Cutting parameter optimization method and device based on wear prediction, equipment and medium

Abstract

The invention discloses a cutting parameter optimization method and device based on wear prediction, equipment and a medium. The cutting parameter optimization method based on wear prediction comprises the steps that a real-time wear image of a target tool and real-time tool machining parameters are collected; based on a pre-trained tool wear prediction model, according to the real-time wear image and the real-time tool machining parameters, determining a predicted wear curve of the target tool; the real-time abrasion loss of the target tool under the preset machining condition is determined based on the real-time tool machining parameters; and on the basis of the real-time abrasion loss and the predicted abrasion loss of the target tool, tool machining parameters of the target tool are optimized. According to the technical scheme, the service life of the cutter is prolonged and the quality of the machined surface is improved while the cutting machining efficiency is ensured.

Description

Technical Field

[0001] The present invention relates to the field of mechanical processing technology, and in particular to a cutting parameter optimization method, device, equipment and medium based on wear prediction. Background Art

[0002] In the field of machining, tool wear is one of the core issues affecting machining quality, production efficiency, and manufacturing costs. With the increasing demand for high-speed and high-precision machining, tool wear behavior under complex working conditions exhibits nonlinear, time-varying, and multi-factor coupled characteristics.

[0003] Traditionally, tool wear status is typically determined through empirical judgment or offline testing, which makes it difficult to reflect the dynamic wear evolution during machining. Optimization of cutting parameters (such as cutting speed, feed rate, and depth of cut) is often based on surface quality or static empirical models of machined parts. This lacks the ability to adaptively control tool wear status, leading to problems such as low machining efficiency, shortened tool life, and fluctuating workpiece surface quality. Summary of the Invention

[0004] The present invention provides a cutting parameter optimization method, device, equipment and medium based on wear prediction, establishes coupling between tool wear and cutting parameters, adaptively adjusts cutting parameters according to tool wear status, and improves tool life and processing quality.

[0005] According to one aspect of the present invention, a cutting parameter optimization method based on wear prediction is provided, the method comprising:

[0006] Collect real-time wear images of target tools and real-time tool processing parameters;

[0007] Based on a pre-trained tool wear prediction model, and according to the real-time wear image and the real-time tool processing parameters, a predicted wear curve of the target tool is determined; wherein the predicted wear curve is used to represent a curve showing a change in the predicted wear amount over the cutting time;

[0008] Determining the real-time wear amount of the target tool under preset machining conditions based on the real-time tool machining parameters;

[0009] Based on the real-time wear amount and the predicted wear amount of the target tool, the tool processing parameters of the target tool are optimized; wherein the predicted wear amount is the wear amount corresponding to the predicted wear curve under the preset processing conditions.

[0010] According to another aspect of the present invention, a cutting parameter optimization device based on wear prediction is provided, the device comprising:

[0011] A data acquisition module is used to collect real-time wear images of target tools and real-time tool processing parameters;

[0012] A wear curve module is configured to determine a predicted wear curve of the target tool based on a pre-trained tool wear prediction model, the real-time wear image, and the real-time tool processing parameters; wherein the predicted wear curve is used to represent a curve showing changes in the predicted wear amount over cutting time;

[0013] a wear determination module, configured to determine the real-time wear amount of the target tool under preset machining conditions based on the real-time tool machining parameters;

[0014] A parameter optimization module is used to optimize the tool processing parameters of the target tool based on the real-time wear amount and predicted wear amount of the target tool; wherein the predicted wear amount is the wear amount corresponding to the predicted wear curve under the preset processing conditions.

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

[0016] at least one processor;

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

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the cutting parameter optimization method based on wear prediction described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the cutting parameter optimization method based on wear prediction described in any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the cutting parameter optimization method based on wear prediction according to any embodiment of the present invention is implemented.

[0021] The technical solution of the embodiment of the present invention generates a predicted wear curve of the target tool, processes the cutting parameters of the target tool according to the real-time wear amount and predicted wear amount of the target tool during the cutting process, couples the tool wear amount with the cutting parameters, and establishes a nonlinear mapping relationship between tool wear and cutting parameters. This solves the limitations of single-factor parameter optimization in traditional methods, and dynamically adjusts the cutting parameters according to the tool wear status, thereby improving the tool life and processing surface quality while ensuring cutting efficiency.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a cutting parameter optimization method based on wear prediction according to the first embodiment of the present invention;

[0025] Figure 2 is a flow chart of a cutting parameter optimization method based on wear prediction according to a second embodiment of the present invention;

[0026] Figure 3 1 is a schematic structural diagram of a cutting parameter optimization device based on wear prediction according to a third embodiment of the present invention;

[0027] Figure 4 It is a structural schematic diagram of an electronic device for implementing the cutting parameter optimization method based on wear prediction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Example 1

[0031] Figure 1 A flowchart of a cutting parameter optimization method based on wear prediction is provided for the first embodiment of the present invention. This embodiment is applicable to the case of optimizing the cutting parameters of a tool. The method can be executed by a cutting parameter optimization device based on wear prediction. The cutting parameter optimization device based on wear prediction can be implemented in the form of hardware and / or software. The cutting parameter optimization device based on wear prediction can be configured in various general-purpose computing devices. Figure 1 As shown, the method includes:

[0032] S110 , collecting a real-time wear image of a target tool and real-time tool processing parameters.

[0033] The target tool may be a tool for which cutting parameters are to be optimized. It should be noted that there may be multiple target tools, ie tools with different tool parameters (tool material, tool geometry, tool coating, etc.).

[0034] Tool processing parameters may include cutting parameters (such as cutting speed, feed rate, and cutting depth), tool parameters, workpiece parameters (such as workpiece material and workpiece hardness), and processing environment parameters (such as cooling method and lubrication conditions). It should be noted that workpiece parameters may refer to parameters of the processing target of the cutting process. In embodiments of the present invention, the parameter objects for which tool parameters need to be optimized are cutting parameters.

[0035] In an embodiment of the present invention, tool wear images can be acquired using a high-resolution microscope, an industrial camera, an embedded vision sensor, or a scanning electron microscope (SEM), etc., to capture images of the tool wear area at different time points during the tool's cutting process. Optionally, the acquired wear images can be preprocessed, such as by grayscaling, noise reduction, and contrast enhancement, to improve the wear image quality and facilitate subsequent extraction of tool wear characteristics.

[0036] S120 , based on the pre-trained tool wear prediction model, according to the real-time wear image and the real-time tool processing parameters, determining the predicted wear curve of the target tool.

[0037] Among them, the predicted wear curve can be used to characterize the curve of the change of the wear amount of the predicted tool with cutting time.

[0038] Optionally, the tool wear prediction model can be obtained by training a neural network model using a machine learning algorithm. Furthermore, the tool wear prediction model can be a multivariate linear regression model trained based on a sequence of historical tool wear images in a tool wear database to characterize the linear relationship between at least two cutting parameters of a target tool and the amount of wear of the target tool.

[0039] Optionally, the tool wear database stores historical tool wear image sequences corresponding to at least two tool processing parameters.

[0040] In an optional embodiment of the present invention, the process of generating a tool wear database includes: performing image acquisition on the tool wear area of ​​the target tool at at least two time nodes in the historical cutting experiment to obtain a historical tool wear image; performing image recognition on the historical tool wear image to extract the wear area features of the tool wear area, and determining the wear label of the historical tool wear image based on the wear area features; storing the historical tool wear images carrying the wear label to generate a tool wear database.

[0041] Specifically, a pre-trained convolutional neural network can be used to extract and classify features from historical tool wear images, determine the wear type and wear state of the historical tool wear images, generate a wear label, and use the wear label as the data label for the corresponding cutting parameters. Optionally, the wear state can include initial wear, normal wear, and severe wear, and the wear type can include rake face wear, flank face wear, boundary wear, and chipping. The wear label can be used to characterize the tool's wear state and wear type, such as initial rake face wear and severe flank face wear.

[0042] It should be noted that in the tool wear database, historical tool wear images corresponding to different types of tools can be classified and stored. For example, corresponding sub-databases can be established for different types of tools based on the tool parameters, and corresponding database tags can be established for each sub-database. The database tags can be determined based on the tool parameters. Optionally, the data in the tool wear database can be dynamically updated to increase the data size.

[0043] By storing the vertical machining conditions and historical tool wear images of the tool, effective data support can be provided for the cutting analysis of the tool, further improving the effectiveness and accuracy of the cutting analysis results.

[0044] S130 , determining the real-time wear amount of the target tool under preset processing conditions based on the real-time tool processing parameters.

[0045] The preset processing condition may refer to a workpiece object that has been processed by the tool for a preset processing time or a preset number of workpieces. It should be noted that the preset processing time and the preset number of workpieces can be adaptively set by those skilled in the art.

[0046] It should be noted that in the embodiment of the present invention, the real-time wear of the tool can be obtained by detecting the wear of the tool after the tool passes the preset handover conditions. Optionally, the wear of the tool can be determined by a pre-trained convolutional neural network.

[0047] S140 , optimizing tool processing parameters of the target tool based on the real-time wear amount and the predicted wear amount of the target tool.

[0048] The predicted wear amount may be the wear amount corresponding to the predicted wear curve under preset processing conditions.

[0049] The technical solution of the embodiment of the present invention generates a predicted wear curve of the target tool, processes the cutting parameters of the target tool according to the real-time wear amount and predicted wear amount of the target tool during the cutting process, couples the tool wear amount with the cutting parameters, and establishes a nonlinear mapping relationship between tool wear and cutting parameters. This solves the limitations of single-factor parameter optimization in traditional methods, and dynamically adjusts the cutting parameters according to the tool wear status, thereby improving the tool life and processing surface quality while ensuring cutting efficiency.

[0050] Example 2

[0051] Figure 2 This is a flowchart of a cutting parameter optimization method based on wear prediction provided in the second embodiment of the present invention. This embodiment further refines the above embodiment and provides specific steps for "optimizing the tool processing parameters of the target tool based on the real-time wear amount and predicted wear amount of the target tool". It should be noted that, for the parts not described in detail in the embodiment of the present invention, please refer to the relevant descriptions of other embodiments, which will not be repeated here. Figure 2 As shown, the method includes:

[0052] S210 , collecting a real-time wear image of a target tool and real-time tool processing parameters.

[0053] S220 , based on the pre-trained tool wear prediction model, according to the real-time wear image and the real-time tool processing parameters, determine the predicted wear curve of the target tool.

[0054] S230 : Determine the real-time wear amount of the target tool under the preset processing conditions based on the real-time tool processing parameters.

[0055] S240: If the difference between the real-time wear amount and the predicted wear amount is greater than a preset wear amount threshold, determine a target wear label in the tool wear database according to the real-time wear amount.

[0056] It should be noted that the wear threshold can be adaptively set according to those skilled in the art.

[0057] In an embodiment of the present invention, the sub-database corresponding to the target tool can be determined in the tool wear database based on the tool parameters of the target tool, and the sub-database can be traversed based on the real-time wear amount of the target tool to determine the wear label that matches the real-time wear amount as the target wear label.

[0058] Optionally, in an embodiment of the present invention, if the difference between the real-time wear amount and the predicted wear amount is equal to or less than a preset wear amount threshold, there is no need to optimize the cutting parameters of the target tool.

[0059] S250 , determining target tool processing parameters matching the target wear tag in a tool wear database according to the target wear tag.

[0060] It should be noted that, in the embodiment of the present invention, the target tool processing parameters may specifically refer to the cutting parameters of the tool during the cutting process.

[0061] Specifically, after determining that the target tool corresponds to the target wear label in the tool wear database, the cutting parameters of the tool stored under the target wear label can be determined in the tool wear database according to the target wear label as the target tool processing parameters of the target tool.

[0062] S260: Using the target tool processing parameters, controlling the new target tool to re-cut, and optimizing the tool processing parameters of the target tool according to the wear amount of the new target tool during the cutting process.

[0063] Specifically, the target tool processing parameters of the target tool are determined from the tool wear database, a new target tool can be re-cut according to the target tool processing parameters, and the wear of the cutting process is monitored, and the cutting parameters of the target tool are optimized according to the monitored wear.

[0064] Optionally, the tool processing parameters of the target tool are optimized according to the wear of the new target tool during the cutting process, including: determining the wear of the new target tool under preset processing conditions during the re-cutting process of the new target tool; if the difference between the wear of the new target tool and the wear of the new target tool is less than or equal to a preset wear threshold, then optimizing the cutting parameters of the target tool according to the target tool processing parameters.

[0065] Optionally, if the difference between the wear amount of the new target tool and the wear amount of the new target tool is less than or equal to a preset wear amount threshold, steps S40-S260 may be repeated until a satisfactory machining effect and tool life are achieved.

[0066] The technical solution of the embodiment of the present invention introduces a closed-loop feedback mechanism to compare the actual wear of the target tool with the predicted wear, optimize the cutting parameters of the target tool, ensure that the cutting parameters are adaptively adjusted as the tool life decays, and achieve the global optimization of processing efficiency, surface quality and tool life.

[0067] Example 3

[0068] Figure 3 This is a schematic diagram of the structure of a cutting parameter optimization device based on wear prediction provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0069] The data acquisition module 310 is used to acquire the real-time wear image of the target tool and the real-time tool processing parameters;

[0070] A wear curve module 320 is configured to determine a predicted wear curve of the target tool based on a pre-trained tool wear prediction model, the real-time wear image, and the real-time tool processing parameters; wherein the predicted wear curve is used to represent a curve showing a change in the predicted wear amount over cutting time;

[0071] a wear determination module 330 for determining a real-time wear amount of the target tool under preset machining conditions based on the real-time tool machining parameters;

[0072] The parameter optimization module 340 is used to optimize the tool processing parameters of the target tool based on the real-time wear amount and predicted wear amount of the target tool; wherein the predicted wear amount is the wear amount corresponding to the predicted wear curve under the preset processing conditions.

[0073] The technical solution of the embodiment of the present invention generates a predicted wear curve of the target tool, processes the cutting parameters of the target tool according to the real-time wear amount and predicted wear amount of the target tool during the cutting process, couples the tool wear amount with the cutting parameters, and establishes a nonlinear mapping relationship between tool wear and cutting parameters. This solves the limitations of single-factor parameter optimization in traditional methods, and dynamically adjusts the cutting parameters according to the tool wear status, thereby improving the tool life and processing surface quality while ensuring cutting efficiency.

[0074] Optionally, the parameter optimization module 340 includes:

[0075] a label determination unit, configured to determine a target wear label in a tool wear database according to the real-time wear amount if a difference between the real-time wear amount and the predicted wear amount is greater than a preset wear amount threshold;

[0076] A parameter matching unit, configured to determine, in the tool wear database, target tool processing parameters that match the target wear tag;

[0077] The parameter optimization unit is used to control the new target tool to re-cut the target tool by adopting the target tool processing parameters, and optimize the tool processing parameters of the target tool according to the wear amount of the new target tool during the cutting process.

[0078] Optionally, the parameter optimization sheet can be specifically used for: determining the wear amount of the new target tool under preset processing conditions during the process of re-cutting the new target tool; if the difference between the wear amount of the new target tool and the wear amount of the new target tool is less than or equal to a preset wear amount threshold, then optimizing the cutting parameters of the target tool according to the target tool processing parameters.

[0079] Optionally, the device further includes:

[0080] The tool wear database generation module can be specifically used to collect images of the tool wear area of ​​the target tool at at least two time nodes in the historical cutting experiment to obtain historical tool wear images; perform image recognition on the historical tool wear images, extract the wear area features of the tool wear area, and determine the wear labels of the historical tool wear images based on the wear area features; store the historical tool wear images carrying the wear labels to generate a tool wear database.

[0081] Optionally, the tool wear database stores historical tool wear image sequences corresponding to at least two tool processing parameters.

[0082] Optionally, the tool wear prediction model is a multiple linear regression model trained based on a historical tool wear image sequence in a tool wear database and used to characterize the linear relationship between at least two cutting parameters of a target tool and the wear amount of the target tool.

[0083] The cutting parameter optimization device based on wear prediction provided by the embodiment of the present invention can execute the cutting parameter optimization method based on wear prediction provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0084] Example 4

[0085] Figure 4 A schematic diagram of the structure of an electronic device 410 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0086] like Figure 4 As shown, the electronic device 410 includes at least one processor 411, and a memory connected to the at least one processor 411, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 411 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 to the random access memory (RAM) 413. Various programs and data required for the operation of the electronic device 410 can also be stored in the RAM 413. The processor 411, ROM 412 and RAM 413 are connected to each other via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0087] Multiple components in electronic device 410 are connected to I / O interface 415, including an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a magnetic disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows electronic device 410 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0088] The processor 411 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 411 executes the various methods and processes described above, such as the cutting parameter optimization method based on wear prediction.

[0089] In some embodiments, the wear prediction-based cutting parameter optimization method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program can 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 wear prediction-based cutting parameter optimization method described above can be performed. Alternatively, in other embodiments, processor 411 can be configured to execute the wear prediction-based cutting parameter optimization method in any other appropriate manner (e.g., via firmware).

[0090] Various embodiments of the systems and techniques described above 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), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs for implementing 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 the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0093] 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 can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0095] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0096] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0097] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A cutting parameter optimization method based on wear prediction, characterized in that: include: Collect real-time wear images of target tools and real-time tool processing parameters; Based on a pre-trained tool wear prediction model, and according to the real-time wear image and the real-time tool processing parameters, a predicted wear curve of the target tool is determined; wherein the predicted wear curve is used to represent a curve showing a change in the predicted wear amount over the cutting time; Determining the real-time wear amount of the target tool under preset machining conditions based on the real-time tool machining parameters; Based on the real-time wear amount and the predicted wear amount of the target tool, the tool processing parameters of the target tool are optimized; wherein the predicted wear amount is the wear amount corresponding to the predicted wear curve under the preset processing conditions.

2. The method according to claim 1, characterized in that The optimizing of the tool processing parameters of the target tool based on the real-time wear amount and the predicted wear amount of the target tool comprises: If the difference between the real-time wear amount and the predicted wear amount is greater than a preset wear amount threshold, determining a target wear tag in a tool wear database according to the real-time wear amount; Determining target tool processing parameters matching the target wear tag in the tool wear database according to the target wear tag; The target tool processing parameters are adopted to control a new target tool to perform cutting processing again, and the tool processing parameters of the target tool are optimized according to the wear amount of the new target tool during the cutting processing.

3. The method according to claim 2, characterized in that The optimizing of the tool processing parameters of the target tool according to the wear amount of the new target tool during the cutting process includes: During the re-cutting process of the new target tool, the wear amount of the new target tool is determined under the preset processing conditions; If the difference between the wear amount of the new target tool and the wear amount of the new target tool is less than or equal to a preset wear amount threshold, the cutting parameters of the target tool are optimized according to the target tool processing parameters.

4. The method according to claim 2, characterized in that The process of generating the tool wear database includes: Capturing images of tool wear areas of a target tool at at least two time points in a historical cutting experiment to obtain a historical tool wear image; Performing image recognition on the historical tool wear image, extracting wear region features of the tool wear region, and determining a wear label of the historical tool wear image based on the wear region features; The historical tool wear images with wear labels are stored to generate a tool wear database.

5. The method according to claim 2, characterized in that The tool wear database stores historical tool wear image sequences corresponding to at least two tool processing parameters.

6. The method according to claim 1, characterized in that The tool wear prediction model is a multiple linear regression model trained based on a historical tool wear image sequence in a tool wear database and used to characterize the linear relationship between at least two cutting parameters of a target tool and the wear amount of the target tool.

7. A cutting parameter optimization device based on wear prediction, characterized in that: include: A data acquisition module is used to collect real-time wear images of target tools and real-time tool processing parameters; A wear curve module is configured to determine a predicted wear curve of the target tool based on a pre-trained tool wear prediction model, the real-time wear image, and the real-time tool processing parameters; wherein the predicted wear curve is used to represent a curve showing changes in the predicted wear amount over cutting time; a wear determination module, configured to determine the real-time wear amount of the target tool under preset machining conditions based on the real-time tool machining parameters; A parameter optimization module is used to optimize the tool processing parameters of the target tool based on the real-time wear amount and predicted wear amount of the target tool; wherein the predicted wear amount is the wear amount corresponding to the predicted wear curve under the preset processing conditions.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the cutting parameter optimization method based on tool wear prediction according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the cutting parameter optimization method based on tool wear prediction according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the cutting parameter optimization method based on tool wear prediction according to any one of claims 1 to 6.

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