Numerical control machine tool operation control system and double-station sawing and drilling integrated machine tool

Abstract

The invention discloses a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool, and relates to the technical field of numerical control machine tool operation. A multi-axis linkage state, a gas cylinder stress state, vibration frequency, cutting force and deformation data are acquired in real time through a data acquisition module; the dynamic modeling module constructs a digital twinborn model by using finite element analysis and real-time sensing data, and accurately simulates stress dynamic change and deformation trend; the intelligent error compensation module dynamically evaluates the multi-axis linkage precision by analyzing the machining data, generates an error compensation instruction and optimizes the operation parameters of the machine tool; the closed-loop feedback control module adjusts the cutting speed, the feeding depth and the clamping force in real time through a fuzzy logic algorithm, a closed-loop adjusting mechanism is formed, the machining precision and stability are greatly improved, the machining error and the workpiece surface damage risk are reduced, and the machining quality and efficiency of complex workpieces are effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of numerical control machine tool operation, and in particular to a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool. Background Art

[0002] With the rapid development of industrial production, gas cylinders, as important containers for storing and transporting gas, have been widely used in many fields, such as energy, medical and chemical industries. In the manufacturing and maintenance of gas cylinders, end face sawing and end drilling are essential key processes. The processing quality of these processes not only directly affects the sealing performance and service life of the gas cylinder, but also plays an important role in the subsequent assembly and safety performance.

[0003] Traditional gas cylinder processing methods usually rely on manual or semi-automated equipment for sawing and drilling operations. However, these methods have many shortcomings, such as low processing accuracy, low production efficiency, and high labor intensity for workers. In addition, since both ends of the gas cylinder need to be precisely positioned and fixed on the processing platform, traditional equipment often finds it difficult to achieve efficient automated operation control during the processing process, which easily leads to positioning errors, thus affecting the consistency and stability of gas cylinder processing. In recent years, CNC machine tools have been promoted and applied in the field of gas cylinder processing due to their high precision, high efficiency and high degree of automation. By integrating advanced control systems, CNC machine tools can effectively coordinate the sawing and drilling processes to ensure the stability and consistency of processing quality.

[0004] The prior art has the following disadvantages: The accuracy of real-time coordinated control of multi-axis linkage during processing is difficult to guarantee, especially when the two processes of sawing and drilling are switched dynamically and the stress state of complex workpieces changes. Since gas cylinders are usually cylindrical thin-walled structures, during the sawing and drilling process, uneven stress on the ends of the gas cylinders may cause deformation, thereby causing positioning deviation and processing errors. In addition, the existing control system lacks intelligent error compensation algorithms when dealing with multi-axis linkage, and it is difficult to effectively deal with process deviations caused by dynamic changes in the force of gas cylinders and vibrations, which not only affects the processing quality, but may also cause surface damage to the gas cylinders or safety hazards. Summary of the invention

[0005] The purpose of the present invention is to provide a CNC machine tool operation control system and a double-station sawing and drilling integrated machine tool to solve the shortcomings of the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a CNC machine tool operation control system, comprising a data acquisition module, a dynamic modeling module, an intelligent error compensation module and a closed-loop feedback control module; The data acquisition module includes a force sensor, a vibration sensor, an optical measuring instrument and a data acquisition main control unit, which is used to collect machine tool operation parameters and processing status data in real time, including multi-axis linkage status, cylinder stress status, vibration frequency, cutting force and cylinder end deformation; The dynamic modeling module builds a digital twin model of the processing process by integrating finite element analysis algorithms and real-time sensor data to simulate the dynamic changes in force and deformation trends during cylinder processing; The intelligent error compensation module comprehensively analyzes the dynamic changes of force and deformation trends during the processing of gas cylinders, determines the accuracy of real-time coordinated control of multi-axis linkage during the processing, and dynamically generates error compensation instructions for real-time adjustment of machine tool operating parameters; The closed-loop feedback control module dynamically adjusts the cutting speed, feed depth and clamping force of the machine tool through fuzzy logic algorithm based on the real-time collected processing status data and the accuracy of real-time coordinated control of multi-axis linkage during the processing process to ensure the accuracy of the processing process.

[0007] Preferably, in the intelligent error compensation module, the dynamic force fluctuation index is generated after analyzing the dynamic force change during the processing of the gas cylinder. The method for obtaining the dynamic force fluctuation index is: Obtain the force signal F(t) during the machining process from the force sensor to construct a discrete time series F[n], n=0,1,2,...,N-1; set the sampling rate of the sensor , the number of sampling points is N, that is, the total number of points for collecting signals, and the mean μ of the force signal is calculated: ; De-DC the signal: F′[n]=F[n]-μ; Perform a fast Fourier transform on the de-DC signal F′[n] to obtain the frequency domain representation: ;in: It is the complex value of the frequency domain signal at the kth frequency point, indicating the amplitude and phase information. N is the number of sampling points, which determines the frequency resolution. is the kernel function of Fourier transform; take the amplitude part of the frequency domain signal to get the amplitude spectrum , the expression is: ; Where: Re(F[k]) and Im(F[k]) are the real and imaginary parts of F[k] respectively, and the normalized amplitude spectrum is: ; In the formula, is the normalized amplitude spectrum; according to the sampling frequency and the number of Fourier transform points, the actual frequency of each frequency point k is calculated : ; Calculate the total spectrum energy and high frequency energy , the expression is: ; ;in, is the set high-frequency threshold frequency, which is used to distinguish low-frequency and high-frequency components and calculate the dynamic fluctuation index of force. The expression is: ; EHK is the dynamic fluctuation index of force.

[0008] Preferably, in the intelligent error compensation module, the vibration over-limit abnormality index is generated after analyzing the deformation trend in the gas cylinder processing, and the method for obtaining the vibration over-limit abnormality index is: The vibration signal x(t) is collected from the vibration sensor, the vibration signal is decomposed by wavelet, the wavelet basis function ψ(t) is selected, and the discrete wavelet transform is used to decompose the signal into detail components of different scales. and approximation component : ; Each level of wavelet component Corresponding frequency range: ; The energy of each level component Defined as the integral of the squared values ​​of the components: ; The total energy of the signal is the sum of the energies of all wavelet components: ; is the approximate component energy of the lowest frequency band. According to the machining vibration characteristics and abnormal frequency range, the abnormal frequency band corresponding to the wavelet component is selected. The total energy of the abnormal frequency band for: ; Calculate the vibration over-limit abnormality index, which is defined as the ratio of the energy in the abnormal frequency band to the total energy: ; In the formula, GMH is the vibration over-limit abnormality index.

[0009] Preferably, in the intelligent error compensation module, the dynamic fluctuation index of force and the vibration over-limit abnormality index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the precision value label of the real-time coordinated control of the multi-axis linkage during the processing as the prediction target, and takes minimizing the sum of the prediction errors of the precision value labels of the real-time coordinated control of the multi-axis linkage during all processing processes as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The precision value of the real-time coordinated control of the multi-axis linkage during the processing is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0010] Preferably, the acquired precision value of the real-time coordinated control of the multi-axis linkage during the processing is compared with a precision value reference threshold pre-set according to historical data. If the precision value of the real-time coordinated control of the multi-axis linkage during the processing is greater than or equal to the pre-set precision value reference threshold, it means that the precision of the real-time coordinated control of the multi-axis linkage during the processing is high. At this time, no early warning signal is generated and no additional adjustment is required. If the precision value of the real-time coordinated control of the multi-axis linkage during the processing is less than the pre-set precision value reference threshold, it means that the precision of the real-time coordinated control of the multi-axis linkage during the processing is low. At this time, a early warning signal is generated, and an error compensation instruction is dynamically generated for real-time adjustment of the machine tool operating parameters.

[0011] Preferably, in the closed-loop feedback control module, based on the real-time collected processing status data and the accuracy of the real-time coordinated control of the multi-axis linkage during the processing, the cutting speed, feed depth and clamping force of the machine tool are dynamically adjusted through the fuzzy logic algorithm to ensure the accuracy of the processing process, specifically: The real-time collected processing state data and the precision value of the real-time coordinated control of multi-axis linkage during the processing are used as the input items of the fuzzy logic, and the cutting speed, feed depth and clamping force of the machine tool are used as the output items of the fuzzy logic, among which the processing state data is the force value; Define fuzzy sets for input items and output items respectively; Fuzzy rules designed based on processing experience and system characteristics are used to associate input items with output items; Convert the specific value of the input item into the membership degree in the fuzzy set and use the membership function to define the fuzzy set: ; a is the left endpoint of the fuzzy set, which is the point where the membership starts to increase from 0, b is the center point of the fuzzy set, which is the point where the membership is 1, and c is the right endpoint of the fuzzy set, which is the point where the membership decreases from 1 to 0; Use the fuzzy rule base to map the membership of input items to the fuzzy set of output items; The reasoning process combines the input membership to calculate the confidence of each rule; The fuzzy set of output items is converted into specific values ​​through defuzzification method, which is used to dynamically adjust the operating parameters of the machine tool.

[0012] Preferably, the cutting speed is dynamically adjusted according to the inference result. If the force value is high or the precision is low, the cutting speed is reduced to reduce the cutting load and force fluctuation; if the force value is low and the precision is high, the cutting speed is increased to improve the processing efficiency; Adjust the feed depth. When the force and vibration are large, reduce the cutting depth to reduce the processing load; when the force is stable, increase the cutting depth to improve the processing efficiency; Adjust the clamping force. When the workpiece is not stable enough or is subjected to large forces, increase the clamping force. When the clamping force is too large and causes the workpiece to deform, reduce the clamping force.

[0013] A double-station sawing and drilling integrated machine tool, comprising a machine base and a machine table, wherein a positioning frame and two sets of supporting guide rails are fixedly arranged on the upper surface of the machine base, two front fixed ports are arranged on the surface of the positioning frame, a support is slidably mounted on the surface of the supporting guide rail, a rear top bowl is fixedly mounted on the surface of the support, and the rear top bowl cooperates with the corresponding front fixed port to process and position the gas cylinder; The surface of the machine table is provided with a sawing module and a drilling module, which are located on a side of the positioning frame away from the rear top bowl and correspond to one of the front fixing ports respectively.

[0014] Preferably, a material table is fixedly provided on the upper surface of the machine base, a conveying plate is slidably provided on the surface of the material table, and at least two groups of "V"-shaped support plates are provided on the upper surface of the conveying plate, and the support plates are located between the positioning frame and the rear top bowl.

[0015] Preferably, a lower guide rail is fixedly provided on the surface of the machine, and two groups of bases are slidably installed on the surface of the lower guide rail. The sawing module and the drilling module are respectively slidably installed on the upper surfaces of the corresponding bases, and the sliding direction is perpendicular to the lower guide rail.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention realizes all-round monitoring and high-precision control of the gas cylinder processing process through the collaborative work of data acquisition, dynamic modeling, intelligent error compensation and closed-loop feedback control modules. The system uses the processing status data collected in real time, combined with finite element analysis to build a digital twin model, accurately simulates the dynamic changes in force and deformation trends during the processing process, generates the force dynamic fluctuation index and vibration overlimit abnormality index through the intelligent error compensation module, and dynamically predicts the accuracy value of multi-axis linkage real-time coordinated control in combination with the polynomial regression model, and adjusts the cutting speed, feed depth and clamping force of the machine tool in real time. This closed-loop control mechanism significantly improves the stability and accuracy of multi-axis linkage control, and reduces the risk of processing errors and workpiece surface damage.

[0017] 2. The present invention overcomes the problem of insufficient control accuracy of the prior art under complex working conditions, especially in the dynamic switching of sawing and drilling processes, and can effectively deal with processing deviations caused by uneven force and abnormal vibration through fuzzy logic algorithms and dynamic error compensation instructions. Its innovative design not only improves processing quality and efficiency, but also significantly reduces safety hazards in gas cylinder processing. It has the characteristics of high adaptability and intelligence, and provides reliable technical guarantees for the precision processing of complex structure workpieces.

[0018] 3. The present invention can realize integrated processing of sawing and drilling of gas cylinders through the combined design of the machine base surface positioning frame and the two groups of rear top bowls and the coordinated use of the machine surface sawing module and the drilling module, which is beneficial to improving the efficiency of gas cylinder processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a system module diagram of the present invention; Figure 2 The sawing and drilling integrated machine tool structure of the present invention is a three-dimensional Figure 1 ; Figure 3 The sawing and drilling integrated machine tool structure of the present invention is a three-dimensional Figure 2 ; Figure 4 The sawing and drilling integrated machine tool structure of the present invention is a three-dimensional Figure 3 .

[0021] In the figure: 1. machine base; 2. positioning frame; 201. front fixing port; 3. support rail; 4. support; 401. rear top bowl; 5. cylinder 1; 6. material table; 7. conveyor plate; 701. support plate; 8. machine table; 801. lower rail; 802. base; 9. sawing module; 10. drilling module. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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.

[0023] For examples, see Figure 1 As shown, a CNC machine tool operation control system described in this embodiment includes a data acquisition module, a dynamic modeling module, an intelligent error compensation module and a closed-loop feedback control module; The data acquisition module includes a force sensor, a vibration sensor, an optical measuring instrument and a data acquisition main control unit, which is used to collect machine tool operation parameters and processing status data in real time, including multi-axis linkage status, cylinder stress status, vibration frequency, cutting force and cylinder end deformation; The dynamic modeling module builds a digital twin model of the processing process by integrating finite element analysis algorithms and real-time sensor data to simulate the dynamic changes in force and deformation trends during cylinder processing; The intelligent error compensation module comprehensively analyzes the dynamic changes of force and deformation trends during the processing of gas cylinders, determines the accuracy of real-time coordinated control of multi-axis linkage during the processing, and dynamically generates error compensation instructions for real-time adjustment of machine tool operating parameters; The closed-loop feedback control module dynamically adjusts the cutting speed, feed depth and clamping force of the machine tool through fuzzy logic algorithm based on the real-time collected processing status data and the accuracy of real-time coordinated control of multi-axis linkage during the processing process to ensure the accuracy of the processing process.

[0024] In the data acquisition module, force sensors are installed on the tool spindle, workpiece clamping device and other key nodes that may be subjected to force. Real-time monitoring of the cutting force, clamping force and other mechanical forces on the cylinder during processing. The force fluctuations caused by changes in cutting parameters (such as feed speed and cutting depth) are collected to provide a basis for subsequent error analysis. The collected data includes: the force applied by the clamping device, and the axial and radial force values ​​of the spindle during cutting.

[0025] Vibration sensors are distributed on the machine tool base, tool spindle, and cylinder clamping area to cover the main vibration sources of the machining process. Capture vibration signals caused by mechanical vibration, cutting force changes, or material inhomogeneity during machining. Monitor the dynamic stability of the machine tool and identify potential vibration problems such as cutting chatter or resonance. The collected data includes: vibration frequency, amplitude, and its dynamic trend over time.

[0026] The optical measuring instrument includes a laser rangefinder, a high-precision camera and an optical scanner, which are installed at key locations in the machine tool processing area to measure the deformation and positioning error of the cylinder end in real time. Accurately measure the flatness of the cylinder end surface and the drilling position deviation. Monitor the dimensional changes of the workpiece during dynamic processing and capture the deformation caused by force. The collected data includes: end surface deformation value, drilling deviation, and geometric feature changes in the processing area.

[0027] Data acquisition control unit: Embedded computing unit with built-in high-speed data processing chip and interface module. Receive and integrate data from force sensors, vibration sensors and optical measuring instruments. Time align the signals collected by multiple sensors to ensure data synchronization. Use real-time filtering algorithms (such as Kalman filtering) to remove noise from the signal and improve data accuracy. Transmit the processed data to the dynamic modeling module of the system.

[0028] The multi-axis linkage status data includes: dynamic status data such as displacement, speed, acceleration, angle deviation, etc. of each axis. Real-time monitoring of the coordinated operation of each motion axis of the machine tool ensures the accuracy and stability of multi-axis linkage. Provide real-time data support for error prediction and multi-axis path optimization.

[0029] The cutting force and clamping force distribution of the gas cylinder during the clamping and processing process is used to identify whether the clamping device has insufficient or excessive force problems to avoid deformation of the gas cylinder due to uneven force. The clamping force is dynamically adjusted to adapt to the force changes in the cutting process to improve processing accuracy.

[0030] Vibration frequency and cutting force: The frequency and amplitude of cutting vibration, and the changing trend of cutting force. Vibration data can be used to determine whether chatter or resonance occurs during cutting. Processing parameters (such as cutting speed and feed depth) can be optimized to reduce vibration and improve surface quality.

[0031] Cylinder end deformation: cylinder end surface flatness, end deformation trend and drilling position error. Real-time detection of end deformation caused by force changes during processing to prevent deformation beyond the allowable range. Provide accurate feedback data for the error compensation module to achieve real-time error correction.

[0032] The data acquisition control unit transmits data to the dynamic modeling module in real time through a high-speed interface (such as CAN bus or Ethernet). The data collected by different sensors are time-stamped to ensure that all data can be used on a unified timeline. Key operating data can be stored in a local database for subsequent processing performance evaluation and model optimization.

[0033] In the dynamic modeling module, the finite element analysis unit uses the finite element analysis (FEA) algorithm to establish the initial mechanical model of the workpiece based on the geometric shape, material properties and processing parameters of the gas cylinder. The force distribution, deformation trend and stress concentration of the gas cylinder during the processing are simulated. The gas cylinder is simplified into a finite element mesh, and the stress, strain and displacement of each mesh unit are calculated by solving the mechanical equations. Combined with the processing parameters (such as cutting force, clamping force, etc.), the force boundary conditions in the model are dynamically updated.

[0034] The real-time data interface is connected to the data acquisition module to receive data such as multi-axis linkage status, vibration frequency, cutting force, and cylinder end deformation collected by sensors in real time. The input conditions of the finite element model are dynamically updated to reflect the actual force state during the processing. The data types include real-time mechanical parameters (such as cutting force, clamping force), dynamic geometric data (such as deformation), and environmental data (such as temperature and humidity).

[0035] The digital twin generator constructs a digital twin model of the gas cylinder processing process by integrating finite element analysis results and real-time sensor data, dynamically reflecting the state changes during the processing process. Real-time virtual simulation is achieved, which is highly consistent with the actual processing process. Visualization support: Generate a 3D visualization interface to display the force distribution, stress concentration location and deformation trend of the gas cylinder in real time, providing intuitive feedback to operators.

[0036] The risk assessment unit analyzes the dynamic data of the digital twin model and identifies abnormal situations that may occur during the processing, such as excessive deformation or uneven force. It provides real-time warning information to support the error compensation and path optimization modules. Output content: excessive deformation warning, stress concentration risk prompt, processing path deviation prediction, etc.

[0037] The construction of the digital twin model includes: Initial modeling: Establish an initial finite element model based on the design parameters of the gas cylinder (such as material properties, geometric dimensions) and the processing path. Define processing constraints, such as clamping position, cutting direction, and external force application point. Dynamic update: Obtain cutting force, vibration data, and deformation data in real time, and dynamically update them to the finite element model as boundary conditions. Ensure that the model reflects the changes in the actual processing status in real time throughout the entire processing process.

[0038] The stress distribution, strain distribution and deformation trend of the gas cylinder during processing are calculated through the finite element solution algorithm. The key areas where stress is concentrated are identified to predict possible processing defects. Application scenarios: Assist in judging whether the clamping force is reasonable to avoid deformation or processing errors caused by improper clamping. Early warning of the risk of workpiece damage caused by excessive force.

[0039] Deformation trend prediction: Based on real-time sensor data and historical processing data, combined with finite element models, the deformation trend of the gas cylinder in subsequent processing steps is predicted. A real-time deformation distribution map is provided to help adjust processing parameters (such as feed speed and cutting depth). The drilling position offset or end surface unevenness caused by deformation is reduced, and the processing accuracy is improved.

[0040] Analyze the dynamic data of the digital twin model to identify abnormal forces, excessive vibrations, or deformations exceeding the set threshold. Output risk warning information to support machining path optimization and error compensation modules. Identify quality problems that may occur during machining in advance, such as end face warping due to uneven forces. Guide the operator or control system to adjust the machining path or process parameters to reduce machining risks.

[0041] The intelligent error compensation module comprehensively analyzes the dynamic changes in force and deformation trends during gas cylinder processing, determines the accuracy of real-time coordinated control of multi-axis linkage during processing, and dynamically generates error compensation instructions for real-time adjustment of machine tool operating parameters.

[0042] After analyzing the dynamic changes of force in the processing of gas cylinders, the dynamic fluctuation index of force is generated. The method for obtaining the dynamic fluctuation index of force is as follows: Obtain the force signal F(t) during the machining process from the force sensor to form a discrete time series F[n], n=0,1,2,...,N-1; set the sampling rate of the sensor , the unit is Hz (sampling points per second). The number of sampling points is N, that is, the total number of points of the collected signal. In order to remove the DC offset in the signal, the mean value μ of the force signal is calculated: ; De-DC the signal: F′[n]=F[n]-μ; Perform a fast Fourier transform on the de-DC signal F′[n] to obtain the frequency domain representation: ;in: It is the complex value of the frequency domain signal at the kth frequency point, indicating the amplitude and phase information. N is the number of sampling points, which determines the frequency resolution. is the kernel function of Fourier transform; take the amplitude part of the frequency domain signal to get the amplitude spectrum , the expression is: ; Where: Re(F[k]) and Im(F[k]) are the real and imaginary parts of F[k], respectively, and the amplitude spectrum is normalized to make it independent of the number of sampling points and signal strength: ; In the formula, is the normalized amplitude spectrum; according to the sampling frequency and the number of Fourier transform points, the actual frequency of each frequency point k is calculated : ; Calculate the total spectrum energy and high frequency energy , the expression is: ; ;in, It is the high-frequency threshold frequency set to distinguish low-frequency and high-frequency components, in Hz. The dynamic fluctuation index of force is calculated as follows: ; EHK is the dynamic fluctuation index of force.

[0043] When the dynamic force fluctuation index is large, it means that the high-frequency component of the force signal during processing accounts for a high proportion, and the force state shows significant rapid fluctuations. This usually indicates that there are deficiencies in the real-time coordinated control of multi-axis linkage, which may lead to poor synchronization between the machine tool motion axes, or mismatch between the processing path planning and the actual force state of the workpiece. Rapidly fluctuating forces may cause chatter or excessive vibration during the cutting process, resulting in reduced processing accuracy and affecting the surface quality and dimensional consistency of the workpiece.

[0044] On the contrary, when the dynamic fluctuation index of force is small, it means that the high-frequency component of the force signal during processing is low and the force state is more stable. This reflects that the real-time coordinated control of multi-axis linkage is more accurate, the synchronization between the motion axes is good, and the processing path planning and force distribution are more reasonable. A stable force state helps to reduce processing errors, ensure the surface finish and dimensional accuracy of the workpiece, and thus improve processing quality and stability.

[0045] After analyzing the deformation trend in the gas cylinder processing, the vibration over-limit abnormality index is generated. The method for obtaining the vibration over-limit abnormality index is as follows: Collect vibration signal x(t) from vibration sensor, perform wavelet decomposition on vibration signal, select wavelet basis function ψ(t), and select appropriate wavelet basis function (such as Daubechies wavelet, Haar wavelet, etc.) according to signal characteristics. Use discrete wavelet transform (DWT) to decompose the signal into detail components of different scales and approximation component : ; Each level of wavelet component Corresponding frequency range: ; The energy of each level component Defined as the integral of the squared values ​​of the components: ; The total energy of the signal is the sum of the energies of all wavelet components: ; is the approximate component energy of the lowest frequency band. According to the machining vibration characteristics and abnormal frequency range, select one or more abnormal frequency bands corresponding to the wavelet components. Total energy of abnormal frequency band for: ; Calculate the vibration over-limit abnormality index, which is defined as the ratio of the energy in the abnormal frequency band to the total energy: ; In the formula, GMH is the vibration over-limit abnormality index.

[0046] When the vibration over-limit abnormality index is larger, it means that the proportion of vibration energy in the abnormal frequency range during the processing process has increased significantly, and the vibration fluctuation is stronger. This indicates that the accuracy of the real-time coordinated control of multi-axis linkage may be insufficient, the synchronization between the moving axes is poor, or the processing path fails to fully consider the force distribution and vibration characteristics of the workpiece. Strong vibration may lead to unstable cutting, reduced surface quality of the workpiece, and even cause equipment loss and processing defects. Therefore, a larger index usually means that the real-time coordinated control of multi-axis linkage needs to be optimized.

[0047] When the vibration over-limit abnormality index is small, it means that the vibration energy in the abnormal frequency range accounts for a low proportion, and the vibration is stable during processing, indicating that the accuracy of multi-axis linkage real-time coordinated control is high. The synchronization between the motion axes is good, and the motion trajectory is highly matched with the stress state of the workpiece, which can effectively suppress abnormal vibration. In this case, the processing accuracy and stability are high, the surface finish and dimensional consistency of the workpiece are guaranteed, and the operating life of the equipment will be extended accordingly.

[0048] The dynamic fluctuation index of force and the vibration over-limit abnormal index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the accuracy value label of the real-time coordinated control of multi-axis linkage during the processing as the prediction target, and takes minimizing the sum of the prediction errors of the accuracy value labels of the real-time coordinated control of multi-axis linkage during all processing processes as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The accuracy value of the real-time coordinated control of multi-axis linkage during the processing is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0049] The method for obtaining the accuracy value of multi-axis linkage real-time coordinated control during machining is as follows: from the comprehensive feature vector training data of the trained machine learning model, the corresponding function expression is obtained: ; In the formula, is the output function of the model, EHK is the dynamic fluctuation index of force, GMH is the vibration over-limit abnormal index, It is the accuracy value of real-time coordinated control of multi-axis linkage during machining.

[0050] The obtained precision value of the real-time coordinated control of the multi-axis linkage during the processing is compared with the precision value reference threshold pre-set according to historical data. If the precision value of the real-time coordinated control of the multi-axis linkage during the processing is greater than or equal to the pre-set precision value reference threshold, it means that the precision of the real-time coordinated control of the multi-axis linkage during the processing is high. At this time, no early warning signal is generated and no additional adjustment is required. If the precision value of the real-time coordinated control of the multi-axis linkage during the processing is less than the pre-set precision value reference threshold, it means that the precision of the real-time coordinated control of the multi-axis linkage during the processing is low. At this time, a early warning signal is generated, and an error compensation instruction is dynamically generated for real-time adjustment of the machine tool operating parameters.

[0051] If the EHK value is high, it means that the force fluctuation is significant, and the feed speed can be adjusted dynamically to reduce the force fluctuation: In the area where the cutting force is insufficient, increasing the feed speed can improve the cutting efficiency and balance the force. In the area where the cutting force is large, reducing the feed speed can reduce the processing load and reduce the force fluctuation.

[0052] If the EHK or GMH value is high and indicates abnormal machining forces and vibrations, the cutting depth can be adjusted dynamically: Reduce cutting depth: Reduce cutting depth in abnormal areas to reduce tool load, reduce abnormal vibrations and force fluctuations.

[0053] If the force fluctuation index EHK is high, the clamping force distribution can be optimized to ensure the stability of the workpiece: when the workpiece shows signs of loosening during processing, increase the clamping force to reduce the movement of the workpiece. When the clamping force is too large and causes the workpiece to deform, reduce the clamping force appropriately.

[0054] If the GMH value is high, it indicates that the abnormal vibration energy is concentrated, and the multi-axis linkage path can be adjusted: Optimize path planning: Recalculate the multi-axis path to smooth the trajectory and avoid vibration amplification caused by sudden changes. Dynamically adjust the movement speed: Slow down at the vibration abnormality point to reduce the vibration excitation effect.

[0055] The EHK and GMH values ​​are monitored in real time through sensors and data acquisition modules, and the machine learning model is input to predict the multi-axis linkage coordination accuracy value. The error compensation module is started based on the comparison result between the model output and the threshold. Error compensation decision and instruction generation: Combined with the processing parameter database and historical data, specific compensation instructions are generated through optimization algorithms (such as genetic algorithms or particle swarm optimization). Instruction issuance and execution: The dynamic compensation instructions are transmitted to the machine tool control system to adjust key parameters such as feed speed, cutting depth, and clamping force in real time. By monitoring the compensated EHK and GMH values, the compensation effect is evaluated, a closed-loop control mechanism is formed, and the machine tool operation status is continuously optimized.

[0056] The closed-loop feedback control module dynamically adjusts the cutting speed, feed depth and clamping force of the machine tool through fuzzy logic algorithm based on the real-time collected processing status data and the accuracy of real-time coordinated control of multi-axis linkage during the processing process to ensure the accuracy of the processing process.

[0057] The real-time collected processing status data and the accuracy value of the real-time coordinated control of multi-axis linkage during the processing are used as the input items of fuzzy logic, and the cutting speed, feed depth and clamping force of the machine tool are used as the output items of fuzzy logic; among them, the processing status data is the force value: it represents the cutting force of the workpiece during the processing, unit: N (Newton), which is collected by a force sensor.

[0058] Processing status data (force value): Cutting force, clamping force or vibration force collected in real time, reflecting the force change of the workpiece and processing stability. Example: The force range is from 0 to Fmax, divided into three fuzzy sets of "low", "medium" and "high".

[0059] Precision value of multi-axis linkage real-time coordinated control: predicted by machine learning model, reflecting the accuracy level of current processing coordination. Example: The precision value ranges from 0 to 1 and is divided into three fuzzy sets: "low precision", "medium precision", and "high precision".

[0060] Output items include: Cutting speed: controls the speed of the tool on the workpiece, affecting the cutting force and surface quality. Example: divided into three fuzzy sets: "slow", "medium", and "fast". Feed depth: controls the cutting depth of the tool, affecting the cutting load and processing efficiency. Example: divided into three fuzzy sets: "shallow", "medium", and "deep". Clamping force: controls the strength of the workpiece fixation, affecting the stability and deformation of the workpiece. Example: divided into three fuzzy sets: "light", "medium", and "heavy".

[0061] Fuzzy rules designed based on processing experience and system characteristics associate input items with output items. Example rule: If the "Force Value" is "High" and the "Precision Value" is "Low Precision", then the "Cutting Speed" is "Slow", the "Feed Depth" is "Shallow", and the "Clamping Force" is "Heavy".

[0062] If the "Force value" is "Medium" and the "Precision value" is "Medium precision", the "Cutting speed" is "Medium speed", the "Feed depth" is "Medium", and the "Clamping force" is "Medium".

[0063] Convert the specific values ​​of the input items (force value, accuracy value) into membership in the fuzzy set, and use the membership function to define the fuzzy set, such as the triangle membership function: ; a is the left endpoint of the fuzzy set, which is the point where the membership starts to increase from 0, b is the center point of the fuzzy set, which is the point where the membership is 1 (the membership reaches the maximum). c is the right endpoint of the fuzzy set, which is the point where the membership decreases from 1 to 0. For example, if the force value is 150N, and the membership functions are "low" μlow(x), "medium" μmedium(x), and "high" μhigh(x), then the membership degrees are calculated as follows: μlow(150)=0.2, μmedium(150)=0.8, and μhigh(150)=0.0.

[0064] Use the fuzzy rule base to map the membership of the input items to the fuzzy set of the output items: if the rule is: "The force value is 'medium' and the accuracy value is 'medium accuracy', then the cutting speed is 'medium speed', the feed depth is 'medium', and the clamping force is 'medium'".

[0065] The reasoning process combines the input membership to calculate the confidence of each rule: ; Example: If μcenter (force value) = 0.8, μcenter precision (precision value) = 0.6, then the confidence level is min(0.8,0.6) = 0.6.

[0066] The fuzzy set of output items is converted into specific values ​​through defuzzification method, which is used to dynamically adjust the machine tool operation parameters. The commonly used method is the Centroid Method: ; For example: for cutting speed, calculate the center of gravity of the membership distribution to obtain the adjusted specific speed value.

[0067] The cutting speed is dynamically adjusted according to the reasoning results. If the force value is high or the precision is low, the cutting speed is reduced to reduce the cutting load and force fluctuation; if the force value is low and the precision is high, the cutting speed is increased to improve the processing efficiency.

[0068] Adjust the feed depth. When the force and vibration are large, reduce the cutting depth to reduce the processing load; when the force is stable, increase the cutting depth to improve the processing efficiency.

[0069] Adjust the clamping force. When the workpiece is not stable enough or is subjected to large forces, increase the clamping force. When the clamping force is too large and causes the workpiece to deform, reduce the clamping force.

[0070] In this embodiment, precise control of the gas cylinder processing process is achieved through collaborative work. The data acquisition module obtains processing status data such as multi-axis linkage status, gas cylinder stress status, vibration frequency, cutting force and gas cylinder end deformation in real time through force sensors, vibration sensors, optical measuring instruments and other equipment; the dynamic modeling module integrates finite element analysis algorithm and real-time sensor data to build a digital twin model of the processing process, simulating the dynamic change of force and deformation trend; the intelligent error compensation module conducts a comprehensive analysis of the force and deformation data, evaluates the accuracy of real-time coordinated control of multi-axis linkage, and dynamically generates error compensation instructions to optimize the machine tool operating parameters in real time; the closed-loop feedback control module is based on fuzzy logic algorithm, using real-time processing data and control accuracy indicators to dynamically adjust the cutting speed, feed depth and clamping force, forming a closed-loop optimization mechanism to ensure high precision and high stability of the processing process.

[0071] See also Figure 2-Figure 4 As shown, a dual-station sawing and drilling integrated machine tool includes a machine base 1 and a machine table 8. A positioning frame 2 and two sets of support rails 3 are fixedly arranged on the upper surface of the machine base 1. Two front fixed ports 201 are arranged on the surface of the positioning frame 2. The front fixed ports 201 are adapted to the open end of the gas cylinder. A support 4 is slidably installed on the surface of the support rail 3. A cylinder 5 is installed in the machine base 1. The support 4 is controlled to slide on the surface of the support rail 3 by the cylinder 5. A rear top bowl 401 is fixedly installed on the surface of the support 4. The rear top bowl 401 is adapted to the bottom of the gas cylinder. The rear top bowl 401 cooperates with the corresponding front fixed port 201 to process and position the gas cylinder. The position of the rear top bowl 401 is controlled by the cylinder 5, and the rear top bowl 401 cooperates with the corresponding front fixed port 201 to fix the gas cylinder to be processed.

[0072] A sawing module 9 and a drilling module 10 are provided on the surface of the machine table 8. The sawing module 9 and the drilling module 10 are located on the side of the positioning frame 2 away from the rear top bowl 401, and respectively correspond to one of the front fixed ports 201. The sawing module 9 is used to perform radial sawing processing on the front port of the gas cylinder, and the drilling module 10 is used to drill the end face of the gas cylinder after sawing.

[0073] On this basis, a material table 6 is fixedly provided on the upper surface of the machine base 1, and a conveying plate 7 is slidably provided on the surface of the material table 6. The conveying plate 7 is driven by a cylinder or a linear screw installed in the material table 6. There are no less than two groups of "V"-shaped support plates 701 on the upper surface of the conveying plate 7. The support plate 701 is located between the positioning frame 2 and the rear top bowl 401. The conveying plate 7 and the support plate 701 are used to transport the gas cylinder to different workstations for processing.

[0074] It should be noted that a lower guide rail 801 is fixedly provided on the surface of the machine table 8, and two groups of bases 802 are slidably installed on the surface of the lower guide rail 801. The base 802 is driven by a cylinder or a linear screw installed in the machine table 8. The sawing module 9 and the drilling module 10 are respectively slidably installed on the upper surfaces of the corresponding bases 802, and the sliding direction is perpendicular to the lower guide rail 801. The sawing module 9 and the drilling module 10 are driven by a cylinder installed on the surface of the corresponding base 802. Through the cooperation of the lower guide rail 801 and the base 802, the multi-directional movement of the sawing module 9 and the drilling module 10 can be realized, so as to realize the sawing and drilling processing of the gas cylinder.

[0075] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A CNC machine tool operation control system, characterized in that: It includes data acquisition module, dynamic modeling module, intelligent error compensation module and closed-loop feedback control module; The data acquisition module includes a force sensor, a vibration sensor, an optical measuring instrument and a data acquisition main control unit, which is used to collect machine tool operation parameters and processing status data in real time, including multi-axis linkage status, cylinder stress status, vibration frequency, cutting force and cylinder end deformation; The dynamic modeling module builds a digital twin model of the processing process by integrating finite element analysis algorithms and real-time sensor data to simulate the dynamic changes in force and deformation trends during cylinder processing; The intelligent error compensation module comprehensively analyzes the dynamic changes of force and deformation trends during the processing of gas cylinders, determines the accuracy of real-time coordinated control of multi-axis linkage during the processing, and dynamically generates error compensation instructions for real-time adjustment of machine tool operating parameters; The closed-loop feedback control module dynamically adjusts the cutting speed, feed depth and clamping force of the machine tool through fuzzy logic algorithm based on the real-time collected processing status data and the accuracy of real-time coordinated control of multi-axis linkage during the processing process to ensure the accuracy of the processing process.

2. A CNC machine tool operation control system according to claim 1, characterized in that: In the intelligent error compensation module, the dynamic force fluctuation index is generated after analyzing the dynamic force changes in the gas cylinder processing. The method for obtaining the dynamic force fluctuation index is as follows: Obtain the force signal F(t) during the machining process from the force sensor to construct a discrete time series F[n], n=0,1,2,...,N-1; set the sampling rate of the sensor , the number of sampling points is N, that is, the total number of points for collecting signals, and the mean μ of the force signal is calculated: ; De-DC the signal: F′[n]=F[n]-μ; Perform a fast Fourier transform on the de-DC signal F′[n] to obtain the frequency domain representation: ;in: It is the complex value of the frequency domain signal at the kth frequency point, indicating the amplitude and phase information. N is the number of sampling points, which determines the frequency resolution. is the kernel function of Fourier transform; take the amplitude part of the frequency domain signal to get the amplitude spectrum , the expression is: ; Where: Re(F[k]) and Im(F[k]) are the real and imaginary parts of F[k] respectively, and the normalized amplitude spectrum is: ; In the formula, is the normalized amplitude spectrum; according to the sampling frequency and the number of Fourier transform points, the actual frequency of each frequency point k is calculated : ; Calculate the total spectrum energy and high frequency energy , the expression is: ; ;in, is the set high-frequency threshold frequency, which is used to distinguish low-frequency and high-frequency components and calculate the dynamic fluctuation index of force. The expression is: ; EHK is the dynamic fluctuation index of force.

3. A CNC machine tool operation control system according to claim 2, characterized in that: In the intelligent error compensation module, the deformation trend in the gas cylinder processing is analyzed to generate the vibration over-limit abnormality index. The method for obtaining the vibration over-limit abnormality index is as follows: The vibration signal x(t) is collected from the vibration sensor, the vibration signal is decomposed by wavelet, the wavelet basis function ψ(t) is selected, and the discrete wavelet transform is used to decompose the signal into detail components of different scales. and approximation component : ; Each level of wavelet component Corresponding frequency range: ; The energy of each level component Defined as the integral of the squared values ​​of the components: ; The total energy of the signal is the sum of the energies of all wavelet components: ; is the approximate component energy of the lowest frequency band. According to the machining vibration characteristics and abnormal frequency range, the abnormal frequency band corresponding to the wavelet component is selected. The total energy of the abnormal frequency band for: ; Calculate the vibration over-limit abnormality index, which is defined as the ratio of the energy in the abnormal frequency band to the total energy: ; In the formula, GMH is the vibration over-limit abnormality index.

4. A CNC machine tool operation control system according to claim 3, characterized in that: In the intelligent error compensation module, the dynamic fluctuation index of force and the vibration over-limit abnormal index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the accuracy value label of the real-time coordinated control of multi-axis linkage during the processing as the prediction target, and takes minimizing the sum of the prediction errors of the accuracy value labels of the real-time coordinated control of multi-axis linkage during all processing processes as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The accuracy value of the real-time coordinated control of multi-axis linkage during the processing is determined according to the model output results. Among them, the machine learning model is a polynomial regression model.

5. A CNC machine tool operation control system according to claim 4, characterized in that: The obtained precision value of the real-time coordinated control of the multi-axis linkage during the processing is compared with the precision value reference threshold pre-set according to historical data. If the precision value of the real-time coordinated control of the multi-axis linkage during the processing is greater than or equal to the pre-set precision value reference threshold, it means that the precision of the real-time coordinated control of the multi-axis linkage during the processing is high. At this time, no early warning signal is generated and no additional adjustment is required. If the precision value of the real-time coordinated control of the multi-axis linkage during the processing is less than the pre-set precision value reference threshold, it means that the precision of the real-time coordinated control of the multi-axis linkage during the processing is low. At this time, a early warning signal is generated, and an error compensation instruction is dynamically generated for real-time adjustment of the machine tool operating parameters.

6. The CNC machine tool operation control system according to claim 1, characterized in that: In the closed-loop feedback control module, based on the real-time collected processing status data and the accuracy of multi-axis linkage real-time coordinated control during the processing, the cutting speed, feed depth and clamping force of the machine tool are dynamically adjusted through the fuzzy logic algorithm to ensure the accuracy of the processing process. Specifically: The real-time collected processing state data and the precision value of the real-time coordinated control of multi-axis linkage during the processing are used as the input items of the fuzzy logic, and the cutting speed, feed depth and clamping force of the machine tool are used as the output items of the fuzzy logic, among which the processing state data is the force value; Define fuzzy sets for input items and output items respectively; Fuzzy rules designed based on processing experience and system characteristics are used to associate input items with output items; Convert the specific value of the input item into the membership degree in the fuzzy set and use the membership function to define the fuzzy set: ; a is the left endpoint of the fuzzy set, which is the point where the membership starts to increase from 0, b is the center point of the fuzzy set, which is the point where the membership is 1, and c is the right endpoint of the fuzzy set, which is the point where the membership decreases from 1 to 0; Use the fuzzy rule base to map the membership of input items to the fuzzy set of output items; The reasoning process combines the input membership to calculate the confidence of each rule; The fuzzy set of output items is converted into specific values ​​through defuzzification method, which is used to dynamically adjust the operating parameters of the machine tool.

7. A CNC machine tool operation control system according to claim 6, characterized in that: Dynamically adjust the cutting speed based on the inference results. If the force value is high or the precision is low, reduce the cutting speed to reduce the cutting load and force fluctuation; if the force value is low and the precision is high, increase the cutting speed to improve the processing efficiency. Adjust the feed depth. When the force and vibration are large, reduce the cutting depth to reduce the processing load; when the force is stable, increase the cutting depth to improve the processing efficiency; Adjust the clamping force. When the workpiece is not stable enough or is subjected to large forces, increase the clamping force. When the clamping force is too large and causes the workpiece to deform, reduce the clamping force.

8. A dual-station sawing and drilling integrated machine tool, using a CNC machine tool operation control system as claimed in any one of claims 1 to 7, comprising a machine base (1) and a machine table (8), characterized in that: A positioning frame (2) and two groups of support guide rails (3) are fixedly arranged on the upper surface of the machine base (1); two front fixed ports (201) are arranged on the surface of the positioning frame (2); a support (4) is slidably mounted on the surface of the support guide rail (3); a rear top bowl (401) is fixedly mounted on the surface of the support (4); the rear top bowl (401) cooperates with the corresponding front fixed port (201) to process and position the gas cylinder; A sawing module (9) and a drilling module (10) are provided on the surface of the machine table (8); the sawing module (9) and the drilling module (10) are located on a side of the positioning frame (2) away from the rear top bowl (401), and respectively correspond to one of the front fixed ports (201).

9. A dual-station sawing and drilling integrated machine tool according to claim 8, characterized in that: A material platform (6) is fixedly provided on the upper surface of the machine base (1), a conveying plate (7) is slidably provided on the surface of the material platform (6), and at least two groups of "V"-shaped support plates (701) are provided on the upper surface of the conveying plate (7), and the support plates (701) are located between the positioning frame (2) and the rear top bowl (401).

10. The double-station sawing and drilling integrated machine tool according to claim 9, characterized in that: A lower guide rail (801) is fixedly arranged on the surface of the machine table (8), two groups of bases (802) are slidably mounted on the surface of the lower guide rail (801), and the sawing module (9) and the drilling module (10) are respectively slidably mounted on the upper surfaces of the corresponding bases (802), and the sliding direction is perpendicular to the lower guide rail (801).

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