Machine tool processing method, device, computer equipment, computer readable storage medium and computer program product

Abstract

The present application relates to a machine tool processing method, device, computer equipment, computer-readable storage medium and computer program product, and relates to the field of machine tool processing technology. The method includes: in response to a processing instruction for a target machine tool, determining the error compensation amount adapted to the current processing scene of the target machine tool through the error compensation model of the target machine tool; the error compensation model is a model constructed based on error compensation parameters matching the deviation data of the processing scene for each processing scene matching the target machine tool; generating an error compensation instruction matching the error compensation amount; according to the error compensation instruction, performing error compensation on the processing instruction, generating an actual processing instruction for the target machine tool; executing the actual processing instruction, controlling the target machine tool to process, and obtaining the processing result of the target machine tool. The use of this method can ensure the processing accuracy of the machine tool under different processing scenarios.

Description

Technical Field

[0001] The present application relates to the field of machine tool processing technology, and in particular to a machine tool processing method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] With the development of science and technology, the requirements for machining accuracy are getting higher and higher, and CNC machining technology has emerged. CNC machining technology has the characteristics of intelligent machining, intelligent management and intelligent maintenance. Among them, intelligent maintenance refers to the intelligent monitoring of the state of the CNC machine tool in its own production process, and self-control according to these states to ensure the normal operation of the machine tool. Machine tool error compensation is one of the important operations of intelligent maintenance, which is of great significance to improving the machining accuracy of CNC machine tools.

[0003] In traditional technology, the error data of the machine tool under a certain calibration state is often measured and recorded to establish an error model, and the error model is used for compensation in various processing states. However, since the machine tool error is a systematic error, the machine tool error will also change under different processing states. Therefore, the error model established under the machine tool calibration state cannot adapt to various processing scenarios, and thus cannot guarantee the processing accuracy of the machine tool under different processing scenarios. Summary of the invention

[0004] Based on this, it is necessary to provide a machine tool processing method, device, computer equipment, computer readable storage medium and computer program product that can ensure the machine tool processing accuracy in response to the above technical problems.

[0005] In a first aspect, the present application provides a machine tool processing method, comprising: in response to a processing instruction for a target machine tool, determining an error compensation amount adapted to the current processing scenario of the target machine tool through an error compensation model of the target machine tool; the error compensation model is a model constructed based on error compensation parameters matching deviation data of the processing scenario for each processing scenario matching the target machine tool; generating an error compensation instruction matching the error compensation amount; performing error compensation on the processing instruction according to the error compensation instruction, and generating an actual processing instruction for the target machine tool; executing the actual processing instruction, controlling the target machine tool to perform processing, and obtaining a processing result of the target machine tool.

[0006] In one of the embodiments, the process of constructing the error compensation model also includes: determining at least one processing scenario that matches the target machine tool, and for each processing scenario, controlling the target machine tool to perform processing under the processing scenario; obtaining expected processing data and actual processing data of the target machine tool under the processing scenario; when the actual processing data is inconsistent with the expected processing data, determining error compensation parameters based on deviation data between the actual processing data and the expected processing data; and establishing an error compensation model of the target machine tool based on the error compensation parameters corresponding to each processing scenario.

[0007] In one of the embodiments, error compensation parameters are determined based on deviation data between actual processing data and expected processing data, including: comparing the actual processing data with the expected processing data to obtain deviation data between the actual processing data and the expected processing data; determining target compensation parameters that match the deviation data; based on the target compensation parameters, controlling the target machine tool to perform processing in the processing scenario until the actual processing data is consistent with the expected processing data, and updating the target compensation parameters to the error compensation parameters.

[0008] In one of the embodiments, the method also includes: adjusting the servo parameters of the target machine tool based on the deviation data to obtain parameter adjustment results of the servo parameters; based on the target compensation parameters, controlling the target machine tool to perform processing in a processing scenario, including: controlling the target machine tool to perform processing in the processing scenario according to the target compensation parameters and the parameter adjustment results.

[0009] In one of the embodiments, the method further includes: determining adjustable model parameters in the error compensation model; and adjusting the adjustable model parameters based on the machining results of the target machine tool to obtain an optimized error compensation model.

[0010] In one of the embodiments, the method further includes: performing instruction splitting on the processing instructions to obtain interpolation instructions for each machine tool axis in the target machine tool; performing error compensation on the processing instructions to generate actual processing instructions for the target machine tool, including: performing error compensation on the interpolation instructions for each machine tool axis to generate actual processing instructions for each machine tool axis.

[0011] In the second aspect, the present application also provides a machine tool processing device, including: an error compensation module, which is used to respond to a processing instruction for a target machine tool, and determine the error compensation amount adapted to the current processing scene of the target machine tool through the error compensation model of the target machine tool; the error compensation model is a model constructed for each processing scene matching the target machine tool based on error compensation parameters matching the deviation data of the processing scene; a compensation instruction generation module, which is used to generate an error compensation instruction matching the error compensation amount; an actual processing instruction generation module, which is used to perform error compensation on the processing instruction according to the error compensation instruction, and generate an actual processing instruction for the target machine tool; a machine tool processing module, which is used to execute the actual processing instruction, control the target machine tool to perform processing, and obtain the processing result of the target machine tool.

[0012] In a third aspect, the present application also provides a computer device, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program: in response to a processing instruction for a target machine tool, determining an error compensation amount adapted to the current processing scene of the target machine tool through an error compensation model of the target machine tool; the error compensation model is a model constructed for each processing scene matching the target machine tool based on error compensation parameters matching the deviation data of the processing scene; generating an error compensation instruction matching the error compensation amount; performing error compensation on the processing instruction according to the error compensation instruction, and generating an actual processing instruction for the target machine tool; executing the actual processing instruction, controlling the target machine tool to perform processing, and obtaining a processing result of the target machine tool.

[0013] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the following steps when the computer program is executed by a processor: in response to a processing instruction for a target machine tool, determining an error compensation amount adapted to the current processing scenario of the target machine tool through an error compensation model of the target machine tool; the error compensation model is a model constructed for each processing scenario matching the target machine tool based on error compensation parameters matching the deviation data of the processing scenario; generating an error compensation instruction matching the error compensation amount; performing error compensation on the processing instruction according to the error compensation instruction, and generating an actual processing instruction for the target machine tool; executing the actual processing instruction, controlling the target machine tool to perform processing, and obtaining a processing result of the target machine tool.

[0014] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the following steps when executed by a processor: in response to a processing instruction for a target machine tool, determining an error compensation amount adapted to the current processing scenario of the target machine tool through an error compensation model of the target machine tool; the error compensation model is a model constructed for each processing scenario matching the target machine tool based on error compensation parameters matching the deviation data of the processing scenario; generating an error compensation instruction matching the error compensation amount; performing error compensation on the processing instruction according to the error compensation instruction, and generating an actual processing instruction for the target machine tool; executing the actual processing instruction, controlling the target machine tool to perform processing, and obtaining a processing result of the target machine tool.

[0015] The above-mentioned machine tool processing method, device, computer equipment, computer-readable storage medium and computer program product, in response to the processing instruction for the target machine tool, first determine the error compensation amount adapted to the current processing scene of the target machine tool through the error compensation model of the target machine tool. Since the error compensation model is a model constructed based on the error compensation parameters matching the deviation data of the processing scene for each processing scene matching the target machine tool, the error compensation model can accurately predict the processing error of the target machine tool in the current processing scene, thereby obtaining the error compensation amount matching the processing error and the corresponding error compensation instruction. According to the error compensation instruction, the processing instruction can be error compensated, thereby generating an actual processing instruction for the target machine tool. Finally, by executing the actual processing instruction, the target machine tool can be controlled to perform processing and obtain the processing result of the target machine tool. In this way, the error compensation model can be applied to various processing scenes of the target machine tool, and can predict the processing error in each processing scene, so as to accurately compensate for the error, thereby improving the accuracy of the error compensation. In this way, the processing accuracy and processing quality of the machine tool in each processing scene adapted to it are guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 An application environment diagram of a machine tool processing method in an embodiment;

[0018] Figure 2 A schematic flow chart of a machine tool processing method in one embodiment;

[0019] Figure 3A schematic diagram of a construction process of an error compensation model in one embodiment;

[0020] Figure 4 A schematic diagram of a process for determining error compensation parameters in one embodiment;

[0021] Figure 5 A schematic diagram of a flow chart of adjusting model parameters in one embodiment;

[0022] Figure 6 A schematic diagram of a module of a machine tool processing flow in a specific embodiment;

[0023] Figure 7 A schematic diagram of a construction process of an error compensation model in a specific embodiment;

[0024] Figure 8 A schematic diagram of the actual processing flow of a machine tool in a specific embodiment;

[0025] Fig. 9 is a structural block diagram of a machine tool processing device in one embodiment;

[0026] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0028] The machine tool processing method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the controller 104 of the machine tool numerical control processing system through the network. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and Internet of Things devices. The controller 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The data storage system can store the data that the controller 104 needs to process. The data storage system can be integrated on the controller 104, or it can be placed on the cloud or other network servers.

[0029] The controller 104 of the CNC machining system of the machine tool responds to the machining instruction for the target machine tool sent by the terminal 102. First, the error compensation model of the target machine tool pre-integrated in the controller 104 can be used to perform error compensation on the machining instruction according to the error compensation amount adapted to the current machining scene of the target machine tool to obtain the error compensation instruction. Thus, the actual machining instruction of the target machine tool is determined based on the error compensation instruction. Finally, according to the actual machining instruction, the target machine tool is controlled to perform machining to obtain the machining result of the target machine tool. It can be understood that the error compensation model is a model constructed in advance for each machining scene matched with the target machine tool based on the error compensation parameters matching the deviation data of each machining scene.

[0030] In an exemplary embodiment, Figure 2 As shown, a machine tool processing method is provided, which is applied to Figure 1 The controller 104 in FIG. 1 is used as an example to illustrate, including:

[0031] Step S202 : in response to a machining instruction for a target machine tool, determining an error compensation amount adapted to a current machining scenario of the target machine tool through an error compensation model of the target machine tool.

[0032] Among them, machine tools are basic production equipment for manufacturing machines and machinery. Machine tools can process metal or other material blanks or workpieces such as cutting, drilling, milling, grinding, sawing and rotating to obtain the required geometric shape. The target machine tool can refer to the machine tool that needs to perform the processing task, which can be but not limited to lathes, drilling machines, boring machines, grinders, gear processing machines, thread processing machines and other types of machine tools. Processing instructions refer to instructions for instructing the target machine tool to perform processing tasks. For example, for a drilling machine, the processing instruction can be a drilling instruction to instruct the drilling machine to drill holes on the workpiece.

[0033] Optionally, the processing instructions of the target machine tool may be generated based on the processing requirements of the target machine tool in the current processing scenario. In other words, the processing instructions of the target machine tool may include the processing requirements of the target machine tool in the current processing scenario. The current processing scenario refers to the current processing scenario of the target machine tool. It can be understood that processing different materials or workpieces may belong to different processing scenarios. This embodiment does not limit the specific processing scenario as long as it belongs to a processing scenario that is compatible with the target machine tool.

[0034] It should be noted that different types of machine tools may have different processing scenarios. In other words, the machine tool will perform processing tasks under various processing scenarios that are adapted to it. For different types of machine tools, a corresponding error compensation model can be constructed specifically so that the error compensation model can be applied to various processing scenarios of the machine tool. It can be understood that the current processing scenario of the target machine tool should belong to any one of the various processing scenarios applied in the process of constructing the error compensation model corresponding to the target machine tool.

[0035] Error compensation refers to reducing or eliminating various errors generated by machine tools during the machining process through a series of technical means, thereby improving the machining accuracy of machine tools and the quality of machined products. In this embodiment, error compensation is mainly performed using an error compensation model. The error compensation model is a mathematical model that analyzes and compensates for various errors generated during the machining process of machine tools. These error sources include but are not limited to thermal deformation, machine tool stiffness, tool wear, etc. These factors will lead to the generation of machining errors. Through the error compensation model, these errors can be offset, thereby improving machining accuracy and efficiency. Considering that the error model in traditional technology is established under the calibration state of the machine tool, it cannot adapt to various machining scenarios. Therefore, the error compensation model in this embodiment is pre-targeted for each machining scenario that matches the target machine tool, and obtains the deviation data between the actual machining data of the target machine tool in the machining scenario and the machining requirements of the machining scenario, thereby constructing it based on the error compensation parameters that match the deviation data. In this way, the error compensation model of the target machine tool can adapt to each machining scenario of the target machine tool, thereby ensuring the machining accuracy of the target machine tool in each machining scenario.

[0036] Error compensation refers to the correction amount obtained by predicting the machining error of the target machine tool. During the machining process, due to errors in the machine tool itself or irregularities in the workpiece material, there will be deviations between the actual machining results and the expected results. In order to ensure machining accuracy, these deviations need to be compensated. This is the error compensation of the machine tool. Since the error compensation model is established based on the error compensation parameters corresponding to the machining deviation data of the target machine tool in different machining scenarios, the error compensation model can accurately generate the error compensation of the target machine tool in the current machining scenario.

[0037] For example, when the controller receives a processing instruction for a target machine tool, before sending the processing instruction to the target machine tool, the controller may first perform error compensation on the processing instruction. That is, through the error compensation model pre-built and embedded in the controller, the error compensation amount of the target machine tool in the current processing scenario is predicted.

[0038] Optionally, the error compensation model can be specifically embedded in the mechanism model of the target machine tool pre-constructed in the controller, that is, the mechanism model of the target machine tool includes the error compensation model of the target machine tool. The mechanism model of the machine tool is a mathematical model established according to the machining process of the machine tool, which is used to describe the working process and performance of the machine tool. By establishing a mechanism model, the dynamic characteristics of the machine tool and various physical phenomena in the machining process can be simulated, thereby optimizing the design and performance of the machine tool. For example, when developing a processing program for a machine tool, simulation processing can be performed through a mechanism model, so that the processing program can be continuously modified and optimized to ensure that the machine tool can accurately perform the expected machining tasks according to the NC (Numerical Control) code generated by the processing program.

[0039] Optionally, the mechanism model of the target machine tool may include, but is not limited to, a servo model corresponding to the machine tool servo, a motor model corresponding to the machine tool motor, a load model corresponding to the machine tool load, a transmission model corresponding to the machine tool transmission assembly, and a mechanical model corresponding to the machine tool body.

[0040] Step S204: Generate an error compensation instruction that matches the error compensation amount.

[0041] The error compensation instruction may be understood as an instruction including an error compensation amount.

[0042] Exemplarily, after the controller predicts the error compensation amount of the target machine tool through the error compensation model, it can generate an error compensation instruction including the error compensation amount.

[0043] Step S206 , performing error compensation on the machining instruction according to the error compensation instruction, and generating an actual machining instruction for the target machine tool.

[0044] The actual processing instruction may refer to the processing instruction actually issued to the target machine tool. It can be understood that the actual processing instruction is obtained by combining the error compensation instruction with the original processing instruction.

[0045] For example, after generating the error compensation instruction of the target machine tool, the controller can combine the error compensation instruction with the original processing instruction. This is because the original processing instruction contains basic processing requirements, processing parameters, processing paths and other information, while the error compensation instruction is a correction of this information. By combining the two, a processing instruction that meets the processing requirements and is optimized by error compensation can be obtained.

[0046] Step S208, executing the actual processing instruction, controlling the target machine tool to perform processing, and obtaining the processing result of the target machine tool.

[0047] The processing results may include but are not limited to the processing tracks of the mechanical ends of each machine tool axis in the target machine tool, workpiece shape, workpiece size, processing accuracy, processing time, processing quality, etc. The specific processing results can be determined according to the actual processing situation, and this embodiment does not limit this.

[0048] Exemplarily, after the controller obtains the actual processing instruction, it can send the actual processing instruction to the target machine tool, thereby controlling the target machine tool to perform processing and obtaining the processing result of the target machine tool in real time.

[0049] Optionally, in addition to the controller, the CNC machining system of the machine tool may also include a servo module, a motor module, a transmission module, and a mechanical module, that is, the machine tool body. The controller can send the actual machining instructions to the servo module, and the servo module drives the motor module based on the actual machining instructions to achieve the speed and steering control of the motor. The motor module then drives the transmission parts in the transmission module, such as ball screws, linear guides, etc., to transmit the movement of the motor to the actuators, such as workbenches, tool holders, etc., to achieve machining. The mechanical module provides support and positioning for the transmission module to ensure the normal movement of the transmission parts and actuators.

[0050] Optionally, the machining result of the target machine tool may be used to perform machining quality analysis on the target machine tool, that is, to analyze the actual machining quality of the target machine tool, so as to facilitate optimization and improvement of the target machine tool.

[0051] Optionally, the processing results and even the entire processing flow of the target machine tool can be displayed in real time on the front-end display interface of the machine tool CNC processing system so that the technicians can clearly understand the processing status of the machine tool.

[0052] In this embodiment, the controller responds to the processing instruction for the target machine tool, and first determines the error compensation amount adapted to the current processing scene of the target machine tool through the error compensation model of the target machine tool. Since the error compensation model is a model constructed based on the error compensation parameters matching the deviation data of the processing scene for each processing scene matching the target machine tool, the error compensation model can accurately predict the processing error of the target machine tool under the current processing scene, thereby obtaining the error compensation amount matching the processing error and the corresponding error compensation instruction. According to the error compensation instruction, the processing instruction can be error compensated, thereby generating an actual processing instruction for the target machine tool. Finally, the controller can control the target machine tool to process by executing the actual processing instruction, and obtain the processing result of the target machine tool. In this way, the error compensation model can be applied to various processing scenes of the target machine tool, and can predict the processing error under various processing scenes, so as to accurately compensate for the error, thereby improving the accuracy of the error compensation. The processing accuracy and processing quality of the machine tool in various processing scenes adapted to it are guaranteed.

[0053] In an exemplary embodiment, Figure 3 As shown, the construction process of the error compensation model also includes:

[0054] Step S302: determine at least one processing scenario that matches the target machine tool, and for each processing scenario, control the target machine tool to perform processing under the processing scenario.

[0055] Exemplarily, when constructing the error compensation model, the controller may first determine at least one processing scenario adapted by the target machine tool. Different target machine tools are adapted to different processing scenarios, and the error compensation model corresponding to each target machine tool can only be applied to the processing scenarios adapted by the target machine tool, and is not applicable to the processing scenarios of other machine tools. For each processing scenario, the controller will obtain the processing requirements for the target machine tool under the processing scenario, and generate a processing instruction carrying the processing requirements. It can be understood that the processing requirements refer to the processing requirements that the machine tool needs to meet or achieve in a certain processing scenario. The processing requirements may be the expected processing data, processing accuracy, processing time, processing error rate, processing quantity, etc. The processing requirements corresponding to different processing scenarios may be different. After the controller generates the corresponding processing instructions based on the processing requirements of each processing scenario, the processing instructions can be issued to the target machine tool to control the target machine tool for processing. It can be understood that the processing instruction can be a multi-axis debugging instruction for the target machine tool, that is, a test instruction for debugging the matching between the various machine tool axes of the target machine tool, and it is necessary to control the actual operation of the various machine tool axes of the target machine tool.

[0056] Step S304, obtaining expected processing data and actual processing data of the target machine tool in the processing scenario.

[0057] The expected processing data refers to the ideal processing result of the target machine tool during processing, such as the ideal shape and size of the workpiece. The actual processing data refers to the data generated by the target machine tool during actual processing, or the processing data generated by the target machine tool during actual processing under multi-axis debugging instructions. It may include but is not limited to the processing trajectory of the mechanical end of each machine tool axis, the shape and size of the workpiece, the processing time, the processing speed, etc.

[0058] For example, after the target machine tool completes processing, the controller can collect the processing data of the target machine tool through the multi-axis feedback information recognition module. The multi-axis feedback information recognition module can be understood as a data collection module, which can be used to collect the processing data generated by each machine tool axis of the target machine tool during the actual processing process.

[0059] Optionally, after the multi-axis feedback information recognition module collects the actual processing data of the target machine tool, in order to ensure the accuracy of the data, the multi-axis feedback information recognition module will also process the actual processing data. Specifically, the actual processing data can be cleaned to remove abnormal data, invalid data, etc. In this way, valid processing data is retained as data support for the establishment of subsequent error compensation models. Of course, in actual applications, the specific operation of data processing is not limited to data cleaning, and other data processing operations can also be set.

[0060] Optionally, this embodiment does not limit the specific form of the multi-axis feedback information identification module, which may be a special data collection tool or data acquisition software, as long as it can collect the actual processing data of the target machine tool.

[0061] Step S306: when the actual processing data is inconsistent with the expected processing data, an error compensation parameter is determined according to the deviation data between the actual processing data and the expected processing data.

[0062] The deviation data refers to the difference between the actual processing data and the expected processing data, such as the difference between the ideal size of the workpiece and the actual size of the workpiece. The error compensation parameter refers to the parameter used to correct and compensate the processing error generated by the target machine tool during the processing process, which may include but is not limited to parameters such as processing speed and processing position.

[0063] Exemplarily, after the multi-axis feedback information recognition module completes data cleaning of the actual processing data, the cleaned actual processing data can be sent to the multi-axis feedback information processing module. The multi-axis feedback information processing module compares the cleaned actual processing data with the expected processing data in the processing scenario to determine whether the actual processing effect meets expectations. If the actual processing data is inconsistent with the expected processing data, it means that the actual processing effect of the target machine tool does not meet expectations, then the deviation data between the actual processing data and the expected processing data is further calculated, so as to determine the error compensation parameters based on the deviation data.

[0064] Optionally, the multi-axis feedback information processing module is a module for identifying the error compensation parameters of the target machine tool. More specifically, the multi-axis feedback information processing module is a module for identifying the error compensation parameters of each machine tool axis in the target machine tool. Similar to the multi-axis feedback information identification module, this embodiment does not limit the specific form of the multi-axis feedback information processing module. It can be a special data processing tool or data processing software, as long as it can realize the comparison between the actual processing data and the expected processing data and determine the error compensation parameters of the target machine tool.

[0065] Step S308: establishing an error compensation model of the target machine tool based on the error compensation parameters corresponding to each processing scenario.

[0066] Exemplarily, the multi-axis feedback information processing module sends the error compensation parameters of each machine tool axis of the target machine tool in each processing scenario to the error model parameter module preset in the controller. The error model parameter module can forward the error compensation parameters to the error model of the target machine tool. The error model here can be understood as a mathematical model architecture or framework. By adding the error compensation parameters to the architecture or framework, the entire error model can be improved, thereby obtaining the error compensation model of the target machine tool.

[0067] Optionally, the error model parameter module can also receive the error compensation parameters corresponding to each round of processing of the target machine tool. Of course, this is not the parameter ultimately used to establish the error compensation model. After the target machine tool performs the latest round of processing, the error model parameter module will replace the error compensation parameters stored in the previous round of processing with the error compensation parameters obtained in the latest round, and so on, and update continuously. When the actual processing data is consistent with the expected processing data, the error compensation parameters obtained in this round are used as the parameters ultimately used to establish the error compensation model.

[0068] In this embodiment, the target machine tool is multi-axis debugged in each processing scenario adapted by the target machine tool to obtain the actual processing data of the target machine tool. The actual processing data is compared with the expected processing data in the processing scenario. When the actual processing data is inconsistent with the expected processing data, the deviation data between the two is calculated to determine the corresponding error compensation parameters for establishing the error compensation model. In this way, it can be ensured that the error compensation model is adapted to each processing scenario of the target machine tool, thereby ensuring the processing accuracy of the target machine tool in each processing scenario. Moreover, the error compensation parameters of the error compensation model are identified according to the actual processing data of the target machine tool, which can further improve the reliability and accuracy of the error compensation parameters, thereby improving the reliability and accuracy of the error compensation model, ensuring accurate error compensation for the target machine tool, and improving the processing accuracy of the target machine tool.

[0069] In an exemplary embodiment, Figure 4 As shown, according to the deviation data between the actual processing data and the expected processing data, the error compensation parameters are determined, including:

[0070] Step S402: Compare the actual processing data with the expected processing data to obtain deviation data between the actual processing data and the expected processing data.

[0071] Step S404, determining target compensation parameters that match the deviation data.

[0072] Among them, the target compensation parameter refers to the error compensation parameter that matches the deviation data. The target compensation parameter at this time can be understood as a temporary error compensation parameter. Different error compensation parameters can be matched after each round of processing and debugging. Until the actual processing data is consistent with the expected processing data, the target compensation parameter of the current round is the final error compensation parameter, which is used to build the error compensation model.

[0073] Exemplarily, after the multi-axis feedback information processing module compares the actual processing data with the expected processing data to obtain the deviation data between the actual processing data and the expected processing data. Based on the correspondence between the deviation data and each candidate compensation parameter, the target compensation parameter matching the deviation data can be queried. That is, the multi-axis feedback information processing module can pre-store the association relationship between the deviation data and each candidate compensation parameter, and the specific form of the association relationship is not limited. In this way, after the multi-axis feedback information processing module calculates the deviation data between the actual processing data and the expected processing data, the corresponding target compensation parameter can be obtained by directly querying the association relationship. Of course, in actual applications, appropriate error compensation parameters can also be selected according to the actual processing situation.

[0074] Step S406 , based on the target compensation parameters, controlling the target machine tool to perform processing in the processing scenario until the actual processing data is consistent with the expected processing data, and updating the target compensation parameters to error compensation parameters.

[0075] Exemplarily, after matching the target compensation parameters of the target machine tool, the multi-axis feedback information processing module can send the target compensation parameters to the controller, which will use the target compensation parameters to execute the steps of: controlling the target machine tool to process in the processing scenario; obtaining the expected processing data and actual processing data of the target machine tool in the processing scenario. This cycle is repeated until the actual processing data is consistent with the expected processing data, and the target compensation parameters corresponding to this round are used as the final error compensation parameters.

[0076] In this embodiment, based on the target compensation parameter, the target machine tool is controlled to re-process until the actual processing data of the target machine tool is consistent with the expected processing data, and the target compensation parameter is used as the final error compensation parameter. The accuracy of the error compensation parameter is ensured, thereby improving the accuracy of the error compensation model subsequently established based on the error compensation parameter, thereby improving the processing accuracy of the machine tool.

[0077] In an exemplary embodiment, the machine tool processing method further includes: adjusting the servo parameters of the target machine tool based on the deviation data to obtain parameter adjustment results of the servo parameters.

[0078] The servo parameters refer to the parameters of the servo module of the target machine tool, or the parameters of the actual servo system of the target machine tool. The servo parameters may be, but are not limited to, gain, speed, voltage, etc. The parameter adjustment result may include the adjusted servo parameters.

[0079] Exemplarily, when the actual processing data is inconsistent with the expected processing data, the controller can also automatically adjust the parameters of the servo module of the target machine tool according to the deviation data between the actual processing data and the expected processing data. Specifically, which parameters in the servo module are adjusted and the specific adjustment amount can be set according to the actual situation. This embodiment does not limit this, so that the consistency between the servo module and the actual processing servo can be ensured. For each machine tool axis, if the actual processing effect of a machine tool axis does not meet the processing requirements, the servo module corresponding to the machine tool axis can be adjusted.

[0080] In an exemplary embodiment, based on the target compensation parameters, controlling the target machine tool to perform processing in a processing scenario includes: controlling the target machine tool to perform processing in the processing scenario according to the target compensation parameters and the parameter adjustment result.

[0081] Exemplarily, after completing the parameter adjustment of the servo module, the controller can send the parameter adjustment result to the servo module to update and optimize the servo module. And re-debug the multi-axis of the target machine tool, that is, the controller will perform error compensation on the last multi-axis debugging instruction based on the target compensation parameter, so as to obtain a new multi-axis debugging instruction. And re-send the new multi-axis debugging instruction to the optimized servo module, and the servo module drives the motor module, driving the transmission module and the mechanical module to perform processing. This cycle is repeated until the actual processing data of the target machine tool is consistent with the expected processing data.

[0082] Optionally, to ensure the accuracy of the actual processing data, the target machine tool can use new parts or workpieces for processing when performing each round of processing. Of course, the processed workpieces can also be reused according to actual conditions, and this embodiment does not limit this.

[0083] In this embodiment, when the actual processing data of the target machine tool is inconsistent with the expected processing data, the parameters of the servo module of the target machine tool are adjusted to debug and optimize the actual servo, so that the machine tool servo can achieve more accurate processing control. In this way, during actual processing, the processing accuracy of the target machine tool can be further ensured under the dual protection of the optimized servo system and the high-precision error compensation model.

[0084] In an exemplary embodiment, Figure 5 As shown, the machine tool processing method also includes:

[0085] Step S502: determining adjustable model parameters in the error compensation model.

[0086] Among them, the adjustable model parameters refer to the model parameters that can be adjusted in the error compensation model. The model parameters here can be error compensation parameters or attribute parameters of the error compensation model itself. The specific adjustable model parameters and the number of adjustable model parameters can be set according to the actual situation. Even the degree of parameter adjustment, such as whether it needs to be fine-tuned within a limited numerical range or can be adjusted at will, can also be set according to the actual situation.

[0087] Exemplarily, the controller may determine the error compensation parameters that can be adjusted in the error compensation model or its own attribute parameters. Of course, there is no restriction on the timing of determining the adjustable model parameters. It may be pre-defined which parameters belong to the adjustable model parameters, or it may be determined which parameters can be adjusted according to the actual processing conditions of the target machine tool.

[0088] Step S504: based on the machining result of the target machine tool, the adjustable model parameters are adjusted to obtain an optimized error compensation model.

[0089] Exemplarily, the controller may adjust the adjustable model parameters according to the actual processing results of the target machine tool. Specifically, the processing results of the target machine tool may be obtained through the model parameter adjustment module in the mechanism model of the target machine tool. Based on the processing results, the adjustable model parameters are adjusted, and the adjusted parameters are sent to the error compensation model for model update to obtain an optimized error compensation model.

[0090] Optionally, in addition to adjusting the parameters of the error compensation model according to the processing results, the model parameter adjustment module can also adjust the model parameters of other models in the mechanism model according to the processing results, such as adjusting the model parameters of the servo model, motor model, transmission model, mechanical model, etc., to ensure that the mechanism model of the target machine tool is consistent with the actual working condition of the target machine tool.

[0091] In this embodiment, by adjusting the parameters of the error compensation model in real time based on the actual processing results of the target machine tool, the error compensation model is made to better fit the actual processing scenario of the target machine tool, thereby further ensuring the processing accuracy of the target machine tool.

[0092] In an exemplary embodiment, the machine tool processing method further includes: splitting the processing instructions to obtain interpolation instructions for each machine tool axis in the target machine tool.

[0093] It should be noted that the machine tool axis can also be understood as the coordinate axis of the machine tool, including the X-axis, Y-axis and Z-axis. The X-axis refers to the horizontal direction of the machine tool, which is used to control the lateral movement of the machine tool. When the workpiece needs to be processed horizontally, the X-axis will start working. The Y-axis is the vertical direction of the machine tool, which is mainly used to control the up and down movement of the machine tool, and is commonly used in processes such as drilling and boring. The Z-axis is the feed direction of the machine tool, which is mainly used to control the feed direction and depth of the workpiece. Through the linkage between the machine tool axes, the machine tool can accurately control the position and posture of the workpiece, thereby completing various complex processing tasks. Of course, in actual applications, the machine tool can also have axes other than the above-mentioned machine tool axes, such as a rotating axis for controlling the rotation of the workpiece. The specific number of machine tool axes is not limited in this embodiment.

[0094] In this embodiment, the controller splits the original processing instructions to obtain interpolation instructions for each machine tool axis. The interpolation instructions are instructions that specify the speed and position of each machine tool axis to achieve various motion trajectories such as straight lines, arcs, spirals, etc.

[0095] Optionally, each machine tool axis may correspond to an interpolation instruction. The controller may send the interpolation instructions of each machine tool axis to the servo module corresponding to the machine tool axis. The servo module controls the motor module of the machine tool axis, thereby driving the transmission module and mechanical module of the machine tool axis to realize the processing movement between multiple axes. Each machine tool axis may have a corresponding servo module, motor module, mechanical module, transmission module, etc. For example, for the X-axis, the controller may send the interpolation instruction for the X-axis to the servo module corresponding to the X-axis. The servo module will drive the motor module corresponding to the X-axis, drive the transmission module and mechanical module corresponding to the X-axis, and realize the processing movement of the X-axis. For the Y-axis, the controller may send the interpolation instruction for the Y-axis to the servo module corresponding to the Y-axis. The servo module will drive the motor module corresponding to the Y-axis, drive the transmission module and mechanical module corresponding to the Y-axis, and realize the processing movement of the Y-axis. For the Z-axis, the controller can send the interpolation instruction for the Z-axis to the servo module corresponding to the Z-axis. The servo module will drive the motor module corresponding to the Z-axis, drive the transmission module and mechanical module corresponding to the Z-axis, and realize the processing movement of the Z-axis.

[0096] Optionally, before the controller issues the interpolation instructions of each machine tool axis to the corresponding machine tool axis, it may first send the interpolation instructions of each machine tool axis to the mechanism model of the target machine tool, and the mechanism model pre-processes each interpolation instruction. The specific pre-processing method may be, but is not limited to, adjusting the parameters of the interpolation instruction, such as increasing or decreasing the speed parameter in the interpolation instruction. The purpose of adjusting the interpolation instruction by the mechanism model in this embodiment is to further ensure the rationality and feasibility of the parameters in the interpolation instruction, thereby ensuring the accuracy of the interpolation instruction. Therefore, in practical applications, other parameters in the interpolation instruction can be selected for adjustment according to actual conditions, and this embodiment does not limit the specific adjusted parameters and the number of adjusted parameters.

[0097] In an exemplary embodiment, error compensation is performed on the machining instruction to generate actual machining instructions for the target machine tool, including: error compensation is performed on the interpolation instruction of each machine tool axis to generate actual machining instructions for each machine tool axis.

[0098] Exemplarily, error compensation can be performed for each machine tool axis. That is to say, the interpolation instructions for each machine tool axis can be error compensated by the error compensation model of the target machine tool, thereby obtaining the actual processing instructions for each machine tool axis, which can be used to control the processing motion of each machine tool axis.

[0099] Optionally, in the current processing scenario of the target machine tool, the error compensation model of the target machine tool can also determine the error compensation amount adapted to each machine tool axis in the target machine tool, thereby generating an error compensation instruction for each machine tool axis according to the error compensation amount. Then, according to the error compensation instruction of each machine tool axis, the interpolation instruction of each machine tool axis is error compensated to generate the actual processing instruction for each machine tool axis. The controller can send the actual processing instruction of each machine tool axis to the servo module of each machine tool axis, thereby controlling the processing motion of each machine tool axis.

[0100] Optionally, the controller may integrate the interpolation instructions of each machine tool axis into an overall instruction, and perform error compensation on the overall instruction through the error compensation model. Of course, the error compensation model may also be used to perform error compensation on the interpolation instructions of each machine tool axis separately.

[0101] Optionally, the controller can also integrate the actual processing instructions of each machine tool axis to obtain an overall processing instruction, and send the overall processing instruction to the servo module of each machine tool axis respectively, so as to control the processing movement of each machine tool axis. In this way, the processing parameters between the machine tool axes can be shared, which is conducive to the coordinated movement between the machine tool axes and improves the overall processing quality of the machine tool.

[0102] In a specific embodiment, Figure 6 As shown, the machine tool processing method may include the following modules: instruction module 1, parameter identification module 2, mechanism model 3, error compensation module 4, servo module 5, motor module 6, transmission and mechanical module 7 (transmission module and mechanical module). Among them, instruction module 1 is mainly used for instruction splitting, that is, splitting the processing instruction into interpolation instructions for each axis of the machine tool. Parameter identification module 2 is used for parameter identification, including the identification of error compensation parameters and the identification of adjustable model parameters. Mechanism model 3 is used to pre-process each interpolation instruction, such as adjusting the processing speed. Error compensation module 4 is used to perform error compensation on the processing instruction and obtain an actual processing instruction. The actual processing instruction will enter the servo module 5, and the servo module 5 drives the motor module 6, thereby driving the transmission and mechanical module 7 to realize the processing of the machine tool.

[0103] Figure 7 The flowchart of error compensation model construction is shown. Among them, the controller initiates a multi-axis debugging command in each processing scenario adapted by the target machine tool to debug the matching degree between the axes of the target machine tool. The multi-axis adjustment command will be split by the command module 1 to obtain interpolation commands for the X-axis, Y-axis, and Z-axis respectively. These interpolation commands will be issued to the X-axis servo, Y-axis servo, and Z-axis servo accordingly. The X-axis servo will drive the X-axis motor based on the interpolation command for the X-axis, thereby driving the X-axis mechanical & transmission to perform processing movement on the X-axis. The Y-axis servo will drive the Y-axis motor based on the interpolation command for the Y-axis, thereby driving the Y-axis mechanical & transmission to perform processing movement on the Y-axis. Similarly, the Z-axis servo will drive the Z-axis motor based on the interpolation command for the Z-axis, thereby driving the Z-axis mechanical & transmission to perform processing movement on the Z-axis.

[0104] It can be understood that the X-axis servo, Y-axis servo, and Z-axis servo all belong to the servo module 5. The X-axis motor, Y-axis motor, and Z-axis motor all belong to the motor module 6. The X-axis mechanical & transmission, Y-axis mechanical & transmission, and Z-axis mechanical & transmission all belong to the transmission and mechanical module 7.

[0105] For the processing process of each axis, the multi-axis feedback information identification module is used to collect the information processing data such as the mechanical end trajectory and speed of each axis. At the same time, these processing data are cleaned and the effective processing data is retained. The effective processing data will be sent to the multi-axis feedback information processing module, which will compare the effective processing data with the expected processing data corresponding to the processing scene, that is, determine whether the effective processing data is consistent with the expected processing data. If not, the error compensation parameters of each axis are identified, including X-axis error compensation parameter identification, Y-axis error compensation parameter identification, and Z-axis error compensation parameter identification. Specifically, the multi-axis feedback information processing module can calculate the deviation value between the actual processing data and the expected processing data, so as to query the error compensation parameters that match the deviation value. At the same time, based on the deviation value, the servo parameters are adjusted, including X-axis servo parameter adjustment, Y-axis servo parameter adjustment, and Z-axis servo parameter adjustment. The adjusted servo parameters are sent to the X-axis servo, Y-axis servo, and Z-axis servo. The controller will compensate the original multi-axis debugging instruction based on the error compensation parameter, obtain a new multi-axis debugging instruction, and send it to the optimized and adjusted servos of each axis to re-execute the entire processing process. This cycle will continue until the effective processing data is consistent with the expected processing data. The latest error compensation parameters are passed to the error model parameter module. The error model parameter module will send the error compensation parameters to the error compensation model module for building the error compensation model.

[0106] Figure 8 A schematic diagram of the actual processing flow of the machine tool is shown. The controller responds to the processing instruction for the target machine tool, which also belongs to a multi-axis instruction. The instruction will first be split to obtain interpolation instructions for the X-axis, Y-axis, and Z-axis respectively. At this time, these instructions can enter the mechanism model for preprocessing. The error compensation model and the model parameter adjustment module are both deployed in the mechanism model. After preprocessing by the mechanism model, multiple interpolation instructions will enter the error compensation model for error compensation analysis to obtain the error compensation amount corresponding to each interpolation instruction in the current processing scenario, so as to obtain error compensation instructions for the X-axis, Y-axis, and Z-axis based on each error compensation amount.

[0107] The error compensation instruction will be combined with the original interpolation instruction to obtain an actual processing instruction. The actual processing instruction will be sent to the servo of the corresponding axis, and each axis servo will drive the motor of each axis, thereby driving the mechanical and transmission of each axis to achieve the final processing. The model parameter adjustment module in the mechanism model will obtain the processing results of each axis in real time to fine-tune the adjustable model parameters in the error compensation model, and update the adjusted parameters to the error compensation model to achieve real-time optimization of the error compensation model. In addition, the model parameter adjustment module can also adjust the parameters of other models (servo model, motor model, transmission model, mechanical model) in the mechanism model of the target machine tool according to the processing results of each axis to ensure that the mechanism model of the target machine tool is consistent with the actual working conditions of the target machine tool.

[0108] In this embodiment, the controller responds to the processing instruction for the target machine tool, and first determines the error compensation amount adapted to the current processing scene of the target machine tool through the error compensation model of the target machine tool. Since the error compensation model is a model constructed based on the error compensation parameters matching the deviation data of the processing scene for each processing scene matching the target machine tool, the error compensation model can accurately predict the processing error of the target machine tool under the current processing scene, thereby obtaining the error compensation amount matching the processing error and the corresponding error compensation instruction. According to the error compensation instruction, the processing instruction can be error compensated, thereby generating an actual processing instruction for the target machine tool. Finally, the controller can control the target machine tool to process by executing the actual processing instruction, and obtain the processing result of the target machine tool. In this way, the error compensation model can be applied to each processing scene of the target machine tool, and can predict the processing error under each processing scene, so as to accurately compensate the error, thereby improving the accuracy of the error compensation. Thereby, the processing accuracy and processing quality of the machine tool in each processing scene adapted to it are guaranteed.

[0109] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0110] Based on the same inventive concept, the embodiment of the present application also provides a machine tool processing device for implementing the machine tool processing method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more machine tool processing device embodiments provided below can refer to the limitations on the machine tool processing method above, and will not be repeated here.

[0111] In an exemplary embodiment, Fig. 9 As shown, a machine tool processing device is provided, including: an error compensation module 902, which is used to respond to a processing instruction for a target machine tool, and determine the error compensation amount adapted to the current processing scene of the target machine tool through the error compensation model of the target machine tool; the error compensation model is a model constructed for each processing scene matching the target machine tool based on error compensation parameters matching the deviation data of the processing scene; a compensation instruction generation module 904, which is used to generate an error compensation instruction matching the error compensation amount; an actual processing instruction generation module 906, which is used to perform error compensation on the processing instruction according to the error compensation instruction, and generate an actual processing instruction for the target machine tool; a machine tool processing module 908, which is used to execute the actual processing instruction, control the target machine tool to perform processing, and obtain the processing result of the target machine tool.

[0112] In one of the embodiments, the device also includes: a processing module, which is used to determine at least one processing scenario that matches the target machine tool, and for each processing scenario, controls the target machine tool to perform processing under the processing scenario; a processing data acquisition module, which is used to acquire expected processing data and actual processing data of the target machine tool under the processing scenario; a module for determining error compensation parameters, which is used to determine error compensation parameters according to deviation data between the actual processing data and the expected processing data when the actual processing data is inconsistent with the expected processing data; and a module for establishing an error compensation model, which is used to establish an error compensation model of the target machine tool based on the error compensation parameters corresponding to each processing scenario.

[0113] In one of the embodiments, the error compensation parameter determination module is also used to: compare the actual processing data with the expected processing data to obtain deviation data between the actual processing data and the expected processing data; determine the target compensation parameters that match the deviation data; based on the target compensation parameters, control the target machine tool to perform processing in the processing scenario until the actual processing data is consistent with the expected processing data, and update the target compensation parameters to the error compensation parameters.

[0114] In one of the embodiments, the device is also used to: adjust the servo parameters of the target machine tool based on the deviation data to obtain parameter adjustment results of the servo parameters; the error compensation parameter determination module is also used to: control the target machine tool to perform processing in a processing scenario according to the target compensation parameters and the parameter adjustment results.

[0115] In one of the embodiments, the device is also used to: determine adjustable model parameters in the error compensation model; and adjust the adjustable model parameters based on the processing results of the target machine tool to obtain an optimized error compensation model.

[0116] In one of the embodiments, the device is also used to: split the processing instructions to obtain interpolation instructions for each machine tool axis in the target machine tool; the actual processing instruction generation module 906 is also used to: perform error compensation on the interpolation instructions of each machine tool axis to generate actual processing instructions for each machine tool axis.

[0117] Each module in the above-mentioned machine tool processing device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0118] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store machine tool processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a machine tool processing method is implemented.

[0119] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: in response to a processing instruction for a target machine tool, determining an error compensation amount adapted to a current processing scenario of the target machine tool through an error compensation model of the target machine tool; the error compensation model is a model constructed based on error compensation parameters matching deviation data of the processing scenario for each processing scenario matching the target machine tool; generating an error compensation instruction matching the error compensation amount; performing error compensation on the processing instruction according to the error compensation instruction, and generating an actual processing instruction for the target machine tool; executing the actual processing instruction, controlling the target machine tool to perform processing, and obtaining a processing result of the target machine tool.

[0121] In one embodiment, when the processor executes the computer program, the following steps are also implemented: determining at least one processing scenario that matches the target machine tool, and for each processing scenario, controlling the target machine tool to perform processing under the processing scenario; obtaining expected processing data and actual processing data of the target machine tool under the processing scenario; when the actual processing data is inconsistent with the expected processing data, determining error compensation parameters based on deviation data between the actual processing data and the expected processing data; and establishing an error compensation model of the target machine tool based on the error compensation parameters corresponding to each processing scenario.

[0122] In one embodiment, when the processor executes the computer program, the following steps are also implemented: comparing the actual processing data with the expected processing data to obtain deviation data between the actual processing data and the expected processing data; determining target compensation parameters that match the deviation data; based on the target compensation parameters, controlling the target machine tool to perform processing in the processing scenario until the actual processing data is consistent with the expected processing data, and updating the target compensation parameters to error compensation parameters.

[0123] In one embodiment, when the processor executes the computer program, the following steps are also implemented: based on the deviation data, the servo parameters of the target machine tool are adjusted to obtain parameter adjustment results of the servo parameters; based on the target compensation parameters, the target machine tool is controlled to perform processing in a processing scenario, including: according to the target compensation parameters and the parameter adjustment results, the target machine tool is controlled to perform processing in the processing scenario.

[0124] In one embodiment, when the processor executes the computer program, the following steps are also implemented: determining adjustable model parameters in the error compensation model; adjusting the adjustable model parameters based on the processing results of the target machine tool to obtain an optimized error compensation model.

[0125] In one embodiment, when the processor executes the computer program, the following steps are also implemented: the processing instructions are split to obtain interpolation instructions for each machine tool axis in the target machine tool; the processing instructions are error compensated to generate actual processing instructions for the target machine tool, including: the interpolation instructions for each machine tool axis are error compensated to generate actual processing instructions for each machine tool axis.

[0126] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: in response to a processing instruction for a target machine tool, an error compensation amount adapted to the current processing scene of the target machine tool is determined through an error compensation model of the target machine tool; the error compensation model is a model constructed based on error compensation parameters matching the deviation data of the processing scene for each processing scene matching the target machine tool; an error compensation instruction matching the error compensation amount is generated; according to the error compensation instruction, error compensation is performed on the processing instruction to generate an actual processing instruction for the target machine tool; the actual processing instruction is executed to control the target machine tool to perform processing, and a processing result of the target machine tool is obtained.

[0127] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining at least one processing scenario that matches the target machine tool, and for each processing scenario, controlling the target machine tool to perform processing under the processing scenario; obtaining expected processing data and actual processing data of the target machine tool under the processing scenario; when the actual processing data is inconsistent with the expected processing data, determining error compensation parameters based on deviation data between the actual processing data and the expected processing data; and establishing an error compensation model of the target machine tool based on the error compensation parameters corresponding to each processing scenario.

[0128] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: comparing the actual processing data with the expected processing data to obtain deviation data between the actual processing data and the expected processing data; determining target compensation parameters that match the deviation data; based on the target compensation parameters, controlling the target machine tool to perform processing in the processing scenario until the actual processing data is consistent with the expected processing data, and updating the target compensation parameters to error compensation parameters.

[0129] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the deviation data, the servo parameters of the target machine tool are adjusted to obtain parameter adjustment results of the servo parameters; based on the target compensation parameters, the target machine tool is controlled to perform processing in a processing scenario, including: according to the target compensation parameters and the parameter adjustment results, the target machine tool is controlled to perform processing in the processing scenario.

[0130] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining adjustable model parameters in the error compensation model; adjusting the adjustable model parameters based on the processing results of the target machine tool to obtain an optimized error compensation model.

[0131] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the processing instructions are split to obtain interpolation instructions for each machine tool axis in the target machine tool; the processing instructions are error compensated to generate actual processing instructions for the target machine tool, including: the interpolation instructions for each machine tool axis are error compensated to generate actual processing instructions for each machine tool axis.

[0132] In one embodiment, a computer program product is provided, comprising a computer program, which implements the following steps when executed by a processor: in response to a processing instruction for a target machine tool, determining an error compensation amount adapted to a current processing scenario of the target machine tool through an error compensation model of the target machine tool; the error compensation model is a model constructed based on error compensation parameters matching deviation data of the processing scenario for each processing scenario matching the target machine tool; generating an error compensation instruction matching the error compensation amount; performing error compensation on the processing instruction according to the error compensation instruction, and generating an actual processing instruction for the target machine tool; executing the actual processing instruction, controlling the target machine tool to perform processing, and obtaining a processing result of the target machine tool.

[0133] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining at least one processing scenario that matches the target machine tool, and for each processing scenario, controlling the target machine tool to perform processing under the processing scenario; obtaining expected processing data and actual processing data of the target machine tool under the processing scenario; when the actual processing data is inconsistent with the expected processing data, determining error compensation parameters based on deviation data between the actual processing data and the expected processing data; and establishing an error compensation model of the target machine tool based on the error compensation parameters corresponding to each processing scenario.

[0134] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: comparing the actual processing data with the expected processing data to obtain deviation data between the actual processing data and the expected processing data; determining target compensation parameters that match the deviation data; based on the target compensation parameters, controlling the target machine tool to perform processing in the processing scenario until the actual processing data is consistent with the expected processing data, and updating the target compensation parameters to error compensation parameters.

[0135] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the deviation data, the servo parameters of the target machine tool are adjusted to obtain parameter adjustment results of the servo parameters; based on the target compensation parameters, the target machine tool is controlled to perform processing in a processing scenario, including: according to the target compensation parameters and the parameter adjustment results, the target machine tool is controlled to perform processing in the processing scenario.

[0136] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining adjustable model parameters in the error compensation model; adjusting the adjustable model parameters based on the processing results of the target machine tool to obtain an optimized error compensation model.

[0137] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the processing instructions are split to obtain interpolation instructions for each machine tool axis in the target machine tool; the processing instructions are error compensated to generate actual processing instructions for the target machine tool, including: the interpolation instructions for each machine tool axis are error compensated to generate actual processing instructions for each machine tool axis.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0139] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0140] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0141] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A machine tool processing method, characterized in that: The method comprises: In response to a machining instruction for a target machine tool, an error compensation amount adapted to a current machining scenario of the target machine tool is determined through an error compensation model of the target machine tool; the error compensation model is a model constructed based on error compensation parameters matching deviation data of each machining scenario matching the target machine tool; generating an error compensation instruction matching the error compensation amount; According to the error compensation instruction, error compensation is performed on the processing instruction to generate an actual processing instruction for the target machine tool; The actual processing instruction is executed to control the target machine tool to perform processing and obtain the processing result of the target machine tool.

2. The method according to claim 1, characterized in that The process of constructing the error compensation model includes: Determine at least one processing scenario that matches the target machine tool, and for each of the processing scenarios, control the target machine tool to perform processing under the processing scenario; Acquire expected processing data and actual processing data of the target machine tool in the processing scenario; In the case where the actual processing data is inconsistent with the expected processing data, determining an error compensation parameter according to deviation data between the actual processing data and the expected processing data; Based on the error compensation parameters corresponding to each of the processing scenarios, an error compensation model of the target machine tool is established.

3. The method according to claim 2, characterized in that The step of determining the error compensation parameter according to the deviation data between the actual processing data and the expected processing data comprises: Comparing the actual processing data with the expected processing data to obtain deviation data between the actual processing data and the expected processing data; determining a target compensation parameter that matches the deviation data; Based on the target compensation parameters, the target machine tool is controlled to perform processing in the processing scenario until the actual processing data is consistent with the expected processing data, and the target compensation parameters are updated to error compensation parameters.

4. The method according to claim 3, characterized in that The method further comprises: Based on the deviation data, the servo parameters of the target machine tool are adjusted to obtain parameter adjustment results of the servo parameters; The controlling the target machine tool to perform processing in the processing scenario based on the target compensation parameter includes: According to the target compensation parameter and the parameter adjustment result, the target machine tool is controlled to perform processing in the processing scenario.

5. The method according to claim 1, characterized in that The method further comprises: determining adjustable model parameters in the error compensation model; Based on the machining result of the target machine tool, the adjustable model parameters are adjusted to obtain an optimized error compensation model.

6. The method according to claim 1, characterized in that The method further comprises: Decomposing the machining instructions to obtain interpolation instructions for each machine tool axis in the target machine tool; The error compensation of the machining instruction to generate an actual machining instruction for the target machine tool includes: Error compensation is performed on the interpolation instruction of each of the machine tool axes to generate actual processing instructions for each of the machine tool axes.

7. A machine tool processing device, characterized in that: The device comprises: an error compensation module, for responding to a machining instruction for a target machine tool, and determining an error compensation amount adapted to a current machining scenario of the target machine tool through an error compensation model of the target machine tool; the error compensation model is a model constructed based on error compensation parameters matching deviation data of each machining scenario matching the target machine tool; A compensation instruction generating module, used for generating an error compensation instruction matching the error compensation amount; An actual processing instruction generating module, used for performing error compensation on the processing instruction according to the error compensation instruction, and generating an actual processing instruction for the target machine tool; The machine tool processing module is used to execute the actual processing instruction, control the target machine tool to perform processing, and obtain the processing result of the target machine tool.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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