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

Abstract

The invention provides a PCB processing resonance suppression method, device and equipment and a storage medium, and the method comprises the steps: collecting the material attribute of a PCB processing workpiece and the characteristic data of processing equipment, carrying out the modal analysis and frequency response analysis, and generating a stability lobe graph of a PCB processing system. And according to the stability lobe graph, a processing parameter space is divided, processing parameters are evaluated, and then a resonance suppression strategy is generated. In the machining process, vibration signals of a workpiece are collected in real time through multiple types of sensors, the vibration signals are analyzed, and current resonance state information is obtained. And performing cooperative control on processing parameters of the processing equipment according to the resonance state information in combination with a preset resonance suppression strategy to realize real-time resonance suppression. According to the invention, through real-time vibration monitoring and dynamic control of the processing system, the resonance phenomenon in the processing process can be effectively inhibited, and the PCB processing quality, the processing precision and the operation stability of equipment are improved.

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 processing accuracy and complexity requirements of PCB (printed circuit board) are constantly increasing. However, in the high-precision processing of PCB, resonance phenomenon has become an important factor affecting the processing quality. Resonance can cause severe vibration of processing equipment, resulting in increased tool wear, reduced processing accuracy and deterioration of workpiece surface quality. Especially under high-speed processing conditions, the particularity of PCB materials and the frequency response of processing equipment can easily cause unstable vibration, resulting in resonance of the processing system.

[0003] At present, traditional resonance suppression methods usually rely on structural improvements to processing equipment or use fixed processing parameters to avoid specific resonant frequencies. However, these methods cannot adapt to the dynamic vibration changes that occur during the processing process, and the resonance phenomenon is still difficult to avoid under various material properties and complex processing conditions, resulting in limited processing 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 occurring during the processing, resulting in limited processing efficiency and accuracy; A first aspect of the present invention provides a PCB processing resonance suppression method, which is applied to a PCB processing system. The PCB processing system includes a PCB processing workpiece and a PCB processing device. The PCB processing resonance suppression method includes: 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; Dividing and evaluating a processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy of the PCB processing system according to the processing parameter space; In the process of the PCB processing equipment processing the PCB processing workpiece, the vibration signal of the PCB processing workpiece is collected by multiple types of sensors on the PCB processing equipment, and the resonance state of the vibration signal is evaluated to obtain the 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.

[0005] 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: Parameterizing the PCB material properties and the processing equipment characteristic data to obtain a multi-dimensional parameter model of the PCB processing system; Using a finite element method to discretize the PCB processing system according to the multidimensional parameter model, to obtain a numerical model of the PCB processing system; Performing eigenvalue decomposition and singular value decomposition on the numerical model to obtain the natural frequency and mode shape of the PCB processing system; Performing a frequency scanning analysis on the PCB processing system according to the natural frequency and the mode vibration shape to obtain a first frequency response function of the PCB processing system; 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.

[0006] Optionally, in a second implementation of the first aspect of the present invention, 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: The frequency response function is transformed by using a dynamic stiffness method to obtain a dynamic stiffness matrix of the PCB processing system; By using an analytical continuation method, the system response of the PCB processing system is extended from the real frequency domain to the complex frequency domain according to the dynamic stiffness matrix, so as 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 through a parameter mapping method.

[0007] Optionally, in a third implementation manner of the first aspect of the present invention, 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 a stable region and an unstable region 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 a plurality of 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 of each processing parameter subspace; Using a multi-objective optimization algorithm, the 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.

[0008] Optionally, in a fourth implementation manner 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 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; According to the second frequency response function, a stability evaluation is performed on each processing parameter point to obtain a corresponding stability index, and a resonant frequency of the PCB processing system is extracted 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.

[0009] Optionally, in a fifth implementation manner of the first aspect of the present invention, 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.

[0010] 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: 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, 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.

[0011] 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 a PCB processing device. The PCB processing resonance suppression device includes: An acquisition and analysis module is used 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 the PCB processing system and stability lobe diagram; A strategy generation module, used to divide and evaluate the processing parameter space of the PCB processing system according to the stability lobe diagram, and generate a resonance suppression strategy of the PCB processing system according to the processing parameter space; A state evaluation module is used to collect vibration signals of the PCB processing workpiece through multiple types of sensors on the PCB processing equipment during the process of the PCB processing workpiece being processed by the PCB processing equipment, and to perform resonance state evaluation on the vibration signal 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.

[0012] A third aspect of the present invention provides a PCB processing resonance suppression device, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via a line; and the at least one processor calls the instructions in the memory so that the PCB processing resonance suppression device performs the steps of the above-mentioned PCB processing resonance suppression method.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the above-mentioned PCB processing resonance suppression method.

[0014] 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, and improve the quality of PCB processing, processing accuracy and operation stability of the equipment.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] 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

[0017] 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; Figure 2 A schematic diagram of an embodiment of a PCB processing resonance suppression device in an embodiment of the present invention; Figure 3 It is a schematic diagram of an embodiment of a PCB processing resonance suppression device in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0020] 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, and the PCB processing system includes a PCB processing workpiece and a PCB processing device. Figure 1 As shown, the method comprises the following steps: 101. Collect PCB material properties of PCB processing workpieces and processing equipment characteristic data of PCB processing equipment, and perform 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; In one embodiment of the present invention, the modal analysis and frequency response analysis of the PCB processing system based on the PCB material properties and the processing equipment characteristic data to obtain the stability lobe diagram of the PCB processing system includes: parameterizing 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 according to 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 the natural frequency and modal vibration shape of the PCB processing system; performing frequency scanning analysis on the PCB processing system according to the natural frequency and modal vibration shape 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.

[0021] Specifically, the PCB material properties and processing equipment characteristic data are parameterized to obtain a multi-dimensional parameter model of the PCB processing system. This step involves converting the raw data into quantifiable parameters, such as the elastic modulus, density, Poisson's ratio of the material, and the stiffness, mass, damping coefficient of the equipment. By establishing a mathematical model, these parameters are organized into a multi-dimensional vector or matrix to form a parametric representation of the system. This representation method makes subsequent analysis and calculation feasible while retaining the key characteristics of the system.

[0022] Specifically, the finite element method is used to discretize the PCB processing system according to the multidimensional parameter model to obtain the numerical model of the PCB processing system. The finite element method divides the continuous physical system into a finite number of units, each of which is connected by nodes. Based on the previously established parameter model, the corresponding material properties and geometric characteristics are assigned to each unit. Then, by constructing the mass matrix, stiffness matrix and damping matrix, the dynamic behavior of the entire system is described as a set of algebraic equations. This process converts the complex continuous system into a discrete mathematical model that can be solved by computer.

[0023] Specifically, the numerical model is subjected to eigenvalue decomposition and singular value decomposition to obtain the natural frequencies and modal vibration shapes of the PCB processing system. Eigenvalue decomposition is used to solve the natural frequencies of the system, which represent the natural vibration frequencies of the system without external forces. Singular value decomposition is used to determine the modal vibration shapes of the system, that is, the deformed shape of the system at each natural frequency. The combination of these two decomposition methods can fully reveal the dynamic characteristics of the system. By analyzing these eigenvalues ​​and eigenvectors, the key vibration modes and frequency ranges of the system can be identified. Based on the natural frequencies and modal vibration shapes, a frequency sweep analysis is performed on the PCB processing system to obtain the first frequency response function of the PCB processing system. Frequency sweep analysis involves applying harmonic excitation to the system within a certain frequency range and calculating the response of the system at each frequency point. By comparing the input excitation and the output response, the frequency response function of the system is constructed. This function describes the amplitude and phase characteristics of the system at different frequencies and is an important tool for understanding the dynamic behavior of the system.

[0024] Specifically, the stability analysis of the PCB processing system is carried out using the first frequency response function through the dynamic stiffness method and the analytical continuation method, and the stability lobe diagram of the PCB processing system is obtained. The dynamic stiffness method expresses the dynamic characteristics of the system as a function of frequency, while the analytical continuation law 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 set of nonlinear equations, and usually requires the use of numerical iteration methods. The stability lobe diagram finally generated intuitively shows the stable and unstable areas of the system under different processing parameters, providing an important basis for subsequent parameter optimization and control strategy formulation.

[0025] Furthermore, the stability analysis of the PCB processing system is performed using the frequency response function through 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 the dynamic stiffness method to obtain the 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 through the 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 the stability judgment result of the PCB processing system under different processing parameters; and drawing the stability lobe diagram of the PCB processing system according to the stability judgment result through the parameter mapping method.

[0026] 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 an expression of dynamic stiffness. Specifically, the dynamic stiffness matrix is ​​the inverse of the frequency response function, 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 dynamic characteristics of the system from the perspective of frequency response to the perspective of stiffness, which is more suitable for subsequent stability analysis. The dynamic stiffness matrix contains the stiffness information of the system at different frequencies, reflecting the change of the system's ability to resist deformation with frequency.

[0027] Specifically, the system response of the PCB processing system is extended from the real frequency domain to the complex frequency domain according to the dynamic stiffness matrix through the analytical continuation method, and the complex frequency domain characteristic equation of the system is obtained. The core idea of ​​the analytical continuation method is to extend the function of the real frequency domain 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. Through this replacement, the dynamic characteristics of the system are extended to a wider complex plane. In the complex frequency domain, the characteristic equation of the system 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.

[0028] 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. For different combinations of processing parameters, the characteristic equation needs to be solved repeatedly. The criterion for stability judgment 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 changing the processing parameters (such as spindle speed, cutting depth, etc.) and performing eigenvalue analysis on each set of parameters, a series of stability judgment results can be obtained. These results constitute the basic data of the stability lobe diagram.

[0029] Specifically, the stability lobe diagram of the PCB processing system is drawn according to the stability judgment results through the 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 according to 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.

[0030] 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 of the PCB processing system according to the processing parameter space; In one embodiment of the present invention, dividing and evaluating the processing parameter space of the PCB processing system according to the stability lobe diagram, and generating the resonance suppression strategy of the PCB processing system according to the processing parameter space includes: performing image segmentation and boundary extraction on the stability lobe diagram to obtain a stable area and an unstable area of ​​the PCB processing system; dividing the processing parameter space of the PCB processing system according to the stable area and the unstable area by using a clustering algorithm to obtain a plurality of processing parameter subspaces; performing stability evaluation and risk quantification on the plurality of processing parameter subspaces to obtain a stability index and a resonance risk level of each processing parameter subspace; selecting and combining processing parameters according to the stability index and the resonance risk level by using a multi-objective optimization algorithm to obtain an initial resonance suppression parameter set; and applying a dynamic adjustment rule to the initial resonance suppression parameter set to generate the resonance suppression strategy of the PCB processing system.

[0031] Specifically, the stability lobe map is segmented and the boundary is extracted to obtain the stable area and unstable area of ​​the PCB processing system. This step involves analyzing the stability lobe map using image processing technology. Specifically, an edge detection algorithm (such as the Canny algorithm or the Sobel algorithm) can be used to identify the boundary line between the stable area and the unstable area. Then, the image is segmented using a region growing algorithm or a watershed algorithm to divide the entire parameter space into different regions. In order to improve the accuracy of the segmentation, morphological processing, such as opening and closing operations, can also be introduced to eliminate noise and small isolated areas. Finally, through these image processing techniques, the stability lobe map is converted into a clear binary image in which the stable area and the unstable area are clearly identified.

[0032] Specifically, according to the stable area and the unstable area, the processing parameter space of the PCB processing system is divided by a clustering algorithm to obtain multiple processing parameter subspaces. The purpose of this step is to classify parameter points with similar stability characteristics into the same subspace. The K-means clustering algorithm or the hierarchical clustering algorithm can be used to achieve this goal. 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 mark. The clustering process takes into account the Euclidean distance between points and the similarity of stability characteristics. The number of clusters can be determined by the silhouette coefficient or the elbow rule. After clustering, each subspace represents a set of processing parameter combinations with similar stability characteristics. This division method helps to formulate differentiated resonance suppression strategies for different subspaces in the future.

[0033] Specifically, stability assessment and risk quantification are performed on multiple processing parameter subspaces to obtain the stability index and resonance risk level of each processing parameter subspace. This step involves assigning a quantitative stability index to each subspace. The stability index can be determined by calculating the proportion of stable points in 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 evaluated based on the average resonance amplitude or frequency of the points in the subspace. Specifically, the fuzzy comprehensive evaluation method can be used to comprehensively consider multiple factors such as stability index, resonance amplitude, frequency offset, etc., so as to obtain a comprehensive risk assessment result. In addition, sensitivity analysis can be introduced to evaluate the degree of influence of parameter changes on system stability, which helps to identify high-risk parameter combinations. Through this quantitative evaluation, each subspace is assigned a clear stability index and risk level, which provides an important basis for subsequent parameter optimization.

[0034] Specifically, the multi-objective optimization algorithm is used to select and combine the processing parameters according to the stability index and the resonance risk level to obtain the initial resonance suppression parameter set. The goal of this step is to find the best parameter combination to suppress resonance while ensuring the processing quality. Multi-objective optimization problems usually involve multiple conflicting objectives, such as maximizing the stability index, minimizing the resonance risk, maximizing the processing efficiency, etc. The non-dominated sorting genetic algorithm II (NSGA-II) or the multi-objective particle swarm optimization algorithm (MOPSO) can be used to solve this problem. During the optimization process, it is necessary to define appropriate objective functions and constraints. For example, the stability index can be used as the maximization target and the resonance risk level can be used as the minimization target, while considering constraints such as processing 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 different objectives.

[0035] 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 is intended 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 according to 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 from historical processing data. By applying these dynamic adjustment rules, the initial parameter set is transformed into a complete resonance suppression strategy, which not only includes the optimal initial parameter settings, but also has the ability to make real-time adjustments according to the actual processing conditions, so that the resonance phenomenon in the PCB processing process can be more effectively suppressed.

[0036] 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 using a system identification method to perform dynamic response analysis on the representative processing parameter point set to obtain a second frequency response function of each processing parameter point; based on the second frequency response function, performing stability assessment on 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; 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.

[0037] Specifically, discrete sampling is performed on each processing parameter subspace to obtain a representative processing parameter point set, and the system identification method is used to perform dynamic response analysis on the representative processing parameter point set to obtain the second frequency response function of each processing parameter point. This step involves selecting representative parameter points in each subspace, and using methods such as uniform sampling or Latin hypercube sampling to ensure the uniform distribution and representativeness of the sampling points. For each sampling point, a system identification experiment is required, by applying a known excitation signal (such as white noise or pseudo-random binary sequence) to the PCB processing system and measuring the output response of the system. Then, the input and output data are processed using a system identification algorithm (such as the least squares method, subspace identification method, or maximum likelihood estimation method) to establish a mathematical model of the system. Based on this model, the second frequency response function 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 evaluation.

[0038] Specifically, according to the second frequency response function, the stability of each processing parameter point is evaluated to obtain the corresponding stability index, and the resonant frequency of the PCB processing system is extracted from the stability lobe diagram. The stability evaluation can be performed by analyzing the characteristics of the frequency response function, such as the Nyquist stability criterion or the phase margin and gain margin analysis. Specifically, the phase margin (the difference between the phase response and -180° when the system gain is 0dB) and the gain margin (the difference between the gain response and 0dB when the system phase is -180°) of the system can be calculated. The stability index can be defined as the weighted sum of these two margins or other composite indicators. At the same time, by analyzing the peak value of the frequency response function, the resonant frequency of the system can be identified. These resonant frequencies usually correspond to the local maximum points on the amplitude curve of the frequency response function. In order to improve the accuracy of resonant frequency identification, the peak detection algorithm and the curve fitting technique can be used in combination. In this way, not only the stability evaluation results of each parameter point are obtained, but also the key resonant frequencies of the system are determined.

[0039] Specifically, a preset fuzzy inference system is used to conduct risk assessment on each processing parameter subspace according to the resonance frequency and stability index to obtain the corresponding resonance risk level. The design of the fuzzy inference system is based on expert knowledge and historical data, with the resonance frequency and stability index as input variables and the resonance risk level as output variable. First, the fuzzy sets and membership functions of the input variables need to be defined. For example, the resonance frequency can be divided into three fuzzy sets of "low", "medium", and "high", and the stability index can be divided into three fuzzy sets of "poor", "general", and "good". Then, a fuzzy rule base is designed, such as "if the resonance frequency is high and the stability index is poor, the resonance risk level is high". Then, a fuzzy inference mechanism (such as Mamdani reasoning or Sugeno reasoning) is used to fuzzify, rule reason and defuzzify the input to obtain the final resonance risk level. In order to improve the accuracy of risk assessment, an adaptive mechanism can be introduced to continuously adjust the fuzzy rules and membership functions according to the actual processing data. Through this fuzzy reasoning method, the quantitative stability index and resonance frequency are converted into risk levels that are easier to understand and apply, providing intuitive guidance for the subsequent formulation of resonance suppression strategies.

[0040] 103. When the PCB processing equipment processes the PCB processing workpiece, a vibration signal of the PCB processing workpiece is collected by using 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; 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 signal 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 feature vector after dimension reduction; 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.

[0041] Specifically, the vibration signals collected by the acceleration sensors, strain sensors and acoustic emission sensors on the PCB processing equipment are subjected to multi-source data fusion processing to obtain the fused time-domain vibration signal. This step involves synchronously collecting data from different types of sensors and preprocessing and fusing these data. Preprocessing includes denoising, filtering and normalization to eliminate the influence of environmental interference and equipment noise. Data fusion can use methods such as Kalman filtering or Bayesian fusion, which can 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 of prediction and update, while Bayesian fusion is based on a probability model to integrate the measurement results of multiple sensors. The fusion process needs to consider parameters such as the sampling rate, measurement range and sensitivity of various sensors to ensure the accuracy and real-time nature of the fusion results. Through this multi-source data fusion, a time-domain signal that comprehensively reflects the vibration state during PCB processing can be obtained, and this signal contains richer and more reliable resonance information.

[0042] Specifically, the fused time-domain vibration signal is subjected to wavelet packet decomposition and singular value decomposition to obtain a multi-scale feature matrix, and the multi-scale feature matrix is ​​subjected to dimensionality reduction processing using the principal component analysis method to obtain the eigenvector after dimensionality reduction. Wavelet packet decomposition can decompose the signal into sub-signals of different frequency bands, providing multi-resolution analysis of the signal in the time-frequency domain. In specific implementation, it is necessary to select appropriate wavelet basis functions (such as Daubechies wavelet or Morlet wavelet) and decomposition layers to capture the detailed characteristics of the signal. For each sub-band, statistical features such as energy, entropy, and standard deviation are calculated to form a preliminary feature matrix. Then, singular value decomposition is performed on this feature matrix to extract the main characteristic structure of the signal. Singular value decomposition can decompose 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 can effectively represent the main features of the signal. Finally, the principal component analysis method is used to reduce the dimensionality of the obtained multi-scale feature matrix. Principal component analysis converts the original features into a set of linearly independent principal components through orthogonal transformation, and selects the first few principal components according to the variance contribution rate, thereby achieving dimensionality reduction of the feature space. This series of processing not only extracts the key features of the vibration signal, but also significantly reduces the dimension of the data, laying the foundation for the subsequent resonance state classification.

[0043] Specifically, the feature vector after dimension reduction is input into the pre-trained deep neural network model to classify the resonance state of the PCB workpiece and obtain the resonance state information. The design and training of the deep neural network model is the key to this step. You can choose a network structure suitable for time series data processing, such as a convolutional neural network (CNN) or a long short-term memory network (LSTM). The input layer of the network receives the feature vector after dimension reduction, and contains multiple hidden layers in the middle. Each hidden layer consists of multiple neurons, and nonlinear transformation is introduced through activation functions (such as ReLU or tanh). The output layer uses the softmax function to convert the network output into the probability distribution of each resonance state category. The training of the network requires a large amount of labeled historical data, and the back propagation algorithm and optimizer (such as Adam or RMSprop) are used to minimize the loss function (such as cross entropy loss). In order to improve the generalization ability of the model, dropout, L2 regularization and other techniques can be used to prevent overfitting. In addition, cross-validation can be used to evaluate model performance and adjust hyperparameters. Once the model training is completed, the new feature vector can be input into the model to obtain the resonance state classification result of the PCB workpiece. This result contains information such as the type and intensity of the resonance. 104. According to the resonance state information and the resonance suppression strategy, the PCB processing equipment is collaboratively controlled to achieve real-time resonance suppression.

[0044] In one embodiment of the present invention, the collaborative control of the PCB processing equipment according to 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 according to the multi-axis collaborative control instruction sequence to achieve real-time resonance suppression.

[0045] Specifically, the resonance state information and the resonance suppression strategy are fuzzified to obtain a fuzzy control rule set. This step involves converting precise numerical information into linguistic variables in order to better handle the uncertainty and complexity in the system. Specifically, it is necessary to define appropriate linguistic variables and membership functions for the resonance state information (such as resonance frequency, amplitude) and the suppression strategy parameters (such as cutting speed, feed rate). For example, the resonance frequency can be defined as "low", "medium", and "high", the amplitude can be defined as "small", "medium", and "large", and the cutting speed can be defined as "slow", "medium", and "fast". The membership function can be selected as a triangle, trapezoid, or Gaussian function. Next, based on expert knowledge and historical data, a fuzzy rule base is constructed. These rules are in the form of IF-THEN, such as "IF the resonance frequency is high AND the amplitude is large THEN reduce the cutting speed". The formulation of the rules needs to comprehensively consider the effect of resonance suppression and the processing efficiency. In order 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 processing data. Finally, the fuzzy inference mechanism (such as Mamdani inference) is used to fuzzify the input, match the rules, and defuzzify the input to obtain the final fuzzy control rule set. This rule set provides a decision-making basis for subsequent collaborative control and can flexibly adjust the control strategy according to different resonance states.

[0046] Specifically, a preset distributed model predictive control algorithm is used to generate collaborative control instructions for multiple actuators of PCB processing equipment based on a fuzzy control rule set, and a multi-axis collaborative control instruction sequence is obtained. The core idea of ​​the distributed model predictive control (DMPC) algorithm is to decompose the entire system into multiple subsystems, each of which corresponds to an actuator, while considering the coupling relationship between the subsystems. First, a dynamic model of each actuator needs to be established, which can be achieved through system identification or physical modeling. The model should be able to accurately describe the dynamic characteristics of the actuator and its interaction with other mechanisms. Then, based on these models and the fuzzy control rule set, a predictive controller is designed. The objective function of the predictive controller should include multiple aspects such as resonance suppression effect, smoothness of control input, and energy consumption. In each control cycle, the controller predicts the behavior of the system in the future and optimizes the control sequence to minimize the objective function. The physical constraints of the actuator, such as speed limit and acceleration limit, need to be considered during the optimization process. In order to achieve distributed control, the alternating direction multiplier method (ADMM) or distributed optimization algorithm can be used to coordinate the control decisions of each subsystem. This method can reduce the computational complexity while ensuring the global optimum. Ultimately, the DMPC algorithm generates a series of optimal control instructions for each actuator, which constitute a multi-axis coordinated control instruction sequence. This sequence not only takes into account the current resonant state, but also predicts and prepares for possible future state changes, thereby suppressing resonance in a more proactive manner.

[0047] Specifically, the preset multi-agent consistency control algorithm is used to coordinate the control of the actuators of the PCB processing equipment according to the multi-axis collaborative control instruction sequence to achieve real-time resonance suppression. The core idea of ​​the multi-agent consistency control algorithm is to regard each actuator as an agent and achieve global consistency through local interaction. First, it is necessary to define the state variables and control objectives of each agent. The state variables may include position, velocity, acceleration, etc., and the control objective is to follow the multi-axis collaborative control instruction sequence while maintaining coordination with adjacent agents. Then, the communication topology between agents is designed, which determines the mode and scope of information exchange. Undirected graphs or directed graphs are usually used to describe the agent network, and the connectivity of the graph directly affects the control performance. Next, the control protocol is designed based on the consistency theory. The control protocol defines how each agent uses its own information and neighbor information to update its control input. Typical protocols include average consistency protocol and leader following protocol. In order to improve the robustness and adaptability of the system, an adaptive mechanism can be introduced to enable the control parameters to be dynamically adjusted according to the resonant state. In the actual execution process, each agent calculates the control input according to the protocol and applies it to the corresponding actuator. Through continuous interaction and coordination between agents, the entire system can quickly converge to the desired state, thereby effectively suppressing resonance. This distributed control method can not only adapt to complex multi-axis systems, but also has good scalability and fault tolerance, and can maintain the stability of the system when the actuator fails or communication is interrupted.

[0048] 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 and improve the quality of PCB processing, processing accuracy and operation stability of the equipment through real-time vibration monitoring and dynamic control of the processing system.

[0049] The above describes the PCB processing resonance suppression method in the embodiment of the present invention. The following describes the PCB processing resonance suppression device in the embodiment of the present invention. The PCB processing resonance suppression device is applied to a PCB processing system. The PCB processing system includes a PCB processing workpiece and a PCB processing device. Figure 2, an embodiment of the PCB processing resonance suppression device in the embodiment of the present invention includes: The acquisition and analysis module 201 is used to acquire the PCB material properties of the PCB processing workpiece and the 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 the PCB processing system and stability lobe diagram; A strategy generation module 202, 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 of the PCB processing system according to the processing parameter space; The state evaluation module 203 is used to collect vibration signals of the PCB processing workpiece through multiple types of sensors on the PCB processing equipment during the process of the PCB processing workpiece being processed by the PCB processing equipment, and to perform resonance state evaluation on the vibration signal to obtain resonance state information; The resonance suppression module 204 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.

[0050] In an embodiment of the present invention, the PCB processing resonance suppression device runs the above-mentioned PCB processing resonance suppression method, and the PCB processing resonance suppression device collects material properties of PCB processing workpieces and characteristic data of 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 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 combined with 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, and improve the quality of PCB processing, processing accuracy and operation stability of the equipment.

[0051] 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.

[0052] Figure 33 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 have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be temporary storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the PCB processing resonance suppression device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the PCB processing resonance suppression device 300 to implement the steps of the above-mentioned PCB processing resonance suppression method.

[0053] The PCB processing resonance suppression device 300 may also 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 Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated 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 a combination of certain components, or a different arrangement of components.

[0054] 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. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the PCB processing resonance suppression method.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0056] If the integrated unit is implemented in the form of 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 is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0057] 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 aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may 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 a PCB processing device, and the PCB processing resonance suppression method includes: 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; Dividing and evaluating a processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy of the PCB processing system according to the processing parameter space; In the process of the PCB processing equipment processing the PCB processing workpiece, the vibration signal of the PCB processing workpiece is collected by multiple types of sensors on the PCB processing equipment, and the resonance state of the vibration signal is evaluated to obtain the 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 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 stability lobe diagram of the PCB processing system includes: Parameterizing the PCB material properties and the processing equipment characteristic data to obtain a multi-dimensional parameter model of the PCB processing system; Using a finite element method to discretize the PCB processing system according to the multidimensional parameter model, to obtain a numerical model of the PCB processing system; Performing eigenvalue decomposition and singular value decomposition on the numerical model to obtain the natural frequency and mode shape of the PCB processing system; Performing a frequency scanning analysis on the PCB processing system according to the natural frequency and the mode vibration shape to obtain a first frequency response function of the PCB processing system; 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.

3. The PCB processing resonance suppression method according to claim 2, characterized in that: The stability analysis of the PCB processing system is performed by using the frequency response function through the dynamic stiffness method and the analytical continuation method to obtain the stability lobe diagram of the PCB processing system, including: The frequency response function is transformed by using a dynamic stiffness method to obtain a dynamic stiffness matrix of the PCB processing system; By using an analytical continuation method, the system response of the PCB processing system is extended from the real frequency domain to the complex frequency domain according to the dynamic stiffness matrix, so as 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 through a parameter mapping method.

4. The PCB processing resonance suppression method according to claim 1, characterized in that: The step of dividing and evaluating the processing parameter space of the PCB processing system according to the stability lobe diagram, and generating a resonance suppression strategy of the PCB processing system according to the processing parameter space includes: Performing image segmentation and boundary extraction on the stability lobe map to obtain a stable region and an unstable region 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 a plurality of 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 of each processing parameter subspace; Using a multi-objective optimization algorithm, the 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.

5. The PCB processing resonance suppression method according to claim 4, 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; According to the second frequency response function, a stability evaluation is performed on each processing parameter point to obtain a corresponding stability index, and a resonant frequency of the PCB processing system is extracted 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.

6. 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 comprises: 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.

7. 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, 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.

8. 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 a PCB processing device, and the PCB processing resonance suppression device includes: An acquisition and analysis module is used 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 the PCB processing system and stability lobe diagram; A strategy generation module, used to divide and evaluate the processing parameter space of the PCB processing system according to the stability lobe diagram, and generate a resonance suppression strategy of the PCB processing system according to the processing parameter space; A state evaluation module is used to collect vibration signals of the PCB processing workpiece through multiple types of sensors on the PCB processing equipment during the process of the PCB processing workpiece being processed by the PCB processing equipment, and to perform resonance state evaluation on the vibration signal 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.

9. A PCB processing resonance suppression device, characterized in that: The PCB processing resonance suppression device comprises: a memory and at least one processor, wherein instructions are stored in the memory; 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 as described in any one of claims 1-7.

10. 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 1-7 are implemented.

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