Machine tool chatter identification and suppression method, system, electronic device, and readable medium

Abstract

The application provides a machine tool chatter identification and suppression method, system, electronic device and readable medium. The machine tool chatter identification method comprises: acquiring characteristic information, the characteristic information comprising vibration time domain amplitudes corresponding to a machine tool feed shaft grating ruler displacement signal, a sampling time of each vibration time domain amplitude, a sampling frequency of the displacement signal, a main shaft rotation frequency and a gear tooth passing frequency; inputting the characteristic information into a chatter identification model to obtain a machining state of the machine tool. The application uses the grating ruler of the machine tool itself to collect displacement signals, and can obtain vibration information in the machining process online without any external sensor, thereby reducing hardware cost and system integration difficulty. Through the characteristic information and the chatter identification model, the chatter state and its incubation stage are identified in real time under the condition that the machining is not interrupted. After the machine tool is identified to be in the chatter or chatter incubation state, the main shaft rotation speed is adjusted to a target stable rotation speed, so that the chatter is actively suppressed without stopping the operation of the machine tool.

Description

Technical Field

[0001] This invention belongs to the field of CNC machine tool technology, specifically relating to a machine tool chatter identification and suppression method, system, electronic device, and readable medium. Background Technology

[0002] Machining vibration is one of the main factors affecting the machining efficiency, tool life, workpiece surface quality, and form and position accuracy of CNC machine tools. Among various machining vibrations, self-excited chatter is the most harmful, therefore it must be effectively suppressed during the machining process.

[0003] Currently, methods for identifying and suppressing machine tool chatter are mainly divided into two categories: offline methods and online methods.

[0004] Offline methods typically begin by acquiring modal parameters at the tool tip using accelerometers and a force hammer, followed by calibrating the cutting force coefficient using a force gauge. Based on this, a stability lobe diagram is calculated using a machining dynamics model. Finally, appropriate cutting parameters (such as depth of cut and spindle speed) are selected according to the lobe diagram to avoid chatter instability regions and achieve chatter-free machining. However, this approach has several significant drawbacks: firstly, it requires expensive dynamic testing equipment, resulting in high initial investment costs; secondly, modal parameter extraction and stability analysis require operators with deep professional knowledge and extensive experimental experience, which is difficult for ordinary field process personnel to handle; and thirdly, the experimental and calculation processes are time-consuming, reducing production efficiency. More importantly, the tool tip dynamic parameters measured offline often differ from the actual online machining conditions, meaning that theoretically calculated stability parameters cannot completely prevent chatter.

[0005] Online methods acquire machining signals in real time using external devices such as accelerometers, microphones, force sensors, or torque sensors. Once chatter symptoms are detected, passive measures such as stopping the machine or reducing speed are taken to suppress its further development. However, these methods rely on a large number of external sensors and analysis devices, which not only consumes too many external interfaces of the CNC system but also makes deep integration with the machine tool body difficult. In addition, stopping or reducing speed will interrupt the normal machining process and significantly reduce production efficiency.

[0006] In summary, offline methods are limited by equipment costs, professional expertise, time consumption, and the difference between offline and online states, making it difficult to flexibly and accurately suppress chatter in actual machining. Online methods, on the other hand, rely on numerous external sensors, have many interfaces, low integration, and experience reduced production efficiency due to downtime, thus failing to meet the requirements of high-efficiency, real-time machining. Neither of these methods can achieve effective identification and suppression of machine tool chatter without external sensors or interruption of the machining process. Summary of the Invention

[0007] The purpose of this invention is to provide a method, system, electronic device, and readable medium for identifying and suppressing machine tool chatter, in order to solve the problems described in the background art.

[0008] In a first aspect, a specific embodiment of the present invention provides a machine tool chatter identification method, the machine tool chatter identification method comprising: acquiring feature information, the feature information including the vibration time-domain amplitude corresponding to the displacement signal of the machine tool feed axis grating ruler, the sampling time of each vibration time-domain amplitude, the sampling frequency of the displacement signal, the spindle rotation frequency and the cutting tooth passing frequency; inputting the feature information into a chatter identification model to obtain the machining state of the machine tool.

[0009] In one or more embodiments of the present invention, the acquisition of feature information includes: preprocessing the displacement signal of the machine tool feed axis grating ruler to obtain a preprocessed displacement signal, and extracting the corresponding vibration time-domain amplitude from the preprocessed displacement signal.

[0010] In one or more embodiments of the present invention, the step of acquiring feature information includes: acquiring tool information and spindle speed, and calculating spindle frequency and tooth pass frequency based on the tool information and spindle speed.

[0011] In one or more embodiments of the present invention, the flutter identification model is an online identification model constructed based on a clustering algorithm.

[0012] In one or more embodiments of the present invention, the step of inputting the feature information into the chatter identification model to obtain the machining state of the machine tool includes: using the clustering algorithm to perform cluster decomposition on the time-domain amplitude data signal in the feature information to obtain a number of signal segments, wherein the signal segments have feature values ​​for representing frequency; identifying chatter signal segments in each of the signal segments; calculating the ratio of the total energy of all chatter signal segments to the total energy of the displacement signal; determining whether the ratio is greater than a preset threshold; if so, determining that the machining state of the machine tool is a chatter state or a chatter incubation state.

[0013] In one or more embodiments of the present invention, identifying the chatter signal segment in each of the signal segments includes: determining, based on the feature value, whether the frequency of the signal segment conforms to an integer multiple ratio with the spindle rotation frequency or the pass frequency of the cutting teeth; if not, determining the corresponding signal segment as a chatter signal segment.

[0014] Secondly, a specific embodiment of the present invention provides a machine tool chatter suppression method, the machine tool chatter suppression method comprising: determining the machining state of the machine tool according to the above-mentioned machine tool chatter identification method; when the machining state of the machine tool is a chatter state or a chatter incubation state, determining a target stable speed based on the chatter frequency of the machine tool, and adjusting the spindle speed to the target stable speed.

[0015] In one or more embodiments of the present invention, determining the target stable speed based on the chatter frequency of the machine tool includes: obtaining a set of stable speeds based on the chatter frequency and stable speed solution model of the machine tool; selecting the stable speed closest to the current spindle speed of the machine tool from the set of stable speeds and determining it as the target stable speed.

[0016] In one or more embodiments of the present invention, the stable rotational speed solution model is n0 = (60 * fc) / k; where n0 is the stable rotational speed / rpm, k is a positive integer harmonic coefficient, and fc is the flutter frequency / Hz.

[0017] Thirdly, a specific embodiment of the present invention provides a machine tool chatter identification system, which includes an information acquisition module and a chatter identification module. The information acquisition module is configured to acquire feature information, including the vibration time-domain amplitude corresponding to the displacement signal of the machine tool feed axis grating ruler, the sampling time of each vibration time-domain amplitude, the sampling frequency of the displacement signal, the spindle rotation frequency, and the tool tooth passage frequency. The chatter identification module is configured to receive the feature information and input it into a chatter identification model to obtain the machining state of the machine tool.

[0018] Fourthly, a specific embodiment of the present invention provides a machine tool chatter suppression system, which includes the aforementioned machine tool chatter identification system. The machine tool chatter suppression system further includes a speed calculation module and a spindle control module. The speed calculation module is configured to determine a target stable speed based on the machine tool's chatter frequency when the machine tool is in a chatter state or a chatter incubation state. The spindle control module is configured to adjust the spindle speed to the target stable speed.

[0019] Fifthly, a specific embodiment of the present invention provides an electronic device, the electronic device including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the above-mentioned machine tool chatter identification method or the above-mentioned machine tool chatter suppression method.

[0020] In a sixth aspect, a specific embodiment of the present invention provides a computer-readable medium storing computer instructions for causing a computer to execute the above-described machine tool chatter identification method or the above-described machine tool chatter suppression method.

[0021] Compared with existing technologies, this invention directly utilizes the optical encoders equipped on the machine tool's feed axes to collect displacement signals, enabling online acquisition of vibration information during machining without the need for any external sensors. This significantly reduces hardware costs and system integration complexity. By extracting feature information from the displacement signals and inputting it into a chatter identification model, chatter states and their incubation stages can be identified in real time without interrupting machining. This avoids the delays and operational barriers associated with traditional methods that rely on offline modal experiments or complex spectral analysis. Once chatter or its incubation stage is identified, the optimal target stable speed is automatically calculated based on a stable speed solution model, and the spindle speed is smoothly adjusted to the target stable speed, thereby actively suppressing chatter without stopping the machine tool. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a machine tool chatter identification and suppression method in one embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the structure of a machine tool chatter identification and suppression system in one embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0027] In the description of this invention, it should be understood that the terms "top", "bottom", "upper", "lower", "front", "rear", "left", "right", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0028] Furthermore, the term "first" is used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, features defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] In one embodiment, reference is made to Figure 1 As shown, the present invention provides a machine tool chatter identification method, which includes the following steps S1 and S2.

[0030] Step S1: Obtain feature information.

[0031] Specifically, in step S1, the feature information includes the vibration time-domain amplitude corresponding to the displacement signal of the machine tool feed axis grating ruler, the sampling time of each vibration time-domain amplitude, the sampling frequency of the displacement signal, the spindle rotation frequency, and the tooth passing frequency.

[0032] When the machine tool performs a cutting task, the linear encoder moves with the feed axis and continuously outputs displacement signals reflecting position changes. The machine tool's CNC system acquires the displacement signals output by the linear encoder through an internal high-speed interface. The acquired displacement signals are time series, denoted as x(t), y(t), and z(t), corresponding to the instantaneous displacement values ​​of each feed axis (X-axis, Y-axis, and Z-axis), respectively. The entire displacement signal acquisition process does not require external accelerometers or force sensors; it utilizes the machine tool's own linear encoder and CNC system to acquire displacement signals in real time and continuously during machining.

[0033] Furthermore, during machine tool operation, displacement signals are acquired at a sampling frequency of not less than 20kHz. This sampling frequency is sufficient to cover the chatter characteristic frequency band (100Hz~2000Hz) and meets the sampling requirements in most cases.

[0034] After obtaining the displacement signal, it is first preprocessed to obtain a preprocessed displacement signal that accurately reflects the chatter characteristics. Preprocessing includes, but is not limited to, at least one of detrending, bandpass filtering, and normalization. Detrending eliminates low-frequency trend terms caused by large-scale machine tool movement or temperature drift. Bandpass filtering preserves components within the chatter-sensitive frequency band while suppressing low-frequency forced vibration and high-frequency electrical noise. Normalization adjusts the filtered signal to a normalized sequence with zero mean and a variance of 1, eliminating the influence of signal amplitude scale differences under different processing conditions on subsequent steps.

[0035] After obtaining the preprocessed displacement signal, the vibration time-domain amplitude sequence and the sampling time corresponding to each amplitude are extracted from it. The vibration time-domain amplitude is the instantaneous value of the preprocessed displacement signal at each sampling point, in millimeters or the position feedback resolution of a grating ruler. The sampling time is in seconds and serves as the time label for each amplitude.

[0036] Simultaneously, the spindle speed and cutter tooth pass-through frequency are obtained from the CNC system. These frequencies can typically be calculated from the current spindle speed and the number of cutter teeth. The spindle speed is equal to one-sixtieth of the spindle rotation speed, while the cutter tooth pass-through frequency is equal to the spindle speed multiplied by the number of cutter teeth. Since the spindle speed and number of cutter teeth can usually be directly obtained from the CNC system, and the calculation of spindle speed and cutter tooth pass-through frequency can generally be completed within the CNC system itself, the operator can directly obtain these frequencies from the CNC system.

[0037] Furthermore, feature information can be constructed in the form of feature vectors.

[0038] Step S2: Input the feature information into the chatter identification model to obtain the machining status of the machine tool.

[0039] Specifically, in step S2, the feature information obtained in step S1 is input into the flutter identification model. The flutter identification model includes, but is not limited to, an online identification model pre-built based on a clustering algorithm, which performs the following processing on the input feature information.

[0040] First, a clustering algorithm is used to decompose the time-domain amplitude data signal in the feature information into several signal segments, each with a feature value representing its frequency. Specifically, the basic principle of the clustering algorithm is to group data points with similar waveform shapes into the same category, while different categories correspond to significantly different local waveform features. By iteratively calculating the distance between each data point and the category center and updating the center, the algorithm can automatically segment the original signal into several signal segments with different feature values. Each signal segment has a consistent waveform shape, corresponding to a specific excitation frequency component; the differences in feature values ​​between different signal segments essentially reflect the different frequencies they dominate. In other words, segments with different feature values ​​correspond to different frequency components. It should be noted that this decomposition is performed directly in the time domain, without the need for Fourier transform or complex peak searches in the frequency domain. The algorithm relies solely on the morphological similarity of the signal waveforms to separate different frequency components, thus avoiding the accuracy loss caused by window selection, spectral leakage, and other issues in traditional frequency domain analysis.

[0041] As a non-limiting example, a flutter identification model can be constructed using the K-means clustering machine learning clustering algorithm. This type of algorithm is computationally efficient, easy to implement online, and can complete clustering decomposition without requiring a large number of labeled samples.

[0042] Compared to the traditional method of first performing a Fast Fourier Transform (FFT) on the signal and then searching for flutter frequencies in the spectrum, the clustering decomposition method used in this step is more computationally efficient. The clustering algorithm directly processes the original amplitude sequence in the time domain, with a computational load far lower than that of the FFT and its subsequent spectral analysis. Furthermore, the signal decomposition accuracy is higher. The clustering algorithm segments based on the local waveform morphology, enabling more precise differentiation of excitation components from different sources, avoiding the dependence of the FFT on signal stationarity and the frequency aliasing problem caused by spectral resolution limitations. These advantages make this method more suitable for real-time operation within CNC systems.

[0043] After obtaining several signal segments, the chatter signal segments within each segment are identified. Specifically, the frequency of each signal segment is extracted (e.g., its dominant frequency is estimated by calculating the average time interval between adjacent peaks within the segment), and this frequency is compared with the known normal cutting excitation frequency.

[0044] During normal cutting, vibration energy is mainly concentrated in integer multiples of the spindle rotation frequency and the cutting tooth passing frequency. Chatter, as a self-excited vibration, typically does not have a simple integer multiple ratio with these frequencies. Therefore, it can be identified by whether the frequency ratio is an integer multiple. Specifically, the normal cutting excitation frequencies include the spindle rotation frequency and the cutting tooth passing frequency. If the frequency of a signal segment is not an integer multiple of either the spindle rotation frequency or the cutting tooth passing frequency, then the signal segment is determined to be a chatter signal segment. Conversely, if the frequency of a signal segment is an integer multiple of either the spindle rotation frequency or the cutting tooth passing frequency, then the signal segment is determined not to be a chatter signal segment.

[0045] It should be noted that in practical engineering applications, due to factors such as the accuracy of speed reading, control errors, and signal frequency estimation deviations, the calculated frequency ratio is often difficult to be strictly equal to an integer. Therefore, there is usually an error tolerance range when calculating the frequency ratio. When the relative error between the calculated frequency ratio and the theoretical integer multiple frequency is within ±2%, or the absolute error is within 10Hz, the frequency ratio is still considered to satisfy the integer multiple ratio relationship.

[0046] As a non-limiting example, assume the current spindle frequency is 100Hz. If the measured frequency of a certain signal segment is 398.5Hz, its ratio to the spindle frequency of 100Hz is approximately 3.985, with a relative error of 1.5%, which is within the ±2% tolerance range. Therefore, this segment's frequency is still considered proportional to the spindle frequency and is not identified as a chatter signal segment. If the measured frequency of another signal segment is 430Hz, the ratio to the spindle frequency is 4.3, with a relative error of 30%, which exceeds the ±2% tolerance range. In this case, the segment's frequency is determined to be disproportionate to the spindle frequency, and thus it is identified as a chatter signal segment.

[0047] Additionally, it should be noted that since the frequency of the cutting teeth is equal to the spindle rotation frequency multiplied by the number of cutting teeth, in practical applications, based on this mathematical relationship, only the frequency of the signal segment needs to be compared with the spindle rotation frequency. This simplifies the identification process, reduces computational complexity, and does not affect the accuracy of the identification.

[0048] After identifying the flutter signal segments, the ratio of the total energy of all flutter signal segments to the total energy of the displacement signal is calculated. Specifically, the energy of each flutter signal segment is obtained by summing the squares of the amplitudes of all sampling points within that segment; the total energy of all flutter signal segments is the sum of the energies of each segment. The total energy of the displacement signal is the sum of the squares of the amplitudes of all sampling points within the entire time window. This ratio reflects the proportion of flutter energy in the total vibration energy and serves as the basis for judging the severity of flutter.

[0049] After calculating the ratio of the total energy of all chatter signal segments to the total energy of the displacement signal, this ratio is compared with a preset threshold. If the ratio is greater than the preset threshold, the machine tool's machining state is determined to be either a chatter state or a chatter incubation state. If the ratio is less than or equal to the preset threshold, the machine tool's machining state is determined to be a stable state. The preset threshold can be pre-set based on actual machining conditions and experience. As a non-limiting example, the preset threshold can be specifically set to 0.2.

[0050] Understandably, the chatter incubation stage refers to the transitional phase from stable cutting to full chatter development. During this stage, weak and unstable vibrations appear in the cutting process, but have not yet reached a level that can significantly affect machining quality or cause tool damage.

[0051] In one embodiment, reference is made to Figure 1 As shown, the present invention provides a machine tool chatter suppression method, which includes the above-mentioned steps S1 to S2, and further includes the following steps S31 to S33.

[0052] Step S31: Determine the machining status of the machine tool. Is the machine tool in a chattering state or a chattering incubation state?

[0053] Step S32: If the machine tool is in a chattering state or a chattering incubation state, then determine the target stable speed based on the machine tool's chattering frequency, and adjust the spindle speed to the target stable speed.

[0054] Specifically, if the machine tool is in a chattering state or a chattering incubation state, active vibration suppression control is triggered. The core of vibration suppression control lies in adjusting the spindle speed to a target stable speed that can suppress chatter. This control method is based on the theory of regenerative chatter stability.

[0055] According to the theory of regenerative chatter stability, there is a fundamental relationship between the limiting stable cutting depth of the cutting system and the frequency response function: b = -1 / (2Kμ·Re[G]).

[0056] Where b is the maximum stable depth of cut, K is the cutting coefficient, μ is the direction coefficient, and Re[G] is the real part of the system's frequency response function. When Re[G] approaches zero, the limiting depth of cut approaches infinity, and the system can theoretically withstand any depth of cut without chattering, i.e., it reaches the highest stable state. At this time, the regeneration phase difference ε satisfies ε=2π-2arctan(Re[G] / Im[G]). When Re[G] approaches 0, ε approaches 2π. This means that the vibration of the current cutting tooth is completely in phase with the ripples left by the previous cutting tooth, the chip thickness no longer changes with vibration, the regeneration effect disappears, and chattering is suppressed. This is the essence of the formation of the peak values ​​(most stable points) of each lobe in the stability lobe diagram.

[0057] Substituting ε=2π into the synchronization relationship between chatter frequency and tooth passage frequency, we obtain fc=(n·m) / [60(N+ ε / 2π)], where fc is the chatter frequency, n is the spindle speed, m is the number of tool teeth, and N is the integer number of vibration cycles experienced between adjacent tool teeth. Substituting ε=2π into the above formula, we get fc = (n·m) / [60(N+1)]. After rearranging this formula, we get n = [60 fc(N+1)] / m. Let m / (N+1) equal the harmonic coefficient k, then we get n= (60fc) / k.

[0058] The above derivation shows that when the spindle speed n and the chatter frequency fc satisfy the above relationship, the system is at the peak position of the stability lobe diagram, and theoretically, the optimal chatter suppression effect can be obtained.

[0059] Based on the above theory, a stable rotational speed solution model is constructed in this step. The calculation formula is n0 = (60 × fc) / k, where n0 is the stable rotational speed, fc is the flutter main frequency extracted from the flutter signal segment, and k is the frequency multiplication factor, which is a positive integer.

[0060] By sequentially taking different integer values ​​of k=1, 2, 3, etc., the corresponding stable speed n0 is calculated until the stable speed n0 is lower than the minimum allowable speed of the machine tool or higher than the maximum speed, thus obtaining a target stable speed set consisting of several stable speeds n0. After obtaining the target stable speed set, each stable speed is compared with the current actual speed of the spindle, and the stable speed with the smallest difference from the current speed is selected as the final target stable speed. Selecting the stable speed n0 closest to the current speed as the optimal speed can reduce the amplitude of speed adjustment, avoid the impact of sudden speed changes on the cutting process, enter a stable cutting state in the shortest time, and thus suppress chatter to the greatest extent.

[0061] After determining the target stable speed, the CNC system sends a speed adjustment command to the spindle motor through the spindle drive interface to adjust the spindle speed to the target stable speed in a smooth gradient.

[0062] After the speed adjustment is completed, you can return to step S1 to continue collecting displacement signals and perform a new round of chatter identification to form closed-loop control and ensure the long-term stability of the machining process.

[0063] In step S33, if the machine tool's machining state is neither a chattering state nor a chattering incubation state, but a stable state, it indicates that the current machining parameters of the machine tool can maintain stable cutting, and under normal circumstances, there is no need to adjust the spindle speed. Therefore, the spindle can continue to run at the current speed, while returning to step S1 to continuously collect and monitor displacement signals to ensure timely response when machining conditions change.

[0064] In one embodiment, reference is made to Figure 2 As shown, this invention provides a machine tool chatter identification system, which is used to implement the aforementioned machine tool chatter identification method. Specifically, the system includes an information acquisition module 11 and a chatter identification module 12. The information acquisition module 11 is configured to acquire the aforementioned feature information, including the vibration time-domain amplitude corresponding to the displacement signal of the machine tool feed axis grating ruler, the sampling time of each vibration time-domain amplitude, the sampling frequency of the displacement signal, the spindle rotation frequency, and the tool tooth passage frequency. After receiving the feature information, the chatter identification module 12 inputs it into a chatter identification model constructed based on a clustering algorithm. It identifies chatter signal segments through cluster decomposition and calculates the chatter energy ratio, thereby determining whether the machining state is stable, chatter incubation, or chattering.

[0065] In one embodiment, reference is made to Figure 2As shown, this invention provides a machine tool chatter suppression system. This system includes the aforementioned machine tool chatter identification system, and further includes a speed calculation module 13 and a spindle control module 14. When chatter or a chatter incubation state is detected, the speed calculation module 13 calculates a set of stable speeds and selects the stable speed closest to the current spindle speed as the target stable speed. After obtaining the target stable speed, the spindle control module 14 sends this target stable speed to the CNC system. The CNC system adjusts the spindle speed to the target stable speed with a smooth gradient, and continuously monitors the chatter energy during the adjustment process, forming a closed-loop control until the chatter is effectively suppressed.

[0066] In one embodiment, reference is made to Figure 3 As shown, the present invention provides an electronic device including at least one processor 21, a memory 22 (e.g., non-volatile memory), a main memory 23, and a communication interface 24, wherein the at least one processor, the memory, the main memory, and the communication interface are connected together via an internal bus 25. The at least one processor 21 is configured to invoke at least one program instruction stored or encoded in the memory 22 to cause the at least one processor 21 to perform various operations and functions of the machine tool chatter identification method or machine tool chatter suppression method described in the various embodiments of this specification.

[0067] In embodiments of the present invention, electronic devices may include, but are not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile electronic devices, smartphones, tablet computers, cellular phones, personal digital assistants (PDAs), handheld devices, messaging devices, wearable electronic devices, consumer electronic devices, etc.

[0068] An embodiment of the present invention also provides a computer-readable medium that may have instructions (i.e., the elements implemented in software as described above), which, when executed by a machine, cause the machine to perform the above-described combinations in the various embodiments of this specification. Figures 1 to 3 The various operations and functions described. Specifically, a system or apparatus equipped with a readable medium on which software program code implementing the functions of any of the embodiments described above is stored, and which enables the computer or processor of the system or apparatus to read and execute the instructions stored in the readable medium.

[0069] The computer-readable medium in this invention can be a computer-readable signal medium, a computer-readable medium, or any combination thereof. The computer-readable medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, the computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0070] In this invention, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0072] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

[0074] It will be apparent to those skilled in the art that this disclosure is not limited to the details of the exemplary embodiments described above, and that this disclosure can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of this disclosure is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this disclosure. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0075] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for identifying machine tool chatter, characterized in that, The machine tool chatter identification method includes: Acquire feature information, which includes the vibration time-domain amplitude corresponding to the displacement signal of the machine tool feed axis grating ruler, the sampling time of each vibration time-domain amplitude, the sampling frequency of the displacement signal, the spindle speed and the tooth passing frequency; The feature information is input into the chatter identification model to obtain the machining state of the machine tool.

2. The machine tool chatter identification method according to claim 1, characterized in that, The acquisition of feature information includes: preprocessing the displacement signal of the machine tool feed axis grating scale to obtain a preprocessed displacement signal, and extracting the corresponding vibration time-domain amplitude from the preprocessed displacement signal; and / or, The acquisition of feature information includes: acquiring tool information and spindle speed, and calculating spindle frequency and tooth pass frequency based on the tool information and spindle speed.

3. The machine tool chatter identification method according to claim 1, characterized in that, The flutter identification model is an online identification model built based on a clustering algorithm.

4. The machine tool chatter identification method according to claim 3, characterized in that, The step of inputting the feature information into the chatter identification model to obtain the machining state of the machine tool includes: The clustering algorithm described above is used to cluster and decompose the data signal of time-domain amplitude in the feature information to obtain several signal segments, each of which has a feature value for representing frequency. Identify the flutter signal segments in each of the aforementioned signal segments; Calculate the ratio of the total energy of all flutter signal segments to the total energy of the displacement signal; Determine whether the ratio is greater than a preset threshold; If so, the machine tool's processing state is determined to be either a chattering state or a chattering incubation state.

5. The machine tool chatter identification method according to claim 4, characterized in that, The identification of flutter signal segments in each of the signal segments includes: Based on the aforementioned characteristic values, determine whether the frequency of the signal segment conforms to an integer multiple proportional relationship with the spindle rotation frequency or the pass frequency of the cutting teeth; If not, the corresponding signal segment is identified as a flutter signal segment.

6. A method for suppressing machine tool chatter, characterized in that, The machine tool chatter suppression method includes: The machining state of the machine tool is determined according to the machine tool chatter identification method as described in any one of claims 1 to 5; When the machine tool is in a chattering state or a chattering incubation state, the target stable speed is determined based on the chattering frequency of the machine tool, and the spindle speed is adjusted to the target stable speed.

7. The machine tool chatter suppression method according to claim 6, characterized in that, The determination of the target stable rotational speed based on the machine tool chatter frequency includes: The set of stable speeds is obtained by solving the model based on the chatter frequency and stable speed of the machine tool; The target stable speed is determined by selecting the stable speed that is closest to the current spindle speed of the machine tool from the set of stable speeds.

8. The machine tool chatter suppression method according to claim 7, characterized in that, The stable rotational speed solution model is n0 = (60 * fc) / k; Where n0 is the stable rotational speed in rpm, k is a positive integer multiplication factor, and fc is the flutter frequency in Hz.

9. A machine tool chatter identification system, characterized in that, The machine tool chatter identification system includes: The information acquisition module is configured to acquire feature information, which includes the vibration time-domain amplitude corresponding to the displacement signal of the machine tool feed axis grating ruler, the sampling time of each vibration time-domain amplitude, the sampling frequency of the displacement signal, the spindle speed and the tooth passing frequency; The chatter identification module is configured to receive the feature information and input the feature information into the chatter identification model to obtain the machining state of the machine tool.

10. A machine tool chatter suppression system, characterized in that, The machine tool chatter suppression system includes the machine tool chatter identification system as described in claim 9, and the machine tool chatter suppression system further includes: The rotational speed calculation module is configured to determine the target stable rotational speed based on the machine tool's chatter frequency when the machine tool is in a chattering state or a chattering incubation state during machining. The spindle control module is configured to adjust the spindle speed to the target stable speed.

11. An electronic device, characterized in that, The electronic device includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the machine tool chatter identification method according to any one of claims 1 to 5, or the machine tool chatter suppression method according to any one of claims 6 to 8.

12. A computer-readable medium, characterized in that, The computer-readable medium stores computer instructions for causing a computer to perform the machine tool chatter identification method according to any one of claims 1 to 5, or the machine tool chatter suppression method according to any one of claims 6 to 8.

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