CN111665786B - Error compensation method and device for machine tool, processor and electronic device - Google Patents
Error compensation method and device for machine tool, processor and electronic device Download PDFInfo
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Abstract
The invention discloses an error compensation method and device of a machine tool, a processor and an electronic device. The method comprises the following steps: determining an error compensation prediction value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool; superposing the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool to obtain a target control value; and controlling a motor of the machine tool to operate according to the target control value. The invention solves the technical problem of single-factor error compensation limitation in the related technology.
Description
Technical Field
The invention relates to the field of industrial control, in particular to an error compensation method and device of a machine tool, a processor and an electronic device.
Background
The superfinishing numerical control machine tool is an important foundation for obtaining high-precision processing products in the field of industrial manufacturing. The triaxial ultra-precision numerical control machine tool is typical ultra-precision machining equipment, and geometric error compensation of the triaxial ultra-precision numerical control machine tool is an important guarantee for realizing precise control of the triaxial ultra-precision numerical control machine tool. The geometric error compensation module of the three-axis ultra-precise numerical control system solves the error problem between the set geometric position and the actual position under the machining coordinate system due to the influence of a plurality of factors in the machining process of a machine tool. In the process of processing a workpiece, when the geometric position is set through a computer program instruction, the spatial geometric position positioning under the workpiece coordinate is influenced by factors such as the temperature, the pitch clearance, the cutting force, the abrasion of a transmission part and the like in the processing process, so that the size and the surface quality of the workpiece are difficult to meet the actual requirements.
The types of numerical control systems commonly used in the related art are generally non-open source systems, making it difficult to know the error compensation schemes used in these numerical control systems. Currently, the error compensation scheme provided in the related art that can be determined may include: single factor error compensation schemes such as basic compensation pitch and reverse backlash compensation, and hardware error compensation schemes such as secondary compensation devices for non-machine products.
However, the obvious drawback of using a single-factor error compensation scheme is that: the comprehensive influence of various factors on error compensation cannot be considered, so that the method has certain limitation on the improvement of the machining precision.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
At least some embodiments of the present invention provide an error compensation method, apparatus, processor and electronic apparatus for a machine tool, so as to at least solve the technical problem of single-factor error compensation limitation in the related art.
According to an embodiment of the present invention, there is provided an error compensation method for a machine tool, including:
determining an error compensation prediction value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool; superposing the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool to obtain a target control value; and controlling a motor of the machine tool to operate according to the target control value.
Optionally, determining the error compensation prediction value based on the plurality of machine tool characteristic parameters comprises: determining a feedback pulse based on a plurality of machine tool characteristic parameters; acquiring a target data model adapted to the feedback pulse, wherein the target data model is used for determining an error fitting result corresponding to the feedback pulse; and setting the feedback pulse as an input parameter of the target data model, and outputting an error compensation predicted value after error fitting processing.
Optionally, the setting the feedback pulse as an input parameter, and after the error fitting process, outputting the error compensation prediction value includes: determining a detection multiplication ratio corresponding to each machine tool characteristic parameter in the plurality of machine tool characteristic parameters based on the detection frequency of the feedback pulse; acquiring a preset instruction multiplication ratio; and setting the detection multiplication ratio and the instruction multiplication ratio corresponding to each machine tool characteristic parameter by using the target data model, and outputting an error compensation predicted value.
Optionally, the superimposing the error compensation predicted value and the initial control value to obtain the target control value includes: determining an input pulse equivalent of each shaft control motor in each processing shaft motor based on the initial control value; converting the error compensation predicted value into a compensation pulse equivalent corresponding to each shaft control motor in each processing shaft motor; and respectively carrying out superposition processing on the input pulse equivalent and the compensation pulse equivalent of each shaft control motor in each processing shaft motor to obtain a target control value.
Optionally, the method further includes: acquiring detection data of a plurality of machine tool characteristic parameters, wherein the detection data comprises: temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data; and preprocessing the detection data to obtain processed data.
Optionally, the method further includes: dividing the processed data into a training set, a verification set and a test set; obtaining an initial data model through machine learning training by using a training set; verifying the initial data model by using a verification set, and adjusting the hyper-parameters of the initial data model to obtain an intermediate data model; and using the test set to carry out generalization error estimation on the intermediate data model to obtain a target data model.
According to an embodiment of the present invention, there is also provided an error compensation apparatus for a machine tool, including:
a determination module for determining an error compensation prediction value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool; the first processing module is used for performing superposition processing on the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool to obtain a target control value; and the control module is used for controlling the motor of the machine tool to operate according to the target control value.
Optionally, the determining module is configured to determine the feedback pulse based on a plurality of machine tool characteristic parameters; acquiring a target data model adapted to the feedback pulse, wherein the target data model is used for determining an error fitting result corresponding to the feedback pulse; and setting the feedback pulse as an input parameter of the target data model, and outputting an error compensation predicted value after error fitting processing.
Optionally, the determining module is configured to determine, based on the detection frequency of the feedback pulse, a detection multiplication ratio corresponding to each of the plurality of machine tool characteristic parameters; acquiring a preset instruction multiplication ratio; and setting the detection multiplication ratio and the instruction multiplication ratio corresponding to each machine tool characteristic parameter by using the target data model, and outputting an error compensation predicted value.
Optionally, the first processing module is configured to determine an input pulse equivalent of each of the processing axis motors based on the initial control value; converting the error compensation predicted value into a compensation pulse equivalent corresponding to each shaft control motor in each processing shaft motor; and respectively carrying out superposition processing on the input pulse equivalent and the compensation pulse equivalent of each shaft control motor in each processing shaft motor to obtain a target control value.
Optionally, the apparatus further comprises: the second processing module is used for acquiring detection data of a plurality of machine tool characteristic parameters, wherein the detection data comprises: temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data; and preprocessing the detection data to obtain processed data.
Optionally, the apparatus further comprises: the third processing module is used for dividing the processed data into a training set, a verification set and a test set; obtaining an initial data model through machine learning training by using a training set; verifying the initial data model by using a verification set, and adjusting the hyper-parameters of the initial data model to obtain an intermediate data model; and using the test set to carry out generalization error estimation on the intermediate data model to obtain a target data model.
According to an embodiment of the present invention, there is also provided a non-volatile storage medium having a computer program stored therein, wherein the computer program is configured to execute the error compensation method of the machine tool in any one of the above-mentioned items when the computer program is executed.
There is also provided, in accordance with an embodiment of the present invention, a processor for executing a program, wherein the program is arranged to execute, when running, the error compensation method of the machine tool of any one of the above.
There is also provided, in accordance with an embodiment of the present invention, an electronic apparatus including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the error compensation method of the machine tool in any one of the above.
In at least some embodiments of the invention, a method for determining an error compensation predicted value based on a plurality of machine tool characteristic parameters used for determining error data of a machine tool is adopted, and the error compensation predicted value and an initial control value of each processing shaft motor of the machine tool are superposed to obtain a target control value and are operated according to the target control value, so that the purpose of performing geometric error compensation on an ultra-precise numerical control machine tool based on multiple factors is achieved, the limitation of a single-factor compensation mode is reduced, the technical effects of improving the dimensional accuracy and the surface quality of a machined part are achieved, and the technical problem of the limitation of the single-factor error compensation in the related technology is solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of an error compensation method of a machine tool according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of an error compensation process of a three-axis ultra-precision numerically controlled machine tool according to an alternative embodiment of the present invention;
FIG. 3 is a schematic diagram of an error compensated prediction process according to an alternative embodiment of the present invention;
FIG. 4 is a schematic diagram of an error-compensated reverse stacking process according to an alternative embodiment of the present invention;
fig. 5 is a block diagram of an error compensation apparatus of a machine tool according to an embodiment of the present invention;
fig. 6 is a block diagram of an error compensation apparatus of a machine tool according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with one embodiment of the present invention, there is provided an embodiment of an error compensation method for a machine tool, where the steps illustrated in the flowchart of the figure may be performed in a computer system, such as a set of computer executable instructions, and where a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.
The method embodiments may be performed in a computer terminal or similar computing device. Taking the example of running on a computer terminal, the computer terminal may include one or more processors (which may include, but are not limited to, processing devices such as Central Processing Units (CPUs), Graphics Processing Units (GPUs), Digital Signal Processing (DSP) chips, Microprocessors (MCUs), programmable logic devices (FPGAs), neural Network Processors (NPUs), Tensor Processors (TPUs), Artificial Intelligence (AI) type processors, etc.) and memory for storing data. Optionally, the computer terminal may further include a transmission device, an input/output device, and a display device for a communication function. It will be appreciated by persons skilled in the art that the above description of the architecture is illustrative only and is not intended to limit the architecture of the computer terminal described above. For example, the computer terminal may also include more or fewer components than described above, or have a different configuration than described above.
The memory may be used to store a computer program, for example, a software program and a module of application software, such as a computer program corresponding to the error compensation method of the machine tool in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the computer program stored in the memory, that is, implements the error compensation method of the machine tool described above. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The display device may be, for example, a touch screen type Liquid Crystal Display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of the computer terminal. In some embodiments, the computer terminal has a Graphical User Interface (GUI) with which a user can interact by touching finger contacts and/or gestures on a touch-sensitive surface, where the human-machine interaction function optionally includes the following interactions: executable instructions for creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, emailing, call interfacing, playing digital video, playing digital music, and/or web browsing, etc., for performing the above-described human-computer interaction functions, are configured/stored in one or more processor-executable computer program products or readable storage media.
In the present embodiment, there is provided an error compensation method for a machine tool running on the computer terminal, and fig. 1 is a flowchart of an error compensation method for a machine tool according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S11, determining an error compensation predicted value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool;
step S12, overlapping the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool to obtain a target control value;
in step S13, the motor of the machine tool is controlled to operate according to the target control value.
Through the steps, the error compensation predicted value can be determined based on a plurality of machine tool characteristic parameters, the plurality of machine tool characteristic parameters are used for determining error data of the machine tool, the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool are subjected to superposition processing to obtain the target control value, and the operation is carried out according to the target control value, so that the purpose of carrying out geometric error compensation on the ultra-precise numerical control machine tool based on multiple factors is achieved, the limitation of a single-factor compensation mode is reduced, the technical effects of improving the size precision and the surface quality of a machined part are achieved, and the technical problem of the limitation of the single-factor error compensation in the related technology is solved.
The plurality of machine tool characteristic parameters may include, but are not limited to: temperature characteristic parameter, cutting force characteristic parameter, transmission wear characteristic parameter and machine tool vibration characteristic parameter. The machine tool can be a three-axis ultra-precision numerical control machine tool. Each of the processing shaft motors may include: an X-axis motor, a Y-axis motor, a Z-axis motor and an auxiliary motor of the three-axis ultra-precision numerical control machine tool. It should be noted that, some embodiments of the present invention only take a three-axis ultra-precision numerical control machine as an example to further describe details of the implementation process of the present invention, but the present invention is not limited thereto.
Fig. 2 is a schematic diagram of an error compensation process of a three-axis superfinishing numerical control machine according to an alternative embodiment of the present invention, and as shown in fig. 2, X-axis, Y-axis and Z-axis are coordinate axes of the three-axis superfinishing numerical control machine. The grating ruler is used for detecting the geometric coordinate data of the X axis, the Y axis and the Z axis to obtain a detection result, and the detection result is used as basic data of the error compensation prediction library. The machine tool characteristic parameters such as temperature characteristic parameters, cutting force characteristic parameters, transmission wear characteristic parameters, machine tool vibration characteristic parameters and the like can be used as important characteristic parameters influencing the size of the geometric error of the triaxial ultra-precision numerical control machine tool. Besides the self-contained detection system of the triaxial ultra-precision numerical control machine, the value of the characteristic parameter of the machine tool needs to be obtained by an external detection device. These machine tool characteristic parameters are uniformly added to an error compensation prediction library of a Computer Numerical Control (CNC) system. The geometric error compensation process of the three-axis ultra-precision numerical control machine tool is used as a key link of closed-loop control of the numerical control system, an error compensation prediction value can be obtained through calculation of an error compensation prediction library, and then the error compensation prediction value is superposed to an initial control value of each processing shaft motor, so that error compensation of each processing shaft motor is realized. In the field of industrial manufacturing, error compensation is particularly important for the size precision and the surface quality of a machine tool machining product, and by introducing a multi-factor error compensation scheme, linkage error compensation is performed on motors of machining shafts, a CNC interpolator is bypassed, and the problem of priority and the influence on a motion control function are not needed to be considered. Therefore, the problems of single-factor compensation limitation, influence of surface thermal deformation on size and the like are solved, the size precision and the surface quality of a machined part are obviously improved, and the problems of secondary development and function type optimization in a CNC error compensation process are solved, so that the product development efficiency is improved.
Optionally, the method may further include the following steps:
step S14, acquiring detection data of a plurality of machine tool characteristic parameters, wherein the detection data comprises: temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data;
and step S15, preprocessing the detection data to obtain processed data.
In order to ensure the accuracy and rapidity of the detection data of the characteristic parameters of the machine tool, the detection data should be as much as possible, and the detection period should be as short as possible. The detection data of a plurality of machine tool characteristic parameters can be obtained through an external detection device of the triaxial ultra-precision numerical control machine tool. The detection data may include, but is not limited to: temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data. Moreover, preprocessing operations (e.g., data normalization processing operations) are required on the test data before applying the test data to the fitted target data model.
Optionally, the method may further include the following steps:
step S16, dividing the processed data into a training set, a verification set and a test set;
step S17, obtaining an initial data model through machine learning training by using a training set;
step S18, verifying the initial data model by using a verification set, and adjusting the hyper-parameters of the initial data model to obtain an intermediate data model;
and step S19, using the test set to carry out generalization error estimation on the intermediate data model to obtain a target data model.
A suitable multi-factor error value fitting algorithm may be selected in the error-compensated prediction library. For example: support vector regression model (SVR) algorithms, Back Propagation (BP) neural network algorithms, genetic algorithms, and the like. In addition, diagnostic methods such as machine learning may be employed to determine the applicable target data model. Therefore, the preprocessed detection data can be divided into a training set, a verification set and a test set according to a preset proportion, so that the generalization of the target data model can be ensured while various parameters (including hyper-parameters and common parameters) of the target data model are obtained. For example: and (3) preprocessing the detection data according to the ratio of 4: 3: and 3, dividing the ratio into a training set, a verification set and a test set. In an alternative embodiment, an initial data model may first be obtained by machine learning training using a training set; secondly, the initial data model can be verified by using a verification set, and the hyper-parameters of the initial data model are adjusted to obtain an intermediate data model; the test set may then be used to generalize the error estimate for the intermediate data model to arrive at the target data model. Therefore, the problem of universality of products can be solved, the technology in the fields of artificial intelligence, machine learning and the like can be conveniently combined, the interchangeability of a numerical control system is enhanced, and the service life of the products is prolonged.
Different target data models can be obtained for different detection data, since the detection data obtained in different time periods may have different degrees of difference. For example: when the detection data comprise temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data, a target data model 1 can be obtained; when the detection data comprise cutting force error data, transmission wear error data and machine tool vibration error data, a target data model 2 can be obtained; when the detection data comprise temperature error data, transmission wear error data and machine tool vibration error data, a target data model 3 can be obtained; when the detection data comprises temperature error data, cutting force error data and machine tool vibration error data, a target data model 4 can be obtained; when the detected data includes temperature error data, cutting force error data, and transmission wear error data, the target data model 5 can be obtained.
Alternatively, in step S11, determining the error compensation prediction value based on the plurality of machine tool characteristic parameters may include performing the steps of:
step S111, determining feedback pulses based on a plurality of machine tool characteristic parameters;
step S112, acquiring a target data model adapted to the feedback pulse, wherein the target data model is used for determining an error fitting result corresponding to the feedback pulse;
and step S113, setting the feedback pulse as an input parameter of the target data model, and outputting an error compensation predicted value after error fitting processing.
The feedback pulse may be determined based on error data for each of a plurality of machine tool parameters. The feedback pulse corresponding to each machine tool characteristic parameter is different. Different feedback pulses can be determined according to different machine tool characteristic parameters, and the different feedback pulses respectively correspond to different target data models, so that the error compensation predicted value can be output after error fitting processing by acquiring the target data model matched with the feedback pulses and setting the feedback pulses as the input parameters of the target data model.
Alternatively, in step S113, the step of setting the feedback pulse as an input parameter, and outputting the error compensation prediction value after the error fitting process may include the following steps:
step S1131, determining a detection multiplication ratio corresponding to each machine tool characteristic parameter in the plurality of machine tool characteristic parameters based on the detection frequency of the feedback pulse;
step S1132, acquiring a preset instruction multiplication ratio;
and step S1133, setting the detection multiplication ratio and the instruction multiplication ratio corresponding to each machine tool characteristic parameter by using the target data model, and outputting the error compensation predicted value.
For the instruction multiplication ratio (CMR), the value thereof can be usually preset by the user, and the value thereof is usually unchanged in size. For detecting the multiplication ratio (DMR), since the detection frequencies of different characteristic parameters, such as the temperature characteristic parameter, the cutting force characteristic parameter, the transmission wear characteristic parameter, and the machine tool vibration characteristic parameter, are different, the detection frequencies of the feedback pulses respectively corresponding to the different characteristic parameters are also different, so that the DMRs respectively corresponding to the different characteristic parameters are also different. For example: the temperature characteristic parameter corresponds to DMR01, the cutting force characteristic parameter corresponds to DMR02, the transmission wear characteristic parameter corresponds to DMR03, and the machine tool vibration characteristic parameter corresponds to DMR 04.
The CMR and DMR are set to function as: the weight between the instruction unit pulse and the feedback unit pulse is ensured to be consistent. For example: if the minimum movement unit is 0.01m, CMR is 1, DMR is 1/2, the feedback unit pulse of the grating ruler is: 0.0005m, the detection unit is 0.001 m.
Fig. 3 is a schematic diagram of an error compensated prediction process according to an alternative embodiment of the present invention, wherein the feedback pulse may be determined based on error data of a plurality of machine tool characteristic parameters, as shown in fig. 3. A suitable multi-factor error value fitting algorithm may be selected in the error-compensated prediction library, for example: SVR algorithms, BP neural network algorithms, genetic algorithms, and the like. The value of the CMR is preset by a user, and the value size of the CMR is kept unchanged. The thermal drift temperature factor corresponds to DMR01, the cutting force factor corresponds to DMR02, …, and the drive wear factor corresponds to DMR0 n. After acquiring the minimum shift unit of the pulse, CMR and DMR01, DMR02, …, DMR0n, the following equations are used:
the minimum motion compensation unit corresponding to each factor is calculated to determine the minimum error and convert the minimum error into the minimum motion compensation amount (i.e., the error compensation prediction value).
Alternatively, in step S12, the superimposing the error compensation prediction value and the initial control value to obtain the target control value may include the following steps:
step S121, determining the input pulse equivalent of each shaft control motor in each processing shaft motor based on the initial control value;
step S122, converting the error compensation predicted value into a compensation pulse equivalent corresponding to each shaft control motor in each processing shaft motor;
and step S123, respectively carrying out superposition processing on the input pulse equivalent and the compensation pulse equivalent of each shaft control motor in each processing shaft motor to obtain a target control value.
The pulse equivalent is the linear distance moved by the screw or the degree of rotation of the rotating shaft when the numerical control system sends out a pulse, namely the minimum unit which can be controlled by the numerical control system. If the value of the pulse equivalent is smaller, the machining precision of the machine tool and the surface quality of the workpiece are higher. If the value of the pulse equivalent is larger, the maximum feeding speed of the machine tool is larger.
The input pulse equivalent is used as the output of the CNC, and is stored in different data buffer addresses (e.g., data buffer address 1, data buffer address 2, …, data buffer address n-1, data buffer address n) of the data register. And respectively storing the input pulse equivalent of the motors with different axes to different data cache addresses. The minimum movement compensation amount of each shaft control motor in each processing shaft motor is different. The input pulse equivalent stored in the data register and stored in the data cache address corresponding to the shaft can be subjected to reverse phase superposition by using the minimum motion compensation quantity of each shaft control motor in each processing shaft motor, so that the value of the actual pulse equivalent is adjusted to obtain a target control value. That is, the actual pulse equivalent is input pulse equivalent ± the minimum movement compensation amount for each of the machining axis motors that are CNC-output.
Fig. 4 is a schematic diagram of an error compensation reverse superposition process according to an alternative embodiment of the present invention, and as shown in fig. 4, input pulse equivalent of the X-axis motor, input pulse equivalent of the Y-axis motor, input pulse equivalent of the Z-axis motor, and input pulse equivalent of the auxiliary motor for CNC output are respectively stored to different data buffer addresses. The minimum movement compensation amount of the X-axis motor, the minimum movement compensation amount of the Y-axis motor, the minimum movement compensation amount of the Z-axis motor, and the minimum movement compensation amount of the auxiliary motor are different from each other. The input pulse equivalent stored in the data register corresponding to the axis can be subjected to reverse phase superposition by using the minimum movement compensation quantity of the X-axis motor, so that the value of the actual pulse equivalent of the X-axis motor is adjusted to obtain the target control value of the X-axis motor. And the input pulse equivalent stored in the data register corresponding to the axis and stored in the data cache address can be subjected to reverse phase superposition by using the minimum motion compensation quantity of the Y-axis motor, so that the value of the actual pulse equivalent of the Y-axis motor is adjusted to further obtain the target control value of the Y-axis motor. And the input pulse equivalent stored in the data register and the data cache address corresponding to the axis can be subjected to reverse phase superposition by using the minimum movement compensation quantity of the Z-axis motor, so that the value of the actual pulse equivalent of the Z-axis motor is adjusted to further obtain the target control value of the Z-axis motor. The input pulse equivalent stored in the data register corresponding to the shaft and stored in the data cache address can be subjected to reverse phase superposition by using the minimum motion compensation quantity of the auxiliary motor, so that the value of the actual pulse equivalent of the auxiliary motor is adjusted to obtain the target control value of the auxiliary motor. And finally, converting the target control value of each shaft control motor in each processing shaft motor into a pulse through an amplifier and a D/A converter, and further synchronously applying the pulse to the corresponding shaft motor to realize error compensation, thereby ensuring the accuracy of linkage compensation.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, an error compensation device for a machine tool is further provided, and the error compensation device is used to implement the above embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram showing the structure of an error compensation apparatus for a machine tool according to an embodiment of the present invention, as shown in fig. 5, the apparatus including: a determination module 10 for determining an error compensation prediction value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool; the first processing module 20 is configured to perform superposition processing on the error compensation predicted value and the initial control value of each processing axis motor of the machine tool to obtain a target control value; and the control module 30 is used for controlling the motor of the machine tool to operate according to the target control value.
Optionally, a determination module 10 for determining a feedback pulse based on a plurality of machine tool characteristic parameters; acquiring a target data model adapted to the feedback pulse, wherein the target data model is used for determining an error fitting result corresponding to the feedback pulse; and setting the feedback pulse as an input parameter of the target data model, and outputting an error compensation predicted value after error fitting processing.
Optionally, the determining module 10 is configured to determine, based on the detection frequency of the feedback pulse, a detection multiplication ratio corresponding to each of the plurality of machine tool characteristic parameters; acquiring a preset instruction multiplication ratio; and setting the detection multiplication ratio and the instruction multiplication ratio corresponding to each machine tool characteristic parameter by using the target data model, and outputting an error compensation predicted value.
Optionally, the first processing module 20 is configured to determine an input pulse equivalent of each of the processing axis motors based on the initial control value; converting the error compensation predicted value into a compensation pulse equivalent corresponding to each shaft control motor in each processing shaft motor; and respectively carrying out superposition processing on the input pulse equivalent and the compensation pulse equivalent of each shaft control motor in each processing shaft motor to obtain a target control value.
Alternatively, fig. 6 is a block diagram of an error compensation apparatus for a machine tool according to an embodiment of the present invention, as shown in fig. 6, the apparatus includes, in addition to all modules shown in fig. 5: a second processing module 40, configured to obtain detection data of a plurality of machine tool characteristic parameters, where the detection data includes: temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data; and preprocessing the detection data to obtain processed data.
Optionally, as shown in fig. 6, the apparatus includes, in addition to all the modules shown in fig. 5: a third processing module 50, configured to divide the processed data into a training set, a verification set, and a test set; obtaining an initial data model through machine learning training by using a training set; verifying the initial data model by using a verification set, and adjusting the hyper-parameters of the initial data model to obtain an intermediate data model; and using the test set to carry out generalization error estimation on the intermediate data model to obtain a target data model.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a non-volatile storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the above-mentioned nonvolatile storage medium may be configured to store a computer program for executing the steps of:
s1, determining an error compensation predicted value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool;
s2, overlapping the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool to obtain a target control value;
and S3, controlling the motor of the machine tool to operate according to the target control value.
Optionally, in this embodiment, the nonvolatile storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, determining an error compensation predicted value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool;
s2, overlapping the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool to obtain a target control value;
and S3, controlling the motor of the machine tool to operate according to the target control value.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (13)
1. An error compensation method for a machine tool, comprising:
determining an error compensation prediction value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool;
superposing the error compensation predicted value and an initial control value of each processing shaft motor of the machine tool to obtain a target control value;
controlling a motor of the machine tool to operate according to the target control value;
wherein determining the error compensation prediction value based on the plurality of machine tool characteristic parameters comprises: determining a feedback pulse based on the plurality of machine tool characteristic parameters; acquiring a target data model adapted to the feedback pulse, wherein the target data model is used for determining an error fitting result corresponding to the feedback pulse; and setting the feedback pulse as an input parameter of the target data model, and outputting the error compensation predicted value after error fitting processing.
2. The method of claim 1, wherein setting the feedback pulse as the input parameter, and outputting the error compensation prediction value after an error fitting process comprises:
determining a detection multiplication ratio corresponding to each of the plurality of machine tool characteristic parameters based on the detection frequency of the feedback pulse;
acquiring a preset instruction multiplication ratio;
and setting the detection multiplication ratio and the instruction multiplication ratio corresponding to each machine tool characteristic parameter by using the target data model, and outputting the error compensation predicted value.
3. The method of claim 1, wherein superimposing the error compensation predicted value with the initial control value to obtain the target control value comprises:
determining an input pulse equivalent of each of the processing shaft motors based on the initial control value;
converting the error compensation predicted value into a compensation pulse equivalent corresponding to each shaft control motor in each processing shaft motor;
and respectively superposing the input pulse equivalent and the compensation pulse equivalent of each shaft control motor in each processing shaft motor to obtain the target control value.
4. The method of claim 1, further comprising:
acquiring detection data of the plurality of machine tool characteristic parameters, wherein the detection data comprises: temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data;
and preprocessing the detection data to obtain processed data.
5. The method of claim 4, further comprising:
dividing the processed data into a training set, a verification set and a test set;
obtaining an initial data model through machine learning training by using the training set;
verifying the initial data model by using the verification set, and adjusting the hyper-parameters of the initial data model to obtain an intermediate data model;
and using the test set to carry out generalization error estimation on the intermediate data model to obtain a target data model.
6. An error compensation device for a machine tool, comprising:
a determination module for determining an error compensation prediction value based on a plurality of machine tool characteristic parameters, wherein the plurality of machine tool characteristic parameters are used for determining error data of the machine tool;
the first processing module is used for carrying out superposition processing on the error compensation predicted value and the initial control value of each processing shaft motor of the machine tool to obtain a target control value;
the control module is used for controlling a motor of the machine tool to operate according to the target control value;
wherein the determination module is configured to determine a feedback pulse based on the plurality of machine tool characteristic parameters; acquiring a target data model adapted to the feedback pulse, wherein the target data model is used for determining an error fitting result corresponding to the feedback pulse; and setting the feedback pulse as an input parameter of the target data model, and outputting the error compensation predicted value after error fitting processing.
7. The apparatus of claim 6, wherein the determining module is configured to determine a detection multiplication ratio corresponding to each of the plurality of machine tool characteristic parameters based on a detection frequency of the feedback pulse; acquiring a preset instruction multiplication ratio; and setting the detection multiplication ratio and the instruction multiplication ratio corresponding to each machine tool characteristic parameter by using the target data model, and outputting the error compensation predicted value.
8. The apparatus of claim 6, wherein said first processing module is configured to determine an input pulse equivalent for each of said respective machine axis motors based on said initial control value; converting the error compensation predicted value into a compensation pulse equivalent corresponding to each shaft control motor in each processing shaft motor; and respectively overlapping the input pulse equivalent and the compensation pulse equivalent of each shaft motor in each processing shaft motor to obtain the target control value.
9. The apparatus of claim 6, further comprising:
a second processing module, configured to acquire detection data of the plurality of machine tool characteristic parameters, where the detection data includes: temperature error data, cutting force error data, transmission wear error data and machine tool vibration error data; and preprocessing the detection data to obtain processed data.
10. The apparatus of claim 9, further comprising:
the third processing module is used for dividing the processed data into a training set, a verification set and a test set; obtaining an initial data model through machine learning training by using the training set; verifying the initial data model by using the verification set, and adjusting the hyper-parameters of the initial data model to obtain an intermediate data model; and using the test set to carry out generalization error estimation on the intermediate data model to obtain a target data model.
11. A non-volatile storage medium, characterized in that a computer program is stored in the storage medium, wherein the computer program is arranged to execute the method of error compensation of a machine tool according to any one of claims 1 to 5 when running.
12. A processor for running a program, wherein the program is arranged to run to perform the method of error compensation of a machine tool of any one of claims 1 to 5.
13. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of error compensation of a machine tool as claimed in any one of claims 1 to 5.
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