Multi-axis Matching Detection Method, System, Device and Readable Storage Medium

Abstract

The present invention discloses a multi-axis matching determination method, system, device and readable storage medium. The method includes: collecting the speed, acceleration and following error values of the motors of each axis to be detected of a numerical control machine tool; calculating the speed correlation coefficient and acceleration correlation coefficient of the motors of each axis to be detected based on the speed, acceleration and following error values of the motors of each axis to be detected; adjusting the motion states of the motors of each axis to be detected based on the speed correlation coefficient and acceleration correlation coefficient, so that the motion performance of the motors of each axis to be detected meets the preset conditions; detecting the roundness and reverse error between each axis to be detected to judge the matching between each axis to be detected. Compared with the prior art, the present invention can more accurately reflect the motion performance of the motors and the matching between each axis. At the same time, by first debugging the motion performance of each axis, the obtained matching accuracy is higher and can better reflect the upper limit of the motor matching performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of servo control, and particularly relates to a multi-axis matching detection method, system, device and readable storage medium. Background Art

[0002] With the continuous development of high-tech industries such as national defense, aerospace, automotive, and microelectronics, higher requirements are put forward for the manufacturing and processing industries. Ultra-high-speed machining and ultra-precision machining have become two themes for the future development of the machine tool industry. The traditional machine tool feed drive system is a "rotary motor + ball screw" mechanism. This drive system involves many intermediate components, has a large moment of inertia, and the ball screw itself has physical limitations. Therefore, the linear speed, acceleration, and positioning accuracy generated are limited and cannot meet the needs of ultra-high-speed and high-precision machining; thus, linear motors have attracted people's attention. It directly generates linear motion, has a simple structure, a small moment of inertia, a high system stiffness, good fast response characteristics, can achieve precise positioning at high speeds, generates a large thrust, especially the motion speed and acceleration are several times higher than those of the ball screw, the working stroke can be infinitely long, and the maintenance is less and the service life is long. These advantages make it an ideal component for modern machine tool feed drives.

[0003] Currently, linear motor numerical control machine tools are widely used in the manufacturing field, and the multi-axis motion matching performance of linear motor numerical control machine tools is the main factor affecting the machining accuracy and stability of the machine tool. The prior art neglects the judgment of the matching performance of multi-axis motion, so there are often problems of large errors and low efficiency in the multi-axis motion of linear motor numerical control machine tools.

[0004] Therefore, in view of the above technical problems, it is necessary to provide an improved method for detecting the matching between motion axes.

[0005] The information disclosed in this background art section is only intended to enhance the understanding of the overall background of the present invention and should not be regarded as an admission or any form of suggestion that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-axis matching detection method, system, device and readable storage medium, which can intuitively judge the matching performance between the motor motion axes.

[0007] To achieve the above purpose, the technical solution provided by a specific embodiment of the present invention is as follows:

[0008] In a first aspect, the present invention provides a multi-axis matching detection method, which is applied to a multi-axis numerical control machine tool and includes:

[0009] Collect the speed, acceleration and following error values of the motors of each axis to be detected of the numerical control machine tool;

[0010] Calculate the speed correlation coefficient and acceleration correlation coefficient of the motors of each axis to be detected based on the speed, acceleration, and following error values of the motors of each axis to be detected;

[0011] Adjust the motion states of the motors of each axis to be detected based on the speed correlation coefficient and acceleration correlation coefficient, so that the motion performance of the motors of each axis to be detected meets the preset conditions;

[0012] Detect the roundness and reverse error between each axis to be detected to determine the matching between each axis to be detected.

[0013] In one or more embodiments of the present invention, the calculation formulas for the speed correlation coefficient and acceleration correlation coefficient of the motors of each axis to be detected are as follows:

[0014]

[0015] Wherein, VelCorr is the speed correlation coefficient, AccCorr is the acceleration correlation coefficient, N is the total number of collected data, Vel(i) is the speed at the i-th point, FE(i) is the following error at the i-th point, and Acc9i) is the acceleration at the i-th point.

[0016] In one or more embodiments of the present invention, the detection of the roundness and reverse error between each axis to be detected includes:

[0017] Select any two axes to be detected;

[0018] Set a circular machining trajectory based on the selected axes to be detected;

[0019] Collect the actual machining trajectory when the numerical control machine tool performs machining along the circular machining trajectory based on the selected axes to be detected;

[0020] Calculate the roundness, backlash, and reverse overshoot of the actual machining trajectory based on the trajectory parameters of the actual machining trajectory.

[0021] In one or more embodiments of the present invention, the method further includes:

[0022] Based on the selected axes to be detected, make the motor move along a preset circumference for at least two weeks;

[0023] Set the initial point and the cut-off point, and take the data of a complete circumference of motion after the initial point and before the cut-off point for roundness analysis.

[0024] In one or more embodiments of the present invention, the method further includes:

[0025] Collect the coordinates corresponding to each point on the actual motion path during the motion along the motion direction;

[0026] Within a preset analysis interval, collect the maximum value of the distance between the actual motion coordinates and the center of the circle as the peak radius. Along the motion direction, the radius of the motion trajectory before the coordinates corresponding to the peak radius is the radius of the pre-switching circle pattern, and the radius of the motion trajectory after the coordinates corresponding to the peak radius is the radius of the post-switching circle pattern.

[0027] In one or more embodiments of the present invention, the calculation formula for the roundness parameter is:

[0028]

[0029] R max = E max + R; R min = E min + R

[0030] f = R max – R min

[0031] Δb = R g - R b ; Δp = R P - R b

[0032] Among them, E i is the error in the radial direction of each point; the center coordinates of the preset circumference are (X 0 , Y 0 ), and the radius is R; at the i-th measurement point, the coordinates of the actual motion are (X i , Y i ); the roundness of the actual motion path and the preset circumference is f; the backlash is Δb, the reverse overshoot is Δp, the radius of the pre-switching circle pattern is Rb, the radius of the post-switching circle pattern is Rg, and the peak radius is Rp.

[0033] In one or more embodiments of the present invention, the method further includes:

[0034] If the following error is greater than a preset error threshold, then reduce the velocity feedforward and / or acceleration feedforward to make the following error less than or equal to the error threshold.

[0035] In a second aspect, the present invention provides a multi-axis matching detection system, which includes:

[0036] A collection module for collecting the speed, acceleration, and following error values of the motors of each axis to be detected of a numerically controlled machine tool;

[0037] A calculation module, configured to calculate a speed correlation coefficient and an acceleration correlation coefficient of the motors of the axes to be detected based on the speed, acceleration, and following error values of the motors of the axes to be detected;

[0038] An adjustment module, configured to adjust the motion states of the motors of the axes to be detected based on the speed correlation coefficient and the acceleration correlation coefficient, so that the motion performance of the motors of the axes to be detected meets a preset condition;

[0039] An analysis module, configured to detect the roundness and reverse error between the axes to be detected, so as to judge the matching between the axes to be detected.

[0040] In a third aspect, the present invention provides a computer device, which includes: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the multi-axis matching detection method.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the multi-axis matching detection method.

[0042] Compared with the prior art, the multi-axis matching detection method provided by the present invention performs a customized roundness analysis between the motion axes. Based on the roundness parameters, the matching relationship between the motion axes participating in the analysis can be quantified, and the motion performance of the motor and the matching between the axes can be reflected more accurately. At the same time, before performing the roundness analysis, the present invention first debugs the motion performance of each axis to make the motion performance of the motors corresponding to each axis reach the best. The matching accuracy obtained in this state is higher and can better reflect the upper limit of the motor matching performance. Further, based on the improved correlation coefficient and related motion parameters, the motion performance of each axis is debugged, which can maximize the motion performance of the corresponding click of the axis to reach the best. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is a schematic diagram of the multi-axis matching detection method scenario in an embodiment of the present invention;

[0045] Figure 2It is a schematic flow chart of a multi-axis matching detection method in an embodiment of the present invention;

[0046] Figure 3 It is a structural block diagram of a multi-axis matching detection system in an embodiment of the present invention;

[0047] Figure 4 It is a structural block diagram of an electronic device in an embodiment of the present invention;

[0048] Figure 5 It is a schematic diagram for setting the initial point position and the cut-off point position in an embodiment of the present invention;

[0049] Figure 6 It is a schematic diagram for reverse error detection in an embodiment of the present invention. Specific Embodiments

[0050] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0051] Unless otherwise clearly stated, in the whole specification and claims, the term "comprising" or its variations such as "including" or "having" etc. will be understood to include the stated elements or components, without excluding other elements or other components.

[0052] In the existing technical solutions, there are methods for machining parts based on multi-axis linkage. Multi-axis linkage is a control strategy widely used in current industrial automation. It realizes the control and adjustment of complex three-dimensional space movements through the coordinated work of multiple axes. This strategy is also called the multi-axis synchronous control strategy, which means that in most applications of multi-axis drive systems, a certain synchronous operation relationship is maintained between the axes. The multi-axis system is a non-linear, strongly coupled multi-input multi-output system.

[0053] Although under the above control strategy, advantages such as reducing reference conversion, improving machining accuracy, reducing the number of tooling fixtures and floor area, shortening the production process chain, and simplifying production management can be achieved, since the implementation of a motion command for one coordinate depends on the simultaneous operation of two or more motors. Therefore, the matching performance between the motors will directly affect the production efficiency and quality.

[0054] The prior art often relies on the experience of engineers for the matching between motion axes, which is highly subjective and has a large error. The inventor of the present invention found the main drawback that the prior art cannot accurately judge the matching between motion axes, and proposed a new technical implementation idea: first, debug the motion performance of the motors corresponding to each axis to a better level, and on this basis, perform roundness analysis between each motion axis, and quantify the matching performance between the corresponding motion axes based on the analysis results.

[0055] Please refer to Figure 1 , which shows a schematic diagram of the application scenario of the multi-axis matching detection method provided by the present invention under an embodiment. This scenario specifically includes: a user terminal 101, a motion control unit 102, and a data processing unit 103.

[0056] It should be noted that communication connections are provided between the user terminal 101, the motion control unit 102, and the data processing unit 103. The communication network extended by the above communication connections can include various connection types, including but not limited to: wired connection, wireless connection, or fiber optic cable connection, etc. At the same time, this communication network can be a local area network, a metropolitan area network, a wide area network, or any combination of the three.

[0057] Among them, the user terminal 101 is installed with a computer software program that matches the multi-axis matching detection method provided by this method; the user terminal 101 can include but not limited to desktop computers (PC terminals), desktop computers, smart phones, handheld computers, tablet computers, personal digital assistants (PDAs), etc. portable electronic devices or wearable electronic devices. The embodiments of the present invention do not limit the above content.

[0058] At the same time, the user terminal 101 is used for the user to input parameters, functions, etc. necessary for the implementation of the numerical control machine tool and the technical solution provided by the present invention. Specifically, it includes but not limited to: speed correlation coefficient calculation formula, acceleration correlation coefficient calculation formula, preset circular path configuration parameters, setting of data acquisition points, calculation formulas for backlash and reverse overshoot, types of data collected at the data acquisition points, etc.

[0059] Based on the relevant parameters set by the user terminal 101, the motion control unit 102 drives the motor to move along a preset path. In the embodiment of the present invention, first, single-axis motion is performed with each axis to be measured as a reference. The data processing unit 103 collects relevant data of the above single-axis motion at preset points, and calculates the speed correlation coefficient and the acceleration correlation coefficient based on the collected motion data. By adjusting the speed / acceleration feedforward, the speed correlation coefficient and the acceleration correlation coefficient are made to meet the preset conditions.

[0060] Subsequently, any two axes to be measured are selected. Based on the circular path preset by the user terminal 101, the motion control unit 102 drives the motor to move along the above circular path. Similarly, the data processing unit 103 is used to collect the motion data of each point during the motion process. Roundness analysis is performed on the collected data and the reverse error is calculated. Roundness and reverse error can quantitatively reflect the matching of the two axes participating in the circular motion.

[0061] It should also be noted that the multi-axis matching detection method of the embodiments of the present invention can be applied to the multi-axis matching detection system of the embodiments of the present invention. The hierarchical quantization learning system can be configured in a terminal. The terminal can include, but is not limited to, a PC (Personal Computer), a PDA (tablet computer), a smart phone, a smart wearable device, and the like.

[0062] Please refer to Figure 2 shown in the flowchart of multi-axis matching detection in an embodiment of the present invention. The multi-axis matching detection method specifically includes the following steps:

[0063] S201: Collect the speed, acceleration, and following error values of the motors of each axis to be detected of the numerically controlled machine tool;

[0064] In an exemplary embodiment, collecting the speed, acceleration, and following error values of the motors of each axis to be detected of the numerically controlled machine tool includes: based on a preset motion instruction, the linear motors of each axis to be detected move separately; based on preset collection points, the motion parameters of the linear motors during the motion are collected.

[0065] It should be noted that the linear motors of each axis to be detected move separately above, that is, perform single-axis motion and collect single-axis motion data. The single-axis motion can include, but is not limited to: one-way motion, round-trip motion, uniform motion, uniformly variable motion, variable acceleration motion, etc.; the performance parameters can include, but are not limited to: the speed, acceleration, and following error values of the motor. The embodiments of the present invention do not limit the type of single-axis motion and the confirmation of performance parameters in this step.

[0066] It should also be noted that the present invention provides a multi-axis matching detection method, where the points for collecting motion data can be set at intervals based on motion time or set based on motion position. The embodiments of the present invention do not limit this.

[0067] For example, in a specific embodiment, collecting the performance parameters during the single-axis motion of the motor specifically includes: first setting the parameters of the single-axis motion, which can include command speed, command acceleration, distance, etc., and based on these parameters, the motion type and motion direction of the linear motor can also be specified. Based on the setting of the above parameters, an NC file for single-axis reciprocating motion is created, and the system is made to start running the created NC file. Subsequently, a data collection thread is started to collect the actual speed, actual acceleration, and following error data, and at the same time, a Timer thread is started to detect the collection status. When a collection end signal (Flag == 1) is received, the data processing interface is called.

[0068] Among them, the NC file is also known as the G code or M code, which is a text file used on numerically controlled machine tools to describe the machine tool movement, cutting parameters, and machining process. It contains all the information required to control the numerically controlled machine tool, such as workpiece coordinates, tool paths, cutting speeds, feed rates, etc. The NC file is usually generated by CAM (Computer Aided Manufacturing) software and is parsed and executed by the numerically controlled machine tool controller.

[0069] Timer is a thread facility used to schedule tasks to be executed in a background thread. Tasks can be scheduled to execute once or to repeat periodically. It is a timer class used to execute a specified task after a specified delay. It is often used to implement timed tasks, such as setting timeouts or periodically cleaning up data in network communication.

[0070] S202: Calculate the speed correlation coefficient and acceleration correlation coefficient of the motors of each axis to be detected based on the speed, acceleration, and following error values of the motors of each axis to be detected;

[0071] In an exemplary embodiment, the calculation formulas for the correlation coefficient of the speed and the correlation coefficient of the acceleration of the motors of each axis to be detected are respectively:

[0072]

[0073] Among them, VelCorr is the speed correlation coefficient, AccCorr is the acceleration correlation coefficient, N is the total number of collected data, Vel(i) is the speed at the i-th point, FE(i) is the following error at the i-th point, and Acc(i) is the acceleration at the i-th point.

[0074] S203: Adjust the motion states of the motors of each axis to be detected based on the speed correlation coefficient and the acceleration correlation coefficient, so that the motion performance of the motors of each axis to be detected meets the preset conditions;

[0075] As can be seen from the above formula, the calculation result of the correlation coefficient is a number within the range of -1 to 1, and the closer the value of the correlation coefficient is to 0, the better the motion performance of the motor.

[0076] In an exemplary embodiment of the present invention, the available ranges of each axis to be detected are configured based on the user terminal. If, after calculation, both its speed correlation coefficient and acceleration correlation coefficient fall within the corresponding available ranges, it is considered that the motion performance of the current axis to be detected has been adjusted to a good state, and the subsequent roundness analysis and reverse error detection phases can be carried out. Conversely, if the speed correlation coefficient and / or acceleration correlation coefficient do not fall within the corresponding available ranges, the current motion state of the axis to be detected is poor. At this time, directly entering the roundness analysis and reverse error calculation will result in a large error in the output matching data. Therefore, at this time, the motion states of the motors of each axis to be detected should be adjusted, and its speed correlation coefficient and acceleration correlation coefficient should be detected again until both the speed correlation coefficient and acceleration correlation coefficient of the axis fall within the corresponding available ranges.

[0077] It should be noted that the present invention embodiment does not limit the method of adjusting the motion states of the motors of each axis to be detected, which may include but are not limited to: adjusting speed feedforward, adjusting acceleration feedforward, reducing following error, etc.

[0078] It should also be noted that too large speed / acceleration feedforward will cause the system to oscillate, and the intuitive manifestation of system oscillation is an increase in following error. Therefore, in an exemplary embodiment, in order to ensure that the following error is controllable, when increasing the correlation coefficient by increasing the feedforward, an error threshold is set at the same time. If the following error is greater than the preset error threshold, the speed feedforward and / or acceleration feedforward are reduced to make the following error less than or equal to the error threshold.

[0079] S204: Detect the roundness and reverse error between each axis to be detected to determine the matching between each axis to be detected.

[0080] Specifically, detecting the roundness and reverse error between each axis to be detected includes: selecting any two axes to be detected; setting a circular machining trajectory based on the selected axes to be detected; collecting the actual machining trajectory when the numerical control machine tool processes along the circular machining trajectory based on the selected axes to be detected; calculating the roundness, reverse clearance, and reverse overshoot of the actual machining trajectory based on the trajectory parameters of the actual machining trajectory.

[0081] It should be noted that the roundness between the above-mentioned axes to be detected refers to the roundness of the actual motion trajectory during the process of any two axes to be detected cooperating to move along a preset circular path based on the motion instructions input by the user terminal. The detection of reverse error refers to the process of calculating the reverse error value between the actual motion path and the preset circular path, which specifically includes but is not limited to: calculating the reverse clearance, calculating the reverse overshoot, calculating the peak radius, etc.

[0082] For example, in a specific embodiment, the motion parameters of multi-axis linkage are first set, including: command speed, command acceleration, and distance, and an NC file for multi-axis linkage is created. The system starts running the NC file, and then a data acquisition thread is started to collect actual speed, actual acceleration, and following error data. At the same time, a Timer thread is started to detect the acquisition status. When the signal indicating the end of acquisition (Flag == 1) is received, the data processing interface is called to process the collected data.

[0083] It should also be noted that roundness analysis requires collecting data of at least one complete circular motion. However, when the CNC machine tool equipment is started and before it stops working, there will be unstable operating conditions. Therefore, when collecting motion data for roundness analysis, the initial startup period and a period of time before it stops working should be avoided.

[0084] In an exemplary embodiment, based on the selected axis to be detected, the motor is commanded to move along a preset circular path for at least two weeks; the initial position and the cut-off position are set, and data of one complete circular motion after the initial position and before the cut-off position is taken for roundness analysis.

[0085] It can be understood that for convenience of setting, the initial position and the cut-off position can be set as the same point on the circular path. That is, before the first passing through this point and after the last passing through this point, data for roundness analysis and calculating reverse error are not collected.

[0086] For example, as Figure 5 shown, it is a schematic diagram of the setting of the initial position and the cut-off position under an embodiment of the present invention. In this embodiment, point A is the starting point of the motion, and point B is set as both the initial position and the cut-off position. Taking the example of moving along the preset circular path for two weeks, starting from point A, the period corresponding to Figure 5 part I when reaching point B for the first time. At this time, the speed and acceleration of the motion are still unstable, so data is not collected. Here, point B is used as the initial position; since it only moves for two weeks, the second time reaching point B is the last time reaching point B. Therefore, after the second time reaching point B, data is not collected. At this time, point B is used as the cut-off position, corresponding to Figure 5 part III; further, the data on the complete circle before the first passing through point B and before the last reaching point B can be used for roundness analysis, corresponding to Figure 5 part II.

[0087] Furthermore, the motion data of the motor is collected, including: collecting the coordinates corresponding to each point on the actual motion path during the motion along the motion direction, the peak radius within the preset analysis interval, the radius of the pre-switching circle pattern, and the radius of the post-switching circle pattern.

[0088] Among them, the preset analysis interval refers to an interval within a certain angular range on the circumference, and its interval size, number of settings, and setting position can all be dynamically adjusted based on different implementation scenarios. The embodiments of the present invention do not limit this.

[0089] Further, the calculation formula for the roundness parameter is:

[0090]

[0091] R max = E max + R; R min = E min + R

[0092] f = R max – R min

[0093] Δb = R g - R b ; Δp = R p - R b

[0094] Among them, E i is the error in the radial direction of each point; the center coordinates of the preset circumference are (X 0 , Y 0 ), and the radius is R; at the i-th measurement point position, the actual moving coordinates are (X i , Y i ); the roundness of the actual moving path and the preset circumference is f; the backlash is Δb, the reverse overshoot is Δp, the radius of the circle pattern before switching is Rb, the radius of the circle pattern after switching is Rg, and the peak radius is Rp.

[0095] For example, as Figure 6 shown, it is a schematic diagram of reverse error detection in an embodiment of the present invention. In this specific embodiment, the analysis interval can be set to 90° ± 7.5°. Among them, within this interval, the maximum value of the distance between the actual moving coordinates and the center is the peak radius. Along the moving direction, the radius of the moving trajectory before the coordinates corresponding to the peak radius is the radius of the circle pattern before switching, and the radius of the moving trajectory after the coordinates corresponding to the peak radius is the radius of the circle pattern after switching.

[0096] Please refer to Figure 3 shown. Based on the same inventive concept as the foregoing multi-axis matching detection method, an embodiment of the present invention provides a multi-axis matching detection system 300, which includes: an acquisition module 301, a calculation module 302, an adjustment module 303, and an analysis module 304.

[0097] Specifically, the acquisition module 301 is configured to acquire the speed, acceleration, and following error values of the motors of each axis to be detected of the numerically controlled machine tool; the calculation module 302 is configured to calculate the speed correlation coefficient and the acceleration correlation coefficient of the motors of each axis to be detected based on the speed, acceleration, and following error values of the motors of each axis to be detected; the adjustment module 303 is configured to adjust the motion states of the motors of each axis to be detected based on the speed correlation coefficient and the acceleration correlation coefficient, so that the motion performance of the motors of each axis to be detected meets the preset conditions; the analysis module 304 is configured to detect the roundness and reverse error between each axis to be detected to determine the matching between each axis to be detected.

[0098] It should be noted that the analysis module 304 is further configured to select any two axes to be detected; based on the selected axes to be detected, set a circular machining trajectory; acquire the actual machining trajectory when the numerically controlled machine tool performs machining along the circular machining trajectory based on the selected axes to be detected; calculate the roundness, backlash, and reverse overshoot of the actual machining trajectory based on the trajectory parameters of the actual machining trajectory.

[0099] The analysis module 304 is further configured to acquire the coordinates corresponding to each point of the actual motion path during the motion along the motion direction; within a preset analysis interval, acquire the maximum value of the distance between the actual motion coordinates and the center of the circle as the peak radius, and along the motion direction, the radius of the motion trajectory before the coordinate corresponding to the peak radius is the radius of the pre-switching circle pattern, and the radius of the motion trajectory after the coordinate corresponding to the peak radius is the radius of the post-switching circle pattern.

[0100] The adjustment module 303 is further configured to, when the following error is greater than a preset error threshold, reduce the speed feedforward and / or acceleration feedforward to make the following error less than or equal to the error threshold.

[0101] It should also be noted that the multi-axis matching detection system 300 further includes an initialization module, which is configured to, based on the selected axes to be detected, make the motor perform circular motion along a preset circumference for at least two weeks; set an initial point and a cut-off point, and take the data of a complete circle of motion after the initial point and before the cut-off point for roundness analysis.

[0102] Please refer to Figure 4As shown, an embodiment of the present invention further provides an electronic device 400, which includes at least one processor 401, a memory 402 (such as a non-volatile memory), a memory 403, and a communication interface 404, and at least one processor 401, the memory 402, the memory 403, and the communication interface 404 are connected together via a bus 405. The at least one processor 401 is configured to call at least one program instruction stored or encoded in the memory 402, so that the at least one processor 401 performs various operations and functions of the multi-axis matching detection method described in various embodiments of this specification.

[0103] In the embodiments of this specification, the electronic device 400 may include, but is not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile electronic devices, smart phones, tablet computers, cellular phones, personal digital assistants (PDAs), handheld devices, messaging devices, wearable electronic devices, consumer electronic devices, and so on.

[0104] An embodiment of the present invention further provides a computer-readable medium, on which computer-executable instructions are carried. When the computer-executable instructions are executed by a processor, they can be used to implement various operations and functions of the multi-axis matching detection method described in various embodiments of this specification.

[0105] The computer-readable medium in the present invention may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0106] In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0107] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses, systems, and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0109] The foregoing description of specific exemplary embodiments of the present invention is for purposes of illustration and exemplification. These descriptions are not intended to limit the present invention to the precise forms disclosed, and it is obvious that many changes and variations are possible in light of the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical applications, so that those skilled in the art can implement and utilize the various different exemplary embodiments of the present invention, as well as various different selections and changes. The scope of the present invention is intended to be defined by the claims and their equivalents.

Claims

1. A multi-axis matching detection method, applied to a multi-axis CNC machine tool, characterized in that: include: Collect the speed, acceleration and following error value of the motor of each axis to be tested of the CNC machine tool; Based on the speed, acceleration and following error value of the motor of each axis to be detected, the speed correlation coefficient and acceleration correlation coefficient of the motor of each axis to be detected are calculated; Based on the speed correlation coefficient and the acceleration correlation coefficient, adjusting the motion state of the motor of each axis to be detected so that the motion performance of the motor of each axis to be detected meets the preset conditions; The roundness and reverse error between the axes to be tested are tested to determine the matching between the axes to be tested. The calculation of the reverse error includes: the calculation of the reverse clearance, the calculation of the reverse overshoot, and the calculation of the peak radius; The detection of the roundness and reverse error between the axes to be detected includes: selecting any two axes to be detected; based on the selected axes to be detected, making the motor move along a preset circle for at least two times; setting an initial point and a cutoff point, taking data of a complete circle after the initial point and before the cutoff point for roundness analysis, and setting a circular processing trajectory; collecting the actual processing trajectory of the CNC machine tool when processing along the circular processing trajectory based on the selected axes to be detected; and calculating the roundness, reverse clearance, and reverse overshoot of the actual processing trajectory based on the trajectory parameters of the actual processing trajectory; The detection of the roundness and reverse error between each axis to be detected also includes: collecting the coordinates corresponding to each point on the actual motion path during the motion process along the motion direction; within a preset analysis interval, the maximum value of the distance between the actual motion coordinates and the center of the circle is collected as the peak radius, and along the motion direction, the radius of the motion trajectory before the coordinate corresponding to the peak radius is the radius of the circular spectrum before commutation, and the radius of the motion trajectory after the coordinate corresponding to the peak radius is the radius of the circular spectrum after commutation.

2. The multi-axis matching detection method according to claim 1, characterized in that: The calculation formulas for the speed correlation coefficient and acceleration correlation coefficient of the motors of the axes to be detected are: Wherein, VelCorr is the velocity correlation coefficient, AccCorr is the acceleration correlation coefficient, N is the total number of collected data, Veli is the velocity of the i-th point, FEi is the following error of the i-th point, and Acci is the acceleration of the i-th point.

3. The multi-axis matching detection method according to claim 1, characterized in that: The roundness parameter calculation formula is: R max =E max +R;R min =E min +R f=R max –R min Δb=R g -R b ;Δp=R p -R b Among them, E i is the error in the radial direction of each point; the center coordinates of the preset circle are (X0, Y0), and the radius is R; at the i-th measurement point, the actual motion coordinates are (X i , Y i ); the circularity of the actual motion path and the preset circle is f; the reverse clearance is Δb, the reverse overshoot is Δp, the radius of the circular spectrum before commutation is Rb, the radius of the circular spectrum after commutation is Rg, and the peak radius is Rp.

4. The multi-axis matching detection method according to claim 1, characterized in that: The method further comprises: If the following error is greater than a preset error threshold, the velocity feedforward and / or acceleration feedforward is reduced to make the following error less than or equal to the error threshold.

5. A multi-axis matching detection system, characterized in that: include: The acquisition module is used to acquire the speed, acceleration and following error value of the motor of each axis to be detected of the CNC machine tool; A calculation module, used for calculating the speed correlation coefficient and acceleration correlation coefficient of the motor of each axis to be detected based on the speed, acceleration and following error value of the motor of each axis to be detected; An adjustment module, used for adjusting the motion state of the motor of each axis to be detected based on the speed correlation coefficient and the acceleration correlation coefficient, so that the motion performance of the motor of each axis to be detected meets the preset conditions; An analysis module is used to detect the roundness and reverse error between the axes to be detected, so as to determine the matching between the axes to be detected; The detection of the roundness and reverse error between the axes to be detected includes: selecting any two axes to be detected; based on the selected axes to be detected, making the motor move along a preset circle for at least two times; setting an initial point and a cutoff point, taking data of a complete circle after the initial point and before the cutoff point for roundness analysis, and setting a circular processing trajectory; collecting the actual processing trajectory of the CNC machine tool when processing along the circular processing trajectory based on the selected axes to be detected; and calculating the roundness, reverse clearance, and reverse overshoot of the actual processing trajectory based on the trajectory parameters of the actual processing trajectory; The detection of the roundness and reverse error between each axis to be detected also includes: collecting the coordinates corresponding to each point on the actual motion path during the motion process along the motion direction; within a preset analysis interval, the maximum value of the distance between the actual motion coordinates and the center of the circle is collected as the peak radius, and along the motion direction, the radius of the motion trajectory before the coordinate corresponding to the peak radius is the radius of the circular spectrum before commutation, and the radius of the motion trajectory after the coordinate corresponding to the peak radius is the radius of the circular spectrum after commutation.

6. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the multi-axis matching detection method according to any one of claims 1 to 4 by executing the computer instructions.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the multi-axis compatibility detection method according to any one of claims 1 to 4.

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