). If the specific posture changes, the directional indications will also change accordingly. Terms such as "first" and "second" are used to distinguish different structural components. These terms are only for the purpose of simplifying the description of the present invention and should not be construed as limiting the present invention.

[0047] The tool monitoring method provided by this invention is mainly applied to tool wear monitoring in specific cutting scenarios. By acquiring preset parameters, the current tool wear status can be obtained, thereby realizing real-time monitoring of tool wear, providing maintenance reference for operators and maintenance personnel, facilitating timely tool maintenance, and helping to reduce the machining failure rate.

[0048] See Figure 2 This is a flowchart illustrating one embodiment of the tool monitoring method provided by the present invention. It is understood that this flowchart only schematically shows some of the steps involved in controlling the machine tool, and therefore, some steps can be added, removed, or their order adjusted according to different application scenarios and environmental conditions.

[0049] like Figure 2 As shown, the tool monitoring method provided in this embodiment may include the following steps:

[0050] S101: Obtain preset parameters of the tool under the current cutting state. The preset parameters include at least the spindle rotation parameters, cutting feed parameters, and tool temperature change parameters.

[0051] In this step, the cutting state is determined by the movement of the cutting tool relative to the workpiece driven by the machine tool, and may include any of the following: climb, reverse, and side cutting. Climb cutting means the direction of tool movement is the same as the direction of workpiece movement; reverse cutting means the direction of tool movement is opposite to the direction of workpiece movement; side cutting means the direction of tool movement is perpendicular to the workpiece movement.

[0052] The spindle rotation parameters are mainly the speed at which the spindle drives the tool to rotate; the cutting feed parameters are mainly the feed rate and feed amount during cutting; the tool temperature change parameters mainly include the temperature change of the tool from a first moment to a second moment during the cutting process, which can be denoted as Δt. The first moment can be determined based on the starting point of the characteristic load change of the spindle, that is, as a typical moment, when the tool is in contact with the workpiece and subjected to cutting force, it can be used as the first moment.

[0053] S102: Obtain the corresponding standard parameters based on the cutting state.

[0054] In this step, the standard parameters mainly provide reference values. However, prior to this step, the following steps can be performed to determine the standard parameters:

[0055] (1) Acquire and process the cutting big data of the machine tool to obtain a data chain corresponding to each machine tool under different cutting states. Each data chain has corresponding tool wear parameters, spindle rotation parameters, cutting feed parameters, and tool temperature change parameters. For example, different cutting states can be respectively Sc1, Sc2...Scn The data chain 1 corresponding to the cutting state Sc1 may include {A1,B} 1, C 1, Data link 2 can include {A2,B2,C}, where D1} is the data link 2. 2, D2}, data link 3 may include {A3, B} 3, C 3, D3}, data link n, etc. Where A represents spindle rotation parameters; B represents cutting feed parameters; C represents tool temperature variation parameters; and D represents tool wear parameters, which are divided into 5 levels from low to high based on the degree of tool wear. It is understood that parameters in different data links may be identical, but each data link should contain at least one different parameter.

[0056] It is understandable that the data chains 1, 2...n corresponding to the cutting state Sc2 are similar, so we will not go into details here.

[0057] (2) Based on the cutting state corresponding to the current cutting state, determine the matching data chain as the standard data chain. That is, by knowing the current cutting state, obtain the cutting state consistent with the current cutting state and its corresponding data chains. In a specific embodiment, the matching determination may further include:

[0058] ① Obtain the data chains corresponding to the current cutting state as the first data chain group, that is, make a preliminary selection to select the data chains corresponding to the cutting state.

[0059] ② Select data chains with the same spindle rotation parameters and the same cutting feed parameters from the first data chain group to form the second data chain group.

[0060] ③ Obtain the frequency of tool temperature change parameters in the second data link group. This means that the tool temperature change parameters include the temperature at the first moment and the temperature change value from the first moment to the second moment. Preferably, the temperature at the first moment is the same, and the absolute value of the difference between the temperature change value and the preset change value is less than the set temperature difference. These are used as the same tool temperature change parameters for frequency statistics. By obtaining the frequency of the corresponding tool temperature change parameters, higher frequency tool temperature change parameters have greater applicability and versatility.

[0061] ④ Using the data chains corresponding to the most frequent tool temperature change parameters as the third data chain group, calculate the average tool wear parameter based on the tool wear parameters in all data chains within the third data chain group. Here, by calculating the average of the tool wear parameters corresponding to the most frequent tool temperature change parameters, the basic situation of tool wear can be obtained.

[0062] ⑤ Determine the data chain corresponding to the tool wear parameter that is closest to the mean value of the tool wear parameters as the matching data chain. Here, if the tool wear parameters correspond to the levels divided by wear grades as described above, the closest tool wear parameter is the same level (in the case of being exactly equal), or between two close levels, the level involved is the smaller of the absolute values ​​of the differences from the mean.

[0063] (3) Determine the standard parameters based on the standard data link.

[0064] It is understood that the data chain also includes environmental parameters. Accordingly, before acquiring the data chains corresponding to the cutting state consistent with the current cutting state as the first data chain group, the method further includes:

[0065] Acquire data chains that are consistent with the environmental parameters under the current cutting state to serve as a pre-screened data chain group, the environmental parameters including ambient temperature parameters and / or humidity parameters.

[0066] S103: Output tool wear information based on the standard parameters and the preset parameters.

[0067] In this step, the corresponding tool wear information is obtained by matching standard parameters with preset parameters.

[0068] The tool monitoring method provided by this invention uses pre-constructed standard parameters and matches them with preset parameters under the current cutting state to obtain tool wear information corresponding to the matched standard parameters. This enables real-time monitoring of the tool, providing operators and maintenance personnel with maintenance references as early as possible, timely detection of whether the tool meets the expected processing effect, reducing scrap costs caused by tool processing not meeting expectations, improving production efficiency, and ensuring the stability of processing effect.

[0069] Furthermore, the standard parameters of this invention are constructed based on big data, and a tool wear information query database corresponding to each parameter under each cutting state is established using various data chains, which facilitates timely and more accurate acquisition of the corresponding tool wear information and shortens the query time.

[0070] See Figure 2 This is a flowchart of another embodiment of the tool monitoring method provided by the present invention. Similarly, this flowchart only schematically shows some of the steps involved in controlling the machine tool; therefore, some steps can be added, removed, or their order adjusted according to different application scenarios and environmental conditions.

[0071] like Figure 2 As shown, the tool monitoring method provided in this embodiment may include the following steps:

[0072] S201: Obtain preset parameters of the tool under the current cutting state. The preset parameters include at least the spindle rotation parameters, cutting feed parameters, and tool temperature change parameters.

[0073] S202: Obtain the corresponding standard parameters based on the cutting state, wherein the standard parameters have a mapping relationship between the corresponding preset parameters and tool wear information.

[0074] S203: Output tool wear information based on the standard parameters and the preset parameters.

[0075] S204: Obtain the vibration parameters of the current tool, wherein the vibration parameters may include changes in vibration frequency and / or changes in vibration amplitude.

[0076] S205: If the vibration parameters include characteristic parameters, then the tool wear information is corrected; wherein, the characteristic parameters include parameters characterizing brittle fracture of the tool.

[0077] Compared to the previous embodiment, this embodiment obtains the vibration changes of the tool from the vibration parameters of the spindle where the tool is located, such as changes in vibration frequency or vibration amplitude, and identifies brittle fractures of the tool, such as chipping, in order to correct the aforementioned tool wear information.

[0078] Of course, after step S205, the generated control information can also be used to stop the spindle from rotating and / or generate alarm information to promptly inform operators and maintenance personnel of the tool status.

[0079] See Figure 3 The present invention provides a functional module of a tool monitoring device 100, which is mainly used in machine tools. Figure 3 In the tool monitoring method provided in the foregoing embodiments, the tool monitoring device 100 may include an acquisition module 101 and an output module 102, wherein:

[0080] The acquisition module 101 is mainly used to acquire preset parameters of the tool under the current cutting state. The preset parameters include at least spindle rotation parameters, cutting feed parameters, and tool temperature change parameters. Based on the cutting state, the module acquires corresponding standard parameters, which have a mapping relationship between the preset parameters and tool wear information.

[0081] The output module 102 is mainly used to output tool wear information based on the standard parameters and the preset parameters.

[0082] It is understood that the tool monitoring device 100 provided by the present invention is not limited to the functional modules mentioned above. Depending on different application scenarios and / or detection conditions, corresponding functional modules and their functions can be appropriately added or removed. For example, the acquisition module is also used to acquire and process large amounts of cutting data from the machine tool to obtain data chains corresponding to each machine tool under different cutting states. Each data chain has corresponding tool wear parameters, spindle rotation parameters, cutting feed parameters, and tool temperature change parameters. It also acquires data chains corresponding to cutting states consistent with the current cutting state as a first data chain group; and acquires the frequency of tool temperature change parameters in the second data chain group. Of course, a filtering module may also be included, mainly used to filter data chains from the first data chain group that have the same spindle rotation parameters and the same cutting feed parameters as a second data chain group.

[0083] Furthermore, the device may also include a calculation module and a determination module. The calculation module is used to calculate the average value of tool wear parameters based on the tool wear parameters in all data chains of the third data chain group, using the data chains corresponding to the most frequent tool temperature change parameters as a third data chain group. The determination module is mainly used to determine the data chain corresponding to the tool wear parameter that is closest to the average value of the tool wear parameters as the matching data chain.

[0084] The present invention also provides a machine tool, including a spindle and a cutting tool mounted on the spindle, and further including: a machine tool control system, the machine tool control system being used to apply the steps in the cutting tool monitoring method as described above; or the cutting tool monitoring device as described above.

[0085] Furthermore, the present invention also provides a computer comprising a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps of the aforementioned tool monitoring methods, for example... Figure 1 Steps S101 to S103 shown are as follows: Figure 2 Steps 201 to S205, as shown, are examples of this. Alternatively, the processor may execute a computer program to implement the functions of each module or unit in the above-described device embodiments.

[0086] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory and executed by a processor to complete the present invention. The aforementioned one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0087] The aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device through various interfaces and lines.

[0088] The aforementioned memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as acquisition functions, compensation functions, etc.), etc.; the data storage area can store data created according to the use of the terminal device (such as feature location data, sensing data, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0089] When computer-integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the above-described tool monitoring method by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described tool monitoring method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0091] In the several embodiments provided in this application, the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of the functional module units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0094] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0096] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A tool monitoring method, applied in a machine tool, characterized in that, The method includes: Obtain preset parameters of the tool under the current cutting state, the preset parameters including at least spindle rotation parameters, cutting feed parameters and tool temperature change parameters; Based on the cutting state, corresponding standard parameters are obtained, and the standard parameters have a mapping relationship between the corresponding preset parameters and tool wear information. The tool wear information is output based on the standard parameters and the preset parameters.

2. The tool monitoring method as described in claim 1, characterized in that, Before obtaining the corresponding standard parameters based on the cutting state, the method includes: The machine tool's cutting big data is acquired and processed to obtain a data chain corresponding to each machine tool under different cutting conditions. Each data chain has corresponding tool wear parameters, spindle rotation parameters, cutting feed parameters, and tool temperature change parameters. Based on the cutting state corresponding to the current cutting state, a matching data chain is determined as the standard data chain; The standard parameters are determined based on the standard data link.

3. The tool monitoring method as described in claim 2, characterized in that, The step of determining the matching data chain as the standard data chain based on the cutting state corresponding to the current cutting state includes: Obtain the data chains corresponding to the cutting state that is consistent with the current cutting state as the first data chain group; Select data chains with the same spindle rotation parameters and the same cutting feed parameters from the first data chain group to form the second data chain group; Obtain the frequency of tool temperature change parameters in the second data chain group; The data chains corresponding to the most frequent tool temperature change parameters are used as the third data chain group, and the average value of tool wear parameters is calculated based on the tool wear parameters in all data chains in the third data chain group. The data chain corresponding to the tool wear parameter that is closest to the average value of the tool wear parameters is determined as the matching data chain.

4. The tool monitoring method as described in claim 3, characterized in that, The data chain also includes environmental parameters. Before acquiring the data chains corresponding to the cutting state consistent with the current cutting state as the first data chain group, the method further includes: Acquire data chains that are consistent with the environmental parameters under the current cutting state to serve as a pre-screened data chain group, the environmental parameters including ambient temperature parameters and / or humidity parameters.

5. The tool monitoring method as described in claim 1, characterized in that, The cutting state includes any of the following: climb, reverse, and side cut.

6. The tool monitoring method as described in claim 1, characterized in that, The method further includes: Obtain the current vibration parameters of the tool; If the vibration parameters include characteristic parameters, then the tool wear information is corrected; wherein, the characteristic parameters include parameters characterizing brittle fracture of the tool.

7. A tool monitoring device, applied in a machine tool, characterized in that, The device includes: The acquisition module is used to acquire preset parameters of the tool under the current cutting state. The preset parameters include at least spindle rotation parameters, cutting feed parameters, and tool temperature change parameters. Based on the cutting state, the module acquires corresponding standard parameters, which have a mapping relationship between the preset parameters and tool wear information. The output module is used to output tool wear information based on the standard parameters and the preset parameters.

8. The tool monitoring device as described in claim 7, characterized in that, The acquisition module is also used to acquire and process the cutting big data of the machine tool to obtain data chains corresponding to each machine tool under different cutting states. Each data chain has corresponding tool wear parameters, spindle rotation parameters, cutting feed parameters and tool temperature change parameters. The module also acquires the data chains corresponding to the cutting states that are consistent with the current cutting state as the first data chain group. Obtain the frequency of tool temperature change parameters in the second data chain group; The filtering module is used to filter out data chains with the same spindle rotation parameters and the same cutting feed parameters from the first data chain group to form a second data chain group. The device also includes a calculation module, which is used to calculate the average value of tool wear parameters based on the tool wear parameters in all data chains in the third data chain group, according to each data chain corresponding to the most frequent tool temperature change parameters. The determination module is used to determine the data chain corresponding to the tool wear parameter that is closest to the average value of the tool wear parameters as the matching data chain.

9. A machine tool, comprising a spindle and a cutting tool mounted on the spindle, characterized in that, Also includes: A machine tool control system, wherein the machine tool control system is used to apply the steps of the tool monitoring method as described in any one of claims 1 to 6; or The tool monitoring device as described in any one of claims 7 to 8.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the tool monitoring method as described in any one of claims 1 to 6.

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