Cutting parameter determination method and device based on normal distribution, equipment and medium

Abstract

The application discloses a normal distribution-based cutting parameter determination method, device, equipment and medium, the normal distribution-based cutting parameter determination method comprises the following steps: obtaining the expected tool life of a target tool corresponding to an expected cutting parameter, and performing normal distribution verification on the expected tool life to generate an expected normal distribution model; based on the tool material of the target tool and the workpiece material of the workpiece to be machined, traversing a tool database to determine a candidate standard normal distribution model matched with the tool material and the workpiece material; based on the expected normal distribution model and the candidate standard normal distribution model, a target standard normal distribution model is determined; the cutting parameter corresponding to the target standard normal distribution model is determined as the cutting parameter of the target tool. Through the technical scheme, the service life of the tool and the reliability of the cutting data are improved.

Description

Technical Field

[0001] This invention relates to the field of machining technology, and in particular to a method, apparatus, equipment and medium for determining cutting parameters based on normal distribution. Background Technology

[0002] In machining, tool life prediction and cutting parameter optimization are core technical issues for improving machining efficiency and reducing production costs.

[0003] Traditional methods mostly rely on empirical formulas or single-factor experiments to determine cutting parameters, which have the following technical bottlenecks: they cannot effectively utilize the tool test data resources accumulated by enterprises over a long period of time, and they lack research on the distribution law of tool life under the coupling effect of multiple parameters, thus making it impossible to efficiently and accurately determine the cutting parameters of the tool. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for determining cutting parameters based on normal distribution, so as to improve the efficiency and reliability of determining cutting parameters of cutting tools.

[0005] According to one aspect of the present invention, a method for determining cutting parameters based on a normal distribution is provided, the method comprising:

[0006] Obtain the expected tool life of the target tool under the expected cutting parameters, and perform normal distribution verification on the expected tool life to generate an expected normal distribution model; wherein, the expected cutting parameters are the cutting parameters when cutting at least two workpieces to be processed, and the expected tool life is the life of the target tool under the expected cutting parameters.

[0007] Based on the tool material of the target tool and the workpiece material to be processed, the tool database is traversed to determine candidate standard normal distribution models that match the tool material and the workpiece material; wherein, the standard normal distribution models stored in the tool database are generated based on historical cutting data;

[0008] Based on the expected normal distribution model and the candidate standard normal distribution model, the target standard normal distribution model is determined;

[0009] The cutting parameters corresponding to the target standard normal distribution model are determined as the cutting parameters of the target tool.

[0010] According to another aspect of the present invention, a cutting parameter determination device based on a normal distribution is provided, the device comprising:

[0011] The expected model generation module is used to obtain the expected tool life of the target tool under the expected cutting parameters, and to perform normal distribution verification on the expected tool life to generate an expected normal distribution model; wherein, the expected cutting parameters are the cutting parameters when cutting at least two workpieces to be processed, and the expected tool life is the life of the target tool under the expected cutting parameters;

[0012] The candidate model determination module is used to traverse the tool database based on the tool material of the target tool and the workpiece material to be processed, and determine the candidate standard normal distribution model that matches the tool material and the workpiece material; wherein, the standard normal distribution model stored in the tool database is generated based on historical cutting data;

[0013] The target model determination module is used to determine the target standard normal distribution model based on the expected normal distribution model and the candidate standard normal distribution model;

[0014] The parameter determination module is used to determine the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor;

[0017] and a memory communicatively connected to the at least one processor;

[0018] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the cutting parameter determination method based on normal distribution as described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the cutting parameter determination method based on normal distribution as described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer program product is provided, the computer program product storing a computer program that, when executed by a processor, implements the cutting parameter determination method based on normal distribution as described in any embodiment of the present invention.

[0021] The technical solution of this invention generates a standard normal distribution model of tool life under different cutting parameters based on historical cutting data, and generates an expected normal distribution model of tool life based on user-expected cutting data. Based on the standard normal distribution model and the expected normal distribution model, the cutting parameters of the target tool are determined under the coupling effect of multi-dimensional data of cutting parameters and tool life, thereby improving the tool life and the reliability of cutting data.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a cutting parameter determination method based on normal distribution according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of a cutting parameter determination method based on normal distribution according to Embodiment 2 of the present invention;

[0026] Figure 3 This is a schematic diagram of a cutting parameter determination device based on normal distribution according to Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the cutting parameter determination method based on normal distribution according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This document provides a flowchart of a method for determining cutting parameters based on a normal distribution, as described in Embodiment 1 of the present invention. This embodiment is applicable to determining the cutting parameters of a tool to be machined. The method can be executed by a device for determining cutting parameters based on a normal distribution. This device can be implemented in hardware and / or software and can be configured in various general-purpose computing devices. Figure 1 As shown, the method includes:

[0032] S110. Obtain the expected tool life of the target tool under the expected cutting parameters, and perform normal distribution verification on the expected tool life to generate the expected normal distribution model.

[0033] The expected cutting parameters can be the cutting parameters used when machining at least two workpieces, and the expected tool life can be the lifespan of the target tool under the expected cutting parameters. It should be noted that the expected normal distribution model can be used to characterize the tool life distribution under the expected cutting parameters.

[0034] In this embodiment of the invention, during actual cutting operations, multiple workpieces are typically machined simultaneously. Before machining, the user can specify expected cutting parameters as the cutting parameters for the cutting tools. Furthermore, in actual operation, the lifespan of the cutting tools used to machine multiple workpieces will not be identical. The user can specify the expected lifespan of multiple cutting tools under the machining conditions corresponding to the cutting parameters. It should be noted that the materials of the multiple workpieces are the same, and the cutting tools used to machine the multiple workpieces are of the same type.

[0035] Optionally, in this embodiment of the invention, generating the expected normal distribution model further includes: setting a preset confidence level for the expected normal distribution model, and generating a confidence interval for the expected normal distribution model based on the confidence level.

[0036] In this embodiment of the invention, the user can customize the confidence level of the expected normal distribution model (e.g., 80%, 90%, 95%, 99%), and determine the deviation range of the population parameter estimation at a given confidence level, thereby determining the confidence interval of the expected normal distribution model.

[0037] Optionally, the confidence interval of the expected normal distribution model can also be set by the user, for example, taking the minimum tool life and the maximum economic tool life required for cutting.

[0038] S120. Based on the tool material of the target tool and the workpiece material to be processed, traverse the tool database to determine the candidate standard normal distribution model that matches the tool material and the workpiece material.

[0039] The standard normal distribution model stored in the tool database can be generated based on historical cutting data. It should be noted that the index of the standard normal distribution model stored in the tool database can be generated based on the tool material and workpiece material of the corresponding historical cutting process, and there is a mapping relationship between the standard normal distribution model and the cutting parameters of the tool in the corresponding historical cutting process.

[0040] Optionally, in this embodiment of the invention, before traversing the tool database, the method further includes:

[0041] Collect the historical tool life corresponding to the historical cutting parameters of the tool during historical cutting processes; perform normal distribution verification on the historical tool life corresponding to each set of historical cutting parameters; if the historical tool life conforms to a normal distribution, determine the mean and standard deviation of the historical tool life, generate a standard normal distribution model and store it in the tool database.

[0042] Specifically, historical cutting processes refer to past cutting experiments, covering workpieces made of different materials (e.g., alloy steel, aluminum alloy, titanium alloy, etc.), cutting tools made of different materials (e.g., cemented carbide, high-speed steel, ceramic tools, etc.), and different combinations of cutting parameters. Historical cutting parameters and historical tool life can both be derived from the cutting parameters and tool wear recorded in the cutting experiments. It should be noted that the standard normal distribution model can be used to characterize the tool life distribution under historical cutting parameter conditions.

[0043] Optionally, the tool database can also store the constrained machining conditions of historical cutting processes, such as cutting cooling methods and environmental parameters, and establish a mapping relationship with the cutting parameters.

[0044] Optionally, the tool database can be maintained and updated regularly to ensure the timeliness of the data.

[0045] By establishing a tool database, we can ensure that the database can efficiently store and quickly retrieve tool data, and lay the foundation for the determination of subsequent cutting parameters and the evaluation of cutting schemes.

[0046] S130. Based on the expected normal distribution model and the candidate standard normal distribution model, determine the target standard normal distribution model.

[0047] S140. Determine the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool.

[0048] The technical solution of this invention generates a standard normal distribution model of tool life under different cutting parameters based on historical cutting data, and generates an expected normal distribution model of tool life based on user-expected cutting data. Based on the standard normal distribution model and the expected normal distribution model, the cutting parameters of the target tool are determined under the coupling effect of multi-dimensional data of cutting parameters and tool life, thereby improving the tool life and the reliability of cutting data.

[0049] Example 2

[0050] Figure 2 This is a flowchart of a method for determining cutting parameters based on a normal distribution, provided in Embodiment 2 of the present invention. This embodiment further refines the above embodiments, providing specific steps for determining a target standard normal distribution model based on an expected normal distribution model and candidate standard normal distribution models. It should be noted that for parts not described in detail in this embodiment, please refer to the relevant descriptions in other embodiments, which will not be repeated here. Figure 2 As shown, the method includes:

[0051] S210. Obtain the expected tool life of the target tool under the expected cutting parameters, and perform normal distribution verification on the expected tool life to generate the expected normal distribution model.

[0052] S220. Based on the tool material of the target tool and the workpiece material to be processed, traverse the tool database to determine the candidate standard normal distribution model that matches the tool material and the workpiece material.

[0053] S230. Based on the model mean of the expected normal distribution model, traverse at least two candidate standard normal distribution models, and take the candidate standard normal distribution model whose model mean is greater than the model mean of the expected normal distribution model as the target standard normal distribution model.

[0054] By traversing the tool database based on the cutting conditions represented by the tool material and the workpiece material, at least two candidate standard normal distribution models are determined. The candidate standard normal distribution model whose model mean is greater than the expected normal distribution model is selected as the target standard normal distribution model, that is, the target standard normal distribution model whose tool service life is better than the expected service life is selected.

[0055] Optionally, in this embodiment of the invention, if the number of candidate standard normal distribution models is M and the number of target standard normal distribution models is N, then the overall success rate of the compliant cutting scheme can be further determined, i.e., N / M.

[0056] By demonstrating the overall success rate of compliant cutting solutions to users, they can intuitively understand the probability of improved production efficiency and machining quality after changes in cutting parameters.

[0057] S240. Determine the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool.

[0058] Optionally, the cutting parameters corresponding to the target standard normal distribution model are determined as the cutting parameters of the target tool, including: if there is only one target standard normal distribution model, the cutting parameters corresponding to the target standard normal distribution model are directly used as the cutting parameters of the target tool; if there is more than one target standard normal distribution model, at least two target standard normal distribution models are screened according to the actual cutting conditions of the target tool, and the cutting parameters corresponding to the target standard normal distribution model that conforms to the actual cutting conditions are used as the cutting parameters of the target tool.

[0059] It should be noted that when there are multiple target standard normal distribution models, the target standard normal distribution models can be screened according to the current actual cutting conditions, such as cutting cooling methods and environmental parameters, to select the target standard normal distribution model that conforms to the current actual cutting conditions.

[0060] The technical solution of this invention compares the model mean of the expected normal distribution model and the candidate standard normal distribution model, selects the candidate standard normal distribution model whose model mean is greater than that of the expected normal distribution model as the target standard normal distribution model, and uses the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool, thereby improving the tool's service life.

[0061] Example 3

[0062] Figure 3 This is a schematic diagram of a cutting parameter determination device based on a normal distribution, provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0063] The expected model generation module 310 is used to obtain the expected tool life of the target tool under the expected cutting parameters, and to perform normal distribution verification on the expected tool life to generate an expected normal distribution model; wherein, the expected cutting parameters are the cutting parameters when cutting at least two workpieces to be processed, and the expected tool life is the life of the target tool under the expected cutting parameters.

[0064] The candidate model determination module 320 is used to traverse the tool database based on the tool material of the target tool and the workpiece material to be processed, and determine the candidate standard normal distribution model that matches the tool material and the workpiece material; wherein, the standard normal distribution model stored in the tool database is generated based on historical cutting data;

[0065] The target model determination module 330 is used to determine the target standard normal distribution model based on the expected normal distribution model and the candidate standard normal distribution model;

[0066] The parameter determination module 340 is used to determine the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool.

[0067] The technical solution of this invention generates a standard normal distribution model of tool life under different cutting parameters based on historical cutting data, and generates an expected normal distribution model of tool life based on user-expected cutting data. Based on the standard normal distribution model and the expected normal distribution model, the cutting parameters of the target tool are determined under the coupling effect of multi-dimensional data of cutting parameters and tool life, thereby improving the tool life and the reliability of cutting data.

[0068] Optionally, the target model determination module 330 may be specifically used to: based on the model mean of the expected normal distribution model, traverse at least two candidate standard normal distribution models, and take the candidate standard normal distribution model whose model mean is greater than the model mean of the expected normal distribution model as the target standard normal distribution model.

[0069] Optionally, the parameter determination module 340 includes:

[0070] The first parameter determination unit is used to directly use the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool if the number of target standard normal distribution models is one.

[0071] The second parameter determination unit is used to, if the number of target standard normal distribution models is greater than one, screen at least two target standard normal distribution models according to the actual cutting conditions of the target tool, and use the cutting parameters corresponding to the target standard normal distribution model that conforms to the actual cutting conditions as the cutting parameters of the target tool.

[0072] Optionally, the expected model generation module 310 also includes:

[0073] A custom interval unit is used to set a preset confidence level for the expected normal distribution model, and to generate a confidence interval for the expected normal distribution model based on the confidence level.

[0074] Optionally, the device may also include:

[0075] The database generation module is used to: collect historical tool life corresponding to historical cutting parameters of tools during historical cutting processes; perform normal distribution verification on the historical tool life corresponding to each set of historical cutting parameters; if the historical tool life conforms to a normal distribution, determine the mean and standard deviation of the historical tool life, generate a standard normal distribution model, and store it in the tool database.

[0076] Optionally, the index of the standard normal distribution model stored in the tool database is generated based on the tool material of the tool and the workpiece material of the workpiece in the corresponding historical cutting process, and there is a mapping relationship between the standard normal distribution model and the cutting parameters of the tool in the corresponding historical cutting process.

[0077] The cutting parameter determination device based on normal distribution provided in the embodiments of the present invention can execute the cutting parameter determination method based on normal distribution provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0078] Example 4

[0079] Figure 4A schematic diagram of an electronic device 410 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0080] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0081] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0082] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as the method for determining cutting parameters based on a normal distribution.

[0083] In some embodiments, the normal distribution-based cutting parameter determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the normal distribution-based cutting parameter determination method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the normal distribution-based cutting parameter determination method by any other suitable means (e.g., by means of firmware).

[0084] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0086] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0087] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0088] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0089] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0090] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0091] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining cutting parameters based on normal distribution, characterized in that, include: Obtain the expected tool life of the target tool under the expected cutting parameters, and perform normal distribution verification on the expected tool life to generate an expected normal distribution model; wherein, the expected cutting parameters are the cutting parameters when cutting at least two workpieces to be processed, and the expected tool life is the life of the target tool under the expected cutting parameters. Based on the tool material of the target tool and the workpiece material to be processed, the tool database is traversed to determine candidate standard normal distribution models that match the tool material and the workpiece material; wherein, the standard normal distribution models stored in the tool database are generated based on historical cutting data; Based on the expected normal distribution model and the candidate standard normal distribution model, the target standard normal distribution model is determined; The cutting parameters corresponding to the target standard normal distribution model are determined as the cutting parameters of the target tool.

2. The method according to claim 1, characterized in that, The step of determining the target standard normal distribution model based on the expected normal distribution model and the candidate standard normal distribution models includes: Based on the model mean of the expected normal distribution model, at least two candidate standard normal distribution models are iterated, and the candidate standard normal distribution model whose model mean is greater than the model mean of the expected normal distribution model is taken as the target standard normal distribution model.

3. The method according to claim 1, characterized in that, The step of determining the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool includes: If there is only one target standard normal distribution model, then the cutting parameters corresponding to the target standard normal distribution model are directly used as the cutting parameters of the target tool. If the number of target standard normal distribution models is greater than one, then at least two target standard normal distribution models are screened according to the actual cutting conditions of the target tool, and the cutting parameters corresponding to the target standard normal distribution model that conforms to the actual cutting conditions are taken as the cutting parameters of the target tool.

4. The method according to claim 1, characterized in that, The model for generating the expected normal distribution also includes: A preset confidence level is set for the expected normal distribution model, and a confidence interval for the expected normal distribution model is generated based on the confidence level.

5. The method according to claim 1, characterized in that, Before iterating through the tool database, the following is also included: Collect historical cutting parameters of the cutting tool during historical cutting processes, corresponding to historical tool life; For each set of historical cutting parameters, the historical tool life is verified to conform to a normal distribution. If the historical tool life conforms to a normal distribution, the mean and standard deviation of the historical tool life are determined, and a standard normal distribution model is generated and stored in the tool database.

6. The method according to claim 1, characterized in that, The index of the standard normal distribution model stored in the tool database is generated based on the tool material and workpiece material of the tool in the corresponding historical cutting process, and there is a mapping relationship between the standard normal distribution model and the cutting parameters of the tool in the corresponding historical cutting process.

7. A cutting parameter determination device based on normal distribution, characterized in that, include: The expected model generation module is used to obtain the expected tool life of the target tool under the expected cutting parameters, and to perform normal distribution verification on the expected tool life to generate an expected normal distribution model; wherein, the expected cutting parameters are the cutting parameters when cutting at least two workpieces to be processed, and the expected tool life is the life of the target tool under the expected cutting parameters; The candidate model determination module is used to traverse the tool database based on the tool material of the target tool and the workpiece material to be processed, and determine the candidate standard normal distribution model that matches the tool material and the workpiece material; wherein, the standard normal distribution model stored in the tool database is generated based on historical cutting data; The target model determination module is used to determine the target standard normal distribution model based on the expected normal distribution model and the candidate standard normal distribution model; The parameter determination module is used to determine the cutting parameters corresponding to the target standard normal distribution model as the cutting parameters of the target tool.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the cutting parameter determination method based on normal distribution as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for determining cutting parameters based on a normal distribution as described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the method for determining cutting parameters based on a normal distribution as described in any one of claims 1-6.

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