PCB processing resonance suppression method, device, equipment and storage medium

Abstract

The present invention provides a method, device, equipment and storage medium for suppressing resonance in PCB processing. The method comprises: collecting material properties of PCB processing workpieces and characteristic data of processing equipment, performing modal analysis and frequency response analysis, and generating a stability lobe diagram of the PCB processing system. According to the stability lobe diagram, the processing parameter space is divided, and the processing parameters are evaluated to generate a resonance suppression strategy. During the processing, multiple types of sensors are used to collect vibration signals of the workpiece in real time, and the vibration signals are analyzed to obtain current resonance state information. According to the resonance state information and the preset resonance suppression strategy, the processing parameters of the processing equipment are coordinated and controlled to achieve real-time resonance suppression. The present invention can effectively suppress the resonance phenomenon in the processing process through real-time vibration monitoring and dynamic control of the processing system, thereby improving the quality of PCB processing, processing accuracy and operational stability of the equipment.

Description

Technical Field

[0001] The present invention relates to the field of PCB processing, and in particular to a PCB processing resonance suppression method, device, equipment and storage medium. Background Art

[0002] With the development of electronic products, the machining accuracy and complexity requirements for PCBs (printed circuit boards) continue to increase. However, during high-precision PCB machining, resonance has become a significant factor affecting machining quality. Resonance can cause severe vibration in machining equipment, leading to increased tool wear, decreased machining accuracy, and deteriorating workpiece surface quality. Especially under high-speed machining conditions, the unique characteristics of PCB materials and the frequency response of machining equipment can easily induce unstable vibrations, leading to resonance in the machining system.

[0003] Conventional resonance suppression methods typically rely on structural improvements to machining equipment or fixed machining parameters to avoid specific resonant frequencies. However, these methods are unable to adapt to the dynamic vibration changes that occur during machining. Furthermore, resonance is difficult to avoid under diverse material properties and complex machining conditions, limiting machining efficiency and accuracy. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing PCB processing resonance suppression method cannot adapt to the dynamic vibration changes that occur during the processing, resulting in limited processing efficiency and accuracy;

[0005] A first aspect of the present invention provides a method for suppressing resonance during PCB processing, which is applied to a PCB processing system. The PCB processing system includes a PCB processing workpiece and PCB processing equipment. The method includes:

[0006] collecting PCB material properties of the PCB processing workpiece and processing equipment characteristic data of the PCB processing equipment, and performing modal analysis and frequency response analysis on the PCB processing system based on the PCB material properties and the processing equipment characteristic data to obtain the PCB processing system and stability lobe diagram;

[0007] dividing and evaluating a processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy for the PCB processing system according to the processing parameter space;

[0008] During the process of the PCB processing equipment processing the PCB processing workpiece, a vibration signal of the PCB processing workpiece is collected by multiple types of sensors on the PCB processing equipment, and a resonance state evaluation is performed on the vibration signal to obtain resonance state information;

[0009] According to the resonance state information and the resonance suppression strategy, the PCB processing equipment is collaboratively controlled to achieve real-time resonance suppression.

[0010] Optionally, in a first implementation of the first aspect of the present invention, performing modal analysis and frequency response analysis on the PCB processing system based on the PCB material properties and the processing equipment characteristic data to obtain a stability lobe diagram of the PCB processing system includes:

[0011] Performing parameterization on the PCB material properties and the processing equipment characteristic data to obtain a multi-dimensional parameter model of the PCB processing system;

[0012] Discretizing the PCB processing system according to the multi-dimensional parameter model using a finite element method to obtain a numerical model of the PCB processing system;

[0013] Performing eigenvalue decomposition and singular value decomposition on the numerical model to obtain the natural frequency and mode shape of the PCB processing system;

[0014] Performing a frequency sweep analysis on the PCB processing system according to the natural frequency and the mode shape to obtain a first frequency response function of the PCB processing system;

[0015] The stability analysis of the PCB processing system is performed using the first frequency response function through a dynamic stiffness method and an analytical continuation method to obtain a stability lobe diagram of the PCB processing system.

[0016] Optionally, in a second implementation of the first aspect of the present invention, performing stability analysis on the PCB processing system using the frequency response function by using a dynamic stiffness method and an analytical continuation method to obtain a stability lobe diagram of the PCB processing system includes:

[0017] Transforming the frequency response function using a dynamic stiffness method to obtain a dynamic stiffness matrix of the PCB processing system;

[0018] Extending the system response of the PCB processing system from the real frequency domain to the complex frequency domain according to the dynamic stiffness matrix by an analytical continuation method to obtain the complex frequency domain characteristic equation of the system;

[0019] Solving the eigenvalue problem of the complex frequency domain characteristic equation to obtain stability judgment results of the PCB processing system under different processing parameters;

[0020] A stability lobe diagram of the PCB processing system is drawn according to the stability judgment result using a parameter mapping method.

[0021] Optionally, in a third implementation of the first aspect of the present invention, dividing and evaluating a processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy for the PCB processing system according to the processing parameter space includes:

[0022] Performing image segmentation and boundary extraction on the stability lobe map to obtain stable areas and unstable areas of the PCB processing system;

[0023] According to the stable region and the unstable region, a clustering algorithm is used to divide the processing parameter space of the PCB processing system to obtain multiple processing parameter subspaces;

[0024] Performing stability assessment and risk quantification on the multiple processing parameter subspaces to obtain a stability index and a resonance risk level for each processing parameter subspace;

[0025] Using a multi-objective optimization algorithm, processing parameters are selected and combined according to the stability index and the resonance risk level to obtain an initial resonance suppression parameter set;

[0026] A dynamic adjustment rule is applied to the initial resonance suppression parameter set to generate a resonance suppression strategy for the PCB processing system.

[0027] Optionally, in a fourth implementation of the first aspect of the present invention, performing stability assessment and risk quantification on the multiple processing parameter subspaces to obtain a stability index and a resonance risk level for each processing parameter subspace includes:

[0028] Discrete sampling is performed on each processing parameter subspace to obtain a representative processing parameter point set, and a system identification method is used to perform dynamic response analysis on the representative processing parameter point set to obtain a second frequency response function of each processing parameter point;

[0029] performing a stability evaluation on each processing parameter point according to the second frequency response function to obtain a corresponding stability index, and extracting a resonant frequency of the PCB processing system from the stability lobe diagram;

[0030] A preset fuzzy inference system is used to perform risk assessment on each processing parameter subspace according to the resonance frequency and the stability index to obtain a corresponding resonance risk level.

[0031] Optionally, in a fifth implementation of the first aspect of the present invention, performing resonance state evaluation on the vibration signal to obtain resonance state information includes:

[0032] Performing multi-source data fusion processing on the vibration signals collected by the acceleration sensor, strain sensor, and acoustic emission sensor on the PCB processing equipment to obtain a fused time-domain vibration signal;

[0033] Performing wavelet packet decomposition and singular value decomposition on the time domain vibration signal to obtain a multi-scale feature matrix, and performing dimensionality reduction processing on the multi-scale feature matrix using a principal component analysis method to obtain a eigenvector after dimensionality reduction;

[0034] The feature vector is input into a pre-trained deep neural network model to classify the resonance state of the PCB workpiece to obtain resonance state information.

[0035] Optionally, in a sixth implementation of the first aspect of the present invention, the collaboratively controlling the PCB processing equipment according to the resonance state information and the resonance suppression strategy to achieve real-time resonance suppression includes:

[0036] Performing fuzzy processing on the resonance state information and the resonance suppression strategy to obtain a fuzzy control rule set;

[0037] A preset distributed model predictive control algorithm is used to generate collaborative control instructions for multiple actuators of the PCB processing equipment based on the fuzzy control rule set to obtain a multi-axis collaborative control instruction sequence;

[0038] Through a preset multi-agent consistency control algorithm, the various actuators of the PCB processing equipment are coordinated and controlled according to the multi-axis collaborative control instruction sequence to achieve real-time resonance suppression.

[0039] A second aspect of the present invention provides a PCB processing resonance suppression device, which is applied to a PCB processing system. The PCB processing system includes a PCB processing workpiece and PCB processing equipment. The PCB processing resonance suppression device includes:

[0040] an acquisition and analysis module, configured to acquire PCB material properties of the PCB processing workpiece and processing equipment characteristic data of the PCB processing equipment, and perform modal analysis and frequency response analysis on the PCB processing system based on the PCB material properties and the processing equipment characteristic data to obtain a stability lobe diagram of the PCB processing system;

[0041] a strategy generation module, configured to divide and evaluate a processing parameter space of the PCB processing system according to the stability lobe diagram, and generate a resonance suppression strategy for the PCB processing system according to the processing parameter space;

[0042] a state evaluation module configured to collect vibration signals of the PCB workpiece by using multiple types of sensors on the PCB processing equipment during processing of the PCB workpiece by the PCB processing equipment, and to perform resonance state evaluation on the vibration signals to obtain resonance state information;

[0043] The resonance suppression module is used to coordinately control the PCB processing equipment according to the resonance state information and the resonance suppression strategy to achieve real-time resonance suppression.

[0044] A third aspect of the present invention provides a PCB processing resonance suppression device, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; the at least one processor calls the instructions in the memory to enable the PCB processing resonance suppression device to perform the steps of the above-mentioned PCB processing resonance suppression method.

[0045] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the steps of the above-mentioned PCB processing resonance suppression method.

[0046] The above-mentioned PCB processing resonance suppression method, device, equipment and storage medium collect the material properties of the PCB processing workpiece and the characteristic data of the processing equipment, perform modal analysis and frequency response analysis, and generate a stability lobe diagram of the PCB processing system. According to the stability lobe diagram, the processing parameter space is divided, and the processing parameters are evaluated to generate a resonance suppression strategy. During the processing, multiple types of sensors are used to collect the vibration signal of the workpiece in real time, and the vibration signal is analyzed to obtain the current resonance state information. According to the resonance state information and the preset resonance suppression strategy, the processing parameters of the processing equipment are coordinated and controlled to achieve real-time resonance suppression. The present invention can effectively suppress the resonance phenomenon in the processing process through real-time vibration monitoring and dynamic control of the processing system, thereby improving the quality of PCB processing, processing accuracy and operational stability of the equipment.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of a first embodiment of a method for suppressing PCB machining resonance according to an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of an embodiment of a PCB processing resonance suppression device according to an embodiment of the present invention;

[0051] Figure 3 Schematic diagram of an embodiment of a PCB processing resonance suppression device in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] The terms "including," "having," and any variations thereof, as used in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or device.

[0054] To facilitate understanding of this embodiment, a PCB processing resonance suppression method disclosed in an embodiment of the present invention is first described in detail. The PCB processing resonance suppression method is applied to a PCB processing system, which includes a PCB processing workpiece and a PCB processing device. Figure 1 As shown, this method includes the following steps:

[0055] 101. Collecting PCB material properties of PCB processing workpieces and processing equipment characteristic data of PCB processing equipment, and performing modal analysis and frequency response analysis on the PCB processing system based on the PCB material properties and processing equipment characteristic data to obtain a PCB processing system and stability lobe diagram;

[0056] In one embodiment of the present invention, performing modal analysis and frequency response analysis on the PCB processing system based on the PCB material properties and the processing equipment characteristic data to obtain a stability lobe diagram of the PCB processing system includes: performing parameterized processing on the PCB material properties and the processing equipment characteristic data to obtain a multidimensional parameter model of the PCB processing system; discretizing the PCB processing system based on the multidimensional parameter model using a finite element method to obtain a numerical model of the PCB processing system; performing eigenvalue decomposition and singular value decomposition on the numerical model to obtain natural frequencies and modal vibration shapes of the PCB processing system; performing frequency sweep analysis on the PCB processing system based on the natural frequencies and modal vibration shapes to obtain a first frequency response function of the PCB processing system; and performing stability analysis on the PCB processing system using the first frequency response function through a dynamic stiffness method and an analytical continuation method to obtain a stability lobe diagram of the PCB processing system.

[0057] Specifically, the PCB material properties and processing equipment characteristic data are parameterized to generate a multidimensional parametric model of the PCB processing system. This step involves converting the raw data into quantifiable parameters, such as the material's elastic modulus, density, and Poisson's ratio, as well as the equipment's stiffness, mass, and damping coefficient. By establishing a mathematical model, these parameters are organized into a multidimensional vector or matrix, forming a parametric representation of the system. This representation makes subsequent analysis and calculations feasible while preserving the system's key characteristics.

[0058] Specifically, the finite element method (FEM) is used to discretize the PCB processing system based on a multidimensional parametric model, resulting in a numerical model of the system. The FEM divides a continuous physical system into a finite number of elements, each connected by nodes. Based on the previously established parametric model, each element is assigned corresponding material properties and geometric characteristics. The dynamic behavior of the entire system is then described as a set of algebraic equations by constructing mass, stiffness, and damping matrices. This process transforms a complex continuous system into a discrete mathematical model that can be solved by a computer.

[0059] Specifically, the numerical model is subjected to eigenvalue decomposition and singular value decomposition to obtain the natural frequencies and modal shapes of the PCB processing system. Eigenvalue decomposition is used to determine the system's natural frequencies, which represent the system's natural vibration frequencies in the absence of external forces. Singular value decomposition is used to determine the system's modal shapes, that is, the deformed shape of the system at each natural frequency. Combining these two decomposition methods can fully reveal the system's dynamic characteristics. By analyzing these eigenvalues ​​and eigenvectors, the system's key vibration modes and frequency ranges can be identified. Based on the natural frequencies and modal shapes, a frequency sweep analysis is performed on the PCB processing system to obtain the first frequency response function (FRF). Frequency sweep analysis involves applying harmonic excitation to the system within a certain frequency range and calculating the system's response at each frequency point. By comparing the input excitation and the output response, the system's frequency response function (FRF) is constructed. This function describes the amplitude and phase characteristics of the system at different frequencies and is an important tool for understanding the system's dynamic behavior.

[0060] Specifically, the stability of the PCB processing system is analyzed using the first frequency response function through the dynamic stiffness method and the analytical continuation method, resulting in a stability lobe diagram for the PCB processing system. The dynamic stiffness method represents the dynamic characteristics of the system as a function of frequency, while the analytical continuation method extends this representation from the real frequency domain to the complex frequency domain. By analyzing the distribution of the roots of the characteristic equation in the complex frequency domain, the stability of the system under different parameter combinations is determined. This process involves solving a system of nonlinear equations and typically requires the use of numerical iteration methods. The resulting stability lobe diagram intuitively displays the stable and unstable regions of the system under different processing parameters, providing an important basis for subsequent parameter optimization and control strategy formulation.

[0061] Furthermore, the method of performing stability analysis on the PCB processing system using the frequency response function through the dynamic stiffness method and the analytical continuation method to obtain a stability lobe diagram of the PCB processing system includes: transforming the frequency response function using the dynamic stiffness method to obtain a dynamic stiffness matrix of the PCB processing system; extending the system response of the PCB processing system from the real frequency domain to the complex frequency domain based on the dynamic stiffness matrix through the analytical continuation method to obtain a complex frequency domain characteristic equation of the system; solving the eigenvalue problem of the complex frequency domain characteristic equation to obtain a stability judgment result of the PCB processing system under different processing parameters; and drawing a stability lobe diagram of the PCB processing system based on the stability judgment result through a parameter mapping method.

[0062] Specifically, the frequency response function is transformed using the dynamic stiffness method to obtain the dynamic stiffness matrix of the PCB processing system. This step involves converting the frequency response function into a dynamic stiffness expression. Specifically, the dynamic stiffness matrix is ​​the inverse of the frequency response function and is expressed as K(ω) = F(ω) / X(ω), where F(ω) is the force spectrum, X(ω) is the displacement spectrum, and ω is the angular frequency. This transformation converts the system's dynamic characteristics from a frequency response perspective to a stiffness perspective, making it more suitable for subsequent stability analysis. The dynamic stiffness matrix contains information about the system's stiffness at different frequencies, reflecting how the system's ability to resist deformation varies with frequency.

[0063] Specifically, the analytical continuation method, based on the dynamic stiffness matrix, extends the system response of the PCB processing system from the real frequency domain to the complex frequency domain, resulting in the complex frequency domain characteristic equation of the system. The core concept of the analytical continuation method is to extend the real frequency domain function to the complex frequency domain. This process involves replacing the real frequency ω in the dynamic stiffness matrix K(ω) with the complex frequency s = σ + jω, where σ represents the attenuation factor and j is the imaginary unit. This replacement extends the system's dynamic characteristics to a wider complex plane. In the complex frequency domain, the system's characteristic equation can be expressed as det[K(s)] = 0, where det represents the determinant. This characteristic equation contains all the dynamic information of the system in the complex frequency domain, laying the foundation for subsequent stability analysis.

[0064] Specifically, the eigenvalue problem of the characteristic equation in the complex frequency domain is solved to obtain the stability judgment results of the PCB processing system under different processing parameters. This step involves numerical calculation methods, such as the Newton-Raphson iteration method or other efficient eigenvalue solution algorithms. The characteristic equation needs to be solved repeatedly for different processing parameter combinations. The stability judgment criterion is: if the real part of all eigenvalues ​​is negative, the system is stable under this parameter combination; if there is an eigenvalue with a positive real part, the system is unstable. By systematically varying processing parameters (such as spindle speed, cutting depth, etc.) and performing eigenvalue analysis on each parameter group, a series of stability judgment results can be obtained. These results form the basic data for the stability lobe diagram.

[0065] Specifically, a stability lobe diagram of the PCB processing system is drawn based on the stability judgment results through a parameter mapping method. Parameter mapping is the process of mapping a multidimensional parameter space to a two-dimensional plane. Usually, the two most critical processing parameters are selected as the horizontal and vertical coordinates of the lobe diagram, such as spindle speed and cutting depth. For each parameter combination, its stability state is marked at the corresponding coordinate point based on the previous stability judgment results. Stable points and unstable points are represented by different colors or symbols. By connecting points with the same stability, the boundary lines of the stable area and the unstable area are formed, and these boundary lines constitute a lobe-shaped figure.

[0066] 102. Divide and evaluate a processing parameter space of the PCB processing system according to the stability lobe diagram, and generate a resonance suppression strategy for the PCB processing system according to the processing parameter space;

[0067] In one embodiment of the present invention, dividing and evaluating the processing parameter space of the PCB processing system according to the stability lobe map, and generating a resonance suppression strategy for the PCB processing system according to the processing parameter space includes: performing image segmentation and boundary extraction on the stability lobe map to obtain stable regions and unstable regions of the PCB processing system; dividing the processing parameter space of the PCB processing system according to the stable regions and unstable regions using a clustering algorithm to obtain multiple processing parameter subspaces; performing stability assessment and risk quantification on the multiple processing parameter subspaces to obtain a stability index and a resonance risk level for each processing parameter subspace; selecting and combining processing parameters according to the stability index and the resonance risk level using a multi-objective optimization algorithm to obtain an initial resonance suppression parameter set; and applying dynamic adjustment rules to the initial resonance suppression parameter set to generate the resonance suppression strategy for the PCB processing system.

[0068] Specifically, the stability lobe map is segmented and its boundaries extracted to identify the stable and unstable regions of the PCB processing system. This step involves analyzing the stability lobe map using image processing techniques. Specifically, edge detection algorithms (such as the Canny or Sobel algorithms) can be used to identify the boundary between stable and unstable regions. Region growing or watershed algorithms are then used to segment the image, dividing the entire parameter space into distinct regions. To improve segmentation accuracy, morphological processing, such as opening and closing operations, can be used to eliminate noise and small, isolated regions. Ultimately, these image processing techniques transform the stability lobe map into a clear binary image, clearly identifying the stable and unstable regions.

[0069] Specifically, a clustering algorithm is used to partition the processing parameter space of the PCB processing system according to stable and unstable regions, resulting in multiple processing parameter subspaces. The purpose of this step is to group parameter points with similar stability characteristics into the same subspace. This can be achieved using a K-means clustering algorithm or a hierarchical clustering algorithm. Before applying the clustering algorithm, each point in the stability lobe diagram needs to be represented as a multidimensional vector, including its coordinate position and stability label. The clustering process considers the Euclidean distance between points and the similarity of stability characteristics. The number of clusters can be determined using the silhouette coefficient or the elbow rule. After clustering, each subspace represents a set of processing parameter combinations with similar stability characteristics. This partitioning method facilitates the subsequent development of differentiated resonance suppression strategies for different subspaces.

[0070] Specifically, stability assessment and risk quantification are performed on multiple processing parameter subspaces to obtain a stability index and resonance risk level for each processing parameter subspace. This step involves assigning a quantitative stability indicator to each subspace. The stability index can be determined by calculating the proportion of stable points within the subspace, while taking into account the distance between the stable points and the boundary of the unstable region. The resonance risk level can be assessed based on the average resonance amplitude or frequency of the points within the subspace. Specifically, a fuzzy comprehensive evaluation method can be used to comprehensively consider multiple factors such as stability index, resonance amplitude, frequency offset, etc., to obtain a comprehensive risk assessment result. In addition, sensitivity analysis can be introduced to evaluate the impact of parameter changes on system stability, which helps to identify high-risk parameter combinations. Through this quantitative assessment, each subspace is assigned a clear stability index and risk level, providing an important basis for subsequent parameter optimization.

[0071] Specifically, a multi-objective optimization algorithm is used to select and combine machining parameters based on the stability index and resonance risk level to obtain an initial resonance suppression parameter set. The goal of this step is to find the optimal parameter combination to suppress resonance while ensuring machining quality. Multi-objective optimization problems often involve multiple conflicting objectives, such as maximizing the stability index, minimizing the resonance risk, and maximizing machining efficiency. The non-dominated sorting genetic algorithm II (NSGA-II) or the multi-objective particle swarm optimization algorithm (MOPSO) can be used to address this problem. During the optimization process, appropriate objective functions and constraints must be defined. For example, the stability index can be maximized and the resonance risk level minimized, while also considering constraints such as machining efficiency and surface quality. Through iterative optimization, the algorithm generates a set of Pareto optimal solutions, namely the initial resonance suppression parameter set. These parameter combinations achieve the best balance between the different objectives.

[0072] Specifically, dynamic adjustment rules are applied to the initial resonance suppression parameter set to generate a resonance suppression strategy for the PCB processing system. This step aims to make the resonance suppression strategy adaptive and able to adjust according to the real-time processing conditions. The dynamic adjustment rules can be formulated based on fuzzy logic control or reinforcement learning algorithms. Specifically, a series of if-then rules can be designed to fine-tune the initial parameters based on the resonance state monitored in real time. For example, if an increase in the resonance amplitude is detected, the cutting depth can be appropriately reduced or the spindle speed can be adjusted. In addition, an online learning mechanism can be introduced to enable the system to continuously optimize the adjustment rules based on historical processing data. By applying these dynamic adjustment rules, the initial parameter set is transformed into a complete resonance suppression strategy that not only includes the optimal initial parameter settings but also has the ability to make real-time adjustments based on the actual processing conditions, thereby more effectively suppressing the resonance phenomenon during the PCB processing process.

[0073] Furthermore, the stability assessment and risk quantification of the multiple processing parameter subspaces to obtain the stability index and resonance risk level of each processing parameter subspace includes: discrete sampling of each processing parameter subspace to obtain a representative processing parameter point set, and dynamic response analysis of the representative processing parameter point set using a system identification method to obtain a second frequency response function of each processing parameter point; based on the second frequency response function, stability assessment of each processing parameter point to obtain a corresponding stability index, and extracting the resonant frequency of the PCB processing system from the stability lobe diagram; and using a preset fuzzy inference system to perform risk assessment on each processing parameter subspace based on the resonant frequency and the stability index to obtain a corresponding resonance risk level.

[0074] Specifically, discrete sampling is performed on each processing parameter subspace to obtain a representative set of processing parameter points. Dynamic response analysis of this representative set of processing parameter points is then performed using system identification methods to obtain the second frequency response function (FRF) of each processing parameter point. This step involves selecting representative parameter points within each subspace, using methods such as uniform sampling or Latin hypercube sampling to ensure uniform distribution and representativeness of the sampling points. For each sampling point, a system identification experiment is performed by applying a known excitation signal (such as white noise or a pseudo-random binary sequence) to the PCB processing system and measuring the system's output response. Then, a system identification algorithm (such as least squares, subspace identification, or maximum likelihood estimation) is used to process the input and output data and establish a mathematical model of the system. Based on this model, the FRF of each processing parameter point is calculated. This function describes the amplitude and phase response of the system at different frequencies, providing an important basis for subsequent stability assessment.

[0075] Specifically, based on the second frequency response function, a stability assessment is performed for each processing parameter point to obtain a corresponding stability index. The resonant frequency of the PCB processing system is then extracted from the stability lobe plot. Stability assessment can be performed by analyzing characteristics of the frequency response function, such as the Nyquist stability criterion or phase and gain margin analysis. Specifically, the system's phase margin (the difference between the phase response and -180° when the system gain is 0 dB) and gain margin (the difference between the gain response and 0 dB when the system phase is -180°) can be calculated. The stability index can be defined as a weighted sum of these two margins or another composite indicator. Furthermore, by analyzing the peaks of the frequency response function, the system's resonant frequencies can be identified. These resonant frequencies typically correspond to local maxima on the frequency response function's amplitude curve. To improve the accuracy of resonant frequency identification, a peak detection algorithm and curve fitting techniques can be combined. This approach not only provides stability assessment results for each parameter point but also identifies the system's key resonant frequencies.

[0076] Specifically, a pre-defined fuzzy inference system is used to assess the risk of each machining parameter subspace based on the resonant frequency and stability index, generating the corresponding resonance risk level. The fuzzy inference system is designed based on expert knowledge and historical data, with the resonant frequency and stability index as input variables and the resonance risk level as the output variable. First, fuzzy sets and membership functions for the input variables are defined. For example, the resonant frequency can be divided into three fuzzy sets: "low," "medium," and "high," and the stability index can be divided into three fuzzy sets: "poor," "fair," and "good." Then, a fuzzy rule base is designed, such as "If the resonant frequency is high and the stability index is poor, the resonance risk level is high." Next, a fuzzy inference mechanism (such as Mamdani or Sugeno reasoning) is used to fuzzify the input, perform rule inference, and defuzzify the input to obtain the final resonance risk level. To improve the accuracy of the risk assessment, an adaptive mechanism can be introduced to continuously adjust the fuzzy rules and membership functions based on actual machining data. This fuzzy inference method converts the quantitative stability index and resonant frequency into a more understandable and applicable risk level, providing intuitive guidance for the subsequent formulation of resonance suppression strategies.

[0077] 103. During the process of processing the PCB workpiece by the PCB processing equipment, collecting vibration signals of the PCB workpiece by using multiple types of sensors on the PCB processing equipment, and performing resonance state evaluation on the vibration signals to obtain resonance state information;

[0078] In one embodiment of the present invention, the resonance state evaluation of the vibration signal to obtain the resonance state information includes: performing multi-source data fusion processing on the vibration signals collected by the acceleration sensor, strain sensor and acoustic emission sensor on the PCB processing equipment to obtain a fused time-domain vibration signal; performing wavelet packet decomposition and singular value decomposition on the time-domain vibration signal to obtain a multi-scale feature matrix, and using a principal component analysis method to reduce the dimension of the multi-scale feature matrix to obtain a reduced-dimensional feature vector; inputting the feature vector into a pre-trained deep neural network model to classify the resonance state of the PCB processing workpiece to obtain the resonance state information.

[0079] Specifically, multi-source data fusion is performed on vibration signals collected by accelerometers, strain sensors, and acoustic emission sensors on PCB processing equipment to generate a fused time-domain vibration signal. This step involves synchronously collecting data from different sensor types, preprocessing, and fusing the data. Preprocessing includes denoising, filtering, and normalization to eliminate the effects of environmental interference and equipment noise. Data fusion can employ methods such as Kalman filtering or Bayesian fusion, which comprehensively consider the characteristics and measurement accuracy of different sensors to obtain more reliable and comprehensive vibration information. Specifically, Kalman filtering continuously optimizes state estimation through two steps: prediction and update, while Bayesian fusion integrates measurement results from multiple sensors based on a probabilistic model. The fusion process considers parameters such as the sampling rate, measurement range, and sensitivity of each sensor to ensure the accuracy and real-time nature of the fusion result. Through this multi-source data fusion, a time-domain signal that comprehensively reflects the vibration state during PCB processing is generated, containing richer and more reliable resonance information.

[0080] Specifically, the fused time-domain vibration signal undergoes wavelet packet decomposition and singular value decomposition to obtain a multi-scale feature matrix. Principal component analysis (PCA) is then used to reduce the dimensionality of the multi-scale feature matrix and obtain the reduced eigenvectors. Wavelet packet decomposition decomposes the signal into sub-signals of different frequency bands, providing multi-resolution analysis of the signal in the time-frequency domain. In implementation, appropriate wavelet basis functions (such as Daubechies wavelets or Morlet wavelets) and decomposition levels must be selected to capture the signal's detailed features. For each sub-band, statistical features such as energy, entropy, and standard deviation are calculated to form a preliminary feature matrix. This feature matrix is ​​then subjected to singular value decomposition (SVD) to extract the signal's primary characteristic structure. SVD decomposes the matrix into left singular vectors, singular values, and right singular vectors, where the singular values ​​reflect the importance of the features. Selecting larger singular values ​​and their corresponding singular vectors effectively represents the signal's primary features. Finally, PCA is used to reduce the dimensionality of the resulting multi-scale feature matrix. Principal component analysis converts the original features into a set of linearly independent principal components through an orthogonal transformation. The top principal components are selected based on their variance contribution, thereby reducing the dimensionality of the feature space. This series of processes not only extracts the key features of the vibration signal but also significantly reduces the dimensionality of the data, laying the foundation for subsequent resonant state classification.

[0081] Specifically, the reduced-dimensionality feature vector is input into a pre-trained deep neural network model to classify the resonant state of the PCB workpiece and obtain resonant state information. The design and training of the deep neural network model is crucial for this step. Network structures suitable for time series data processing, such as convolutional neural networks (CNNs) or long short-term memory networks (LSTMs), can be selected. The network's input layer receives the reduced-dimensionality feature vector and contains multiple hidden layers. Each hidden layer consists of multiple neurons, and nonlinear transformations are introduced through activation functions (such as ReLU or tanh). The output layer uses a softmax function to convert the network output into a probability distribution for each resonant state category. Network training requires a large amount of well-labeled historical data. Backpropagation algorithms and optimizers (such as Adam or RMSprop) are used to minimize a loss function (such as cross-entropy loss). To improve the model's generalization capabilities, techniques such as dropout and L2 regularization can be used to prevent overfitting. Furthermore, cross-validation can be used to evaluate model performance and adjust hyperparameters. Once the model is trained, new feature vectors can be input into the model to obtain a classification result for the resonant state of the PCB workpiece. This result includes information such as the type and intensity of the resonant state.

[0082] 104. Based on the resonance state information and the resonance suppression strategy, coordinately control the PCB processing equipment to achieve real-time resonance suppression.

[0083] In one embodiment of the present invention, the collaborative control of the PCB processing equipment based on the resonance state information and the resonance suppression strategy to achieve real-time resonance suppression includes: fuzzifying the resonance state information and the resonance suppression strategy to obtain a fuzzy control rule set; using a preset distributed model predictive control algorithm to generate collaborative control instructions for multiple actuators of the PCB processing equipment based on the fuzzy control rule set to obtain a multi-axis collaborative control instruction sequence; and using a preset multi-agent consistency control algorithm to coordinately control the actuators of the PCB processing equipment based on the multi-axis collaborative control instruction sequence to achieve real-time resonance suppression.

[0084] Specifically, the resonance state information and the resonance suppression strategy are fuzzified to generate a set of fuzzy control rules. This step involves converting precise numerical information into linguistic variables to better handle the uncertainty and complexity of the system. Specifically, appropriate linguistic variables and membership functions must be defined for the resonance state information (such as resonant frequency and amplitude) and the suppression strategy parameters (such as cutting speed and feed rate). For example, the resonance frequency can be defined as "low," "medium," and "high," the amplitude as "small," "medium," and "large," and the cutting speed as "slow," "medium," and "fast." Membership functions can be triangular, trapezoidal, or Gaussian. Next, a fuzzy rule base is constructed based on expert knowledge and historical data. These rules take the form of if-then statements, such as "IF the resonance frequency is high AND the amplitude is large THEN reduce the cutting speed." The formulation of rules requires a comprehensive consideration of the resonance suppression effect and machining efficiency. To improve the adaptability of the rule base, a self-learning mechanism can be introduced to continuously optimize and adjust the rules by analyzing historical machining data. Finally, fuzzification, rule matching, and defuzzification of the input are performed using fuzzy inference mechanisms (such as Mamdani inference) to obtain the final fuzzy control rule set. This rule set provides the decision-making basis for subsequent collaborative control and can flexibly adjust the control strategy according to different resonant states.

[0085] Specifically, a pre-defined distributed model predictive control algorithm, based on a set of fuzzy control rules, is used to generate coordinated control instructions for multiple actuators in PCB processing equipment, resulting in a multi-axis coordinated control instruction sequence. The core concept of the distributed model predictive control (DMPC) algorithm is to decompose the entire system into multiple subsystems, each corresponding to an actuator, while also considering the coupling relationships between the subsystems. First, a dynamic model of each actuator must be established, which can be achieved through system identification or physical modeling. The model should accurately describe the actuator's dynamic characteristics and its interactions with other actuators. Then, based on these models and the set of fuzzy control rules, a predictive controller is designed. The objective function of the predictive controller should include multiple aspects such as resonance suppression, control input smoothness, and energy consumption. During each control cycle, the controller predicts the system's behavior for a period of time in the future and optimizes the control sequence to minimize the objective function. The physical constraints of the actuators, such as speed and acceleration limits, must be considered during the optimization process. To achieve distributed control, the alternating direction method of multipliers (ADMM) or a distributed optimization algorithm can be used to coordinate the control decisions of each subsystem. This approach can reduce computational complexity while ensuring global optimality. Ultimately, the DMPC algorithm generates a series of optimal control commands for each actuator, forming a multi-axis coordinated control command sequence. This sequence not only considers the current resonant state but also predicts and prepares for possible future state changes, enabling more proactive suppression of resonance.

[0086] Specifically, a pre-defined multi-agent consensus control algorithm coordinates the control of the various actuators in the PCB processing equipment according to the multi-axis coordinated control instruction sequence, achieving real-time resonance suppression. The core concept of the multi-agent consensus control algorithm is to treat each actuator as an agent and achieve global consensus through local interactions. First, the state variables and control objectives of each agent must be defined. State variables can include position, velocity, and acceleration, while the control objective is to follow the multi-axis coordinated control instruction sequence while maintaining coordination with neighboring agents. Next, the communication topology between agents is designed. This structure determines the method and scope of information exchange. Agent networks are typically described using undirected or directed graphs, and the connectivity of the graph directly affects control performance. Next, a control protocol is designed based on consensus theory. The control protocol defines how each agent uses its own information and that of its neighbors to update its control inputs. Typical protocols include average consensus and leader-follower protocols. To improve the robustness and adaptability of the system, an adaptive mechanism can be introduced to dynamically adjust control parameters based on the resonant state. In actual execution, each agent calculates the control input according to the protocol and applies it to the corresponding actuator. Through continuous interaction and coordination between intelligent agents, the entire system can quickly converge to the desired state, effectively suppressing resonance. This distributed control approach not only adapts to complex multi-axis systems but also has good scalability and fault tolerance, maintaining system stability even when actuators fail or communication is interrupted.

[0087] In this embodiment, by collecting the material properties of the PCB processing workpiece and the characteristic data of the processing equipment, modal analysis and frequency response analysis are performed to generate a stability lobe diagram of the PCB processing system. According to the stability lobe diagram, the processing parameter space is divided, and the processing parameters are evaluated to generate a resonance suppression strategy. During the processing, multiple types of sensors are used to collect the vibration signal of the workpiece in real time, and the vibration signal is analyzed to obtain the current resonance state information. According to the resonance state information and the preset resonance suppression strategy, the processing parameters of the processing equipment are coordinated and controlled to achieve real-time resonance suppression. The present invention can effectively suppress the resonance phenomenon in the processing process through real-time vibration monitoring and dynamic control of the processing system, thereby improving the quality of PCB processing, processing accuracy and operational stability of the equipment.

[0088] The above describes the PCB processing resonance suppression method according to the embodiment of the present invention. The following describes the PCB processing resonance suppression device according to the embodiment of the present invention. The PCB processing resonance suppression device is applied to a PCB processing system, wherein the PCB processing system includes a PCB processing workpiece and a PCB processing device. Figure 2 In one embodiment of the present invention, a PCB processing resonance suppression device includes:

[0089] An acquisition and analysis module 201 is configured to acquire PCB material properties of the PCB processing workpiece and processing equipment characteristic data of the PCB processing equipment, and perform modal analysis and frequency response analysis on the PCB processing system based on the PCB material properties and the processing equipment characteristic data to obtain a stability lobe diagram of the PCB processing system;

[0090] a strategy generating module 202 for dividing and evaluating a processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy for the PCB processing system according to the processing parameter space;

[0091] a state evaluation module 203 configured to collect vibration signals of the PCB workpiece by using multiple types of sensors on the PCB processing equipment during the process of the PCB processing equipment processing the PCB workpiece, and to perform resonance state evaluation on the vibration signals to obtain resonance state information;

[0092] The resonance suppression module 204 is configured to collaboratively control the PCB processing equipment according to the resonance state information and the resonance suppression strategy to achieve real-time resonance suppression.

[0093] In an embodiment of the present invention, the PCB processing resonance suppression device runs the above-mentioned PCB processing resonance suppression method. The PCB processing resonance suppression device collects the material properties of the PCB processing workpiece and the characteristic data of the processing equipment, performs modal analysis and frequency response analysis, and generates a stability lobe diagram of the PCB processing system. According to the stability lobe diagram, the processing parameter space is divided, and the processing parameters are evaluated to generate a resonance suppression strategy. During the processing, multiple types of sensors are used to collect the vibration signal of the workpiece in real time, and the vibration signal is analyzed to obtain the current resonance state information. According to the resonance state information and the preset resonance suppression strategy, the processing parameters of the processing equipment are coordinated and controlled to achieve real-time resonance suppression. The present invention can effectively suppress the resonance phenomenon in the processing process through real-time vibration monitoring and dynamic control of the processing system, thereby improving the quality of PCB processing, processing accuracy and operational stability of the equipment.

[0094] above Figure 2 The PCB processing resonance suppression device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The PCB processing resonance suppression device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0095] Figure 3This is a schematic diagram of the structure of a PCB processing resonance suppression device provided by an embodiment of the present invention. The PCB processing resonance suppression device 300 may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors), memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage medium 330 may be either transient or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions for operating the PCB processing resonance suppression device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, executing the series of instructions stored in the storage medium 330 on the PCB processing resonance suppression device 300 to implement the steps of the aforementioned PCB processing resonance suppression method.

[0096] The PCB processing resonance suppression device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the PCB processing resonance suppression device shown does not constitute a limitation on the PCB processing resonance suppression device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0097] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the PCB processing resonance suppression method.

[0098] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0100] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A PCB processing resonance suppression method, characterized in that: Applied to a PCB processing system, the PCB processing system includes a PCB processing workpiece and PCB processing equipment, and the PCB processing resonance suppression method includes: PCB material properties of the PCB processing workpiece and processing equipment characteristic data of the PCB processing equipment are collected, and the PCB material properties and the processing equipment characteristic data are parameterized to obtain a multidimensional parameter model of the PCB processing system; the PCB processing system is discretized based on the multidimensional parameter model using a finite element method to obtain a numerical model of the PCB processing system; eigenvalue decomposition and singular value decomposition are performed on the numerical model to obtain natural frequencies and modal vibration shapes of the PCB processing system; frequency sweep analysis is performed on the PCB processing system based on the natural frequencies and modal vibration shapes to obtain a first frequency response function of the PCB processing system; and stability analysis of the PCB processing system is performed using the first frequency response function using a dynamic stiffness method and an analytical continuation method to obtain a stability lobe diagram of the PCB processing system. dividing and evaluating a processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy for the PCB processing system according to the processing parameter space; During the process of the PCB processing equipment processing the PCB processing workpiece, a vibration signal of the PCB processing workpiece is collected by multiple types of sensors on the PCB processing equipment, and a resonance state evaluation is performed on the vibration signal to obtain resonance state information; According to the resonance state information and the resonance suppression strategy, the PCB processing equipment is collaboratively controlled to achieve real-time resonance suppression.

2. The PCB processing resonance suppression method according to claim 1, characterized in that: The stability analysis of the PCB processing system is performed using the frequency response function by the dynamic stiffness method and the analytical continuation method to obtain the stability lobe diagram of the PCB processing system, including: Transforming the frequency response function using a dynamic stiffness method to obtain a dynamic stiffness matrix of the PCB processing system; Extending the system response of the PCB processing system from the real frequency domain to the complex frequency domain according to the dynamic stiffness matrix by an analytical continuation method to obtain the complex frequency domain characteristic equation of the system; Solving the eigenvalue problem of the complex frequency domain characteristic equation to obtain stability judgment results of the PCB processing system under different processing parameters; A stability lobe diagram of the PCB processing system is drawn according to the stability judgment result using a parameter mapping method.

3. The PCB processing resonance suppression method according to claim 1, characterized in that: The dividing and evaluating the processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy for the PCB processing system according to the processing parameter space includes: Performing image segmentation and boundary extraction on the stability lobe map to obtain stable areas and unstable areas of the PCB processing system; According to the stable region and the unstable region, a clustering algorithm is used to divide the processing parameter space of the PCB processing system to obtain multiple processing parameter subspaces; Performing stability assessment and risk quantification on the multiple processing parameter subspaces to obtain a stability index and a resonance risk level for each processing parameter subspace; Using a multi-objective optimization algorithm, processing parameters are selected and combined according to the stability index and the resonance risk level to obtain an initial resonance suppression parameter set; A dynamic adjustment rule is applied to the initial resonance suppression parameter set to generate a resonance suppression strategy for the PCB processing system.

4. The PCB processing resonance suppression method according to claim 3, characterized in that: The performing stability assessment and risk quantification on the multiple processing parameter subspaces to obtain the stability index and resonance risk level of each processing parameter subspace includes: Discrete sampling is performed on each processing parameter subspace to obtain a representative processing parameter point set, and a system identification method is used to perform dynamic response analysis on the representative processing parameter point set to obtain a second frequency response function of each processing parameter point; performing a stability evaluation on each processing parameter point according to the second frequency response function to obtain a corresponding stability index, and extracting a resonant frequency of the PCB processing system from the stability lobe diagram; A preset fuzzy inference system is used to perform risk assessment on each processing parameter subspace according to the resonance frequency and the stability index to obtain a corresponding resonance risk level.

5. The PCB processing resonance suppression method according to claim 1, characterized in that: The performing resonance state evaluation on the vibration signal to obtain resonance state information includes: Performing multi-source data fusion processing on the vibration signals collected by the acceleration sensor, strain sensor, and acoustic emission sensor on the PCB processing equipment to obtain a fused time-domain vibration signal; Performing wavelet packet decomposition and singular value decomposition on the time domain vibration signal to obtain a multi-scale feature matrix, and performing dimensionality reduction processing on the multi-scale feature matrix using a principal component analysis method to obtain a eigenvector after dimensionality reduction; The feature vector is input into a pre-trained deep neural network model to classify the resonance state of the PCB workpiece to obtain resonance state information.

6. The PCB processing resonance suppression method according to claim 1, characterized in that: The collaboratively controlling the PCB processing equipment according to the resonance state information and the resonance suppression strategy to achieve real-time resonance suppression includes: Performing fuzzy processing on the resonance state information and the resonance suppression strategy to obtain a fuzzy control rule set; A preset distributed model predictive control algorithm is used to generate collaborative control instructions for multiple actuators of the PCB processing equipment based on the fuzzy control rule set to obtain a multi-axis collaborative control instruction sequence; Through a preset multi-agent consistency control algorithm, the various actuators of the PCB processing equipment are coordinated and controlled according to the multi-axis collaborative control instruction sequence to achieve real-time resonance suppression.

7. A PCB processing resonance suppression device, characterized in that: Applied to a PCB processing system, the PCB processing system includes a PCB processing workpiece and PCB processing equipment, and the PCB processing resonance suppression device includes: an acquisition and analysis module configured to acquire PCB material properties of the PCB processing workpiece and processing equipment characteristic data of the PCB processing equipment, and perform parameterization processing on the PCB material properties and the processing equipment characteristic data to obtain a multidimensional parameter model of the PCB processing system; discretize the PCB processing system based on the multidimensional parameter model using a finite element method to obtain a numerical model of the PCB processing system; perform eigenvalue decomposition and singular value decomposition on the numerical model to obtain natural frequencies and modal vibration shapes of the PCB processing system; perform frequency sweep analysis on the PCB processing system based on the natural frequencies and modal vibration shapes to obtain a first frequency response function of the PCB processing system; and perform stability analysis on the PCB processing system using the first frequency response function using a dynamic stiffness method and an analytical continuation method to obtain a stability lobe diagram of the PCB processing system; a strategy generation module, configured to divide and evaluate a processing parameter space of the PCB processing system according to the stability lobe diagram, and generate a resonance suppression strategy for the PCB processing system according to the processing parameter space; a state evaluation module configured to collect vibration signals of the PCB workpiece by using multiple types of sensors on the PCB processing equipment during processing of the PCB workpiece by the PCB processing equipment, and to perform resonance state evaluation on the vibration signals to obtain resonance state information; The resonance suppression module is used to coordinately control the PCB processing equipment according to the resonance state information and the resonance suppression strategy to achieve real-time resonance suppression.

8. A PCB processing resonance suppression device, characterized in that: The PCB processing resonance suppression device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the PCB processing resonance suppression device to perform the steps of the PCB processing resonance suppression method according to any one of claims 1 to 6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the PCB processing resonance suppression method as described in any one of claims 1 to 6 are implemented.

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