A heavy-load robot milling chatter detection method, device, medium and product

Abstract

The application discloses a heavy-load robot milling chatter detection method and device, medium and product, and relates to the technical field of robot milling processing. The method comprises the following steps: acquiring a real-time acceleration signal at a current moment in a heavy-load robot milling process; determining a characteristic signal by using a variational mode decomposition method according to the real-time acceleration signal; determining a Lyapunov exponent of the characteristic signal as a characteristic Lyapunov exponent; determining a local frequency spectrum broken line similarity of the characteristic signal as a characteristic local frequency spectrum broken line similarity; and determining a state type at the current moment according to the characteristic Lyapunov exponent and the characteristic local frequency spectrum broken line similarity. The application can improve the precision and efficiency of heavy-load robot milling chatter detection by determining the characteristic signal.

Description

Technical Field

[0001] This invention relates to the field of robotic milling technology, and in particular to a method, device, medium, and product for detecting chatter in heavy-duty robotic milling. Background Technology

[0002] In recent years, robotic milling technology has been increasingly applied to the machining of components in aerospace and other fields. Aerospace components are complex in shape and diverse in structure, especially thin-walled parts which have poor rigidity and weak strength, yet require high-quality machining. Therefore, chatter stability research is indispensable when performing precision machining on these components. However, current robotic milling chatter stability modeling theories are not perfect. Therefore, chatter identification based on effective signal processing algorithms is an important approach to chatter stability research. Acceleration signals acquired during machining often contain multiple signal components, such as the machine's main frequency and its harmonics, chatter components, spindle idling components, and random noise components. In practical signal analysis, it is necessary to quickly and accurately separate the effective chatter components to observe the signal state. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, medium, and product for detecting chatter in heavy-duty robot milling, which can improve the accuracy and efficiency of chatter detection in heavy-duty robot milling.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A method for detecting chatter during heavy-duty robot milling includes:

[0006] Acquire the real-time acceleration signal at the current moment during the milling process of the heavy-duty robot;

[0007] Based on the real-time acceleration signal, the characteristic signal is determined using the variational mode decomposition method.

[0008] The Lyapunov exponent of the characteristic signal is determined to be the characteristic Lyapunov exponent;

[0009] The local spectral piecewise similarity of the feature signal is determined as the feature local spectral piecewise similarity;

[0010] The state type at the current moment is determined based on the Lyapunov index and the similarity of the local spectral lines of the feature; the state type is either a fluttering moment or a non-fluttering moment.

[0011] Optionally, based on the real-time acceleration signal, the characteristic signal is determined using a variational mode decomposition method, including:

[0012] Determine the stable and transient signals in the real-time acceleration signal;

[0013] Based on the spectral kurtosis of the stable signal, determine the variational mode decomposition parameters;

[0014] Based on the variational mode decomposition parameters, the transition signal is decomposed and reconstructed using the variational mode decomposition method to obtain the transition residual signal.

[0015] The ratio of the flutter frequency amplitude of the transition residual signal to the flutter frequency amplitude of the real-time acceleration signal is determined as the discriminant.

[0016] Determine whether the discriminant is less than the discriminant threshold to obtain a first judgment result;

[0017] If the first judgment result is yes, then return to the step "Determine the variational mode decomposition parameters based on the spectral kurtosis of the stable signal";

[0018] If the first judgment result is negative, then the transition residual signal is determined to be a characteristic signal.

[0019] Optionally, after determining the transition residual signal as a characteristic signal, the method further includes:

[0020] Based on the variational mode decomposition parameters, the stable signal is decomposed and reconstructed using the variational mode decomposition method to obtain a stable residual signal.

[0021] The Lyapunov exponent of the stable residual signal is determined as the Lyapunov exponent threshold.

[0022] The local spectral similarity of the stable residual signal is determined as the local spectral similarity threshold.

[0023] Optionally, the state type at the current moment is determined based on the Lyapunov index of the feature and the similarity of the feature local spectral lines, including:

[0024] Determine whether the Lyapunov index of the feature is less than the Lyapunov index threshold to obtain a second determination result;

[0025] If the second judgment result is yes, then the current state type is determined to be a non-fluttering moment, the current time is updated and the process returns to the step "obtain the real-time acceleration signal at the current moment during the heavy-duty robot milling process";

[0026] If the second judgment result is negative, then it is determined whether the feature local spectrum piecewise similarity is less than the local spectrum piecewise similarity threshold, and a third judgment result is obtained;

[0027] If the third judgment result is yes, then the current state type is determined to be a non-fluttering moment, the current time is updated and the process returns to the step "obtain the real-time acceleration signal at the current moment during the heavy-duty robot milling process";

[0028] If the third judgment result is negative, then the current state type is determined to be a flutter moment.

[0029] Optionally, the Lyapunov index is:

[0030] Where, λ p Lyapunov index; p-1 L is the distance between the p-th phase space and the (p-1)-th phase space; p Let be the distance between the (p+1)th phase space and the pth phase space.

[0031] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for detecting chatter in heavy-duty robot milling.

[0032] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for detecting chatter in heavy-duty robot milling.

[0033] A computer program product includes a computer program that, when executed by a processor, implements the above-described method for detecting chatter in heavy-duty robot milling.

[0034] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0035] This invention provides a method, device, medium, and product for detecting chatter in heavy-duty robot milling. After acquiring the acceleration signal during robot milling, the method enhances the discriminative power of changes in the processing signal state based on variational mode decomposition. Furthermore, Lyapunov exponent and local spectral piecewise linear similarity based on Fréchet distance are selected as time-domain and frequency-domain monitoring indicators, respectively. The time- and frequency-domain indicators of chatter are preprocessed using signals from stable regions to obtain thresholds for both indicators. Considering the need for rapid chatter monitoring, frequency-domain indicator calculations are performed for verification after the time-domain indicators reach the thresholds, ultimately yielding the chatter identification result. By employing time-domain indicators to achieve chatter monitoring in robot processing, the complexity of spectral Fourier calculations is greatly reduced. Simultaneously, the constraint of dual indicators reduces the possibility of chatter misjudgment, providing a foundation for subsequent robot milling parameter control and demonstrating practical value in chatter identification in actual robot processing. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the heavy-duty robot milling chatter detection method provided in Embodiment 1 of the present invention;

[0038] Figure 2 This is a schematic diagram of the heavy-duty robot milling chatter detection method provided in Embodiment 1 of the present invention;

[0039] Figure 3 This is a schematic diagram of the selection of variational mode decomposition parameters based on spectral kurtosis provided in Embodiment 1 of the present invention;

[0040] Figure 4 The time-domain and frequency-domain reconstruction results of the stable signal based on the variational mode decomposition method provided in Embodiment 1 of the present invention are shown in the figure.

[0041] Figure 5 The time-domain and frequency-domain reconstruction results of the transition signal based on the variational mode decomposition method provided in Embodiment 1 of the present invention are shown in the figure.

[0042] Figure 6 This is a signal state discrimination characterization diagram provided in Embodiment 1 of the present invention;

[0043] Figure 7 This is a schematic diagram illustrating the selection of time-frequency domain index thresholds based on stable region signals provided in Embodiment 1 of the present invention;

[0044] Figure 8 This is a schematic diagram of the time-frequency domain flutter identification effect in the transition region provided in Embodiment 1 of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] The purpose of this invention is to provide a method, device, medium, and product for detecting chatter in heavy-duty robot milling, which can improve the accuracy and efficiency of chatter detection in heavy-duty robot milling.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example 1

[0049] like Figure 1 As shown, a method for detecting chatter during milling in a heavy-duty robot in this embodiment includes:

[0050] Step 101: Obtain the real-time acceleration signal at the current moment during the heavy-duty robot milling process.

[0051] Step 102: Determine the characteristic signal using the variational mode decomposition method based on the real-time acceleration signal.

[0052] Step 103: Determine the Lyapunov exponent of the characteristic signal as the characteristic Lyapunov exponent.

[0053] Step 104: Determine the local spectral similarity of the feature signal as the feature local spectral similarity.

[0054] Step 105: Determine the state type at the current moment based on the characteristic Lyapunov exponent and the characteristic local spectral line similarity. The state type is either a fluttering moment or a non-fluttering moment.

[0055] Step 102 includes:

[0056] Step 102-1: Determine the stable signal and the transition signal in the real-time acceleration signal.

[0057] Step 102-2: Determine the variational mode decomposition parameters based on the spectral kurtosis of the stable signal.

[0058] Step 102-3: Based on the variational mode decomposition parameters, the transition signal is decomposed and reconstructed using the variational mode decomposition method to obtain the transition residual signal.

[0059] Step 102-4: Determine the ratio of the flutter frequency amplitude of the transition residual signal to the flutter frequency amplitude of the real-time acceleration signal as the discriminant.

[0060] Step 102-5: Determine whether the discriminant is less than the discriminant threshold to obtain the first judgment result.

[0061] Step 102-6: If the first judgment result is yes, then return to step 102-2.

[0062] Step 102-7: If the first judgment result is negative, then the transition residual signal is determined to be the characteristic signal.

[0063] Step 102-8: Based on the variational mode decomposition parameters, the stable signal is decomposed and reconstructed using the variational mode decomposition method to obtain the stable residual signal.

[0064] Step 102-9: Determine the Lyapunov exponent of the stable residual signal as the Lyapunov exponent threshold.

[0065] Step 102-10: Determine the local spectral similarity of the stable residual signal as the local spectral similarity threshold.

[0066] Step 105 includes:

[0067] Step 105-1: Determine whether the feature Lyapunov index is less than the Lyapunov index threshold to obtain the second determination result.

[0068] Step 105-2: If the second judgment result is yes, then determine that the current state type is a non-fluttering moment, update the current time and return to step 101.

[0069] Step 105-3: If the second judgment result is negative, then determine whether the feature local spectrum piecewise similarity is less than the local spectrum piecewise similarity threshold to obtain the third judgment result.

[0070] Step 105-4: If the third judgment result is yes, then determine that the current state type is a non-fluttering moment, update the current time and return to step 101.

[0071] Step 105-5: If the third judgment result is negative, then determine the current state type as a flutter moment.

[0072] The Lyapunov index is:

[0073] Where, λ p Lyapunov index. p-1 L represents the distance between the p-th phase space and the (p-1)-th phase space. p Let be the distance between the (p+1)th phase space and the pth phase space.

[0074] Stability research in robotic milling is fundamental to achieving efficient robotic milling, and rapid chatter identification based on machining process signals directly determines the quality and efficiency of the machining process. To meet the requirements of efficient and stable robotic machining, rapid chatter identification based on measured robotic machining process signals is crucial. This invention, based on measured robotic milling signals, employs variational mode decomposition to increase signal state discriminability. Furthermore, it utilizes the Lyapunov exponent time-domain monitoring index and the local spectral piecewise linear similarity frequency-domain monitoring index based on Fréchet distance to effectively achieve rapid monitoring of chatter states in robotic milling, providing a reference for rapid chatter identification based on measured robotic machining process signals.

[0075] like Figure 2 Based on measured machining signals from robotic milling operations, considering the complexity of the measured signal components and the need for chatter feature extraction, variational mode decomposition is used to increase signal state discriminability. Furthermore, Lyapunov exponential time-domain monitoring and local spectral piecewise linear similarity frequency-domain monitoring based on Fréchet distance are employed to achieve rapid monitoring of chatter states in robotic milling operations. In this embodiment, chatter monitoring of robotic milling acceleration signals is taken as an example. The robotic milling acceleration signal is used as the implementation object, and time-frequency domain chatter identification is performed on the measured signal, where t... d This is the update interval.

[0076] The technical solution adopted in this invention is: a method for rapid time-frequency domain monitoring of chatter in robot milling, the method flow of which is as follows:

[0077] S1. Acceleration signals during the milling process are collected by an accelerometer and the data is output in real time through the accompanying software.

[0078] S2. For acceleration signals in the stable region, determine the variational mode decomposition parameters based on spectral kurtosis.

[0079] S3. Using the variational mode decomposition method, the measured acceleration steady signal is decomposed and reconstructed to obtain the residual signal.

[0080] S4. For the acceleration transition signal, based on the flutter frequency and the center frequency distribution of the variational mode decomposition mode, the transition signal is decomposed and reconstructed in combination with the variational mode decomposition to finally obtain the residual signal.

[0081] S5. For the obtained residual signal of the transition signal, analyze its spectral distribution. If the ratio of the dizzying frequency amplitude of the residual signal to the dizzying frequency amplitude of the measured signal is not less than the threshold ρ0, then proceed to S5. Otherwise, reselect appropriate variational mode decomposition parameters and proceed to S3.

[0082] S6. Determine the threshold ρ of the Lyapunov exponential time-domain monitoring index based on the residual signal of the stable signal.L The threshold ρ of the frequency domain monitoring index based on local spectral piecewise linear similarity to the Fraser distance value. F .

[0083] S7. In the transition signal region, the processing state of the signal is determined using the Lyapunov exponent threshold. If the Lyapunov exponent D of the residual signal of the measured signal is... L Reaching the threshold ρ L Mark that moment as a suspicious moment.

[0084] S8. Perform local spectral piecewise linear similarity calculation on the suspicious moments in S7. If the local spectral piecewise linear similarity D of the signal at that moment is... F It also reaches the threshold ρ F If the threshold is not reached, then flutter is determined to have occurred. If the threshold is not reached, then execution S7 will continue to monitor the signal in the time domain.

[0085] Further, in step S2, the method for determining the variational mode decomposition parameters based on spectral kurtosis specifically involves selecting a stable segment of the acquired measured acceleration signal and using the variational mode decomposition method to decompose the stable signal into a certain number of intrinsic mode functions (IMFs), denoted as u1, u2, ..., u k It can be written as:

[0086]

[0087] Where A k The instantaneous amplitude of u is given by the instantaneous frequency.

[0088] For each mode, the correlation analysis signal is calculated using Hilbert transform to obtain a one-sided spectrum. For each mode, the mode spectrum is shifted to the "baseband" by mixing with an exponent adjusted to its respective estimated center frequency.

[0089]

[0090] Here, δ(t) is the unit impulse function. The bandwidth is estimated by the Gaussian smoothness of the demodulated signal (i.e., the square norm of the gradient). The resulting constrained variational problem is as follows:

[0091]

[0092] Where u k ω represents all the patterns obtained from the decomposition. k Let f(t) represent the center frequencies of the corresponding modes, and f(t) be the original signal.

[0093] The variational problem is further constrained by a quadratic penalty term α and a Lagrange multiplier k. Therefore, the augmented Lagrange multiplier is introduced as follows:

[0094]

[0095] Here, λ(t) is the Lagrange multiplier operator. The weight of the penalty term needs to be as large as possible to strictly enforce data fidelity. The Lagrange multiplier is a common method for strictly enforcing constraints. u is updated alternately. k ω k By finding λ, we can locate the saddle point in the augmented Lagrange expression. Then, by setting the first change in the positive frequency to zero, we obtain the solution to this quadratic optimization problem:

[0096]

[0097]

[0098]

[0099] in, and f(t) and u are respectively k Both λ(t) and λ(t) are the results of transforming the time domain (t) to the frequency domain (ω) through Parseval / Plancherel Fourier isometric transform. Specifically, This represents the result of the (n+1)th iteration in the frequency domain of the k-th mode. Let be the absolute value of the amplitude of the k-th modal frequency domain expression at each frequency position after the (n+1)-th iteration. This represents the result of the (n+1)th iteration in the frequency domain of the i-th mode. This represents the result of the (n+1)th iteration for the center frequency of the k-th mode. and These are the results of the nth and (n+1)th iterations of the frequency domain expression of the Lagrange multiplication operator, respectively, where τ is the noise tolerance parameter and n is the iteration number. Based on the stable signal spectrum, the residual signal R after reconstruction under different parameter sets is calculated by traversing the number of mode centers k and the quadratic penalty term α in the variational mode decomposition parameters.

[0100]

[0101] Its corresponding spectral kurtosis Kup:

[0102]

[0103] In the formula, N represents the number of spectral lines in the spectral analysis, and x iLet represent the amplitude of the i-th spectral line, x represent the average amplitude of the spectral line, and σ represent the variance of the amplitude of the spectral line. The set of variational mode decomposition parameters with the smallest spectral kurtosis value is selected. In this embodiment, the number of mode centers k and the quadratic penalty term α in the variational mode decomposition parameters are 4 and 5900, respectively. Figure 3 As shown.

[0104] Further, in step S4, the method for reconstructing the measured acceleration transition signal specifically involves: first observing the spectral distribution of the transition signal and the mode center frequency of the variational mode decomposition method in the stable signal reconstruction; if the flutter frequency in the transition signal spectrum is close to the mode center frequency of the variational mode decomposition method, then in the variational mode decomposition iteration of the transition region, mode center frequencies close to the flutter frequency are eliminated, and the residual signal is obtained using a one-iteration alternating direction multiplier method optimization algorithm. If the mode center frequency of the variational mode decomposition method is not close to the flutter frequency, then the variational mode decomposition parameters of the stable signal are used to carry out the transition signal decomposition and reconstruction. Finally, the residual signal of the transition signal is obtained. In this embodiment, the first case is applied, that is, the signal flutter frequency is close to the variational mode decomposition mode center frequency, such as... Figure 5 As shown.

[0105] Further, in step S5, the comparison of the residual signal spectral distribution with the measured signal spectral distribution and the reselection of variational mode decomposition parameters specifically involve comparing the amplitude of the residual signal's flutter frequency with the amplitude of the measured signal's flutter frequency. The residual signal should retain the vast majority of the flutter components. When the amplitude ratio is not less than the threshold ρ0, the reconstruction effect is considered to meet expectations. When the amplitude ratio is less than the threshold ρ0, it is considered that the residual signal has removed a large portion of the flutter components, and the reconstruction effect is poor.

[0106] Furthermore, in step S5, the expected reconstruction effect is specifically that the residual signal in the stable region and the residual signal in the transition region obtained after reconstruction are more clearly distinguishable. In this embodiment of the invention, the real-time variance iteration rate of change is used to characterize the distinguishability of the residual signal state change, such as... Figure 6 As shown.

[0107] Further, in step S5, reselecting the variational mode decomposition parameters specifically involves: selecting a local non-dominated solution based on the spectral kurtosis three-dimensional plot obtained by traversing the variational mode decomposition parameters, which requires adjusting the selection of the number of mode centers k and the quadratic penalty term α in the variational mode decomposition parameters.

[0108] Further, in step S6, the Lyapunov exponent is calculated as follows: Appropriate reconstruction parameters are selected, namely the embedding dimension m and the time delay τ, to reconstruct the phase space of the time series. For a time series of length N, an m-dimensional phase trajectory consisting of M phase points can be obtained sequentially. Let L... p-1Let λ be the distance between the p-th phase space and the (p-1)-th phase space. The Lyapunov exponent λ is defined based on the change in the distance between the phase spaces. p :

[0109]

[0110] In this embodiment, a time delay τ = 0.01f is selected. s Left and right sampling units, dimension m = 0.2f s Refactoring, f s The sampling frequency.

[0111] Furthermore, in step S6, the method for calculating the local spectral piecewise linear similarity based on the Fraser distance value is as follows: Select the number of time-domain sampling points ξ, and approximate the spectral distribution at that moment with the spectral analysis results of the previous ξ signal values. Since the number of sampling points is small, the spectrum is a piecewise linear curve. Calculate the spectral piecewise linear similarity between consecutive moments to characterize the signal state change.

[0112] The spectral similarity is obtained based on the Frescher distance value:

[0113]

[0114] Where d is the Eulerian distance, A and B represent two spectral polygons of a continuous-time signal segment defined on a unit interval, and β(t) and γ(t) are two reparameterized functions within the unit interval. A smaller Fréchet distance indicates more similar spectral polygons, meaning less variation in spectral energy distribution. In this embodiment, the number of time-domain sampling points ξ = 0.025f is selected. s .

[0115] Further, in step S6, the method for selecting the time-frequency domain index threshold is as follows: the threshold ρ of the Lyapunov exponent time-domain monitoring index is calculated based on the time-domain distribution of the residual signal of the stable signal and the 3σ principle. L The threshold ρ of the frequency domain monitoring index based on local spectral piecewise linear similarity to the Fraser distance value. F The stable signal temporal reconstruction effect and threshold selection in this embodiment are as follows: Figure 4 and Figure 7 As shown.

[0116] Further, in step S7, the Lyapunov exponent threshold monitoring specifically involves: reconstructing the time-domain signal using the same embedding dimension m and time delay τ as the Lyapunov exponent of the stable region signal; if the Lyapunov exponent D at that moment... L Reaching the threshold ρ L Execute S8.

[0117] Furthermore, in step S8, the local spectrum piecewise linear similarity monitoring method specifically involves: for the suspicious moments in S7, calculating the local spectrum piecewise linear similarity D of the transition region at that moment using the same calculation method as the signal in the stable region. F If the threshold ρ is reached... F If the threshold is not reached, then flutter is determined to have occurred. If the threshold is not reached, S7 is executed again to monitor the signal in the time domain. In this embodiment, flutter is detected at the 0.45-second mark. Figure 8 As shown.

[0118] Based on steps S1 to S8 above, the residual signal is obtained after variational mode decomposition and reconstruction of the measured machining signal from robot milling. Using the Lyapunov exponent time-domain monitoring index and the local spectral piecewise linear similarity frequency-domain monitoring index based on the Fréchet distance value, the flutter identification result can be obtained as follows: Figure 7 As shown. At this moment, the milling parameters are adjusted, such as reducing the radial depth of cut or adjusting the spindle speed, to avoid chatter during robot milling.

[0119] This invention utilizes variational mode decomposition (VMD) to preprocess measured acceleration processing signals. Considering the complexity of spectral Fourier transform calculations and reducing the possibility of flutter misjudgment, it selects the Lyapunov exponent and local spectral piecewise linear similarity based on Fréchet distance as time-domain and frequency-domain monitoring indicators for rapid flutter identification of processing signals. The advantages are: solid theoretical foundation and reliable results; using the spectral kurtosis of the residual signal as the judgment criterion; effectively utilizing VMD to decompose and reconstruct the signal, improving the distinguishability of state changes in transition signals; using the Lyapunov exponent as the time-domain flutter monitoring indicator reduces dependence on frequency-domain calculations, significantly reducing computational complexity; and employing local spectral piecewise linear similarity based on Fréchet distance as the frequency-domain flutter identification indicator verifies flutter from the perspective of spectral energy, avoiding misjudgment. This invention can be specifically applied in the field of flutter monitoring in robotic processing.

[0120] Example 2

[0121] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a heavy-duty robot milling chatter detection method as described in Embodiment 1.

[0122] Example 3

[0123] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for detecting chatter in heavy-duty robot milling as described in Embodiment 1.

[0124] Example 4

[0125] A computer program product includes a computer program that, when executed by a processor, implements a method for detecting chatter in heavy-duty robot milling as described in Embodiment 1.

[0126] Example 5

[0127] A computer device, which may be a database, includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores pending transactions. The I / O interfaces facilitate information exchange between the processor and external devices. The communication interface allows communication with external terminals via a network connection. When executed by the processor, the computer program implements a heavy-duty robot milling chatter detection method as described in Embodiment 1.

[0128] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0129] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided by this invention may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0130] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0131] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for detecting chatter during heavy-duty robot milling, characterized in that, include: Acquire the real-time acceleration signal at the current moment during the milling process of the heavy-duty robot; Based on the real-time acceleration signal, the characteristic signal is determined using the variational mode decomposition method. The Lyapunov exponent of the characteristic signal is determined to be the characteristic Lyapunov exponent; The local spectral piecewise similarity of the feature signal is determined as the feature local spectral piecewise similarity; The state type at the current moment is determined based on the Lyapunov index and the local spectral similarity of the features; the state type is either a fluttering moment or a non-fluttering moment. The determination of characteristic signals based on the real-time acceleration signal using a variational mode decomposition method includes: Determine the stable and transient signals in the real-time acceleration signal; Based on the spectral kurtosis of the stable signal, determine the variational mode decomposition parameters; Based on the variational mode decomposition parameters, the transition signal is decomposed and reconstructed using the variational mode decomposition method to obtain the transition residual signal. The ratio of the flutter frequency amplitude of the transition residual signal to the flutter frequency amplitude of the real-time acceleration signal is determined as the discriminant. Determine whether the discriminant is less than the discriminant threshold to obtain a first judgment result; If the first judgment result is yes, then return to the step "determine the variational mode decomposition parameters based on the spectral kurtosis of the stable signal"; If the first determination result is negative, then the transition residual signal is determined to be a characteristic signal; Based on the Lyapunov index and the local spectral similarity, the current state type is determined, including: Determine whether the Lyapunov index of the feature is less than the Lyapunov index threshold to obtain a second determination result; If the second judgment result is yes, then the current state type is determined to be a non-fluttering moment, the current time is updated and the process returns to the step "obtain the real-time acceleration signal at the current moment during the heavy-duty robot milling process"; If the second judgment result is negative, then it is determined whether the feature local spectrum piecewise similarity is less than the local spectrum piecewise similarity threshold, and a third judgment result is obtained; If the third judgment result is yes, then the current state type is determined to be a non-fluttering moment, the current time is updated and the process returns to the step "obtain the real-time acceleration signal at the current moment during the heavy-duty robot milling process"; If the third judgment result is negative, then the current state type is determined to be a flutter moment.

2. The method for detecting chatter in heavy-duty robot milling according to claim 1, characterized in that, After determining the transition residual signal as the characteristic signal, the following steps are also included: Based on the variational mode decomposition parameters, the stable signal is decomposed and reconstructed using the variational mode decomposition method to obtain a stable residual signal. The Lyapunov exponent of the stable residual signal is determined as the Lyapunov exponent threshold. The local spectral similarity of the stable residual signal is determined as the local spectral similarity threshold.

3. The method for detecting chatter in heavy-duty robot milling according to claim 1, characterized in that, The Lyapunov index is: Where, λ p Lyapunov index; p-1 L is the distance between the p-th phase space and the (p-1)-th phase space; p Let be the distance between the (p+1)th phase space and the pth phase space.

4. A computer device, comprising: The memory and processor contain a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a method for detecting chatter in heavy-duty robot milling as described in any one of claims 1-3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for detecting chatter in heavy-duty robot milling as described in any one of claims 1-3.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for detecting chatter in heavy-duty robot milling as described in any one of claims 1-3.

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