Machining control method and device, machine tool and readable storage medium

Abstract

The invention discloses a machining control method and device, a machine tool and a readable storage medium, and relates to the technical field of machine tools. The method comprises the steps that under a preset machine tool program segment, preset type data fed back by a machining actuator is input into a preset optimization model to generate optimized feeding parameters and / or optimized cutting parameters of the corresponding program segment; wherein the machining actuator comprises a feeding actuator and / or a machining head actuator; adjusting the feed parameters and / or cutting parameters in the corresponding program segments to the corresponding optimized feed parameters and / or optimized cutting parameters to generate optimized action program segments; and the processing executor executes the optimized action program segment so as to complete the optimized action. According to the machining control method provided by the invention, automatic optimization of actions can be realized, experience requirements and technical requirements on programmers can be reduced, self-adaptive machining adjustment of the machine tool can be realized, and meanwhile, the machining efficiency of the machine tool can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machine tools, in particular to a machining control method and device, a machine tool and a readable storage medium. BACKGROUND

[0002] In high-precision machining machine tools, the machining efficiency is relatively high. The end execution executes the corresponding code to perform the corresponding machining action, and the effectiveness and simplicity of the machining action are directly determined by the programmer, which tests the technical level and experience of the programmer.

[0003] At present, based on artificial intelligence big data processing, it has been continuously applied in the field of machine tools. For example, the existing patent for invention with publication number CN113238527B discloses an industrial data aggregation method and system, such as Figure 1 The industrial data aggregation system involves heat learning and data synchronization. When the working condition of the numerical control machine tool changes, the data heat of each data point can be obtained, which provides strong technical support for subsequent collection of data points requiring data synchronization.

[0004] However, the existing artificial intelligence has few research and application scenarios in terms of machining optimization control technology. SUMMARY

[0005] The main purpose of the present application is to provide a machining control method, device, machine tool and readable storage medium which can reduce the experience and technical requirements of programmers, and can improve production efficiency and machining efficiency.

[0006] First aspect:

[0007] The present application provides a machining control method applied to a machine tool, which comprises:

[0008] Under a preset machine tool program segment, input the preset type data fed back by the machining executor into a preset optimization model to generate optimized feed parameters and / or optimized cutting parameters corresponding to the program segment; wherein the machining executor comprises a feed executor and / or a machining head executor;

[0009] Adjust the feed parameters and / or cutting parameters in the corresponding program segment to the corresponding optimized feed parameters and / or optimized cutting parameters to generate an optimized action program segment;

[0010] The machining executor executes the optimized action program segment to complete the optimized action.

[0011] In an embodiment of the machining control method, before the preset type data fed back by the machining executor is input into the preset optimization model, the method further comprises:

[0012] Obtaining processing big data and processing to obtain a data set, the data set including a training set, a validation set and a test set;

[0013] Based on the preset type data in the corresponding program segment, the tool usage time length, and the processing time length for completing the corresponding program segment to construct an optimization model;

[0014] The data set is used to train the optimization model until the convergence condition is met, and the preset optimization model is obtained.

[0015] In an embodiment of the machining control method, the preset type data includes tool wear state parameters, and at least one of the following parameters: cutting parameters, feed parameters.

[0016] In an embodiment of the machining control method, the method further comprises:

[0017] When the preset type data includes machining state parameters, if the machining state parameters representing idle cutting are input into the preset optimization model, the optimization feed parameters generated by the preset optimization model correspond to the optimization action program segment generated when executed, which includes controlling the feed actuator to accelerate execution to quickly reach the next program segment; or,

[0018] When the preset type data includes machining position parameters, if the machining position parameters representing that the tool is close to the workpiece to less than a preset distance are input into the preset optimization model, the optimization feed parameters generated by the preset optimization model correspond to the optimization action program segment generated when executed, which includes controlling the feed actuator to execute at a preset low speed; or,

[0019] When the preset type data includes cutting setting parameters, if the cutting setting parameters representing small cutting amount requirements are input into the preset optimization model, the optimization feed parameters generated by the preset optimization model correspond to the optimization action program segment generated when executed, which includes controlling the feed actuator to execute at a preset high speed.

[0020] In an embodiment of the machining control method, during execution of the optimization action program segment by the machining actuator, the method further comprises:

[0021] Real-time acquisition of the cutter relief amount;

[0022] Determining the reference feed speed according to the size of the cutter relief amount;

[0023] Taking the smaller one of the reference feed speed and the feed speed corresponding to the feed parameters as the subsequent feed speed of the machining actuator to complete the optimized action.

[0024] Second aspect:

[0025] The application provides a machining control device applied to a machine tool, characterized in that the device comprises:

[0026] a generating module configured to input preset type data fed back by a machining executor into a preset optimization model under a preset machine tool program segment, so as to generate optimized feed parameters and / or optimized cutting parameters corresponding to the program segment; wherein the machining executor comprises a feed executor and / or a machining head executor;

[0027] an optimization module configured to adjust feed parameters and / or cutting parameters in the corresponding program segment to the corresponding optimized feed parameters and / or optimized cutting parameters to generate an optimized action program segment;

[0028] an execution module configured to execute the optimized action program segment by using the machining executor to complete the optimized action.

[0029] In an embodiment of the machining control device, the device further comprises:

[0030] an acquisition module configured to acquire machining big data and process the machining big data to obtain a data set, wherein the data set comprises a training set, a verification set and a test set;

[0031] a model construction module configured to construct an optimization model based on preset type data in a corresponding program segment, tool use duration and machining duration for completing the corresponding program segment;

[0032] a training module configured to train the optimization model by using the data set until a convergence condition is met, so as to obtain the preset optimization model.

[0033] In an embodiment of the machining control device, the device further comprises:

[0034] a real-time acquisition module configured to acquire a tool compensation amount in real time;

[0035] a determination module configured to determine a reference feed speed according to the size of the tool compensation amount;

[0036] The execution module is further configured to take the smaller one of the reference feed speed and a feed speed corresponding to the feed parameters as a feed speed for subsequent execution of the machining executor to complete the optimized action.

[0037] In a third aspect, the application further provides a machine tool comprising a spindle provided with a tool, characterized in that the machine tool further comprises a machine tool system, wherein the machine tool system comprises the machining control device as described above.

[0038] Alternatively,

[0039] The machine tool system comprises a processor configured to execute the steps in the machining control method as described above.

[0040] In a fourth aspect, the present application further provides a readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the machining control method.

[0041] Compared with the prior art, the present application has the following beneficial effects:

[0042] In the machining control method provided by the present application, the preset optimization model is constructed based on big data, the preset optimization model is used to optimize the feeding parameters and / or cutting parameters in the specified program segment, and the corresponding actions are executed by the machining executor, so as to realize the automatic optimization of the actions, reduce the experience and technical requirements of the programmer, help to realize the adaptive machining adjustment of the machine tool, improve the production efficiency of the programmer, and also help to improve the machining efficiency of the machine tool. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a schematic diagram of an industrial data aggregation system provided by the prior art;

[0044] Figure 2 is a flowchart of the machining control method provided by the present application in an embodiment;

[0045] Figure 3 is a schematic diagram of the tool life change of the tool provided by the present application under different cutting speeds of the same material;

[0046] Figure 4 is a flowchart of the machining control method provided by the present application in another embodiment;

[0047] Figure 5 is a functional module diagram of the machining control device provided by the present application.

[0048] Explanation of reference signs:

[0049] Machining control device 100; generation module 101; optimization module 102; execution module 103. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0051] It should be noted that when an element is referred to as being "disposed on" another element, it can be directly disposed on the other element or there can be an intervening element. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or there can be an intervening element. When an element is referred to as being "mounted on" another element, it can be directly mounted on the other element or there can be an intervening element.

[0052] In addition, it should also be understood that all directional directions in the embodiments (such as up, down, left, right, middle, etc.) are only used to explain the relative positional relationship, motion condition, etc. between components in a certain specific posture (as shown in the figure), and if the specific posture changes, the directional directions will also change accordingly. The terms "first", "second", etc. are used to distinguish different structural components. These terms are only for the convenience of describing the simplified description of the present application and cannot be understood as a limitation on the present application.

[0053] The machining control method provided by the present application is based on the use of machine tools based on machining data such as workpiece data, feed data and cutting data, and the optimization of preset type data to obtain optimized parameters of feed parameters and / or cutting parameters, which is beneficial to improve the machining efficiency, can effectively adapt to various machining scenes, and can make more economical machining control.

[0054] Reference Figure 2 The flowchart of the machining control method provided by the present application in an embodiment. It should be understood that the flowchart only schematically shows part of the control steps of the machine tool, so some steps can be added or removed and / or the order of some steps can be adjusted according to different application scenarios and environmental conditions.

[0055] As Figure 2 shown, the machining control method can include the following steps:

[0056] S101: In a preset machine tool program segment, input the preset type data fed back by the machining executor into the preset optimization model to generate optimized feed parameters and / or optimized cutting parameters corresponding to the program segment; wherein the machining executor includes a feed executor and / or a machining head executor.

[0057] It can be understood that the coordinated action of the machine tool is completed by executing a plurality of program segments, and each action can correspond to a program segment. For example, the first program segment is that the spindle drives the cutter to move from the initial position to a distance of a preset distance from the workpiece, the second program segment is that the spindle drives the cutter to move from the distance of the preset distance from the workpiece to the next position, and so on. Here, no longer described.

[0058] In this embodiment, the preset type data can include a tool wear state parameter, and at least one of the following parameters: a cutting parameter, a feed parameter. Of course, it can also include a spindle load, a motor load of a motion shaft, and / or a cutting speed setting parameter, etc.

[0059] In this embodiment, the preset optimization model can be constructed based on big data, and the construction step can include:

[0060] (1) Obtain machining big data and process to obtain a data set, which can include a training set, a validation set, and a test set. Specifically, a 4G / 5G intelligent data communication gateway can be used as a data processing unit, which has an edge computing function. The corresponding machining parameters before and during machining are collected through a 485 communication interface as machining data, and the corresponding machining data is uploaded to a database for storage. The machining parameters can include tool parameters, feed parameters, cutting parameters, cutting setting parameters, motor loads, position sensor sensing information, angle sensor information, etc.

[0061] It can be understood that the machining parameters of each action segment under the corresponding machining scene and / or machining workpiece conditions can be identified and selected from the machining data, especially the tool wear performance information under different feed control parameters and / or cutting control parameters.

[0062] Subsequently, based on the preset type data in the corresponding program segment, the tool usage time, and the machining time when the corresponding program segment is completed, an optimization model is constructed. Here, the preset type data can include tool wear state parameters, cutting parameters, and feed parameters. The construction process of the optimization model can include:

[0063] (1) Under the same or similar program segment, based on the similar or same tool wear state, the tool wear change of the spindle under different speeds when machining the corresponding position of the same material workpiece or similar material workpiece, and the machining time until the tool fails; wherein the different speeds of the spindle correspond to different cutting speeds as the cutting parameters; the machining time until the tool fails corresponds to the tool usage time; the time to complete the corresponding program segment corresponds to the machining time.

[0064] As Figure 3 , in the cutting process from new to old of the same material tool, curves l1, l2, and l3 are obtained under different cutting speeds, and the three curves correspond to three cutting stages, i.e., an initial rapid wear period I, a stable wear period II, and a wear failure period III. Among them, curve l1 corresponds to the tool life change curve under cutting speed A; curve l2 corresponds to the tool life change curve under cutting speed B; and curve l3 corresponds to the tool life change curve under cutting speed C, wherein cutting speed A> cutting speed B> cutting speed C.

[0065] As can be seen, in the same material cutter, the greater the cutting speed, the more obvious the decrease in service life with the increase of cutting time. However, on the other hand, the machining efficiency will be higher. The smaller the cutting speed, the slower the decrease in service life with the increase of cutting time. However, on the other hand, the machining efficiency may not be as expected. Therefore, if the machining time and the service life of the cutter need to be considered, the cutting speed B can be selected for cutting.

[0066] (2) In the same or similar program segment, based on similar or same cutter wear state, when machining the corresponding position of the same material workpiece or similar material workpiece, the cutter wear change and the machining time when failure occurs under different machining feed rates; wherein the different machining feed rates correspond to different feed parameters. The feed parameters can include feed rate and feed speed.

[0067] Finally, the optimization model is trained using the data set until the convergence condition is met, and the preset optimization model is obtained.

[0068] It can be understood that, in the machining environment corresponding to the above program segment, the machining environment is preferably similar or the same to obtain a more referable data set.

[0069] In this embodiment, after obtaining the corresponding model, the parameters generated by the corresponding model can be used for trial machining, and the machining effect and cutter wear degree obtained by trial machining are used for further optimization, so that a more accurate model is obtained.

[0070] In this embodiment, the feed actuator is mainly a moving device for driving the movement, indexing rotation and / or indexing overturning of the workpiece on the spindle and / or worktable.

[0071] For example, in a three-axis vertical machining center, the moving device can include a Z-axis motor, an X-axis motor and a Y-axis motor, wherein the Z-axis motor is mainly used to drive the spindle to move in the Z-axis direction, and the X-axis motor and the Y-axis motor are used to jointly drive the workpiece on the worktable to move in the XY plane.

[0072] For another example, in a four-axis horizontal machining center, the moving device can include a Z-axis motor, an X-axis motor, a Y-axis motor and a C-axis motor, wherein the X-axis motor and the Y-axis motor are used to drive the workpiece on the worktable to move in the XY plane, the C-axis motor is used to drive the workpiece on the worktable to rotate in the C-axis, and the Z-axis motor is used to drive the spindle to move close to or away from the workpiece in the horizontal direction.

[0073] For example, in a five-axis machining center, such as an AC swing head machining center, the spindle is swung or rotated under the drive of an A-axis motor and a C-axis motor, and the workpiece on the worktable moves in the XY plane under the drive of an X-axis motor and a Y-axis motor.

[0074] The machining head executor is mainly a spindle motor for driving the tool to rotate, which can be a direct drive motor or a servo motor.

[0075] S102: adjusting the feed parameters and / or cutting parameters in the corresponding program segment to the corresponding optimized feed parameters and / or optimized cutting parameters to generate an optimized action program segment.

[0076] S103: the machining executor executes the optimized action program segment to complete the optimized action.

[0077] In this embodiment, the optimized action program is preferably part of the program segment that controls the feed or cutting speed, that is, it does not involve adjustment of the part of the program segment that controls the tool path.

[0078] In this embodiment, the preset optimization model is constructed based on big data, and the preset optimization model is used to optimize the feed parameters and / or cutting parameters in the specified program segment for the machining executor to execute the corresponding action, thereby realizing automatic optimization of the action, reducing the experience and technical requirements for the programmer, facilitating adaptive machining adjustment of the machine tool, improving the production efficiency of the programmer, and also improving the machining efficiency of the machine tool.

[0079] In this embodiment, the preset optimization model is preferably used to generate cutting parameters and / or feed parameters that take into account production efficiency and tool service life to maximize the economic benefits of production.

[0080] In this embodiment, the preset type data can also include machining state parameters, machining position parameters, and cutting setting parameters, etc. At this time, the method can also include:

[0081] (1) When the preset type data includes machining state parameters, if the machining state parameter representing idle cutting is input into the preset optimization model, the optimized action program segment corresponding to the optimized feed parameters generated by the preset optimization model includes control of the feed executor to accelerate execution when executed. Here, the current machining state can be identified by obtaining the load value of the spindle motor. Specifically, if the load value of the spindle motor is small and relatively stable, it can be determined that the tool on the spindle is currently in an idle cutting state. At this time, by optimizing the current program segment, the current program segment is accelerated to complete, and the idle cutting time is reduced, thereby facilitating improvement of the machining efficiency.

[0082] (2), when the preset type data comprises a machining position parameter, if a machining position parameter representing that the cutter is close to the workpiece to less than a preset distance is input into the preset optimization model, the optimization action program segment generated by the optimization feed parameters generated by the preset optimization model comprises controlling the feed executor to execute at a preset low speed when being executed. Here, the relative distance between the workpiece and the cutter can be obtained by a distance sensor or a pressure sensor, and the preset distance can be set to 0, that is, in this program segment, when the cutter is close to the workpiece to mutual contact, the current program segment is optimized to reduce the feed speed, which can reduce the tool chatter, reduce the impact on the cutter, improve the collapse of the blade, and help to prolong the service life of the cutter.

[0083] Of course, when the preset type data comprises a cutting setting parameter, if a cutting setting parameter representing a small cutting amount requirement is input into the preset optimization model, the optimization action program segment generated by the optimization feed parameters generated by the preset optimization model comprises controlling the feed executor to execute at a preset high speed when being executed. Here, by obtaining the cutting setting parameter input by the machining personnel or the default cutting setting parameter of the machine tool, the optimized program segment can be optimized to increase the feed speed to support rapid machining.

[0084] Referring to Figure 4 The flowchart of the machining control method provided by the application in another embodiment. Similarly, the flowchart only schematically shows part of the control steps of the machine tool, so that part of the steps and / or the order of part of the steps can be added or adjusted according to different application scenarios and environmental conditions.

[0085] As Figure 4 shown, the machining control method can comprise the following steps:

[0086] S201: In a preset machine tool program segment, input the preset type data fed back by the machining executor into the preset optimization model to generate the optimization feed parameters and / or the optimization cutting parameters corresponding to the program segment; wherein the machining executor comprises a feed executor and / or a machining head executor.

[0087] S202: Adjust the feed parameters and / or the cutting parameters in the corresponding program segment to the corresponding optimization feed parameters and / or the optimization cutting parameters to generate an optimized action program segment.

[0088] S203: During execution of the optimized action program segment by the machining executor, the cutter relief is obtained in real time.

[0089] S204: Determine the reference feed speed according to the size of the cutter relief.

[0090] S205: taking the smaller one between the reference feed speed and the feed speed corresponding to the feed parameter as the feed speed for the machining executor to execute subsequently to complete the optimized action.

[0091] In this embodiment, the actual executed feed speed is determined according to the size of the relief amount to complete the optimized action. Here, when the relief amount is large (serious relief), the feed speed can be controlled to be reduced to perform low-speed machining. Correspondingly, when the relief amount is small (not obvious relief), the feed speed can be controlled to be increased to perform high-speed machining to reduce the dimensional error. Of course, when the increased feed speed is inconsistent with the feed speed corresponding to the current optimized action program segment, the smaller one between the two is taken.

[0092] Compared with the previous embodiment, in the execution process of the optimized action program segment, the relief condition is further acquired in real time, and the execution of the corresponding action is further controlled according to the relief condition to guarantee better machining effect and facilitate the stability of the machining effect.

[0093] Referring to Figure 5 , the function modules of the machining control device provided by the present application are exemplarily shown, and the machining control device 100 is mainly applied to a machine tool. Figure 5 Corresponding to the machining control method provided by the preceding embodiments, the machining control device 100 can include a generation module 101, an optimization module 102 and an execution module 103, wherein:

[0094] The generation module 101 is mainly used for inputting the preset type data fed back by the machining executor into a preset optimization model under a preset machine tool program segment to generate optimized feed parameters and / or optimized cutting parameters corresponding to the program segment; wherein the machining executor includes a feed executor and / or a machining head executor.

[0095] The optimization module 102 is mainly used for adjusting the feed parameters and / or the cutting parameters in the corresponding program segment to the corresponding optimized feed parameters and / or the optimized cutting parameters to generate an optimized action program segment.

[0096] The execution module 103 is mainly used for executing the optimized action program segment by the machining executor to complete the optimized action.

[0097] It can be understood that the machining control device 100 provided by the application can not be limited to the above-mentioned various functional modules, and the corresponding functional modules can be appropriately increased or decreased according to different application scenarios and / or detection conditions. For example, the acquisition module is used to acquire machining big data and process to obtain a data set, the data set includes a training set, a verification set and a test set; the model construction module is used to construct an optimization model based on preset type data in a corresponding program segment, tool use time length and machining time length for completing the corresponding program segment; the training module is used to train the optimization model by using the data set until the convergence condition is met, and the preset optimization model is obtained.

[0098] Of course, the real-time acquisition module is used to acquire a relief amount in real time; the determination module is used to determine a reference feed speed according to the size of the relief amount; and the execution module is further used to take the smaller one of the reference feed speed and the feed speed corresponding to the feed parameter as the feed speed of subsequent execution of the machining executor to complete the optimized action.

[0099] The application further provides a machine tool, which comprises a spindle with a tool, and further comprises a machine tool system, the machine tool system comprising the machining control device as described above; or the machine tool system comprising a processor, the processor being used to execute the steps in the machining control method as described above.

[0100] In addition, the application further provides a computer, which comprises a processor, a memory and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the various machining control methods described above are implemented, such as Figure 2 the steps S101 to S103 shown in the figure, Figure 4 the steps 201 to S205 shown in the figure, etc. Alternatively, when the processor executes the computer program, the functions of the modules or units in the various device embodiments described above are implemented.

[0101] For example, the computer program can be divided into one or more modules / units, one or more modules / units are stored in the memory and executed by the processor to complete the application. One or more modules / units described above can be a series of computer program instruction segments capable of completing a specific function, which is used to describe the execution process of the computer program in the terminal device.

[0102] The processor described above can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is the control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0103] The memory described above can be used to store computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as an acquisition function, a compensation function, etc.), etc. The data storage area can store data created according to the use of the terminal device (such as feature position data, sensing data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0104] If the computer integrated module / unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes of the above processing control method can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of the above processing control method embodiment can be realized. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

[0106] In several embodiments provided in the present application, the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are only schematic. For example, the division of the above functional module units is only a logical function division. In actual implementation, another division mode can be adopted, for example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0107] The units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0108] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0109] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in the form of a contribution to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disk.

[0110] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.

[0111] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features, as long as the modifications, equivalent replacements, improvements, etc. are within the spirit and principles of the present application, they should be included in the protection scope of the present application.

Claims

1. A machining control method, applied in a machine tool, characterized in that, The method includes: Under a preset machine tool program segment, preset type data fed back by the machining actuator is input into a preset optimization model to generate optimized feed parameters and / or optimized cutting parameters for the corresponding program segment; wherein, the machining actuator includes a feed actuator and / or a machining head actuator; Adjust the feed parameters and / or cutting parameters in the corresponding program segment to the corresponding optimized feed parameters and / or optimized cutting parameters to generate an optimized action program segment; The machining actuator executes the optimized action program segment to complete the optimized action.

2. The processing control method as described in claim 1, characterized in that, Before inputting the preset type data fed back by the machining actuator into the preset optimization model, the method further includes: Acquire and process big data to obtain a dataset, which includes a training set, a validation set, and a test set; An optimization model is constructed based on the preset type data in the corresponding program segment, the tool usage time, and the machining time to complete the corresponding program segment; The optimization model is trained using the dataset until it meets the convergence condition, thus obtaining the preset optimization model.

3. The processing control method as described in claim 2, characterized in that, The preset data type includes tool wear status parameters, and at least one of the following parameters: cutting parameters and feed parameters.

4. The processing control method as described in claim 1, characterized in that, The method further includes: When the preset type data includes machining state parameters, if the machining state parameters characterized as air cutting are input into the preset optimization model, then the optimized action program segment corresponding to the optimized feed parameters generated by the preset optimization model, when executed, includes controlling the feed actuator to accelerate execution to quickly move to the next program segment; or, When the preset type data includes machining position parameters, if the machining position parameters, characterized as the tool approaching the workpiece to a distance less than a preset distance, are input into the preset optimization model, then the optimized action program segment corresponding to the optimized feed parameters generated by the preset optimization model, when executed, includes controlling the feed actuator to execute at a preset low feed speed; or, When the preset type data includes cutting setting parameters, if the cutting setting parameters representing small cutting amount requirements are input into the preset optimization model, the optimized action program segment corresponding to the optimized feed parameters generated by the preset optimization model will include controlling the feed actuator to execute with preset high-speed feed when it is executed.

5. The processing control method according to any one of claims 1 to 4, characterized in that, During the execution of the optimized action program segment by the machining actuator, the method further includes: Real-time acquisition of tool deflection; The reference feed rate is determined based on the magnitude of the deflection amount. The smaller of the reference feed rate and the feed rate corresponding to the feed parameter is used as the feed rate for subsequent execution by the machining actuator to complete the optimized action.

6. A machining control device, applied in a machine tool, characterized in that, The device includes: The generation module is used to input the preset type of data fed back by the machining actuator into the preset optimization model under the preset machine tool program segment, so as to generate the optimized feed parameters and / or optimized cutting parameters of the corresponding program segment; wherein, the machining actuator includes a feed actuator and / or a machining head actuator; The optimization module is used to adjust the feed parameters and / or cutting parameters in the corresponding program segment to the corresponding optimized feed parameters and / or optimized cutting parameters to generate an optimized action program segment; The execution module is used to execute the optimized action program segment using the machining actuator to complete the optimized action.

7. The processing control device as described in claim 6, characterized in that, The device further includes: The acquisition module is used to acquire and process big data to obtain a dataset, which includes a training set, a validation set, and a test set. The model building module is used to build an optimization model based on preset type data in the corresponding program segment, tool usage time, and machining time to complete the corresponding program segment. The training module is used to train the optimization model using the dataset until the convergence condition is met, thereby obtaining the preset optimization model.

8. The processing control device as described in claim 6, characterized in that, The device further includes: The real-time acquisition module is used to acquire the deflection amount in real time. The determining module is used to determine the reference feed rate based on the magnitude of the tool deflection amount; The execution module is further configured to use the smaller of the reference feed rate and the feed rate corresponding to the feed parameter as the feed rate for subsequent execution by the machining actuator to complete the optimized action.

9. A machine tool comprising a spindle having cutting tools, characterized in that, It also includes a machine tool system, the machine tool system comprising the machining control device as described in any one of claims 6 to 8; or, The machine tool system includes a processor for performing the steps in the machining control method as described in any one of claims 1 to 5.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the processing control method as described in any one of claims 1 to 5.

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