A numerical control machine tool machining parameter optimization method and system

Abstract

The application provides a numerical control machine tool machining parameter optimization method and system, which is suitable for the technical field of data processing. The method comprises the following steps: generating initial machine tool machining parameter optimization information according to machine tool machining condition information, tool running state information and an initial machine tool machining parameter optimization model; and generating target machine tool machining parameter optimization information according to historical machining parameter optimization information, the initial machine tool machining parameter optimization information, a machine tool machining parameter division threshold and a target machine tool machining parameter optimization model. The application effectively adapts to the complex and changeable cutting machining scene of the numerical control machine tool, dynamically matches the machine tool machining condition and the tool running state, reduces the error and time consumption of manual debugging of the machining parameter, and thus improves the stability and production efficiency of the numerical control machine tool machining process.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to a method and system for optimizing CNC machine tool machining parameters. Background Technology

[0002] With the continuous development of intelligent manufacturing technology, the industry has put forward higher requirements for the machining accuracy, machining efficiency and operational stability of CNC machine tools. The optimization of machining parameters of CNC machine tools has become a key technical link to improve machining quality and production efficiency.

[0003] In existing technologies, offline simulation calculations or single threshold judgment methods are usually adopted to pre-set machining parameters such as spindle speed, feed rate, and depth of cut. Fixed CNC machine tool parameter adjustment schemes are provided based on human experience data, and the CNC machine tool is adjusted to a limited extent during the machining process.

[0004] However, existing technologies suffer from technical problems such as insufficient matching between CNC machine tool machining parameters and real-time operating conditions, lag in parameter adjustment, poor coordination between tool running status and machining conditions, and insufficient stability of the machining process. Summary of the Invention

[0005] In view of this, the present application provides a method and system for optimizing CNC machine tool machining parameters, aiming to solve the problems in the prior art such as insufficient matching degree between CNC machine tool machining parameters and real-time working conditions, lagging adjustment of CNC machine tool machining parameters, poor coordination between machine tool operating status and machining conditions, and insufficient stability of CNC machine tool machining process.

[0006] The first aspect of this application provides a method for optimizing machining parameters of a CNC machine tool, including: Acquire machining condition information from multiple machine tools and operating status information from multiple cutting tools; Based on the multiple machine tool machining condition information, multiple tool running status information, and the preset initial machine tool machining parameter optimization model, multiple initial machine tool machining parameter optimization information are generated. Based on multiple historical machining parameter optimization information, multiple initial machine tool machining parameter optimization information, multiple preset machine tool machining parameter division thresholds, and a preset target machine tool machining parameter optimization model, multiple target machine tool machining parameter optimization information are generated.

[0007] A second aspect of this application provides a CNC machine tool machining parameter optimization system, including: The machine tool processing condition information and tool running status information acquisition module is used to acquire multiple machine tool processing condition information and multiple tool running status information. The initial machine tool machining parameter optimization information generation module is used to generate multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information and the preset initial machine tool machining parameter optimization model; The target machine tool machining parameter optimization information generation module is used to generate multiple target machine tool machining parameter optimization information based on multiple historical machining parameter optimization information, multiple initial machine tool machining parameter optimization information, multiple preset machine tool machining parameter division thresholds, and a preset target machine tool machining parameter optimization model.

[0008] A third aspect of this application provides a terminal device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the CNC machine tool machining parameter optimization method described in the first aspect above.

[0009] A fourth aspect of this application provides a computer-readable storage medium, comprising: storing a computer program, wherein when executed by a processor, the computer program implements the steps of the CNC machine tool machining parameter optimization method described in the first aspect above.

[0010] The beneficial effects of this application embodiment compared with the prior art are as follows: This application achieves precise optimization of machining parameters of CNC machine tools during the machining process, so as to automatically adapt to different machine tool machining conditions and tool running states, which helps to optimize the machining parameter setting method and machining process control of CNC machine tools, improve the machining accuracy and machining efficiency of machined parts, and at the same time realize the refined and intelligent control of CNC machine tool machining process, reduce the workload and error of manual adjustment of machining parameters, reduce tool wear, and ensure the stability and continuity of CNC machine tool machining process, so as to promote the development of CNC machine tool machining towards intelligence, efficiency and precision. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram illustrating the implementation process of the CNC machine tool machining parameter optimization method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram illustrating the implementation process of the CNC machine tool machining parameter optimization method provided in Embodiment 2 of this application; Figure 3This is a schematic diagram illustrating the implementation process of the CNC machine tool machining parameter optimization method provided in Embodiment 3 of this application; Figure 4 This is a schematic diagram illustrating the implementation process of the CNC machine tool machining parameter optimization method provided in Embodiment 4 of this application; Figure 5 This is a schematic diagram illustrating the implementation process of the CNC machine tool machining parameter optimization method provided in Embodiment 5 of this application; Figure 6 This is a schematic diagram illustrating the implementation process of the CNC machine tool machining parameter optimization method provided in Embodiment Six of this application; Figure 7 This is a schematic diagram illustrating the implementation process of the CNC machine tool machining parameter optimization method provided in Embodiment 7 of this application; Figure 8 This is a schematic diagram of the CNC machine tool machining parameter optimization system provided in the embodiments of this application; Figure 9 This is a schematic diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0015] Figure 1 A flowchart illustrating the implementation of the CNC machine tool machining parameter optimization method provided in Embodiment 1 of this application is shown, and is described in detail below: Step S101: Obtain multiple machine tool processing condition information and multiple tool running status information.

[0016] In this embodiment, during CNC machine tool machining, the machining process triggering event can be triggered by retrieving machining task content from a manually set machining task library and sending the machining task content to the CNC machine tool control system. A complete machining process triggering event can start from the machining start time and end at the machining end time. The start time of the machining process triggering event can be determined according to the time when the machining task content is sent to the CNC machine tool control system, and the end time of the machining process triggering event can be determined according to the time when the CNC machine tool completes the current machining task and the machined part is taken off the line. Multiple machine tool machining condition information can refer to multiple sets of machining condition data generated during a machining process trigger event. Specifically, it can be information corresponding to multiple condition monitoring during a machining process trigger event. This information can be obtained by real-time monitoring during the machining process trigger event. Specifically, it can be obtained by collecting machine tool machining conditions within the complete time range from the start time to the end time of the machining process trigger event. Multiple tool running status information can be data corresponding to various states generated by tool running during a machining process trigger event. Specifically, it can be multiple sets of tool running data generated during the machining process trigger event, including tool spindle speed, tool wear degree, tool cutting load, tool feed state, and tool change frequency, etc. This information can be obtained by automatically detecting tool running status during the machining process trigger event. Specifically, it can be obtained by real-time monitoring of various tool running parameters within the complete time range of the machining process trigger event and classifying them into the tool running data of each machining stage according to the chronological order of the machining sequence. Understandably, multiple machine tool processing status information can be included in the current processing process trigger event, covering various aspects such as the spindle running status of the CNC machine tool, the feed system status, the cutting fluid supply status, and the workpiece positioning status. Multiple tool running status information can comprehensively reflect the real-time working status of the tool during the processing.

[0017] In this embodiment, during a machining process triggering event, the CNC machine tool control system can issue only one machining task to the CNC machine tool. The CNC machine tool control system will not issue the next machining task until the CNC machine tool has completed the current machining task.

[0018] In this embodiment, the machining task received by the CNC machine tool can refer to the specific task instructions used to guide the CNC machine tool to complete machining operations in precision machining tasks in the field of high-end equipment manufacturing. These instructions can take various forms, including workpiece dimensional parameters, machining accuracy requirements, machining process flow, and material specifications. It is understood that the machining task content can be issued to the CNC machine tool during precision machining activities. A dedicated machining database can be artificially constructed around the machining task, and the machining parameters can be precisely optimized based on the machine tool's machining condition information and tool running status information during the CNC machining process.

[0019] In this embodiment, preferably, the processing task content can be presented in the form of a standardized processing instruction sheet. The standardized processing instruction sheet can include structured fields such as processing task number, processing part name, processing accuracy level, processing material properties, processing steps, and processing time limit requirements.

[0020] Step S102: Generate multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information, and the preset initial machine tool machining parameter optimization model.

[0021] In this embodiment, the preset initial machine tool machining parameter optimization model can be manually preset. This model can be a combination of Transformer and LSTM, capable of identifying the machining parameter optimization requirements of the CNC machine tool under different machining conditions and tool operating states. The output can include multiple core machine tool machining parameter optimization information such as spindle speed optimization parameters, feed rate optimization parameters, depth of cut optimization parameters, and cutting load optimization parameters. The preset initial machine tool machining parameter optimization model can be trained using a manually constructed machining parameter optimization dataset. During training, a large amount of machining data under different machining conditions and tool states can be incorporated to ensure the model can accurately adapt to various machining scenarios. Specifically, the process can begin by merging multiple machine tool processing condition information and multiple tool running status information corresponding to each processing stage in the processing sequence triggered by the processing process. After merging, the fused processing condition and tool status information corresponding to each processing stage is obtained. Then, the merged fused processing condition and tool status information is input into a preset initial machine tool processing parameter optimization model. The preset initial machine tool processing parameter optimization model outputs the predicted probability of multiple processing parameter optimization schemes corresponding to each processing stage. This can be calculated by associating the processing parameter optimization information corresponding to the processing stages before each processing stage, thereby generating multiple initial machine tool processing parameter optimization information that can reflect the processing requirements of the current processing stage.

[0022] Step S103: Generate multiple target machine tool processing parameter optimization information based on multiple historical processing parameter optimization information, multiple initial machine tool processing parameter optimization information, multiple preset machine tool processing parameter division thresholds, and a preset target machine tool processing parameter optimization model.

[0023] In this embodiment, the preset target machine tool machining parameter optimization model can be manually preset, or it can be a trained bidirectional long short-term memory network model, a trained gated recurrent unit model, a trained recurrent neural network model, or an attention mechanism-enhanced neural network model. The preset threshold values ​​for multiple machine tool machining parameters can also be manually preset. Specifically, the process can begin by sequentially associating and integrating multiple historical machining parameter optimization information with multiple initial machine tool machining parameter optimization information. This results in the construction of a continuous machining parameter optimization sequence according to the chronological order of machining stages. This sequence is then input into a pre-defined target machine tool machining parameter optimization model. Next, multiple pre-defined machine tool machining parameter thresholds are used to determine thresholds, correct deviations, and filter states for the initial machine tool machining parameter optimization information. This process eliminates parameter optimization results that do not meet machining requirements, retaining only those highly compatible with machine tool machining conditions and tool operating status. The initial machine tool machining parameter optimization information is then iteratively optimized and its rationality verified. Finally, the current optimization results are trend-matched and their consistency verified by combining multiple historical machining parameter optimization information. This process generates stable, reliable target machine tool machining parameter optimization information that is suitable for the current machining scenario. Among them, the historical machining parameter optimization information is obtained by collecting and analyzing multiple machine tool machining condition information and multiple tool running status information during the CNC machine tool machining process in each complete machining process triggering event in the past. It is generated and stored according to the machining time sequence after calculation and optimization by combining the preset initial machine tool machining parameter optimization model, the preset target machine tool machining parameter optimization model and multiple preset machine tool machining parameter division thresholds.

[0024] The CNC machine tool machining parameter optimization method provided in this application embodiment enables precise optimization of machining parameters during the machining process of CNC machine tools. This allows for automatic adaptation to different machine tool machining conditions and tool operating states, helping to optimize the setting of machining parameters and control of the machining process. This improves the machining accuracy and efficiency of the machined parts, while also achieving refined and intelligent control of the CNC machine tool machining process. This reduces the workload and errors of manual adjustment of machining parameters, reduces tool wear, and ensures the stability and continuity of the CNC machine tool machining process, thereby promoting the development of CNC machine tool machining towards intelligence, efficiency, and precision.

[0025] Figure 2The flowchart illustrating the implementation of the CNC machine tool machining parameter optimization method provided in Embodiment 2 of this application is shown. Its difference from Embodiment 1 described above lies in: The multiple machine tool processing condition information includes first machine tool processing condition information and second machine tool processing condition information; The first machine tool processing condition information includes first machine tool condition timing identifier information, first machine tool condition start time information, and first machine tool condition end time information; The second machine tool processing condition information includes the second machine tool condition timing identifier information, the second machine tool condition start time information, and the second machine tool condition end time information; Wherein, the timing identifier information of the first machine tool operating condition is less than that of the timing identifier information of the second machine tool operating condition; Step S102 specifically includes: Step S201: Calculate the machine tool operating time interval information based on the end time information of the first machine tool operating condition and the start time information of the second machine tool operating condition.

[0026] In this embodiment, the first machine tool machining condition information and the second machine tool machining condition information can be two sets of machine tool machining condition information within a single machining process triggering event, i.e., two sets of machine tool machining condition information collected between the start and end times of the machining process triggering event. It is understood that there are at least two sets of machine tool machining condition information within a single machining process triggering event; this embodiment only lists two sets for illustrative purposes. It is understood that the first machine tool condition timing identifier information can be used to indicate the collection time of the first machine tool machining condition information within a single machining process triggering event, and the second machine tool condition timing identifier information can be used to indicate the collection time of the second machine tool machining condition information within the same machining process triggering event. The fact that the first machine tool condition timing identifier information is less than the second machine tool condition timing identifier information can be used to indicate that the collection time of the first machine tool machining condition information takes precedence over the collection time of the second machine tool machining condition information. The machine tool operating condition time interval information refers to the time difference between the end time information of the first machine tool operating condition corresponding to the first machine tool processing operating condition information and the start time information of the second machine tool operating condition corresponding to the second machine tool processing operating condition information. It can be used to reflect the time interval between the two sets of machine tool processing operating condition information.

[0027] Step S202: Determine whether the machine tool operating time interval information is greater than the preset machine tool operating time interval threshold; if yes, proceed to step S203; if no, proceed to step S204.

[0028] In this embodiment, the preset machine tool operating condition time interval threshold can be preset manually. It can be set manually by referring to the threshold corresponding to the operating condition attribution determination rule of adjacent processing stage operating condition interval in the processing process trigger event. It can be used to distinguish whether two sets of machine tool processing operating condition information belong to the same processing stage.

[0029] Step S203: The processing condition information of the first machine tool and the processing condition information of the second machine tool are segmented to obtain multiple processing condition information of the machine tool to be processed.

[0030] In this embodiment, if the machine tool operating condition time interval information is greater than the preset machine tool operating condition time interval threshold, it indicates that the first machine tool processing operating condition information and the second machine tool processing operating condition information belong to two different and independent processing stages. The segmentation process specifically refers to marking the first machine tool processing operating condition information and the second machine tool processing operating condition information as independent machine tool processing operating condition information to be processed, so as to ensure that the operating condition data of each processing stage are not confused.

[0031] Step S204: Perform splicing processing based on the first machine tool processing condition information and the second machine tool processing condition information to obtain the machine tool processing condition information to be processed.

[0032] In this embodiment, if the machine tool operating condition time interval information is less than or equal to a preset machine tool operating condition time interval threshold, it indicates that the first machine tool processing operating condition information and the second machine tool processing operating condition information belong to the operating condition data of the same processing stage. The first machine tool processing operating condition information and the second machine tool processing operating condition information can be integrated according to the order of the first machine tool operating condition timing identifier information and the second machine tool operating condition timing identifier information, and merged into a complete set of operating condition information as the machine tool processing operating condition information to be processed, thereby ensuring the integrity and continuity of the operating condition data of the same processing stage.

[0033] Step S205: Generate multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information, and the preset initial machine tool machining parameter optimization model.

[0034] In this embodiment, the preset initial machine tool machining parameter optimization model can be manually preset. This preset initial machine tool machining parameter optimization model can be a model combining Transformer and LSTM, capable of accurately identifying machining parameter optimization requirements under different machining scenarios based on the integrated machining condition information of the machine tool to be processed and multiple tool running status information. Specifically, it can first fuse multiple machining condition information of the machine tool to be processed with corresponding multiple tool running status information to generate integrated condition and tool status information corresponding to each machining stage. Then, the integrated condition and tool status information of each machining stage is input into the preset initial machine tool machining parameter optimization model. The preset initial machine tool machining parameter optimization model then outputs the predicted probabilities of various machining parameter optimization schemes corresponding to each machining stage. Subsequently, it correlates the relevant data on machining parameter optimization before each machining stage for calculation, thereby generating multiple initial machine tool machining parameter optimization information that accurately reflects the machining requirements of the current machining stage and adapts to the machining condition information of the machine tool to be processed and the tool running status information.

[0035] The CNC machine tool machining parameter optimization method provided in this application improves the accuracy and adaptability of the initial machine tool machining parameter optimization information and the subsequently generated target machine tool machining parameter optimization information, strengthens the refined and intelligent control of CNC machine tool machining parameter optimization, helps improve the machining accuracy and efficiency of machined parts, reduces the deviation of CNC machine tool machining parameter optimization, and ensures the stability of the CNC machine tool machining process.

[0036] Figure 3 The flowchart illustrating the implementation of the CNC machine tool machining parameter optimization method provided in Embodiment 3 of this application is shown. Its difference from Embodiment 1 described above lies in: The multiple tool operating status information includes first tool operating status information and second tool operating status information; The first tool operating status information includes the first tool spindle speed information; The second tool operating status information includes the second tool spindle speed information; Step S102 specifically includes: Step S301: Calculate the difference in spindle speed based on the spindle speed information of the first and second tools.

[0037] In this embodiment, the first tool running status information and the second tool running status information can be two sets of tool running status information within a machining process triggering event. Specifically, they are two sets of tool running status information collected between the start and end times of the machining process triggering event, both containing spindle speed data related to the corresponding tool. It is understood that there are at least two sets of tool running status information within a machining process triggering event. This embodiment only lists two sets for illustrative purposes, focusing on the core parameter of tool spindle speed. The tool spindle speed difference information refers to the numerical difference between the first tool spindle speed information in the first tool running status information and the second tool spindle speed information in the second tool running status information. This difference can be used to reflect the difference in spindle speed between the two sets of tool running status information.

[0038] Step S302: Determine whether the difference in the spindle speed of the tool is greater than a preset threshold for distinguishing the spindle speed of the tool; if yes, proceed to step S303; if no, proceed to step S304.

[0039] In this embodiment, the preset tool spindle speed differentiation threshold can be preset manually. It can be set manually by referring to the threshold corresponding to the spindle speed range division rules for different machining processes of CNC machine tools. It can be used to distinguish whether the spindle speed in the two sets of tool running status information is in the same speed range, and then determine whether the two sets of tool running status belong to the same machining parameter adaptation scenario.

[0040] Step S303: Based on the spindle speed information of the first tool and the spindle speed information of the second tool, segmentation processing is performed to obtain multiple tool running status information to be processed.

[0041] In this embodiment, if the difference in tool spindle speed is greater than the preset tool spindle speed differentiation threshold, it indicates that the first tool spindle speed information in the first tool running status information and the second tool spindle speed information in the second tool running status information belong to different speed ranges, and the corresponding tool running statuses also belong to different machining scenarios. Segmented processing specifically refers to marking the first tool running status information and the second tool running status information as independent tool running status information to be processed, so as to ensure that the tool status data in different speed ranges are independently distinguished.

[0042] Step S304: Combine the spindle speed information of the first tool and the spindle speed information of the second tool to obtain the operating status information of the tool to be processed.

[0043] In this embodiment, if the difference in tool spindle speed is less than or equal to a preset tool spindle speed differentiation threshold, it indicates that the first tool spindle speed information in the first tool running status information and the second tool spindle speed information in the second tool running status information are in the same speed range, and the corresponding tool running status belongs to the same machining scenario. The first tool running status information and the second tool running status information can be integrated according to the order of acquisition time and merged into a complete set of tool running status information as the tool running status information to be processed, thereby ensuring the integrity and consistency of tool status data under the same machining scenario.

[0044] Step S305: Based on the multiple machine tool processing condition information, the multiple tool running status information to be processed, and the preset initial machine tool processing parameter optimization model, generate multiple initial machine tool processing parameter optimization information.

[0045] In this embodiment, the preset initial machine tool machining parameter optimization model can be manually preset. This preset initial machine tool machining parameter optimization model can be a model combining Transformer and LSTM, capable of accurately identifying machining parameter optimization requirements under different spindle speed scenarios based on multiple machine tool machining condition information and multiple tool running status information. Specifically, multiple machine tool machining condition information can be first fused with corresponding multiple tool running status information to generate integrated condition and tool speed information for each machining scenario. This integrated condition and tool speed information for each machining scenario is then input into the preset initial machine tool machining parameter optimization model. The model then outputs predicted probabilities of various machining parameter optimization schemes for each machining scenario. Furthermore, it correlates with previous machining parameter optimization data for each machining scenario for calculation, thereby generating multiple initial machine tool machining parameter optimization information that accurately reflects the current machining scenario requirements and adapts to multiple machine tool machining condition information and tool running status information.

[0046] The CNC machine tool machining parameter optimization method provided in this application provides accurate and suitable tool status data support for the generation of initial machine tool machining parameter optimization information, thereby improving the accuracy and pertinence of the initial machine tool machining parameter optimization information and the target machine tool machining parameter optimization information, strengthening the refined control of CNC machine tool machining parameter optimization, helping to adapt to the machining needs of different spindle speed scenarios, so as to improve the machining accuracy and efficiency of the machined parts, reduce tool wear, and ensure the stability and reliability of the CNC machine tool machining process.

[0047] Figure 4 The flowchart illustrating the implementation of the CNC machine tool machining parameter optimization method provided in Embodiment 4 of this application is shown. The difference between this method and Embodiment 1 is that step S102 specifically includes: Step S401: Calculate the similarity between the processing conditions and the preset CNC machine tool processing task information based on the multiple machine tool processing condition information and the preset CNC machine tool processing task information to obtain the similarity information between the processing conditions and the processing tasks.

[0048] In this embodiment, the preset CNC machine tool machining task information can be manually preset and can refer to the machining content within the same machining process. For example, it can include machining materials, machining accuracy, machining steps, machining dimensions, etc., and can be used to indicate the task theme that needs to be clarified before performing specific machining. Similarity calculation can refer to calculating the similarity between multiple machine tool machining condition information corresponding to multiple initial machine tool machining parameter optimization information and the preset CNC machine tool machining task information. The calculation result serves as the similarity information between machining condition and machining task, which can be used to determine whether the current multiple machine tool machining condition information is operating stably around the preset CNC machine tool machining task information.

[0049] Step S402: Determine whether the similarity information between the processing condition and the processing task is less than a preset similarity threshold between the processing condition and the processing task; if yes, proceed to step S403; if no, proceed to step S404.

[0050] In this embodiment, the preset similarity threshold between processing conditions and processing tasks can be preset manually. By judging the numerical relationship between the similarity information between processing conditions and processing tasks and the preset similarity threshold, it can be used to accurately determine whether the current machine tool processing condition deviates from the main processing task. It can also be used to determine whether multiple machine tool processing condition information revolves around the preset CNC machine tool processing task information, thereby determining whether the CNC machine tool is in a stable and compliant processing stage.

[0051] Step S403: Generate steady-state machining parameter information as initial machine tool machining parameter optimization information.

[0052] In this embodiment, when the similarity information between the machining condition and the machining task is less than the preset similarity threshold between the machining condition and the machining task, it indicates that the machining condition information of multiple machine tools has not been stably expanded around the preset CNC machine tool machining task information. That is, the CNC machine tool is currently in a stable machining stage where no parameter adjustment is required, thereby generating steady-state machining parameter information as the initial machine tool machining parameter optimization information.

[0053] Step S404: Extract the start time information and end time information corresponding to the multiple machine tool processing conditions information to obtain the start time information and end time information of multiple machine tool processing conditions.

[0054] In this embodiment, multiple machine tool processing condition information correspond to the processing condition content of different processing units in the processing process trigger event. By extracting the start time information and end time information corresponding to multiple machine tool processing condition information, the time interval corresponding to each machine tool processing condition information can be determined, which can provide an accurate time anchor point for subsequent calculation of the processing condition duration.

[0055] Step S405: Calculate the duration of the multiple machine tool processing conditions based on the start time information and end time information of the multiple machine tool processing conditions.

[0056] In this embodiment, it can be understood that each machine tool processing condition information corresponds to a unique start time information and end time information. By subtracting the start time information from the end time information corresponding to each machine tool processing condition information, the duration of multiple machine tool processing conditions corresponding to multiple machine tool processing condition information can be obtained, which is used to reflect the continuous running time of each segment of machine tool processing condition.

[0057] Step S406: Generate multiple initial machine tool machining parameter optimization information based on the duration of the multiple machine tool machining conditions, the multiple tool running status information, the preset threshold for the duration of the machine tool machining conditions, and the preset initial machine tool machining parameter optimization model.

[0058] In this embodiment, the preset machine tool machining condition duration threshold can be manually preset, and the preset initial machine tool machining parameter optimization model can also be manually preset. Multiple machine tool machining condition durations and multiple tool running status information can be combined, and a state determination can be made based on the preset machine tool machining condition duration threshold to distinguish between transitional machining states and normal machining states. Then, data meeting the normal machining conditions is input into the preset initial machine tool machining parameter optimization model, thereby generating multiple initial machine tool machining parameter optimization information.

[0059] The CNC machine tool machining parameter optimization method provided in this application accurately identifies steady-state machining and transitional machining states, effectively avoids misjudgment of the initial machine tool machining parameter optimization information, improves the accuracy of multiple initial machine tool machining parameter optimization information, helps optimize the machining parameter settings and machining process control of CNC machine tools, improves the machining accuracy and efficiency of machined parts, and ensures the stability and continuity of the CNC machine tool machining process.

[0060] Figure 5 The flowchart illustrating the implementation of the CNC machine tool machining parameter optimization method provided in Embodiment 5 of this application is shown. The difference between this method and Embodiment 4 above is that step S406 specifically includes: Step S501: Determine whether the duration of the machine tool processing condition is greater than a preset threshold for the duration of the machine tool processing condition; if yes, proceed to step S502; if no, proceed to step S503.

[0061] In this embodiment, the preset machine tool processing condition duration threshold can be preset manually and can be used to distinguish whether the duration of multiple machine tool processing conditions exceeds the normal processing time limit, thereby determining whether the CNC machine tool is in a transitional processing state.

[0062] Step S502: Generate transitional machining parameter information as initial machine tool machining parameter optimization information.

[0063] In this embodiment, when the duration of the machine tool processing condition is greater than the preset threshold for the duration of the machine tool processing condition, it indicates that the CNC machine tool is running abnormally during the processing and has not effectively entered the stable processing stage. It is in a processing transition state, thereby generating transition processing parameter information as initial machine tool processing parameter optimization information, which can be used to mark the current processing state of the CNC machine tool that needs transition adjustment.

[0064] Step S503: Based on the duration of the multiple machine tool processing conditions, the multiple tool running status information, and the preset initial machine tool processing parameter optimization model, generate multiple initial machine tool processing parameter optimization information.

[0065] In this embodiment, if the similarity information between the machining condition and the machining task is greater than or equal to a preset similarity threshold, and the duration of the machine tool machining condition is less than or equal to a preset duration threshold, it indicates that the CNC machine tool is in an effective and stable machining stage based on the preset CNC machine tool machining task information. Subsequently, the durations of multiple machine tool machining conditions and multiple tool running status information can be fused and input into a preset initial machine tool machining parameter optimization model to extract machining data features and perform machining parameter classification optimization. Thus, multiple initial machine tool machining parameter optimization information adapted to the current machining stage can be output through the preset initial machine tool machining parameter optimization model.

[0066] The CNC machine tool machining parameter optimization method provided in this application embodiment effectively improves the adaptability of the initial machine tool machining parameter optimization information generation, thereby strengthening the refined and intelligent parameter optimization and control of the entire CNC machine tool machining process, reducing manual debugging errors, reducing tool wear, and improving CNC machine tool machining accuracy and efficiency.

[0067] Figure 6 The flowchart illustrating the implementation of the CNC machine tool machining parameter optimization method provided in Embodiment Six of this application is shown. Its difference from Embodiment Five described above lies in: Multiple preset machine tool machining parameter division thresholds include preset steady-state machining parameter division thresholds, multiple preset transition machining parameter division thresholds, and multiple preset target machining parameter division thresholds. Step S103 specifically includes: Step S601: Determine whether the initial machine tool machining parameter optimization information is steady-state machining parameter information. If yes, proceed to step S602; otherwise, proceed to step S604.

[0068] In this embodiment, by determining whether the initial machine tool machining parameter optimization information is steady-state machining parameter information, it is ensured that the subsequently generated target machine tool machining parameter optimization information can accurately adapt to the current machining operation state of the CNC machine tool.

[0069] Step S602: Calculate the duration of steady-state machining conditions based on the machine tool machining condition information corresponding to the initial machine tool machining parameter optimization information.

[0070] In this embodiment, the initial machine tool machining parameter optimization information is steady-state machining parameter information. By extracting the start time and end time information of the machine tool machining condition information corresponding to the initial machine tool machining parameter optimization information, and subtracting the corresponding start time information from the end time information of the machine tool machining condition information corresponding to the steady-state machining parameter information, the duration information of the current steady-state machining condition is obtained.

[0071] Step S603: Based on the duration information of the steady-state machining condition and the preset threshold for dividing steady-state machining parameters, generate optimization information for the target machine tool machining parameters.

[0072] In this embodiment, the preset steady-state machining parameter classification thresholds can be manually preset and may include normal thresholds, observation thresholds, and abnormal thresholds for the steady-state machining duration. These thresholds can be used to define different specific situations of the steady-state machining state. The steady-state machining duration information can be compared with these thresholds. If the steady-state machining duration information is within the normal threshold range, a "normal steady-state machining" condition is generated as the target machine tool machining parameter optimization information. If the steady-state machining duration information is between the normal threshold and the observation threshold, a "steady-state machining observation" condition is generated as the target machine tool machining parameter optimization information. If the steady-state machining duration information exceeds the abnormal threshold, a "steady-state machining abnormal" condition is generated as the target machine tool machining parameter optimization information, thereby achieving accurate determination of the steady-state machining state.

[0073] Step S604: Determine whether the initial machine tool machining parameter optimization information is transitional machining parameter information; if yes, proceed to step S605; if no, proceed to step S606.

[0074] In this embodiment, after excluding the case where the initial machine tool machining parameter optimization information is steady-state machining parameter information, the specific situation of the initial machine tool machining parameter optimization information can be further determined.

[0075] Step S605: Based on the duration of the multiple machine tool processing conditions and the threshold values ​​of multiple preset transition processing parameters, generate target machine tool processing parameter optimization information.

[0076] In this embodiment, the multiple preset threshold values ​​for transitional machining parameters can be manually preset and may include normal thresholds, observation thresholds, and abnormal thresholds for the duration of transitional machining. These thresholds can be used to define different specific situations of transitional machining states. The durations of multiple machine tool machining conditions can be compared with these thresholds. If the duration of the machine tool machining condition is within the normal threshold range, normal transitional machining is generated as optimization information for the target machine tool machining parameters. If the duration of the machine tool machining condition is between the normal threshold and the observation threshold, observation of transitional machining is generated as optimization information for the target machine tool machining parameters. If the duration of the machine tool machining condition exceeds the abnormal threshold, abnormal transitional machining is generated as optimization information for the target machine tool machining parameters, thereby achieving accurate determination of the transitional machining state.

[0077] Step S606: Generate multiple target machine tool processing parameter optimization information based on multiple historical processing parameter optimization information, the initial machine tool processing parameter optimization information, multiple preset target processing parameter division thresholds, and preset target machine tool processing parameter optimization models.

[0078] In this embodiment, the multiple preset target machining parameter division thresholds can be manually preset and may include thresholds for machining parameter change span, number of loop repetitions, backtracking span, machining parameter coverage, and machining parameter sequence length. The preset target machine tool machining parameter optimization model can also be manually preset. A complete machining parameter optimization sequence can be constructed by first combining initial machine tool machining parameter optimization information with multiple historical machining parameter optimization information. Then, the initial machine tool machining parameter optimization information is compared with multiple historical machining parameter optimization information. Based on the multiple preset target machining parameter division thresholds, the current machining parameter change type is determined. Then, the machining parameter change is quantitatively determined according to the multiple preset target machining parameter division thresholds. Based on the determination results, the machining parameter state that best matches the current machining stage can be analyzed, i.e., the normal, observed, and abnormal situations under four types of machining parameter changes: stable, adjusting, cyclical, and backtracking. This generates multiple target machine tool machining parameter optimization information.

[0079] The CNC machine tool machining parameter optimization method provided in this application embodiment can accurately distinguish and identify steady-state machining, transitional machining, and the core machining parameter status of the CNC machine tool during the machining process. This improves the accuracy and relevance of the target machine tool machining parameter optimization information, providing reliable data support for the accurate matching of dynamic machining parameter adjustment strategies. This helps to optimize the machining parameter settings and machining process control of the CNC machine tool, ensuring and improving the machining accuracy and efficiency of the machined parts.

[0080] Figure 7 The flowchart illustrating the implementation of the CNC machine tool machining parameter optimization method provided in Embodiment 7 of this application is shown. Its difference from Embodiment 6 described above lies in: Multiple preset target processing parameter division thresholds include preset target processing parameter optimization sequence length thresholds and preset target processing parameter duration thresholds; Step S606 specifically includes: Step S701: Perform time-series splicing processing based on the initial machine tool machining parameter optimization information and multiple historical machining parameter optimization information to obtain multiple machine tool machining parameter optimization sequence information.

[0081] In this embodiment, the multiple historical machining parameter optimization information can be a set of machining parameters arranged in chronological order. Then, the initial machine tool machining parameter optimization information is added to the end of the multiple historical machining parameter optimization information to form a complete machine tool machining parameter optimization sequence information.

[0082] Step S702: Calculate the length information of the optimized sequence of machine tool processing parameters based on the multiple optimized sequence information of machine tool processing parameters.

[0083] In this embodiment, the number of machining parameter optimization results contained in multiple machine tool machining parameter optimization sequence information can be counted to generate machine tool machining parameter optimization sequence length information, which is used to quantitatively evaluate the continuity and integrity of the machining process.

[0084] Step S703: Determine whether the length of the machine tool processing parameter optimization sequence is less than the preset target processing parameter optimization sequence length threshold; if yes, proceed to step S704; if no, proceed to step S705.

[0085] In this embodiment, the preset target machining parameter optimization sequence length threshold can be preset manually, and can be set to 10. It can be used to determine whether the current machine tool machining parameter optimization sequence has reached the length standard for effective analysis.

[0086] Step S704: The multiple machine tool machining parameter optimization sequence information is used as multiple historical machining parameter optimization information and returned to step S101.

[0087] In this embodiment, by updating multiple historical machining parameter optimization information and returning to the acquisition steps of multiple machine tool machining condition information and multiple tool running status information, the continuous collection of multiple machine tool machining condition information and multiple tool running status information in the machining process trigger events is realized, thereby improving the accuracy and continuity of optimizing CNC machine tool machining parameters.

[0088] Step S705: Based on the multiple machine tool processing condition information corresponding to the initial machine tool processing parameter optimization information, obtain the initial machine tool processing condition start time information and the initial machine tool processing condition end time information.

[0089] In this embodiment, the start and end time information of multiple machine tool processing conditions corresponding to the initial machine tool processing parameter optimization information can be extracted to provide a time basis for subsequent calculation of duration.

[0090] Step S706: Based on the initial machine tool processing condition start time information and the initial machine tool processing condition end time information, calculate the initial processing parameter optimization condition duration information.

[0091] In this embodiment, the duration of the initial machining parameter optimization condition can be obtained by subtracting the start time of the initial machine tool machining condition from the end time information, which reflects the running time of the condition corresponding to the current initial machine tool machining parameter optimization information.

[0092] Step S707: Based on multiple historical machining parameter optimization information, the initial machine tool machining parameter optimization information, the initial machining parameter optimization condition duration information, the preset target machining parameter duration threshold, and the preset target machine tool machining parameter optimization model, generate multiple target machine tool machining parameter optimization information.

[0093] In this embodiment, the preset target machining parameter duration threshold can be manually preset, and the preset target machine tool machining parameter optimization model can also be manually preset. The initial machining parameter optimization condition duration information can be compared with the preset target machining parameter duration threshold. Then, multiple historical machining parameter optimization information and the initial machine tool machining parameter optimization information are combined for comprehensive analysis. This information is then input into the preset target machine tool machining parameter optimization model for in-depth optimization and rationality verification, thereby generating stable, reliable, and adaptable target machine tool machining parameter optimization information suitable for the entire current machining process.

[0094] In this embodiment, multiple preset machine tool machining parameter adjustment strategies can be manually set and have a one-to-one matching relationship with multiple target machine tool machining parameter optimization information. Each target machine tool machining parameter optimization information can be a steady-state machining state, a transitional machining state, or a normal, observational, or abnormal situation under four types of machining parameter changes: stable, adjusted, cyclical, and regressive. Multiple target machine tool machining parameter optimization information can be matched with multiple preset machine tool machining parameter adjustment strategies, and different preset machine tool machining parameter adjustment strategies can correspond to different adjustment methods, that is, they can be combined with the real-time operating status information of the CNC machine tool to generate different machine tool machining parameter adjustment information.

[0095] Specifically, we can first determine whether the target machine tool's machining parameter optimization information falls under the category of steady-state machining, transitional machining, stabilization, adjustment, cyclical, or regression. Then, we can determine whether the state change is normal, observational, or abnormal. This information is then used to accurately match multiple preset machine tool machining parameter adjustment strategies. At the same time, we can combine the specific content in the real-time operating status information of the CNC machine tool, such as spindle speed, cutting load, feed rate, and tool wear, to fine-tune the adjustment method. This allows us to select the adjustment method that best fits the current machining scenario from multiple preset machine tool machining parameter adjustment strategies, thereby generating multiple machine tool machining parameter adjustment information.

[0096] In this embodiment, the multiple preset machine tool machining parameter adjustment strategies may include a steady-state machining mild maintenance strategy, with corresponding adjustment methods including "the system displays the current stable operating parameter range such as spindle speed, feed rate, and depth of cut" and "the system pops up a text prompt suggesting maintaining the current stable machining state"; it may include a steady-state machining height protection strategy, with corresponding adjustment methods including "the system automatically locks the current machining parameters, prohibiting manual modification", "the system issues an audible and visual prompt, reminding that the machining state has experienced abnormal fluctuations and needs to be restored to stable operation as soon as possible", and "the system interface displays the duration of this steady-state machining in real time and indicates the duration of stable operation"; it may include a transitional machining strategy. The system offers several adjustment strategies, including: a light adaptation strategy for machining, which involves minor, step-wise adjustments to key parameters such as spindle speed, feed rate, and depth of cut; and parameter guidance prompts based on the pre-transition machining status, such as prompting "Do you need to switch machining parameters to finish-fit values?" when transitioning from roughing to finishing. It may also include a transition machining height adaptation strategy, which involves retrieving and displaying 1-2 sets of optimal machining parameter combinations from similar historical workpieces and automatically loading tool compensation parameters and cooling / lubrication parameters for the current process for auxiliary adaptation. Finally, it may include a smooth machining light-progress strategy, which involves displaying the current machining process on the system interface. The system can be categorized into several stages: a pre-processing stage with arrows indicating the next stage, and a system text prompt that gradually advances the machining process (e.g., "Complete this process using current parameters, prepare to proceed to the next finishing stage"). It can also include a stable machining height constraint strategy, with corresponding adjustment methods including "System prompts that excessively fast machining feed may cause risks such as tool vibration and poor surface quality" and "System forces the maintenance of current machining parameters, e.g., "Please maintain the current finishing parameters for at least 1 minute"). Furthermore, it can include a mild intervention strategy for machining parameter adjustments, with corresponding adjustment methods including "System prompts of sudden changes in machining parameters, displaying the number of adjustments, magnitude, before and after parameter values, and missing transition steps" and "System..." The text warning states that the parameter adjustment range is too large and suggests using a segmented fine-tuning method; it can include a high intervention strategy for machining parameter adjustment, with corresponding adjustment methods including "the system displays missing step-by-step transition parameters and prompts the user to make the adjustments, such as 'Please complete the step-by-step adjustment of the spindle speed within 1 minute'" and "the system provides parameter adjustment guidance, such as prompting 'The feed rate can be adjusted step by step in 3 segments to match the cutting capability of the tool'"; it can also include a light intervention strategy for machining parameter cycles, with corresponding adjustment methods including "the system prompts that the current machining parameters are repeatedly adjusted and enter an invalid cycle" and "the system provides guidance text, such as 'It is recommended to lock a set of stable machining parameters according to the current cutting state'";This can include a high-level intervention strategy for machining parameter cycles, with corresponding adjustment methods including "the system prompts that the parameter adjustment time is too long, requiring immediate locking of the parameters and entry into the formal machining stage, and explaining the requirements of the next process" and "the system provides two guiding texts to help jump out of the parameter cycle adjustment"; it can include a mild intervention strategy for machining parameter rollback, with corresponding adjustment methods including "the system provides text prompts to guide the rollback of parameters and then stabilize them again, for example, when rolling back to roughing parameters, it prompts 'the rationality of the parameters can be evaluated from three aspects: cutting load, tool life, and machining accuracy'"; it can include a high-level intervention strategy for machining parameter rollback, with corresponding adjustment methods including "the system prompts that excessive rollback of machining parameters poses risks such as low efficiency, tool vibration, and overcutting" and "the system suggests stopping the rollback, continuing to advance the machining, and providing two adjustment guidelines, such as 'it is recommended to maintain the current feed, reasonably adjust the depth of cut, and stabilize the machining state as soon as possible'";

[0097] Understandably, when the target machine tool's machining parameter optimization information is the normal situation under the steady-state machining state identification information, no intervention is needed; when the target machine tool's machining parameter optimization information is the observed situation under the steady-state machining state identification information, a mild steady-state machining maintenance strategy can be adopted; when the target machine tool's machining parameter optimization information is the abnormal situation under the steady-state machining state identification information, a high-level steady-state machining guarantee strategy can be adopted.

[0098] When the target machine tool machining parameter optimization information is the normal situation under the transition machining state identification information, no intervention is required; when the target machine tool machining parameter optimization information is the observed situation under the transition machining state identification information, a mild transition machining adaptation strategy can be adopted; when the target machine tool machining parameter optimization information is the abnormal situation under the transition machining state identification information, a high transition machining adaptation strategy can be adopted.

[0099] When the target machine tool's machining parameter optimization information is the normal situation under the stable machining state identification information, no intervention is required; when the target machine tool's machining parameter optimization information is the observed situation under the stable machining state identification information, a stable machining slight advancement strategy can be adopted; when the target machine tool's machining parameter optimization information is the abnormal situation under the stable machining state identification information, a stable machining height constraint strategy can be adopted.

[0100] When the target machine tool's machining parameter optimization information is in a normal state under the machining parameter adjustment status identification information, no intervention is required; when the target machine tool's machining parameter optimization information is in an observed state under the machining parameter adjustment status identification information, a mild intervention strategy for machining parameter adjustment can be adopted; when the target machine tool's machining parameter optimization information is in an abnormal state under the machining parameter adjustment status identification information, a high intervention strategy for machining parameter adjustment can be adopted.

[0101] When the target machine tool's machining parameter optimization information is in a normal state under the machining parameter cycle status identification information, no intervention is required; when the target machine tool's machining parameter optimization information is in an observed state under the machining parameter cycle status identification information, a mild intervention strategy for machining parameter cycles can be adopted; when the target machine tool's machining parameter optimization information is in an abnormal state under the machining parameter cycle status identification information, a high intervention strategy for machining parameter cycles can be adopted.

[0102] When the target machine tool's machining parameter optimization information is in a normal state under the machining parameter rollback status identification information, no intervention is required; when the target machine tool's machining parameter optimization information is in an observational state under the machining parameter rollback status identification information, a mild intervention strategy for machining parameter rollback can be adopted; when the target machine tool's machining parameter optimization information is in an abnormal state under the machining parameter rollback status identification information, a high intervention strategy for machining parameter rollback can be adopted.

[0103] The CNC machine tool machining parameter optimization method provided in this application improves the accuracy and continuity of target machine tool machining parameter optimization information, provides reliable data support for the accurate matching of CNC machine tool adaptive machining parameter adjustment strategies, helps optimize the refined and intelligent management of the entire CNC machine tool machining process, and ensures and improves the machining accuracy, surface quality and machining efficiency of the machined parts.

[0104] Corresponding to the method in the above embodiments, Figure 8 The diagram shows a structural block diagram of the CNC machine tool machining parameter optimization system provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown. Figure 8 The example CNC machine tool machining parameter optimization system can be the execution subject of the CNC machine tool machining parameter optimization method provided in the aforementioned embodiment 1.

[0105] Reference Figure 8 The CNC machine tool machining parameter optimization system includes: The machine tool processing condition information and tool running status information acquisition module 810 is used to acquire multiple machine tool processing condition information and multiple tool running status information; The initial machine tool machining parameter optimization information generation module 820 is used to generate multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information and the preset initial machine tool machining parameter optimization model; The target machine tool machining parameter optimization information generation module 830 is used to generate multiple target machine tool machining parameter optimization information based on multiple historical machining parameter optimization information, multiple initial machine tool machining parameter optimization information, multiple preset machine tool machining parameter division thresholds, and a preset target machine tool machining parameter optimization model.

[0106] The process by which each module in the CNC machine tool machining parameter optimization system provided in this application implements its respective function can be found in the foregoing. Figure 1 The description of Embodiment 1 shown will not be repeated here.

[0107] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0108] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0109] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0110] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0111] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first," "second," etc., are used in the text to describe various elements in some embodiments of this application, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0112] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0113] The CNC machine tool machining parameter optimization method provided in this application embodiment can be applied to terminal devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality / virtual reality devices, laptops, super mobile personal computers, netbooks, and personal digital assistants. This application embodiment does not impose any restrictions on the specific type of terminal device.

[0114] For example, the terminal device may be a station in a WLAN, a cellular phone, a cordless phone, a session initiation protocol phone, a wireless local loop station, a personal digital processing device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a set-top box, a user premises equipment, and / or other devices for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved public terrestrial mobile networks, etc.

[0115] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 Only one is shown in the image), and a memory 91 is stored in which a computer program 92 that can run on the processor 90 is stored. When the processor 90 executes the computer program 92, it implements the steps in the above embodiments of the CNC machine tool machining parameter optimization methods, for example... Figure 1 Steps S101 to S103 are shown. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the above system embodiments, for example... Figure 8 The functions of modules 810 to 830 are shown.

[0116] The terminal device 9 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will understand that... Figure 9 This is merely an example of terminal device 9 and does not constitute a limitation on terminal device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input transmission devices, network access devices, buses, etc.

[0117] The processor 90 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0118] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., equipped on the terminal device 9. Furthermore, the memory 91 may include both internal and external storage units of the terminal device 9. The memory 91 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 91 can also be used to temporarily store data that has been sent or will be sent.

[0119] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0120] This application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, it causes the terminal device to implement the steps in any of the above method embodiments.

[0121] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0122] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0123] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0124] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0125] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing machining parameters of a CNC machine tool, characterized in that, include: Acquire machining condition information from multiple machine tools and operating status information from multiple cutting tools; Based on the multiple machine tool machining condition information, multiple tool running status information, and the preset initial machine tool machining parameter optimization model, multiple initial machine tool machining parameter optimization information are generated. Based on multiple historical machining parameter optimization information, multiple initial machine tool machining parameter optimization information, multiple preset machine tool machining parameter division thresholds, and a preset target machine tool machining parameter optimization model, multiple target machine tool machining parameter optimization information are generated.

2. The method for optimizing CNC machine tool machining parameters as described in claim 1, characterized in that, The multiple machine tool processing condition information includes first machine tool processing condition information and second machine tool processing condition information; The first machine tool processing condition information includes first machine tool condition timing identifier information, first machine tool condition start time information, and first machine tool condition end time information; The second machine tool processing condition information includes the second machine tool condition timing identifier information, the second machine tool condition start time information, and the second machine tool condition end time information; Wherein, the timing identifier information of the first machine tool operating condition is less than that of the timing identifier information of the second machine tool operating condition; The step of generating multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information, and a preset initial machine tool machining parameter optimization model specifically includes: Based on the end time information of the first machine tool operating condition and the start time information of the second machine tool operating condition, the machine tool operating condition time interval information is calculated. Determine whether the machine tool operating condition time interval information is greater than a preset machine tool operating condition time interval threshold; If so, the processing is performed based on the first machine tool processing condition information and the second machine tool processing condition information to obtain multiple machine tool processing condition information to be processed. If not, the processing conditions of the first machine tool and the second machine tool are spliced ​​together to obtain the processing conditions of the machine tool to be processed. Based on the processing condition information of multiple machine tools to be processed, the running status information of multiple cutting tools, and the preset initial machine tool processing parameter optimization model, multiple initial machine tool processing parameter optimization information are generated.

3. The method for optimizing CNC machine tool machining parameters as described in claim 1, characterized in that, The multiple tool operating status information includes first tool operating status information and second tool operating status information; The first tool operating status information includes the first tool spindle speed information; The second tool operating status information includes the second tool spindle speed information; The step of generating multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information, and a preset initial machine tool machining parameter optimization model specifically includes: Based on the spindle speed information of the first tool and the spindle speed information of the second tool, the spindle speed difference information is calculated. Determine whether the tool spindle speed difference information is greater than a preset tool spindle speed differentiation threshold; If so, then segmentation processing is performed based on the spindle speed information of the first tool and the spindle speed information of the second tool to obtain multiple tool running status information to be processed; If not, the spindle speed information of the first tool and the spindle speed information of the second tool are combined to obtain the operating status information of the tool to be processed. Based on the multiple machine tool machining condition information, the multiple tool running status information to be processed, and the preset initial machine tool machining parameter optimization model, multiple initial machine tool machining parameter optimization information are generated.

4. The method for optimizing CNC machine tool machining parameters as described in claim 1, characterized in that, The step of generating multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information, and a preset initial machine tool machining parameter optimization model specifically includes: Based on the multiple machine tool processing condition information and the preset CNC machine tool processing task information, similarity calculation is performed to obtain processing condition and processing task similarity information. Determine whether the similarity information between the processing condition and the processing task is less than a preset similarity threshold between the processing condition and the processing task; If so, then steady-state machining parameter information is generated as initial machine tool machining parameter optimization information; If not, extract the start time information and end time information corresponding to the multiple machine tool processing conditions to obtain the start time information and end time information of multiple machine tool processing conditions. Based on the start time information and end time information of the multiple machine tool processing conditions, the duration of the multiple machine tool processing conditions is calculated. Based on the duration of multiple machine tool machining conditions, multiple tool running status information, preset machine tool machining condition duration thresholds, and preset initial machine tool machining parameter optimization models, multiple initial machine tool machining parameter optimization information are generated.

5. The method for optimizing CNC machine tool machining parameters as described in claim 4, characterized in that, The step of generating multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition durations, multiple tool running status information, preset machine tool machining condition duration thresholds, and preset initial machine tool machining parameter optimization models specifically includes: Determine whether the duration of the machine tool processing condition is greater than a preset threshold for the duration of the machine tool processing condition; If so, then generate transitional machining parameter information as initial machine tool machining parameter optimization information; If not, then based on the duration of the multiple machine tool processing conditions, the multiple tool running status information, and the preset initial machine tool processing parameter optimization model, multiple initial machine tool processing parameter optimization information are generated.

6. The method for optimizing CNC machine tool machining parameters as described in claim 5, characterized in that, Multiple preset machine tool machining parameter division thresholds include preset steady-state machining parameter division thresholds, multiple preset transition machining parameter division thresholds, and multiple preset target machining parameter division thresholds. The step of generating multiple target machine tool machining parameter optimization information based on multiple historical machining parameter optimization information, multiple initial machine tool machining parameter optimization information, multiple preset machine tool machining parameter division thresholds, and a preset target machine tool machining parameter optimization model specifically includes: When the initial machine tool machining parameter optimization information is steady-state machining parameter information, the steady-state machining condition duration information is calculated based on the machine tool machining condition information corresponding to the initial machine tool machining parameter optimization information. Based on the duration information of the steady-state machining condition and the preset threshold for dividing steady-state machining parameters, optimization information for the target machine tool machining parameters is generated. When the initial machine tool machining parameter optimization information is transitional machining parameter information, the target machine tool machining parameter optimization information is generated based on the duration of the multiple machine tool machining conditions and the multiple preset transitional machining parameter division thresholds. When the initial machine tool machining parameter optimization information is not steady-state machining parameter information and is not transitional machining parameter information, multiple target machine tool machining parameter optimization information is generated based on multiple historical machining parameter optimization information, the initial machine tool machining parameter optimization information, multiple preset target machining parameter division thresholds, and preset target machine tool machining parameter optimization models.

7. The method for optimizing CNC machine tool machining parameters as described in claim 6, characterized in that, Multiple preset target processing parameter division thresholds include preset target processing parameter optimization sequence length thresholds and preset target processing parameter duration thresholds; The step of generating multiple target machine tool machining parameter optimization information based on multiple historical machining parameter optimization information, the initial machine tool machining parameter optimization information, multiple preset target machining parameter division thresholds, and a preset target machine tool machining parameter optimization model specifically includes: Based on the initial machine tool machining parameter optimization information and multiple historical machining parameter optimization information, a time sequence splicing process is performed to obtain multiple machine tool machining parameter optimization sequence information; Based on the multiple machine tool machining parameter optimization sequence information, the length information of the machine tool machining parameter optimization sequence is calculated; Determine whether the length of the optimized sequence of machine tool processing parameters is less than a preset target threshold for the length of the optimized sequence of processing parameters; If so, the multiple machine tool machining parameter optimization sequence information is used as multiple historical machining parameter optimization information, and the process is returned to the step of obtaining multiple machine tool machining condition information and multiple tool running status information; If not, then based on the multiple machine tool processing condition information corresponding to the initial machine tool processing parameter optimization information, the start time information and end time information of the initial machine tool processing condition are obtained. Based on the initial machine tool machining condition start time information and the initial machine tool machining condition end time information, the duration information of the initial machining parameter optimization condition is calculated; Based on multiple historical machining parameter optimization information, the initial machine tool machining parameter optimization information, the duration information of the initial machining parameter optimization condition, the preset target machining parameter duration threshold, and the preset target machine tool machining parameter optimization model, multiple target machine tool machining parameter optimization information are generated.

8. A CNC machine tool machining parameter optimization system, characterized in that, include: The machine tool processing condition information and tool running status information acquisition module is used to acquire multiple machine tool processing condition information and multiple tool running status information. The initial machine tool machining parameter optimization information generation module is used to generate multiple initial machine tool machining parameter optimization information based on the multiple machine tool machining condition information, multiple tool running status information and the preset initial machine tool machining parameter optimization model; The target machine tool machining parameter optimization information generation module is used to generate multiple target machine tool machining parameter optimization information based on multiple historical machining parameter optimization information, multiple initial machine tool machining parameter optimization information, multiple preset machine tool machining parameter division thresholds, and a preset target machine tool machining parameter optimization model.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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