Tool life management method, system, apparatus, device, medium, and program product

Abstract

The application relates to a tool life management method, system, device, equipment, medium and program product. The method comprises the following steps: in response to a tool changing instruction, a tool is grabbed and a tag reading operation is triggered, tool information stored in an electronic tag associated with the tool and material information of a workpiece to be processed are acquired through near field wireless communication, and the tool information comprises a current life state; the tool is verified based on the tool information and the material information, and a verification result is obtained; in the case that the verification result represents that the tool passes the verification, a machining task is executed, and actual machining parameters of the tool are acquired during execution of the machining task; based on the actual machining parameters, the material information and the tool information, wear increment of the tool is predicted, and the current life state of the tool is updated based on the wear increment; and after the machining task is completed, the updated current life state is written into the electronic tag. The method can realize reliable intelligent management of the whole life cycle of the tool.

Description

Technical Field

[0001] This application relates to the field of CNC machining and tool management technology, and in particular to a tool life management method, system, device, computer numerical control equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] In CNC machining systems, tool life management primarily relies on time accumulation, machining cycle counting, or sensor-based monitoring methods. Some industrial-grade equipment uses RFID (Radio Frequency Identification) technology to identify tools and combines this with sensor signals such as spindle load, vibration, or acoustic emission to assess wear status; other systems estimate theoretical cutting time by parsing G-codes as a basis for tool life consumption.

[0003] However, these methods have significant limitations when applied to desktop CNC (Computer Numerical Control) equipment: RFID tags are expensive, difficult to reliably install on small metal tool holders, and difficult to accurately predict tool life. Therefore, current solutions are insufficient for accurate tool life management. Summary of the Invention

[0004] Therefore, it is necessary to provide a more accurate tool life management method, system, device, computer numerical control equipment, computer-readable storage medium, and computer program product to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a tool life management method, including:

[0006] In response to a tool change command, the tool is grasped and a tag reading operation is triggered. The tool information stored in the electronic tag associated with the tool and the material information of the workpiece to be processed are obtained through near-field wireless communication. The tool information includes the current life status.

[0007] The tool is verified based on the tool information and the material information to obtain a verification result. The verification includes at least one of material suitability verification, tool diameter verification, and tool life adequacy verification.

[0008] If the verification result indicates that the tool has passed the verification, the machining task is executed, and the actual machining parameters of the tool are obtained during the execution of the machining task.

[0009] Based on the actual machining parameters, the material information, and the tool information, the wear increment generated by the tool after completing the machining task is predicted, and the current life status of the tool is updated based on the wear increment;

[0010] After the processing task is completed, the updated current lifespan status is written to the electronic tag.

[0011] Secondly, this application also provides a tool life management system, including:

[0012] The cutting tool, or its associated carrier, is equipped with an electronic tag that stores cutting tool information, including a list of applicable materials and the current lifespan status.

[0013] The electronic tag reading and writing module is configured to interact with the electronic tag via near-field wireless communication.

[0014] The controller, which is communicatively connected to the electronic tag reader / writer module, is configured as follows:

[0015] In response to a tool change command, the tool is grasped, and the electronic tag reading and writing module is controlled to read the tool information of the tool through near-field wireless communication and obtain the material information of the workpiece to be processed.

[0016] The tool is verified based on the tool information and the material information to obtain a verification result. The verification includes at least one of material suitability verification, tool diameter verification, and tool life adequacy verification.

[0017] If the verification result indicates that the tool has passed the verification, the machining task is executed, and the actual machining parameters of the tool are obtained during the execution of the machining task.

[0018] Based on the actual machining parameters, the material information, and the tool information, the wear increment generated by the tool after completing the machining task is predicted, and the current life status of the tool is updated based on the wear increment;

[0019] After the processing task is completed, the electronic tag reading and writing module is controlled to write the updated current lifespan status into the electronic tag.

[0020] Thirdly, this application also provides a tool life management device, comprising:

[0021] The data acquisition module is used to respond to the tool change command, grab the tool and trigger the tag reading operation, and acquire the tool information stored in the electronic tag associated with the tool and the material information of the workpiece to be processed through near-field wireless communication. The tool information includes the current life status.

[0022] The verification module is used to verify the tool based on the tool information and the material information, and obtain the verification result. The verification includes at least one of material suitability verification, tool diameter verification and tool life adequacy verification.

[0023] The machining control module is used to execute a machining task when the verification result indicates that the tool has passed the verification, and to acquire the actual machining parameters of the tool during the execution of the machining task;

[0024] The lifespan update module is used to predict the wear increment generated by the tool after the tool completes the machining task based on the actual machining parameters, the material information and the tool information, and update the current lifespan status of the tool based on the wear increment;

[0025] The data writing module is used to write the updated current lifespan status to the electronic tag after the processing task is completed.

[0026] Fourthly, this application also provides a computer digital control device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above-described tool life management method embodiments.

[0027] Fifthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in any of the above-described tool life management method embodiments.

[0028] Sixthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above-described tool life management method embodiments.

[0029] The aforementioned tool life management methods, systems, devices, computer numerical control equipment, computer-readable storage media, and computer program products utilize electronic tags as carriers of tool identity and status, ensuring that tool information can be transferred across devices and is available offline. This solves the problems of error-prone traditional manual recording and information silos. By verifying the tool using tool and material information before machining, machining tasks are only executed if the tool passes the verification, effectively reducing machining failures or tool breakage caused by tool misuse or exceeding limits, significantly improving machining safety and success rate. Furthermore, without relying on sensors such as current and vibration sensors, only actual machining parameters are needed to update the tool life status in real time based on material properties and tool information, significantly reducing system costs and improving applicability on desktop CNC equipment. The entire solution achieves intelligent management of the entire tool lifecycle with low cost, high reliability, and strong collaboration. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is an application environment diagram of the tool life management method in one embodiment;

[0032] Figure 2 This is a flowchart illustrating a tool life management method in one embodiment;

[0033] Figure 3 This is a flowchart illustrating a tool life management method in another embodiment;

[0034] Figure 4 This is a flowchart illustrating the step of determining the recommended tool in one embodiment;

[0035] Figure 5 This is a flowchart illustrating the steps for predicting tool wear increments in one embodiment;

[0036] Figure 6 This is a flowchart illustrating the steps for predicting tool wear increments in another embodiment;

[0037] Figure 7 This is a structural block diagram of a tool life management device in one embodiment;

[0038] Figure 8 This is an internal structural diagram of a computer digital control device in one embodiment. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0040] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0041] The tool life management method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, the controller 102 of the computer digital control device communicates with the server 104 via a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104, or it can be located in the cloud or on another network server.

[0042] Specifically, the operator can select the G-code file corresponding to the workpiece to be processed through the human-machine interface or host computer software of the computer digital control equipment, and the controller 102 can parse the material information of the workpiece to be processed. Then, in response to the tool change command, the controller grabs the tool, triggers the tag reading and writing operation, and obtains the tool information stored in the electronic tag associated with the tool through near-field wireless communication. Then, the tool is verified based on the tool information and material information to obtain the verification result. The verification includes at least one of material suitability verification, tool diameter verification, and tool life adequacy verification. If the verification result indicates that the tool has passed the verification, the processing task is executed, and the actual processing parameters of the tool are obtained during the processing task. Then, based on the actual processing parameters, material information, and tool information, the wear increment of the tool is predicted, and the current tool life status is updated based on the wear increment. After the processing task is completed, the updated current life status is written to the electronic tag. Furthermore, the updated current life status can also be uploaded to the server 104.

[0043] Among them, server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0044] In one exemplary embodiment, such as Figure 2 As shown, a tool life management method is provided, which is applied to... Figure 1 The following steps are used as an example, with controller 102 as an example: 100 to 500.

[0045] Step 100: In response to the tool change command, grab the tool and trigger the tag reading operation. Obtain the tool information stored in the electronic tag associated with the tool and the material information of the workpiece to be processed through near-field wireless communication. The tool information includes the current life status.

[0046] In this embodiment, the electronic tag has rewritable capabilities and can be considered a digital identity carrier for the cutting tool. It can be a non-volatile storage medium that can be read and written via near-field wireless communication, used to bind and record information such as the tool's identity, physical attributes, and usage status. Tool information refers to the structured data stored in the electronic tag, including fields such as tool type, diameter, coating, applicable material list, and current lifespan status. Current lifespan status refers to the remaining usable cutting time or the proportion of lifespan already consumed since the tool was first used, reflecting its safe continued use. The material information of the workpiece to be processed refers to identification data describing the workpiece's material type, such as "acrylic," "6061 aluminum alloy," or "carbon fiber sheet," used to determine the tool's suitability.

[0047] In this embodiment, a desktop CNC machine (hereinafter referred to as "the machine") is used as the computer numerical control device, and the controller of the desktop CNC machine is used as the execution subject for the method. In practical applications, after purchasing a new tool, the user first affixes a readable electronic tag to a designated location on the tool magazine tray (usually a non-metallic area on the bottom or side wall to avoid signal shielding); then inserts the new tool into the tray, bringing it close to the electronic tag reader integrated into the machine. When an unregistered electronic tag is detected, a registration window automatically pops up, prompting "New tool detected, please enter information." The user fills in or selects relevant parameters on the interface according to the tool packaging or the actual tool: including tool type (such as flat end mill, ball end mill, etc., selected via drop-down menu), diameter (can be manually entered, or obtained with one click if the machine is equipped with an automatic measuring instrument), number of cutting edges, coating type (such as uncoated, TiN, TiAlN, etc.), and brand (selected from a preset list or entered custom); the controller automatically recommends and fills in the theoretical life value (e.g., "coated carbide tool, recommended life 120 minutes") based on the selected brand and coating combination. After confirming the information is correct, the user submits the data, and the controller writes the complete tool information into the electronic tag in a structured format, including fields such as the default list of applicable materials, initial remaining lifespan, and unique identifier, thus completing the digital registration of the tool. Thereafter, when the tool is used on any compatible device, its identity and status can be automatically identified by reading this tag, enabling full lifecycle management.

[0048] When a user loads a machining task, the controller first extracts the material information of the workpiece from the metadata comments of the G-code file, the process configuration file, or by scanning the QR code attached to the workpiece. Simultaneously, in response to a tool change command, the controller grabs the tool. During the tool change phase (when the automatic tool changer is in place) or after the user manually inserts the tool into the tool holder with reading functionality, the controller activates the electronic tag data read / write module of the I²C interface and sends a read request to a near-field wireless communication tag (such as an NFC tag). After successful communication, the controller parses the returned read data and extracts key fields such as tool ID (e.g., T001-T100), tool type (flat end mill, ball end mill, V-butter, drill bit, etc.), tool diameter (unit: mm), number of cutting edges (usually 1-4 teeth), coating type (e.g., TiAlN titanium aluminum nitride coating, extending life by 30-50%), recommended material list for the tool, theoretical total tool life (unit: seconds), remaining life (unit: seconds), wear percentage (0-100%), and the last 10 usage records (earlier records are uploaded to the cloud and deleted to save space). These are temporarily stored in memory to provide input for subsequent verification.

[0049] In other embodiments, in low-cost desktop CNC equipment without automatic tool changer functionality, the controller can have a "Tool Ready" button on the user interface. When the user manually clicks this button, it triggers an electronic tag data read. At this time, the electronic tag is fixedly installed in the reading station near the spindle. The user must first load the tool into the spindle and then hold the tag close to the reading area to complete the identification. The key to this method is compensating for hardware deficiencies through human-machine collaboration, ensuring the tag deployment avoids metal shielding areas, and using software guidance to ensure the correct operation sequence. In advanced controllers that support multi-tool caching, the controller can perform a batch scan of the entire tool magazine when the device starts or before a task begins, reading the electronic tags corresponding to all tool positions at once to build a local tool status cache table. When executing a specific task, the cached data is directly indexed based on the current tool number, reducing latency caused by repeated readings.

[0050] Step 200: Verify the tool based on tool information and material information to obtain the verification result.

[0051] After obtaining tool and material information, the controller immediately executes the preset verification logic. The verification logic can be multi-dimensional or single-dimensional, depending on the actual situation.

[0052] For example, taking multiple verifications of a cutting tool as an example, the first step is material compatibility judgment: checking whether the material information appears in the "Applicable Material List" in the cutting tool information. If not, it is determined to be incompatible, and the cutting tool passes this verification. The second step is cutting tool diameter verification: judging whether the cutting tool diameter matches the recommended cutting tool diameter. If they do not match, it is determined to be "failed"; otherwise, the cutting tool passes this verification. The third step is lifespan adequacy judgment: combining the current lifespan status in the cutting tool information, judging whether the current lifespan status is greater than or equal to the preset safety margin. If all three conditions are met, the verification result is "passed"; if any one condition is not met, it is "failed". If it fails, the start of the machining task can be prohibited. It can be understood that in some embodiments, at least one of the above three verifications can be performed on the cutting tool.

[0053] In other embodiments, if the applicable material list is not filled in during tool registration, the controller defaults to applying it only to general non-metallic materials (such as wood, acrylic, PVC). Once metallic materials (such as aluminum, copper) are detected, the controller automatically blocks the process and prohibits the execution of the machining task. In other embodiments, the controller can also provide users with an "expert mode," which pops up a confirmation dialog box when verification fails: "The tool is not recommended for this material. Do you want to force continue?" After the user confirms, the restriction can be bypassed, but the controller simultaneously records a high-risk operation log and marks the machining anomaly. This ensures a safety baseline while preserving the operational freedom of professional users.

[0054] Step 300: If the verification result indicates that the tool has passed the verification, execute the machining task and obtain the actual machining parameters of the tool during the execution of the machining task.

[0055] Actual machining parameters refer to the cutting process parameters that actually control the tool's movement trajectory and speed during CNC machining. These mainly include cutting speed, feed rate, axial depth of cut, radial width of cut, spindle speed, and interpolation type (linear G1, circular G2 / G3). In this embodiment, the feed rate is the core parameter reflecting the cutting aggressiveness and is used for wear prediction.

[0056] Following the previous step, machining is only permitted after the tool passes the aforementioned verification. The controller sends a start signal to the motion control module to begin executing G-code for the machining task. Simultaneously, the background instruction parser runs concurrently, scanning the G-code stream line by line, identifying all G1, G2, and G3 instructions and their associated feed rate values, and excluding the G0 rapid traverse segment. For each cutting segment, the controller records its start point, end point, path type, feed rate, and theoretical execution time (derived by dividing the path length by the feed rate), obtaining the actual machining parameters such as the feed rate and cutting time for each cutting segment.

[0057] Step 400: Based on actual machining parameters, material information and tool information, predict the tool wear increment, and update the tool's current life status based on the wear increment.

[0058] In practice, after acquiring the actual feed rate, duration, and material information for each cutting segment, the controller retrieves the hardness coefficient of the corresponding material from the local material database (e.g., 3.0 for carbon fiber and 1.5 for hardwood). It then combines this with the coating flag in the tool information to determine the quality coefficient (0.7 for coated parts and 1.0 for uncoated parts), and calculates the ratio of the actual feed rate to the recommended feed rate. Subsequently, the duration of each cutting segment is multiplied by the corresponding comprehensive coefficient, and the results are accumulated to obtain the total wear increment. This increment is then subtracted from the current lifespan state, updated to a new lifespan state, and temporarily stored in memory. The tag is written back after the task ends.

[0059] In other embodiments, for educational or prototyping scenarios where high precision is not required, the feed rate deviation calculation can be omitted. A fixed wear rate per unit time (e.g., 0.5% of lifespan consumed per minute) can be set according to the material type, and then multiplied by the total cutting time to obtain the total wear increment. This total wear increment is then deducted from the current lifespan state, updated to a new lifespan state, and temporarily stored in memory.

[0060] Step 500: After the processing task is completed, write the updated current lifespan status into the electronic tag.

[0061] After a machining task is completed normally, the controller constructs a data packet containing the updated remaining lifespan status, wear level, most recent use timestamp, and synchronization version number, and writes the data packet to the original electronic tag using a preset protocol. Before writing, the controller verifies whether the tag ID matches the current tool to prevent miswriting. If writing fails (e.g., the tag moves out of the reading range or a communication error occurs), the controller will record the error code and prompt "Tag synchronization failed, please reposition the tool." The user can then retry after confirmation.

[0062] In other embodiments, a dual-tag redundancy strategy can be employed, where one tag is embedded in the tool holder end face and the other is placed in the tool magazine tray. The controller writes both tags sequentially after the task is completed, and synchronization is considered complete only when at least one tag is successfully written. In other scenarios where tools are used multiple times within a single session, a delayed write mode can be used: during continuous multi-task machining, the controller temporarily stores all updated data in local non-volatile memory (such as EEPROM), and writes the tags uniformly before the user actively clicks "Save Tool Status" or before the device is shut down, reducing the impact of frequent read / write operations on tag lifespan.

[0063] The aforementioned tool life management method utilizes electronic tags as a carrier of tool identity and status, ensuring that tool information can be transferred across devices and is available offline, thus solving the problems of error-prone traditional manual recording and information silos. By verifying the tool using tool and material information before machining, machining tasks are only executed if the tool passes the verification, effectively reducing machining failures or tool breakage caused by tool misuse or exceeding limits, significantly improving machining safety and success rate. Furthermore, without relying on expensive sensors such as current and vibration sensors, the method obtains actual machining parameters by parsing machining instructions, and can update the tool life status in real time by combining material characteristics and tool information, significantly reducing controller costs and improving applicability on desktop CNC equipment. The entire solution achieves intelligent management of the entire tool lifecycle with low cost, high reliability, and strong collaboration.

[0064] In an exemplary embodiment, before verifying the tool based on tool information and material information, the method further includes:

[0065] Obtain the planned processing parameters and operating condition coefficients for the processing task.

[0066] Based on tool information, material information, planned machining parameters, and operating condition coefficients, the theoretical basic life of the tool is determined.

[0067] Based on the planned machining parameters, material information, and tool information, the theoretical wear increment generated by the tool after completing the machining task is predicted.

[0068] Based on the theoretical lifespan and theoretical wear increment, the theoretical remaining lifespan of the tool after completing the machining task is predicted.

[0069] Planned machining parameters refer to the machining parameters planned for the current machining task, specifically including but not limited to cutting speed, feed rate, axial depth of cut, and radial width of cut. The operating condition factor is a comprehensive correction factor reflecting the impact of non-geometric factors such as material hardness, cooling method, and tool coating on tool life. Theoretical base life refers to the theoretical usable life of the tool under normal standard cutting conditions (without considering the current machining task), based on the tool's current latest life state. Theoretical wear increment refers to the amount of tool life predicted based on the current machining task parameters after completing the task. Theoretical remaining tool life refers to the estimated remaining usable life of the tool after the current machining task is completed, up to the failure criterion. The failure criteria differ for different types of tools. For end mills, the failure criteria include average flank wear VB_avg >= 0.2 or 0.3 mm, maximum flank wear VB_max >= 0.6 mm, or edge chipping / plastic deformation / severe chatter causing surface roughness to exceed preset limits, or rocker arm edge degradation and the maximum depth KT value of crater wear exceeding the preset KT limit. Crater wear refers to a concave area formed on the rake face of the tool, near the back of the cutting edge, resembling a crescent moon, mainly caused by the friction and diffusion of chips with the rake face at high temperatures.

[0070] In this embodiment, before performing tool verification, the controller first extracts the planned machining parameters from the machining task context (such as G-code metadata, CAM output, or workpiece QR code) to clarify the planned machining parameters to be used in the current plan. Then, based on tool information, material information, planned machining parameters, and working condition coefficients, the theoretical basic lifespan T of the tool is determined. Then, based on the planned machining parameters, material information, and tool information, a preset wear prediction model is called to predict the theoretical wear increment Δteq generated by the tool after completing the current machining task. The theoretical basic lifespan T is subtracted from the theoretical wear increment Δteq to obtain the theoretical remaining lifespan RUL of the tool after completing the machining task, i.e., RUL = T - Δteq.

[0071] In other embodiments, the planned machining parameters and actual machining parameters include cutting speed V, feed rate f (e.g., feed per tooth fz), axial depth of cut ap, and radial width of cut ae. The controller extracts the cutting speed V, feed rate f (e.g., feed per tooth fz), axial depth of cut ap, and radial width of cut ae from the machining task context. Then, combining the read tool information (including coating, material, and type) with the material information of the workpiece to be machined, it queries the local process database for the pre-stored empirical coefficients C, n, x, y, and z calibrated for the tool-material combination. Finally, it invokes the core logic of the extended Taylor model: calculating the basic theoretical lifespan through a power-law decay relationship (i.e., each cutting parameter affects the lifespan in a negative exponential form). Next, the controller determines the operating condition coefficient K based on the current operating conditions (such as dry / wet cutting, material hardness grade, and the presence of a TiAlN coating), and multiplies the basic theoretical lifespan by K to obtain the corrected theoretical basic lifespan T = T0·K. This value represents the total available time of the tool from its latest usage state to the failure criterion under the current complete operating conditions. Based on this, the controller estimates the expected cutting time for the current task based on the planned machining parameters, material information, and tool information. The expected cutting time is then converted into the theoretical wear increment Δteq (equivalent lifespan consumption) according to the current operating conditions. Finally, the controller subtracts the theoretical wear increment Δteq from the theoretical basic lifespan T to obtain the theoretical basic lifespan RUL after the tool completes the current machining task, i.e., RUL = T - Δteq. The theoretical basic lifespan is then used for subsequent lifespan adequacy verification. In some other embodiments, the calculated theoretical basic lifespan can also be displayed on the human-machine interface for the user to decide whether to select the tool.

[0072] In this embodiment, by calculating the theoretical basic lifespan and the theoretical remaining lifespan after the task based on process parameters, material information and tool characteristics before processing, it is possible to predict whether the tool can safely complete the current task under sensorless conditions, thereby improving the reliability of decision-making and the success rate of the task in the processing preparation stage.

[0073] In one exemplary embodiment, the tool information further includes tool applicable material information and tool diameter, wherein the material information includes at least the material type and estimated machining time. The tool is verified based on the tool information and material information to obtain a verification result, including at least one of the following methods:

[0074] The first step is to verify the material suitability of the cutting tool based on the information on the applicable material and the material type, and obtain the material suitability verification result.

[0075] The second step is to obtain the recommended tool diameter, match the current tool diameter with the recommended tool diameter, and obtain the tool diameter verification result.

[0076] The third step involves verifying the tool's lifespan adequacy based on its current lifespan status and expected processing time, and obtaining the lifespan adequacy verification result.

[0077] The verification includes at least one of the following: material suitability verification results, tool diameter verification results, and tool life adequacy verification results.

[0078] Material suitability verification refers to a compliance check to determine whether the material type of the workpiece to be machined falls within the range of applicable materials supported by the cutting tool. Recommended tool diameter refers to the optimal tool diameter value preset by the controller or extracted from the process database for specific material information and machining processes. Tool diameter verification is the process of comparing the actual diameter of the currently installed tool with the recommended tool diameter to assess their geometric fit. Tool life adequacy verification refers to determining whether the tool has a safety margin to complete the machining process based on the relationship between its current tool life and the estimated cutting time required for the current task. Estimated machining time refers to the effective cutting time simulated or estimated based on the machining path length, feed rate, and material type.

[0079] This embodiment illustrates a multi-dimensional verification of the controller based on tool and material information (including material suitability verification, tool diameter matching, and tool life sufficiency verification). After the controller acquires the tool and material information, it initiates the multi-dimensional verification process. First, material suitability verification is performed: the "Applicable Material List" field is extracted from the tool information and matched with the material information (e.g., "carbon fiber") to determine whether the material of the workpiece to be processed is included in the applicable material list. If it is included, the material suitability verification result is "passed," and the process proceeds to tool diameter verification. If the material is not included, the material suitability verification result is "failed."

[0080] Tool diameter verification: The controller scans the QR code information to obtain the recommended tool diameter, or obtains the recommended tool diameter (e.g., "6mm") based on the current material information and machining type from the process configuration, G-code metadata, or cloud process library. It then compares the current tool diameter (e.g., "5.8mm") with the recommended tool diameter and calculates the absolute deviation between them. If the absolute deviation exceeds a preset threshold (e.g., 1mm), a "diameter mismatch" message is generated. However, this usually does not directly block machining; it is only included in the overall judgment as a risk warning. If the absolute deviation of the diameter is less than the preset threshold, then the tool life adequacy verification is initiated. It is understood that the diameter deviation threshold can also be 2mm, which can be set according to actual conditions and is not a unique limitation here.

[0081] Finally, the tool life adequacy is verified: the controller extracts the estimated processing time (e.g., 45 minutes) from the material information and reads the current tool life status (e.g., 50 minutes), determining whether it is greater than or equal to a preset multiple of that time, such as 1.2 times (i.e., 54 minutes). If it is less than that, the tool life adequacy verification result is "insufficient". It is understood that the preset multiple can also be 1.3, 1.5, or other values, depending on the actual situation, and is not limited to a single value here.

[0082] The three checks mentioned above can be performed independently or in combination. The check results are returned in a structured format for subsequent decision-making. The entire process is based on registered tool parameters, workpiece identification information, and program instruction parsing results, without relying on external sensors. All judgment logic is built into the rule engine of the control software, ensuring efficient operation even in resource-constrained environments on desktop CNC equipment. If any check fails, the machining task cannot be started. The controller can generate targeted prompts based on the specific failed check, such as "This tool is not suitable for carbon fiber materials" or "The remaining tool life is insufficient to safely complete the machining," providing users with clear operation guidance and suggestions, such as "The estimated machining time is 20 minutes, the remaining tool life is 30 minutes, it is recommended to replace the tool first."

[0083] In this embodiment, a triple verification mechanism of material suitability, tool diameter matching, and tool life adequacy enables the controller to comprehensively evaluate the compatibility between the tool and the workpiece before machining, thereby improving tool safety and machining success rate without relying on physical sensors.

[0084] In one exemplary embodiment, such as Figure 3 As shown, the method also includes:

[0085] Step 310: If at least one verification result indicates that the tool has failed the verification, push an early warning or prohibit the start of the machining task, select at least one recommended tool from the candidate tool set, push a tool replacement message containing the recommended tool to the user terminal, obtain the selected target tool from the user terminal, and perform a tool replacement operation based on the target tool.

[0086] The candidate tool set refers to the collection of all locally registered and physically accessible tools, whose information comes from electronic tags or local cache. Recommended tools are candidate tools that, under the current machining task requirements, meet conditions such as material compatibility, sufficient tool life, and geometric fit after being screened by the controller. Tool replacement messages are prompts containing a list of recommended tools and their key parameters (such as diameter, remaining tool life, and applicable materials), used to guide the user in performing tool replacement operations. The user terminal refers to the human-machine interface, host computer software, or mobile device connected to the computer numerical control equipment, used to receive prompts and user selection feedback. The target tool is the tool selected by the user from the recommended list to perform the current machining task.

[0087] In practical applications, when the controller determines that at least one verification result fails during the verification phase (such as incompatible material, insufficient lifespan, or excessive diameter deviation), it immediately triggers the exception handling process. First, the controller generates a tiered warning based on the failure type: if the failure is due to material incompatibility or severely insufficient lifespan, the start command is prohibited, and a blocking warning pops up on the human-machine interface; if it is only a slight diameter deviation, a non-blocking warning is displayed. Then, the controller initiates a recommendation mechanism, traversing the local candidate tool set, which consists of all registered tools whose status can be read via electronic tags or cached. For each candidate tool, the controller sequentially performs the same judgments as the original verification, including at least one of the following: whether the material is compatible, whether the remaining lifespan meets the safety margin of the expected processing time, and whether the diameter is within the allowable deviation range. Only when all verification items are met is the candidate included in the valid recommendation pool. Next, the controller sorts the valid candidates according to preset scoring rules, comprehensively considering diameter similarity, remaining lifespan percentage, and the presence of performance-enhancing attributes (such as coatings), and finally selects the top few (e.g., Top 3) with the highest scores as recommended tools. The controller pushes tool change messages containing recommended tool IDs, diameters, remaining tool life, and matching reasons to the user terminal. After viewing the message on the interface, the user selects a target tool, and the terminal sends the selection result back to the controller. Upon receiving the target tool identifier, if the equipment supports automatic tool changing, the controller drives the tool changing mechanism to retrieve the corresponding tool from the tool magazine to complete the change; if it is manual tool changing, the controller prompts the user "Please install the recommended tool!" and, after user confirmation or rereading the new tool label, re-executes the verification and machining process with the target tool. The entire process ensures a workable alternative is provided when the tool is incompatible, maintaining machining continuity.

[0088] In this embodiment, by automatically filtering and pushing recommended tools that meet the conditions when the verification fails, and performing tool replacement operation based on the user's selection, the controller can guide the user to quickly complete the tool replacement without interrupting the workflow, thereby improving the operating efficiency and task completion rate of desktop CNC equipment in multi-material and multi-tool scenarios.

[0089] The method of using the recommended cutting tool is not limited. In one exemplary embodiment, such as Figure 4 As shown, step 310 includes:

[0090] Step 312: Based on at least one of the material information, recommended tool diameter, and estimated machining time, verify each candidate tool in the candidate tool set and select the target candidate tool that passes the verification.

[0091] Step 314: Based on the verification results of each target candidate tool, determine the matching score of each target candidate tool.

[0092] Step 316: Sort the target candidate tools according to the matching degree score, and select at least one target candidate tool as the recommended tool based on the sorting result.

[0093] Target candidate tools refer to a subset of tools in the candidate tool set that meet basic compatibility conditions after preliminary verification. The matching score is a comprehensive score obtained by quantitatively evaluating the target candidate tools based on dimensions such as material information compatibility, tool diameter similarity, and tool life adequacy, reflecting their suitability for the current machining task. Recommended tools refer to one or more high-scoring target candidate tools selected according to their matching scores, used to provide tool replacement suggestions to the user.

[0094] In practical applications, when the controller needs to generate a recommended tool from the candidate tool set, it first obtains the key requirement parameters of the current machining task, including material information type, recommended tool diameter, and estimated machining time. Then, the controller iterates through all locally registered candidate tools (whose information comes from electronic tag reading or local cache) and performs verification on each candidate tool sequentially. In this embodiment, a triple verification is performed on each candidate tool as an example: first, it determines whether its applicable material list contains the current material information; second, it calculates whether the deviation between its actual diameter and the recommended tool diameter is within the allowable range (e.g., ±1mm); and third, it assesses whether its current lifespan is greater than or equal to 1.2 times the estimated machining time as a safety margin. Only when all dimensions of the verification meet the corresponding conditions is the candidate tool included in the target candidate tool set.

[0095] Next, the controller calculates a matching score for each target candidate tool. This score is composed of multi-dimensional weighted factors: for example, complete material compatibility assigns a base score, smaller diameter deviations result in higher scores, a larger proportion of remaining life to total lifespan adds more points, and performance-enhancing attributes such as coatings add extra points. The weighting can be preset according to equipment configuration or user preferences.

[0096] In other embodiments, the matching score can also be calculated using the following formula: Matching score = 100 - Absolute value of diameter deviation × 10 + Remaining lifespan percentage × 20. It is understood that in other embodiments, the matching score calculation method can be set according to circumstances and requirements, and is not limited to a single method here.

[0097] After the matching degree is scored, the controller sorts the target candidate tools from high to low according to the matching degree score, and selects at least one of the top-ranked tools (usually the Top 3) as the final recommended tool.

[0098] In this embodiment, candidate tools are evaluated and matched based on material information, recommended diameter, and estimated machining time in a multi-dimensional manner. The recommended tools are then sorted and selected accordingly. This allows the controller to automatically provide a quantitative optimization solution when the tool is not suitable, thereby improving the user's decision-making efficiency and machining preparation accuracy in a multi-tool environment.

[0099] In another exemplary embodiment, selecting at least one recommended tool from the candidate tool set includes:

[0100] For each candidate tool in the candidate tool set, a hard process constraint check is performed. The hard process constraints include at least one of the following: tool type, diameter tolerance, tool holder interface type, minimum fillet radius, and toolpath space accessibility. If any hard process constraint is not met, the candidate tool is excluded. For the target candidate tool that passes the hard process constraint check, the matching degree score of the target candidate tool is determined based on multiple preset adaptation dimensions. The target candidate tools are sorted according to the matching degree score, and at least one target candidate tool is selected as the recommended tool based on the sorting result.

[0101] Hard process constraints refer to the minimum requirements set by the machining task for the physical or functional properties of the cutting tool. Tool type refers to the basic geometric category of the cutting tool, such as a flat end mill, ball end mill, or chamfering cutter. Diameter tolerance refers to the allowable deviation range between the actual diameter of the cutting tool and the nominal diameter required by the task. Tool holder interface type refers to the mechanical standard for the connection between the tool and the spindle. Minimum fillet radius refers to the minimum internal angle radius that the cutting edge can machine, determined by the tool tip geometry. Toolpath space accessibility refers to whether the tool can enter and complete the specified path without interference within the workpiece geometry. Matching score is a quantitative score that comprehensively reflects the multi-dimensional fit between the candidate cutting tool and the current machining task.

[0102] In this embodiment, when it is necessary to select recommended tools from the candidate tool set, the controller first traverses all registered candidate tools and performs hard process constraint verification on each candidate. Hard process constraints include at least one of the following: tool type, diameter tolerance, tool holder interface type, minimum fillet radius, and toolpath spatial accessibility. Specifically, the verification may be based on the process requirements of the current machining task, sequentially determining whether the tool type matches (e.g., if the task requires a ball end mill but the candidate is a flat end mill, it is excluded), whether the diameter is within the allowable tolerance range, whether the tool holder interface type is compatible with the machine spindle, whether the minimum fillet radius is less than or equal to the minimum interior angle required by the toolpath, and whether the overall tool size can complete the toolpath without collision in the complex structure of the workpiece. If any hard constraint is not met, the candidate tool is immediately excluded and does not participate in subsequent scoring. For target candidate tools that pass all hard constraints, the controller calculates normalized sub-item scores based on multiple preset adaptation dimensions (including material compatibility, geometric matching degree, machine tool capability support, remaining life sufficiency, etc.), and merges them according to preset weights to generate a matching degree score. Subsequently, the controller sorts all target candidate tools from high to low according to their matching scores, and selects at least one of the top-ranked tools (such as Top 1 or Top 3) as recommended tools and pushes them to the user terminal for selection.

[0103] In some embodiments, the matching score calculation process can adopt a "hard constraint filtering + multi-dimensional weighted fusion" strategy. The hard process constraints include tool type, diameter tolerance, tool holder interface type, minimum fillet radius, and toolpath space accessibility, as illustrated below. In this embodiment, the specific steps are as follows: First, the controller performs the above-mentioned hard process constraint checks on the candidate tool; if any constraint is not met, the tool is directly eliminated.

[0104] For a target candidate tool that passes the hard process constraint verification, the controller can obtain the scoring rules corresponding to each adaptation dimension, score the target candidate tool based on each scoring rule, calculate the adaptation score for each adaptation dimension, and then perform weighted fusion processing on each adaptation score to obtain the matching degree score of the target candidate tool. Specifically, the adaptation dimensions include at least: the compatibility between workpiece material and tool material and coating, the degree of matching between tool geometry parameters and current toolpath requirements, the machine tool's dynamic performance support for recommended cutting parameters, the sufficiency of theoretical lifespan relative to task requirements, tool quality rigidity dimension, and tool supply cost dimension.

[0105] For example, the six adaptation dimensions mentioned above are used as examples for explanation. The controller calculates the normalized sub-item scores (scores in the range of [0,1]) based on the six preset adaptation dimensions, and sums them according to preset weights to finally obtain a matching score of 0 to 100.

[0106] The calculation method for each normalized sub-item is as follows: Material matching score M: The score is assigned according to the matching matrix of ISO513 workpiece group and tool material / coating. If it is a perfect match, M is 1; if it is suboptimal, M is 0.7; if it is not a match, M is 0.

[0107] Geometric matching score G: Based on the requirements of toolpath diagonal radius, cutting edge length, number of teeth and helix angle, the deviation between the actual parameters and the target value is input into a continuous penalty function (such as logistic or exponential decay function) for smoothing and score reduction, thus obtaining the geometric matching score G.

[0108] The capability matching score C is calculated by assessing whether the current machine tool can support the recommended cutting parameters (Vc, fz). If the required spindle speed or torque exceeds the equipment limit, a speed penalty term pen_speed and a torque penalty term pen_torque are introduced, and C = exp(-pen_speed-pen_torque) is set to ensure that the more the limit is exceeded, the lower the score.

[0109] The lifespan matching score R is determined by the ratio of the predicted remaining lifespan RUL to the estimated cutting time T_job, i.e., R=min(1,RUL / T_job), where T_job can be directly provided by CAM (Computer-Aided Manufacturing) software, or determined by the path length L_path and the feed rate f, and estimated as T_job≈L_path / f.

[0110] The mass rigidity matching item score Q is calculated by using the beam deflection model to estimate the end deflection δ based on the overhang length L, elastic modulus E, and section moment of inertia I. Then, Q = exp(-δ / δ_lim) is calculated by combining the allowable deflection δ_lim. At the same time, the correction factors for runout and dynamic balance level are superimposed to obtain the normalized mass rigidity matching item score Q.

[0111] Supply cost matching score A: obtained by weighted summation after normalizing inventory quantity and unit purchase cost respectively.

[0112] Finally, the scores of the normalized sub-items above are weighted and fused to obtain the matching degree score of the target candidate tool:

[0113] Score=100×(0.28M+0.18G+0.18C+0.18R+0.10Q+0.08A).

[0114] In this system, all sub-items fall within the range [0,1], with a total weight of 1, ensuring the interpretability and ranking stability of the scoring results. Subsequently, the controller sorts the candidate tools in descending order based on their matching scores, outputting the Top-3 as the recommended results. It is understood that in other embodiments, the weights of the normalized sub-item scores can be adjusted according to actual circumstances, and no single limitation is imposed.

[0115] In this embodiment, a two-stage screening mechanism is used, which first performs hard process constraint filtering and then performs multi-dimensional adaptation scoring. This mechanism can quickly eliminate unusable options from a large number of candidate tools and accurately recommend the optimal alternative, thereby improving the efficiency of tool change decision-making and the continuity of machining tasks.

[0116] In an exemplary embodiment, the actual machining parameters include cutting time and cutting parameter coefficients. Obtaining the actual machining parameters of the tool during the execution of the machining task includes:

[0117] During the execution of the machining task, the machining control instructions are analyzed in real time to obtain the actual feed rate, identify the cutting stroke segment and the non-cutting stroke segment, accumulate the cutting stroke segment to obtain the cutting time, obtain the recommended feed rate of the tool, and determine the cutting parameter coefficients based on the actual feed rate and the recommended feed rate.

[0118] Machining control instructions are program codes used to control the movement of CNC machine tools, typically in G-code form, containing information such as movement type, coordinate position, and feed rate. Actual feed rate refers to the tool movement speed specified by the F-value (feed rate) in the machining control instructions and actually effective during the cutting stroke segment. A cutting stroke segment is the movement segment in the machining control instructions that characterizes material removal behavior, corresponding to interpolation instructions such as G1, G2, or G3. Non-cutting stroke segments refer to rapid positioning or idle stroke segments that do not involve material cutting, typically corresponding to the G0 instruction. Cutting time is the sum of the execution times of all cutting stroke segments, reflecting the cumulative time the tool actually participates in cutting. The recommended feed rate is the optimal feed speed preset in the process database or tool information. Cutting parameter coefficients are weighting factors determined based on the deviation of the actual feed rate from the recommended feed rate, used to quantify the impact of machining aggression on tool wear.

[0119] During the execution of the machining task, the controller can read and analyze the machining control commands sent to the motion control unit line by line in real time. Specifically, it can first identify the type of each command: for example, if it is a G0 command, it is determined to be a non-cutting stroke segment, and its corresponding F value is ignored, and the running time of this segment is not included in the life consumption; if it is a G1, G2, or G3 command, it is determined to be a cutting stroke segment, and the F value of it is extracted as the actual feed rate of this segment. At the same time, the controller combines the path geometry of this segment (the path length L calculated from the start point, end point, and interpolation type) with the actual feed rate F to calculate the theoretical execution time t=L / F of this cutting segment, and sums up the t values ​​of all cutting segments to obtain the total cutting time of this task. Meanwhile, the controller obtains the recommended feed rate under the tool-material combination from the tool information or the process database associated with the current material information. Subsequently, it calculates the ratio of the actual feed rate to the recommended feed rate, and determines the cutting parameter coefficient based on this ratio: cutting parameter coefficient = (actual feed rate / recommended feed rate)^1.5. A coefficient greater than 1 indicates that more aggressive cutting conditions will accelerate tool wear.

[0120] In this embodiment, by analyzing machining control commands in real time during the machining process to distinguish between cutting and non-cutting strokes and accumulate effective cutting time, and combining the actual feed rate and recommended feed rate to determine cutting parameter coefficients, the controller can dynamically quantify the impact of machining aggression on tool life without physical sensors, thereby improving the accuracy and practicality of wear prediction.

[0121] In one exemplary embodiment, the material information includes the material hardness coefficient. For example... Figure 5 As shown, based on actual machining parameters, material information, and tool information, the tool wear increment is predicted, including:

[0122] Step 410: Determine the tool quality coefficient based on the tool information.

[0123] Step 420: Based on cutting time, material hardness coefficient, cutting parameter coefficient, and tool quality coefficient, predict the tool wear increment.

[0124] The tool quality factor is a coefficient used to characterize the wear resistance of a tool, based on its physical properties (such as whether it has a wear-resistant coating, material type, or brand performance level). The material hardness factor is an empirical value preset for the relative wear caused to the tool during cutting by different materials (such as acrylic, hardwood, carbon fiber, or aluminum alloy).

[0125] For example, the material hardness coefficients are shown in Table 1:

[0126] Table 1. Material Hardness Coefficients

[0127]

[0128] In practice, after completing the machining task and obtaining the effective cutting time and cutting parameter coefficients, the controller enters the wear increment prediction stage. First, based on the read tool information, the controller determines the tool quality coefficient: if the tool information indicates a wear-resistant coating such as TiN or TiAlN, a lower quality coefficient (e.g., 0.7) is assigned, indicating slower wear under the same conditions; if it is a common uncoated tool, a default value (e.g., 1.0) is used. Second, the controller queries a locally stored material hardness coefficient table (Table 1) based on material information (e.g., "carbon fiber"). This table is constructed based on measured data, for example, acrylic is 0.3, hardwood is 1.5, and carbon fiber is 3.0, used to quantify the wear intensity of different materials on the tool. Subsequently, the controller calls the cutting time (i.e., the cumulative time of all cutting stroke segments) and cutting parameter coefficients (derived from the ratio of actual feed rate to recommended feed rate) calculated in the previous steps. Based on this, the controller performs wear increment calculation: Wear increment = Cutting time × Material hardness coefficient × Cutting parameter coefficient × Tool quality coefficient. After predicting the wear increment, the wear increment is subtracted from the remaining lifespan and temporarily stored in memory, to be written back to the electronic tag upon task completion. In other embodiments, the wear increment can be calculated by weighting the cutting time, material hardness coefficient, cutting parameter coefficient, and tool quality coefficient.

[0129] In this embodiment, the wear increment is predicted by combining the tool quality coefficient, material hardness coefficient, cutting parameter coefficient and actual cutting time. This enables the controller to achieve dynamic life update of multi-factor coupling without relying on external sensors, thereby improving the precision and practical applicability of tool life management.

[0130] In one exemplary embodiment, such as Figure 6 As shown, step 420 includes:

[0131] Step 422: Using cutting time, material hardness coefficient, cutting parameter coefficient, and tool quality coefficient as inputs, call the trained wear increment prediction model to predict the tool wear increment.

[0132] The wear increment prediction model is trained based on historical machining data and historical tool information of the tool, as well as historical material information of the workpiece.

[0133] In this embodiment, the wear increment prediction model refers to a mathematical or algorithmic model trained based on historical data, used to predict the wear increment of the tool according to the current machining conditions. Historical machining data refers to the actual cutting time, actual feed rate, material information type, and corresponding tool life consumption results recorded in past machining tasks.

[0134] In this embodiment, unlike the method of calculating wear increment using a fixed empirical formula, the controller calls a pre-deployed wear increment prediction model for intelligent prediction after completing the machining task. This model is loaded into local storage before the equipment leaves the factory or via a cloud update mechanism. Its training data comes from a large number of anonymized user-uploaded real usage records, including but not limited to historical machining data of the cutting tools (such as cutting time and actual feed rate for each task), historical tool information (such as coating type, brand, and total lifespan), and historical material information of the corresponding workpiece (such as material type and hardness grade). When executing the current task, the controller first summarizes the key input parameters for this machining: effective cutting time, material hardness coefficient, cutting parameter coefficient, and tool quality coefficient. Then, the controller inputs these four parameters as feature vectors into the wear increment prediction model. This model can be a lightweight regression model (such as linear regression or decision tree) or a compressed neural network, capable of outputting the wear increment prediction value for this task within milliseconds. This value represents the equivalent lifespan unit consumed by the tool under the current combined working conditions. Next, the controller updates the remaining tool life status accordingly and temporarily stores the result, writing it back to the electronic tag after the task is completed. Furthermore, the continuous optimization of the model can rely on the "cloud-edge" collaborative mechanism: each computer digital control device regularly uploads anonymous usage records when connected to the network, the cloud aggregates global data to retrain the model, and pushes the better version to the terminal device through firmware updates, forming a closed loop of "use-feedback-evolution".

[0135] In this embodiment, by calling a wear increment prediction model trained based on historical machining and material data, the wear increment is predicted with cutting time, material hardness coefficient, cutting parameter coefficient and tool quality coefficient as inputs. This enables the controller to achieve data-driven personalized life assessment without increasing hardware costs, and improves the adaptability of the prediction results to different tool brands and actual working conditions.

[0136] In an exemplary embodiment, the method further includes: recording tool usage data, tool failure events, and tool life status data during the execution of a machining task; uploading the tool usage data, tool failure events, and tool life status data to a server so that the server updates the model parameters of the wear increment prediction model; and receiving the model parameters of the wear increment prediction model sent by the server.

[0137] In this embodiment, tool usage data refers to information related to tool operation recorded during a single machining task, including actual cutting time, material type, feed parameters, and environmental context. Tool failure events include, but are not limited to, abnormal tool states marked by the user or inferred by the controller during or after machining, such as breakage, chipping, or premature wear rendering the tool unusable.

[0138] In practice, during the execution of machining tasks, the controller continuously records multi-dimensional data related to the cutting tool. Specifically, while parsing G-code and updating tool life, the controller synchronously generates structured tool usage data, including material information type, effective cutting time, actual feed rate, recommended feed rate, tool ID, and brand information. If tool breakage occurs during machining or the user manually marks "tool damaged" after the task ends, a tool failure event is recorded, and the machining conditions and tool life status at that time are associated with it. In addition, the controller also saves tool life status data before and after the task starts, such as initial remaining life, final remaining life, and whether the warning threshold has been reached. When the device is connected to the network, after the task is completed or during a preset synchronization period, the controller processes the above three types of data—tool usage data, tool failure event data, and tool life status data—(e.g., hashing the tool ID and removing the user identifier), and packages the processed data and uploads it to a remote cloud server.

[0139] After receiving data from a large number of terminals, the cloud server uses it as training samples to recalibrate or iteratively optimize the model parameters of the wear increment prediction model. This includes adjusting the baseline value of the material hardness coefficient, correcting the actual gain ratio of coating on lifespan, or updating the performance deviation factors of different brands of cutting tools. Subsequently, the server distributes the updated model parameters to each terminal device via firmware differential packages or configuration files. Upon receiving the new parameters, the device controller verifies their version validity and replaces the locally stored old parameters, thus achieving online model updates. Throughout the entire process, all data acquisition and uploading are performed with user authorization and privacy protection, without relying on additional sensors. It is entirely based on existing control logic extensions, ensuring stable operation on desktop CNC equipment.

[0140] In this embodiment, by recording tool usage data, failure events, and life status during the machining process and uploading them to the server to update the wear increment prediction model parameters, and then receiving the optimized parameters, the controller can continuously improve the life prediction accuracy based on real global usage feedback, and enhance the model's adaptability to different tool brands and complex working conditions.

[0141] In one exemplary embodiment, obtaining tool information stored in an electronic tag associated with the tool includes:

[0142] After the tool change is completed, a tag reading operation is triggered. Tool information is read from the near-field communication tag associated with the tool via near-field wireless communication. The near-field communication tag is set on the tool change path.

[0143] In this embodiment, the Near Field Communication Tag (NFC tag) is a contactless radio frequency identification device conforming to specific standards such as ISO / IEC 14443A or ISO / IEC 15693, possessing read and write storage capabilities, and used to bind tool identity and status information. The tool change path refers to the physical trajectory area traversed by the tool or tool magazine tray during automatic or manual tool change; placing a reader / writer within this area ensures reliable communication.

[0144] In this embodiment, NFC tags conforming to the ISO / IEC 14443A or ISO / IEC 15693 protocol can be placed on the tool magazine tray, near the spindle, or on the tool holder to achieve on-the-go storage and dynamic updates of tool lifecycle data. In this embodiment, the NFC tag storage capacity is not less than 512 bytes (preferably 888 bytes, such as NTAG216), which can completely record the tool type, diameter, number of cutting edges, coating, list of applicable materials, cumulative usage time, remaining life, wear level, and usage history. The matching NFC reader module is installed in a position accessible to the tool changing action, with low unit cost and low core PCB module cost. It supports I²C interface (for easy integration with embedded controllers) or USB interface (plug and play), with a reading distance of 10–30 mm. The entire reading, writing, and JSON data parsing process takes less than 200 ms.

[0145] Specifically, in automated tool changer scenarios, the NFC reader is fixed to the tool magazine bracket, facing the bottom of the tray, and connected to the CNC controller (such as a Raspberry Pi running Linux CNC or an ESP32 running GRBL). In manual tool changer scenarios, a fixed "card-swiping area" type reading station is set up at the edge of the workbench, requiring the user to actively bring the tool close for reading before and after tool changes. The controller supports multiple triggering mechanisms: automatic reading after a 0.5-second delay after tool change, manual triggering by the user clicking the "Identify Tool" button on the interface, or periodic reading before each machining operation to confirm the current tool status. To address the corrosive effects of sawdust, cutting fluid, and metal dust on electronic components in desktop CNC environments, the NFC tag is encapsulated in IP67-rated epoxy resin and covered with a 0.5mm thick transparent protective film. It is also recommended to clean the reader antenna surface weekly to maintain communication stability.

[0146] In some embodiments, to address the shielding issue of NFC signals by metal tool magazine trays (typically made of aluminum alloy or steel), three engineering solutions are provided: First, a 30mm wide slot is cut into the bottom of the tray to embed the tag and maintain a gap of at least 3mm between the tag and the metal surface; second, an "anti-metal NFC tag" with a built-in ferrite layer is used, which is less expensive; third, the tag is pasted onto a plastic or ceramic isolator before being fixed to the tray. For multi-position tool magazines (e.g., 8-position), the controller performs an initial scan upon startup. The tool magazine rotates sequentially to each position, and the reader reads all tags one by one (total time approximately 10 seconds), caching the results in the controller's memory. Subsequent processing directly calls the cached data to improve efficiency. When the controller detects that the user has manually changed the tool, causing inconsistencies between the cached and actual tags, it automatically prompts "Tool magazine configuration has changed, rescan?". After the processing task is completed, the controller writes the updated lifetime data back to the original NFC tag.

[0147] In this embodiment, by deploying an anti-interference NFC reader and protective tag along the tool change path, and combining automatic triggering, caching mechanisms, and multi-tool position scanning strategies, the controller can stably read tool information in the metal shielding and dusty environments commonly found in desktop CNC, ensuring the reliability and practicality of the tool life management process.

[0148] In one exemplary embodiment, reading tool information from a tool-associated near-field communication tag via near-field wireless communication includes: reading tool information from a tool-associated near-field communication tag disposed around a tool magazine tray, tool holder end face, or tool changer via near-field wireless communication.

[0149] Tool magazine tray integration refers to embedding NFC tags into the tray structure of the automatic tool changer in a CNC machine tool, serving as a physical carrier of tool information. Fixed reading stations are NFC reader / writer devices located near the spindle or edge of the worktable, allowing users to actively approach and identify the tool during manual tool changes. Tool holder end face integration refers to directly embedding ultra-thin NFC tags into the non-cutting end face of the tool holder, achieving integrated binding between the tool body and the electronic tag.

[0150] In practical applications, the core of the controller's acquisition of tool information relies on the appropriate deployment and reliable reading of near-field communication (NFC) tags under different hardware configurations. In some embodiments, a tool magazine tray integration approach can be adopted: NFC Forum Type 2 tags (such as NTAG216) conforming to the ISO / IEC 14443A protocol can be embedded in the bottom or side wall of each tool magazine tray. These tags have 888 bytes of storage capacity, sufficient to hold structured data such as tool type, diameter, coating, applicable material list, remaining lifespan, and usage history. The tags are encapsulated with waterproof adhesive and installed facing the spindle, ensuring that during automatic tool changing, when the tray rotates to the spindle docking position, the NFC reader fixed to the bracket can stably read the data within a distance of 10–30 mm. After detecting the completion signal of the tool change action, the controller triggers the reading process after a delay, acquiring the tag data via I²C or USB interface and parsing it into an internally usable format.

[0151] For equipment without an automatic tool magazine, a fixed reading station is used: before and after tool changing, the user needs to bring the tool close to the reading station on the side of the spindle. The controller activates the reader via manual triggering or an interface button to confirm and switch between the old and new tool information. In high-value applications, an NFC tag can be integrated into the end face of the tool holder. This involves embedding an ultra-thin tag into the non-cutting end of a customized tool holder, using a ceramic or plastic "NFC transparent window" to overcome metal shielding, while controlling the tag weight to be less than 1 gram to maintain dynamic balance. Regardless of the integration method used, the controller uses the read tool information as the input source for subsequent material compatibility verification, lifespan adequacy judgment, and wear prediction, and writes the updated lifespan status back to the original tag after machining is completed.

[0152] In this embodiment, by deploying near-field communication tags on the tool magazine tray, fixed reading station, or tool holder end face, and combining waterproof encapsulation, anti-metal design, and multi-scenario adaptation strategies, the controller can stably read tool information under different desktop CNC configurations, providing a scalable and highly reliable tool identification and status management foundation for low-cost devices.

[0153] In an exemplary embodiment, the method further includes: during the execution of a machining task, if the tool life status is detected to meet a preset early warning mechanism, a graded prompting mechanism is activated, wherein the graded prompting mechanism includes at least one of visual prompts, operation confirmation pop-ups, or interruption of machining.

[0154] A graded prompting mechanism refers to a multi-level human-machine interaction feedback method, from mild to severe, based on different levels of tool wear. Visual prompts involve displaying the tool life status on the human-machine interface using colors, icons, or text; for example, a yellow icon indicates "low tool life." An operation confirmation pop-up is a dialog box that appears when the user attempts to start or continue machining, requiring confirmation whether to continue operation with insufficient tool life.

[0155] During the machining process, the controller continuously monitors the tool wear level, assesses its remaining lifespan, and compares it in real time with preset multi-level warning thresholds. Once the current lifespan falls into a certain warning range, the controller immediately activates the corresponding graded prompt mechanism. For example, when the remaining lifespan is less than 50% (i.e., the wear percentage is greater than or equal to 50%), a yellow prompt is displayed on the human-machine interface ("Tool lifespan is more than halfway through, pay attention to monitoring machining quality"). When the remaining lifespan is less than 30% (i.e., the wear percentage is greater than or equal to 70%), an orange warning is displayed, and a replacement suggestion is pushed: "Tool lifespan is less than 30%, replacement recommended". If the remaining lifespan is less than 10% (i.e., the wear percentage is greater than or equal to 90%), it is considered a high-risk state, and a red warning is displayed with the message "Tool lifespan is less than 10%, must be replaced immediately", prohibiting the start of the machining task. In specific implementation, a status assessment can be triggered every time the wear increment is calculated. All prompt behaviors are based on locally stored tool information and real-time updated lifespan data, without network dependence, and are seamlessly integrated with the aforementioned NFC tag reading and writing, material verification, wear prediction, and other processes. In a desktop CNC resource-constrained environment, the entire mechanism achieves safety boundary control through software policies, balancing operational flexibility with equipment protection requirements.

[0156] In this embodiment, by dynamically triggering visual prompts, operation confirmation pop-ups, or interrupting processing according to the tool life status during the machining process, a graded prompt mechanism can be established to provide user interaction methods that match the risk level at different wear stages, thereby improving operational safety and decision-making rationality.

[0157] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0158] This application also provides a tool life management system, the system comprising:

[0159] The cutting tool, or its associated carrier, is equipped with an electronic tag that stores cutting tool information, including a list of applicable materials and the current lifespan status.

[0160] The electronic tag reader / writer module is configured to interact with the electronic tag via near-field wireless communication.

[0161] The controller, which communicates with the electronic tag reader / writer module, is configured as follows:

[0162] In response to a tool change command, the system grasps the tool and acquires tool information stored in an electronic tag associated with the tool, as well as material information of the workpiece to be machined, via near-field wireless communication. The tool information includes the current tool life status. Based on the tool and material information, the system verifies the tool to obtain a verification result. The verification includes at least one of material suitability verification, tool diameter verification, and tool life adequacy verification. If the verification result indicates that the tool has passed the verification, the system executes the machining task and acquires the actual machining parameters of the tool during the machining task execution. Based on the actual machining parameters, material information, and tool information, the system predicts the tool wear increment and updates the current tool life status based on the wear increment. After the machining task is completed, the system controls the electronic tag reading and writing module to write the updated current tool life status to the electronic tag.

[0163] It is understood that the controller is configured to execute the steps in any of the above tool life management method embodiments. For details, please refer to the content in the above method embodiments, which will not be repeated here.

[0164] In practical applications, the aforementioned tool life management system achieves a complete data closed loop—from tool identification and intelligent verification to dynamic wear prediction and status write-back—through the collaborative work of the tool, electronic tag reading and writing module, and controller. The entire process does not rely on physical sensors such as current or vibration sensors; it only requires near-field communication and G-code parsing to operate efficiently on desktop computer digital control equipment (such as CNC engraving machines equipped with automatic or manual tool changing functions).

[0165] After purchasing new cutting tools, users can affix an NFC tag conforming to the ISO / IEC 14443A protocol to the bottom of the tool magazine tray (or the reading station near the spindle) and enter tool information (such as type, diameter, coating, and list of applicable materials) through the host computer software. The system automatically fills in the theoretical lifespan (e.g., 120 minutes) based on the brand and coating and writes the complete information to the electronic tag. At this point, the tool completes digital registration and acquires an identity that can be recognized by the system.

[0166] When a user loads a machining task, the system first extracts the material information (such as "carbon fiber plate") of the workpiece to be machined from G-code metadata, process documents, or workpiece QR codes. Subsequently, after the tool change is completed (either automatically or manually by the user placing the tool in the reading area), the controller triggers the electronic tag reading and writing module to read the tool information from the current tool's electronic tag via near-field wireless communication, including the list of applicable materials and the current life status.

[0167] The controller then performs a triple check: Diameter check: Obtain the recommended tool diameter (e.g., "6mm"), then compare the current tool's actual diameter (e.g., "5.8mm") with the recommended tool diameter to determine if the absolute value deviation exceeds a preset threshold (e.g., 1mm); Material compatibility check: Determine if the material information is in the tool's applicable material list; Lifespan adequacy check: Combine the estimated processing time (derived from G-code path simulation) to determine if the current lifespan meets the safety margin (e.g., ≥120%). If all three checks pass, the check result is "passed," allowing entry into the processing stage; otherwise, the system blocks processing and displays the reason. Then, it filters candidate tools that meet the conditions from the local tool set, sorts them by matching degree, and pushes the recommended tool list until the user-selected recommended tool passes the above triple check. The calculation process for the matching score of the candidate tools is described in detail in the above embodiment and will not be repeated here.

[0168] After successful verification, the controller initiates the machining task and analyzes and identifies machining instructions in real time: G1 / G2 / G3 and other cutting instruction segments are marked as cutting stroke segments, their F values ​​are extracted as the actual feed rate, and the execution time of each segment is calculated based on the path length. The G0 rapid traverse segment is ignored and not included in the tool life consumption. Next, the execution time of all cutting segments is accumulated to obtain the cutting duration. Simultaneously, the recommended feed rate for this tool-material combination is obtained from the process database, and the deviation between the actual feed rate and the recommended feed rate is calculated to determine the cutting parameter coefficients. Subsequently, the controller calls the wear prediction logic: combining the material hardness coefficient (e.g., 3.0 for carbon fiber), tool quality coefficient (0.7 if TiAlN coating is present), cutting parameter coefficients, and cutting duration, the wear increment for this task is calculated: Wear increment = Cutting duration × Material hardness coefficient × Cutting parameter coefficient × Tool quality coefficient. After predicting the wear increment, it is deducted from the remaining tool life, the tool life status is updated, and temporarily stored in memory. In addition, the tool's lifespan is checked to determine whether the machining can be completed. If the check fails, a tiered prompt is triggered (such as different colored icons, confirmation pop-ups, or forced interruption) to guide the user to operate safely.

[0169] Upon completion of the task, the controller immediately activates the electronic tag reading and writing module to write the updated current lifespan status (including remaining lifespan, wear level, usage timestamp, etc.) back to the original electronic tag. After successful writing, the tool's latest status can be accurately read regardless of which compatible device it is moved to, achieving cross-device status synchronization.

[0170] In some exemplary embodiments, electronic tags are deployed in the area around the tool magazine tray, the tool holder end face, or the tool changing mechanism.

[0171] The deployment location of electronic tags can be flexibly configured according to the equipment type and usage scenario: In desktop CNC systems equipped with automatic tool changers, electronic tags are embedded in the bottom or side wall of each tool magazine tray to ensure stable reading by the NFC reader near the spindle during tool changing; in manual tool changers without tool magazines, tags can be integrated into the non-metallic area of ​​the tool holder end face (such as forming an "NFC transparent window" through a ceramic or plastic end cap), or the user can bring the tool close to a fixed reading station at the edge of the worktable for identification; in addition, tags can also be placed on non-moving parts around the tool changing mechanism, as long as they are within the effective communication range of the reader (10–30 mm).

[0172] In some exemplary embodiments, the electronic tag employs an anti-metal encapsulation structure, or is deployed by creating a non-metallic slot in the tool magazine tray and attaching ceramic or plastic spacers.

[0173] To address the common metal shielding issues in desktop CNC environments (such as interference from aluminum alloy tool magazine trays to radio frequency signals), electronic tags employ an anti-metal encapsulation structure (with a built-in ferrite isolation layer), or embed the tag into a non-metallic groove approximately 30mm wide on the metal tray, maintaining a gap of at least 3mm between the tag and the metal surface, or attach a ceramic or plastic isolation sheet to the tray surface before fixing the tag, thereby effectively ensuring the reliability of near-field communication.

[0174] In some exemplary embodiments, the system further includes a server communicatively connected to the controller, configured to: receive tool usage data, tool failure events, and tool life status data uploaded by at least one controller; update the model parameters of the wear increment prediction model based on the tool usage data, tool failure events, and tool life status data; and send the updated model parameters to the computer numerical control device controller. The wear increment prediction model is used to predict the wear increment of the tool.

[0175] In this embodiment, a cloud server is used as an example for illustration. The controller also establishes a communication connection with the cloud server: After the machining task is completed, the controller processes the recorded tool usage data (including material type, cutting time, actual feed rate), tool failure events (such as chipping or breakage marked by the user), and tool life status data (such as initial remaining life and final remaining life) and uploads them to the server; the server aggregates such data from multiple devices to retrain or fine-tune the model parameters of the wear increment prediction model (e.g., adjusting the material hardness coefficient, coating gain factor, or brand performance deviation), and distributes the optimized model parameters to the controllers of each computer numerical control device through firmware updates or configuration files; after receiving the data, the controller updates its local model to make subsequent wear increment predictions closer to real working conditions. The entire architecture achieves a smooth evolution from single-machine closed-loop to cloud-edge collaboration without relying on additional sensors, taking into account both low-cost deployment and continuous learning capabilities.

[0176] It is understood that for details regarding the specific content of each module in the aforementioned tool life management system, please refer to the description of the relevant content in the aforementioned tool life management method, and will not be repeated here.

[0177] Based on the same inventive concept, this application also provides a tool life management device for implementing the tool life management method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more tool life management device embodiments provided below can be found in the limitations of the tool life management method described above, and will not be repeated here.

[0178] In one exemplary embodiment, such as Figure 7 As shown, a tool life management device 700 is provided, including: a data acquisition module 710, a verification module 720, a machining control module 730, a tool life update module 740, and a data writing module 750, wherein:

[0179] The data acquisition module 710 is used to respond to the tool change command, grab the tool and trigger the tag reading operation, and acquire the tool information stored in the electronic tag associated with the tool and the material information of the workpiece to be processed through near-field wireless communication. The tool information includes the current life status.

[0180] The verification module 720 is used to verify the tool based on tool information and material information, and obtain the verification result. The verification includes at least one of material suitability verification, tool diameter verification and tool life adequacy verification.

[0181] The machining control module 730 is used to execute machining tasks when the verification result indicates that the tool has passed the verification, and to obtain the actual machining parameters of the tool during the execution of the machining task.

[0182] The life update module 740 is used to predict the wear increment generated by the tool after completing the machining task based on actual machining parameters, material information and tool information, and update the current life status of the tool based on the wear increment.

[0183] The data writing module 750 is used to write the updated current lifespan status to the electronic tag after the processing task is completed.

[0184] Each module in the aforementioned tool life management device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the computer numerical control device in hardware form or independent of it, or stored in the memory of the computer numerical control device in software form, so that the processor can call and execute the corresponding operations of each module.

[0185] In one exemplary embodiment, a computer digital control device is provided, the internal structure of which can be shown in the following diagram. Figure 8 As shown, the computer digital control device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a tool life management method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer digital control device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer digital control device, or external keyboards, touchpads, or mice, etc.

[0186] In other embodiments, the computer digital control device may also be a desktop computer digital control device.

[0187] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer digital control device to which the present application is applied. A specific computer digital control device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0188] In one exemplary embodiment, a computer digital control device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in any of the tool life management method embodiments described above.

[0189] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in any of the tool life management method embodiments described above.

[0190] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in any of the tool life management method embodiments described above.

[0191] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0192] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0193] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0194] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A tool life management method, characterized in that, The method includes: In response to a tool change command, the tool is grasped and a tag reading operation is triggered. The tool information stored in the electronic tag associated with the tool and the material information of the workpiece to be processed are obtained through near-field wireless communication. The tool information includes the current life status. The tool is verified based on the tool information and the material information to obtain a verification result. The verification includes at least one of material suitability verification, tool diameter verification, and tool life adequacy verification. If the verification result indicates that the tool has passed the verification, the machining task is executed, and the actual machining parameters of the tool are obtained during the execution of the machining task. Based on the actual machining parameters, the material information, and the tool information, the wear increment generated by the tool after completing the machining task is predicted, and the current life status of the tool is updated based on the wear increment; After the processing task is completed, the updated current lifespan status is written to the electronic tag.

2. The method according to claim 1, characterized in that, During the execution of the machining task, the actual machining parameters of the tool are obtained, including: During the execution of the machining task, the machining control command is analyzed in real time to obtain the actual feed rate, identify the cutting stroke segment and the non-cutting stroke segment, and accumulate the cutting stroke segment to obtain the cutting time. Obtain the recommended feed rate of the tool, and determine the cutting parameter coefficients based on the actual feed rate and the recommended feed rate; The actual machining parameters include the cutting time and the cutting parameter coefficients.

3. The method according to claim 2, characterized in that, The material information includes the material hardness coefficient; The method of predicting the wear increment of the tool based on the actual machining parameters, the material information, and the tool information includes: Based on the tool information, the tool quality coefficient is determined; Based on the cutting time, the material hardness coefficient, the cutting parameter coefficient, and the tool quality coefficient, the wear increment of the tool is predicted.

4. The method according to claim 3, characterized in that, The method of predicting the wear increment of the tool based on the cutting time, the material hardness coefficient, the cutting parameter coefficient, and the tool quality coefficient includes: Using the cutting time, the material hardness coefficient, the cutting parameter coefficient, and the tool quality coefficient as inputs, a trained wear increment prediction model is invoked to predict the wear increment of the tool. The wear increment prediction model is trained based on historical machining data and historical tool information of the tool, as well as historical material information of the workpiece.

5. The method according to any one of claims 1 to 4, characterized in that, Before verifying the tool based on the tool information and the material information, the method further includes: Obtain the planned processing parameters and operating condition coefficients for the processing task; Based on the tool information, the material information, the planned machining parameters, and the working condition coefficient, the theoretical basic life of the tool is determined; Based on the planned machining parameters, the material information, and the tool information, predict the theoretical wear increment that the tool will generate after completing the machining task; Based on the theoretical lifespan and the theoretical wear increment, the theoretical remaining lifespan of the tool after completing the machining task is predicted.

6. The method according to claim 5, characterized in that, The planned machining parameters and actual machining parameters include cutting speed, feed rate, axial depth of cut, and radial width of cut; determining the theoretical life of the tool based on the tool information, material information, planned machining parameters, and operating condition coefficients includes: Based on the tool information and the material information, an empirical coefficient is determined; Based on the cutting speed, feed rate, axial depth of cut, radial width of cut, and empirical coefficient, the initial theoretical life of the tool is determined through a power-law decay relationship. The initial theoretical life is adjusted based on the operating condition coefficient to obtain the theoretical life.

7. The method according to claim 5, characterized in that, The tool information also includes tool applicable material information and tool diameter, and the material information includes at least the material type and the estimated processing time; The verification of the tool based on the tool information and the material information includes at least one of the following methods: The first step is to perform a material suitability check on the tool based on the tool's applicable material information and the material type. If the material type is not included in the tool's applicable material information, the tool is determined to have failed the material suitability check. The second step is to obtain a recommended tool diameter, match the tool diameter with the recommended tool diameter, and determine that the tool has failed the tool diameter verification if the difference between the tool diameter and the recommended tool diameter is less than or equal to a preset difference threshold. The third step involves verifying the tool's lifespan adequacy based on the current lifespan status and the estimated processing time. If the current lifespan status is less than a preset safety multiple of the estimated processing time, the tool is deemed to have failed the lifespan adequacy verification.

8. The method according to claim 7, characterized in that, The method further includes: If at least one of the verification results indicates that the tool has failed the verification, a warning message will be sent or the machining task will be prohibited from starting. Select at least one recommended tool from the candidate tool set; Push a tool replacement message containing the recommended tool to the user terminal; Obtain the selected target tool from the user terminal, and perform a tool change operation based on the target tool.

9. The method according to claim 8, characterized in that, The step of selecting at least one recommended tool from the candidate tool set includes: Based on at least one of the material information, the recommended tool diameter, and the estimated processing time, each candidate tool in the candidate tool set is verified, and the target candidate tool that passes the verification is selected. Determine the matching score for each target candidate tool; The target candidate tools are sorted according to the matching score, and at least one target candidate tool is selected as the recommended tool based on the sorting result.

10. The method according to claim 9, characterized in that, The step of selecting at least one recommended tool from the candidate tool set includes: For each candidate tool in the candidate tool set, a hard process constraint check is performed. The hard process constraints include at least one of the following: tool type, diameter tolerance, tool holder interface type, minimum fillet radius, and toolpath space accessibility. If any hard process constraint is not met, the candidate tool is excluded; For a target candidate tool that passes the hard process constraint verification, the matching score of the target candidate tool is determined based on multiple preset adaptation dimensions. The target candidate tools are sorted according to the matching score, and at least one target candidate tool is selected as the recommended tool based on the sorting result.

11. The method according to claim 10, characterized in that, The determination of the matching score of the target candidate tool based on multiple preset adaptation dimensions includes: Obtain the scoring rules corresponding to each adaptation dimension, score the target candidate tool based on each scoring rule, and generate corresponding adaptation scores respectively; The matching scores are weighted and fused to determine the matching score of the target candidate tool. The matching dimensions include at least: the compatibility between workpiece material and tool material and coating, the degree of matching between tool geometry parameters and current toolpath requirements, the machine tool's power performance's ability to support recommended cutting parameters, the sufficiency of the theoretical lifespan relative to task requirements, tool quality rigidity dimension, and tool supply cost dimension.

12. The method according to claim 4, characterized in that, The method further includes: During the execution of machining tasks, record tool usage data, tool failure events, and tool life status data; The tool usage data, tool failure events, and tool life status data are uploaded to the server so that the server can update the model parameters of the wear increment prediction model. Receive the model parameters of the wear increment prediction model sent by the server.

13. The method according to any one of claims 1 to 4, characterized in that, The process of acquiring tool information stored in an electronic tag associated with the tool via near-field wireless communication includes: Tool information is read from a tool-associated near-field communication tag located on the tool magazine tray, tool holder end face, or tool changer around the tool changer via near-field wireless communication. The near-field communication tag is located on the tool changer path.

14. A tool life management system, characterized in that, The system includes: The cutting tool, or its associated carrier, is equipped with an electronic tag that stores cutting tool information, including a list of applicable materials and the current lifespan status. The electronic tag reading and writing module is configured to interact with the electronic tag via near-field wireless communication. The controller, which is communicatively connected to the electronic tag reader / writer module, is configured as follows: In response to a tool change command, the tool is grasped, and the tool information stored in the electronic tag associated with the tool and the material information of the workpiece to be processed are obtained through near-field wireless communication. The tool information includes the current life status. The tool is verified based on the tool information and the material information to obtain a verification result. The verification includes at least one of material suitability verification, tool diameter verification, and tool life adequacy verification. If the verification result indicates that the tool has passed the verification, the machining task is executed, and the actual machining parameters of the tool are obtained during the execution of the machining task. Based on the actual machining parameters, the material information, and the tool information, the wear increment generated by the tool after completing the machining task is predicted, and the current life status of the tool is updated based on the wear increment; After the processing task is completed, the updated current lifespan status is written to the electronic tag.

15. The system according to claim 14, characterized in that, The electronic tag is deployed in the tool magazine tray, tool holder end face, or around the tool changing mechanism. The electronic tag adopts an anti-metal encapsulation structure, or is deployed by opening a non-metallic groove in the tool magazine tray and attaching a ceramic or plastic isolation plate.

16. A tool life management device, characterized in that, The device includes: The data acquisition module is used to respond to the tool change command, grab the tool, and acquire the tool information stored in the electronic tag associated with the tool and the material information of the workpiece to be processed through near-field wireless communication. The tool information includes the current life status. The verification module is used to verify the tool based on the tool information and the material information, and obtain the verification result. The verification includes at least one of material suitability verification, tool diameter verification and tool life adequacy verification. The machining control module is used to execute a machining task when the verification result indicates that the tool has passed the verification, and to acquire the actual machining parameters of the tool during the execution of the machining task; The lifespan update module is used to predict the wear increment generated by the tool after completing the machining task based on the actual machining parameters, the material information and the tool information, and update the current lifespan status of the tool based on the wear increment; The data writing module is used to write the updated current lifespan status to the electronic tag after the processing task is completed.

17. A computer digital control device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 13.

18. A computer-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 method according to any one of claims 1 to 13.

19. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 13.

Images