A high-precision method and system for cutting metal plates by fiber laser

Abstract

The present application relates to the technical field of fiber laser cutting, and particularly relates to a high-precision fiber laser cutting metal plate method and system, the method comprising the following steps: obtaining an original capacitance value of a capacitive induction ring; separating the original capacitance value of the capacitive induction ring into an average capacitance value and a high-frequency vibration signal; extracting a vibration amplitude from the high-frequency vibration signal; according to a nonlinear relationship between the capacitance value of the capacitive induction ring and the distance, the vibration amplitude and the average capacitance value, calculating a direct current deviation caused by high-frequency vibration; subtracting the direct current deviation from the average capacitance value to obtain a corrected average capacitance value; and controlling the distance between the nozzle and the metal plate according to the corrected average capacitance value. This is helpful to improve the cutting quality and dimensional accuracy.

Description

Technical Field

[0001] This invention relates to the technical field of fiber laser cutting, and specifically to a high-precision fiber laser cutting method and system for cutting metal sheets. Background Technology

[0002] In laser cutting, especially in the mass production of high-value titanium alloy sheets, a zero-pitch, shared-edge cutting layout strategy is often adopted to improve material utilization and reduce costs, where adjacent parts share a single cutting path. This strategy is highly effective in saving material, but in actual processing, the existing through-cut kerf alters the boundary conditions of the working chamber below the nozzle. The auxiliary gas flow no longer faces the continuous rigid surface entirely, but partially enters directly into the kerf, leading to a significant increase in local velocity and a rearrangement of pressure distribution, thus triggering a series of complex physical and control problems. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings by proposing a high-precision fiber laser cutting method and system for metal sheets.

[0004] The present invention adopts the following technical solution: A method for high-precision fiber laser cutting of metal sheets, the method comprising the following steps: Obtain the original capacitance value of the capacitive sensing loop; The original capacitance value of the capacitive sensing loop is separated into the average capacitance value and the high-frequency vibration signal. Extracting vibration amplitude from high-frequency vibration signals; Based on the nonlinear relationship between the capacitance value and distance of the capacitive sensing ring, the vibration amplitude, and the average capacitance value, the DC deviation caused by high-frequency vibration is calculated. The corrected average capacitance value is obtained by subtracting the DC deviation from the average capacitance value. The distance between the nozzle and the metal sheet is controlled based on the corrected average capacitance value.

[0005] This technical solution can effectively identify and correct the DC deviation of the capacitive sensing ring caused by high-frequency vibration of the sheet metal, thereby achieving precise control of the distance between the nozzle and the metal sheet metal. It avoids the reciprocating oscillation of the Z-axis of the cutting head caused by false displacement signals, and significantly improves the cutting quality and dimensional accuracy.

[0006] This application also discloses a high-precision fiber laser cutting metal sheet system, applied to the aforementioned high-precision fiber laser cutting metal sheet method, the system comprising: The module acquires the original capacitance value of the capacitive sensing loop. The separation module separates the original capacitance value of the capacitive sensing loop into an average capacitance value and a high-frequency vibration signal; The extraction module extracts the vibration amplitude from the high-frequency vibration signal; The calculation module calculates the DC deviation caused by high-frequency vibration based on the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance, the vibration amplitude, and the average capacitance value. The correction module subtracts the DC deviation from the average capacitance value to obtain the corrected average capacitance value. The control module controls the distance between the nozzle and the metal sheet based on the corrected average capacitance value.

[0007] This technical solution provides a system for achieving the aforementioned high-precision fiber laser cutting of metal sheets. Through modular design, it effectively executes various functions, ensuring the accuracy and stability of the cutting process and solving the cutting quality problem caused by high-frequency vibration in the prior art.

[0008] This application precisely identifies and corrects the DC deviation of the capacitive sensor caused by high-frequency vibration, avoiding the control system's response to false displacement signals, thereby eliminating the periodic oscillation of the cutting head's Z-axis and ensuring the stability of the laser focus position. Accordingly, this application can significantly improve the edge quality and dimensional accuracy of the cut surface, especially in zero-pitch common-edge cutting applications of high-value metal sheets, effectively improving material utilization and processing efficiency, reducing production costs, and demonstrating significant practical value and technological advancement.

[0009] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0010] Figure 1 This is a flowchart of a high-precision fiber laser cutting method for metal sheets according to the present invention; Figure 2 This is a schematic diagram of the structure of a high-precision fiber laser cutting system for metal sheets according to the present invention. Detailed Implementation

[0011] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0012] This embodiment provides a high-precision fiber laser cutting method and system for metal sheets, combined with... Figure 1 and Figure 2 As shown.

[0013] refer to Figure 1 A method for high-precision fiber laser cutting of metal sheets, the method comprising the following steps: Obtain the original capacitance value of the capacitive sensing loop; The original capacitance value of the capacitive sensing loop is separated into the average capacitance value and the high-frequency vibration signal. Extracting vibration amplitude from high-frequency vibration signals; Based on the nonlinear relationship between the capacitance value and distance of the capacitive sensing ring, the vibration amplitude, and the average capacitance value, the DC deviation caused by high-frequency vibration is calculated. The corrected average capacitance value is obtained by subtracting the DC deviation from the average capacitance value. The distance between the nozzle and the metal sheet is controlled based on the corrected average capacitance value.

[0014] The capacitive sensing ring is a non-contact displacement sensor that measures distance by utilizing the characteristic that capacitance changes with the distance between plates. During laser cutting, the capacitive sensing ring is typically installed around the nozzle of the cutting head to monitor the distance between the nozzle and the metal sheet in real time. The raw capacitance value refers to the unprocessed capacitance measurement directly output by the capacitive sensing ring, which includes information on the average distance between the nozzle and the sheet, as well as high-frequency fluctuations caused by sheet vibration. The average capacitance value reflects the portion of the raw capacitance value that reflects the average distance between the nozzle and the sheet, while the high-frequency vibration signal represents the portion of the capacitance value that changes rapidly due to the micro-vibrations of the sheet. The vibration amplitude is the intensity of the high-frequency vibration signal, reflecting the severity of the sheet vibration. The nonlinear relationship between capacitance and distance refers to the inverse nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance between the nozzle and the sheet; that is, the closer the distance, the more sensitive the capacitance value becomes. DC deviation refers to the fact that due to the nonlinear relationship between capacitance and distance, even symmetrical vibration of the sheet can cause a shift in the average value of the output signal of the capacitive sensing ring, manifesting as false signals of the sheet moving closer or further away. The corrected average capacitance value is a more accurate average distance information obtained by subtracting the DC deviation from the original average capacitance value.

[0015] This application provides a high-precision fiber laser cutting method for metal sheets, the core of which lies in accurately processing the signal of the capacitive sensing ring to eliminate measurement errors caused by high-frequency vibration.

[0016] First, the method involves acquiring the raw capacitance value of a capacitive sensing loop. This can be achieved by connecting the capacitive sensing loop to a data acquisition system. For example, the capacitive sensing loop can be a ring electrode with the metal sheet to be cut underneath, forming a parallel-plate capacitor. When the laser cutting system is operating, the capacitive sensing loop continuously outputs the capacitance value it senses. These raw capacitance values ​​typically contain both DC and AC components, where the DC component reflects the average distance between the nozzle and the sheet, while the AC component includes high-frequency fluctuations caused by sheet vibration.

[0017] Secondly, the original capacitance value of the capacitive sensing loop is separated into an average capacitance value and a high-frequency vibration signal. One approach is to use a low-pass filter to filter the original capacitance value, thus removing the high-frequency vibration signal and obtaining the average capacitance value. For example, a low-pass filter with a cutoff frequency lower than the vibration frequency of the material can be designed. The original capacitance value is input into this filter, and the output is the average capacitance value. The high-frequency vibration signal can be obtained by subtracting the average capacitance value from the original capacitance value, or it can be extracted directly from the original capacitance value using a high-pass filter.

[0018] Next, the vibration amplitude is extracted from the high-frequency vibration signal. This can be achieved by rectifying and filtering the high-frequency vibration signal. For example, the high-frequency vibration signal can be fully rectified and then passed through a low-pass filter to obtain an envelope signal that reflects the vibration intensity; the amplitude of this envelope signal is the vibration amplitude. Another approach is to calculate the root mean square (RMS) value of the high-frequency vibration signal, which can also serve as a quantification of the vibration amplitude.

[0019] Next, based on the nonlinear relationship between the capacitance of the capacitive sensing ring and distance, the vibration amplitude, and the average capacitance value, the DC deviation caused by high-frequency vibration is calculated. Since the capacitance is inversely proportional to distance, even symmetrical vibration of the plate will cause a shift in the average capacitance value output by the capacitive sensing ring. To calculate this DC deviation, a nonlinear relationship model between capacitance and distance can be pre-established, such as an inverse proportional function model. Then, the currently measured vibration amplitude is substituted into this model, and combined with the average capacitance value, the DC deviation caused by the nonlinear effect is estimated through numerical calculation or table lookup. For example, a lookup table can be created that records the possible DC deviation values ​​under different average distances and vibration amplitudes.

[0020] Next, the DC deviation is subtracted from the average capacitance value to obtain the corrected average capacitance value. This step is crucial for eliminating the effects of high-frequency vibration. By subtracting the previously calculated DC deviation from the original average capacitance value, a more accurate correction value reflecting the actual average distance between the nozzle and the metal sheet can be obtained. For example, if the original average capacitance value falsely indicates that the sheet is close due to high-frequency vibration, then subtracting the DC deviation will result in a corrected average capacitance value that is closer to the true average distance.

[0021] Finally, the distance between the nozzle and the metal sheet is controlled based on the corrected average capacitance value. The corrected average capacitance value is then fed into the Z-axis servo control system of the laser cutting system. This control system adjusts the height of the cutting head by driving the Z-axis servo motor according to the difference between the preset target cutting distance and the corrected average capacitance value, thereby precisely controlling the distance between the nozzle and the metal sheet and ensuring the laser focus is always at the optimal position. For example, if the corrected average capacitance value indicates that the distance between the nozzle and the sheet is too large, the control system will drive the cutting head down; conversely, it will drive it up.

[0022] The high-precision fiber laser cutting method for metal sheets proposed in this application effectively solves the problem of decreased cutting accuracy caused by high-frequency vibration of the sheet in traditional laser cutting by refining the original capacitance value of the capacitive sensing ring.

[0023] Specifically, this method first obtains the raw capacitance values ​​of the capacitive sensing ring. These values ​​include the average distance information between the nozzle and the metal sheet, as well as high-frequency fluctuations caused by the micro-vibrations of the sheet. Next, a separation module decomposes the raw capacitance values ​​into an average capacitance value and a high-frequency vibration signal; this step is fundamental to subsequent precise processing. Then, the vibration amplitude is extracted from the high-frequency vibration signal, quantifying the severity of the sheet vibration.

[0024] The core innovation lies in its full consideration of the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance. When the plate vibrates at high frequency, due to this nonlinear relationship, even if the plate vibrates symmetrically around an average position, the average capacitance value output by the capacitive sensing ring will produce a false offset, i.e., a DC deviation, due to this nonlinear relationship. This application calculates the DC deviation caused by high-frequency vibration and subtracts this deviation from the average capacitance value to obtain a corrected average capacitance value. This correction process effectively eliminates the interference of high-frequency vibration on distance measurement, allowing the obtained average capacitance value to more accurately reflect the actual average distance between the nozzle and the metal plate.

[0025] Ultimately, based on the corrected average capacitance value, the control module precisely controls the distance between the nozzle and the metal sheet. In this way, the Z-axis servo system no longer responds to false distance signals caused by high-frequency vibrations, but adjusts according to the true average distance, thereby avoiding unnecessary reciprocating oscillations of the cutting head.

[0026] This application further proposes a method for calculating the DC deviation caused by high-frequency vibration based on the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance, the vibration amplitude, and the average capacitance value. Real-time monitoring of pressure fluctuations in the auxiliary gas flow field, and extraction of pressure fluctuation amplitude and frequency; The dielectric constant parameter in the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance is dynamically adjusted based on the pressure fluctuation amplitude and frequency. Based on the dynamically adjusted dielectric constant parameter, vibration amplitude, and average capacitance value, the DC deviation caused by high-frequency vibration is calculated.

[0027] Specifically, real-time monitoring of pressure fluctuations in the auxiliary gas flow field refers to continuously collecting gas pressure data by arranging pressure sensors in the auxiliary gas flow field to obtain dynamic information about its changes over time. The pressure fluctuation amplitude can be understood as the difference between the maximum and minimum values ​​of the pressure signal within a certain time window, or its root mean square value, used to quantify the severity of pressure changes. The pressure fluctuation frequency represents the periodicity of the pressure fluctuations, obtained, for example, through spectral analysis methods such as Fourier transform, with the aim of capturing any periodic disturbances that may exist in the auxiliary gas flow field.

[0028] The dynamic adjustment of the dielectric constant parameter in the nonlinear relationship between the capacitance value and distance of the capacitive sensing ring, based on the pressure fluctuation amplitude and frequency, refers to the real-time correction of the dielectric constant used to describe the dielectric properties between the capacitive sensing ring and the metal plate using monitored pressure fluctuation information. In practical applications, the dielectric constant parameter can be adjusted according to a pre-established pressure-dielectric constant mapping relationship or physical model. For example, when the pressure fluctuation amplitude increases or the frequency changes significantly, the dielectric constant parameter will be corrected accordingly to more accurately reflect the actual dielectric characteristics of the current auxiliary gas flow field. The aim is to ensure that the nonlinear relationship model between capacitance value and distance can adapt to the dynamic changes in the auxiliary gas flow field, thereby improving the accuracy of distance estimation.

[0029] Therefore, based on the dynamically adjusted dielectric constant parameter, vibration amplitude, and average capacitance value, the DC deviation caused by high-frequency vibration is calculated. This involves updating the dielectric constant parameter and substituting it into the original DC deviation calculation model. This model is typically based on the nonlinear relationship between capacitance and distance, and corrects for the influence of vibration amplitude on the capacitance measurement to eliminate the DC offset effect of high-frequency vibration on the average capacitance value. By using a more accurate dielectric constant parameter, the calculated DC deviation can be closer to the true value.

[0030] This application's solution, by introducing real-time monitoring of pressure fluctuations in the auxiliary gas flow field, can promptly acquire information on external disturbances affecting changes in the dielectric constant. It is precisely because pressure fluctuations in the auxiliary gas flow field directly affect the dielectric constant of the medium between the capacitive sensing ring and the metal sheet that traditional fixed dielectric constant models have limitations in dynamic cutting environments. By extracting the pressure fluctuation amplitude and frequency, these disturbances can be quantified, and the dielectric constant parameter in the nonlinear relationship between capacitance and distance can be dynamically adjusted based on this quantified information. This dynamic adjustment mechanism allows the capacitance model to adapt to changes in the cutting environment in real time, thus more accurately reflecting the true distance between the nozzle and the metal sheet. Finally, when calculating the DC deviation caused by high-frequency vibration, the use of dynamically corrected dielectric constant parameters makes the calculation results of the DC deviation more accurate, effectively avoiding calculation errors caused by changes in the dielectric constant.

[0031] In some preferred embodiments, it is assumed that during the fiber laser cutting of metal sheets, the supply pressure of the auxiliary gas (e.g., oxygen) fluctuates periodically or randomly due to gas source fluctuations or changes in pipeline resistance.

[0032] First, highly sensitive miniature pressure sensors are installed near the capacitive sensing ring, such as inside the nozzle or at the auxiliary gas outlet. These sensors acquire pressure data of the auxiliary gas flow field in real time. For example, during a certain cutting stage, the pressure sensors detect gas pressure fluctuations at a frequency of 50Hz within the range of 100kPa ± 5kPa.

[0033] Next, the system processes the collected pressure data and extracts the pressure fluctuation amplitude as 5 kPa and the pressure fluctuation frequency as 50 Hz.

[0034] Then, based on a pre-established pressure-dielectric constant mapping table or physical model, the system dynamically adjusts the dielectric constant parameter in the nonlinear relationship between the capacitance value and distance of the capacitive sensing loop using the extracted pressure fluctuation amplitude and frequency. For example, when the pressure fluctuation amplitude is 5 kPa and the frequency is 50 Hz, the system dynamically adjusts the dielectric constant parameter from the initially set 1.0005 (the dielectric constant of air) to 1.0006.

[0035] Finally, this dynamically adjusted dielectric constant parameter, along with the vibration amplitude extracted from the high-frequency vibration signal and the average capacitance value of the capacitive sensing loop, is substituted into the DC deviation calculation model. For example, without dynamic adjustment, the calculated DC deviation might be 0.02 pF, while after dynamically adjusting the dielectric constant parameter, the calculated DC deviation might be corrected to 0.021 pF. This corrected DC deviation is subtracted from the average capacitance value to obtain a more accurate corrected average capacitance value, thereby achieving more precise control over the distance between the nozzle and the metal sheet. In this way, even when there are pressure fluctuations in the auxiliary gas flow field, the stability and accuracy of the cutting distance can be ensured.

[0036] This application further proposes that the steps for extracting vibration amplitude from high-frequency vibration signals include: Spectral analysis of high-frequency vibration signals identifies the characteristic frequency range of micro-vibrations in the sheet metal caused by aeroacoustic excitation. Based on the characteristic frequency range of the micro-vibration of the board, an adaptive bandpass filter is designed. The center frequency and bandwidth of the adaptive bandpass filter are used to track the dominant frequency and spectral width of the micro-vibration of the board. The high-frequency vibration signal is filtered by an adaptive bandpass filter to obtain the filtered signal. The envelope of the filtered signal is extracted to obtain the envelope. Calculate the root mean square value of the envelope as the amplitude of the vibration.

[0037] Specifically, spectral analysis of high-frequency vibration signals refers to converting the time-domain high-frequency vibration signal into a frequency-domain spectrum using digital signal processing techniques such as Fourier transform, in order to reveal the various frequency components contained in the signal and their corresponding energy or amplitude. Its purpose is to identify the specific frequency range of minute vibrations in the metal sheet caused by aeroacoustic effects during laser cutting (such as sound waves generated by the interaction of auxiliary gas and the cutting kerf), thereby distinguishing effective vibration information from background noise.

[0038] In this context, designing an adaptive bandpass filter based on the characteristic frequency range of the sheet metal's micro-vibration can be understood as dynamically constructing a filter that selectively passes signals within a specific frequency range, based on the frequency characteristics of the sheet metal's micro-vibration identified through spectral analysis. The center frequency and bandwidth of the adaptive bandpass filter are designed to track the dominant frequency and spectral width of the sheet metal's micro-vibration in real time. This means that the filter can automatically adjust its filtering parameters according to changes in actual working conditions (such as cutting speed, material thickness, and auxiliary gas pressure) to ensure that it always focuses on the target vibration signal.

[0039] In practical applications, high-frequency vibration signals are filtered by an adaptive bandpass filter. For example, a digital signal processor or field-programmable gate array can be used to implement the filtering algorithm. The original high-frequency vibration signal is input into the adaptive bandpass filter to remove noise and interference components outside the target frequency range, thereby obtaining a purer and more accurate filtered signal that reflects the micro-vibration of the board.

[0040] Furthermore, envelope extraction of the filtered signal involves using methods such as Hilbert transform, peak detection, or low-pass filtering to obtain the instantaneous amplitude change trajectory of the filtered signal, forming an envelope. The purpose is to eliminate carrier frequency components within the signal, retaining only the amplitude change information of the vibration, which is crucial for accurate subsequent calculation of the vibration amplitude.

[0041] Therefore, the root mean square (RMS) value of the envelope is calculated as the vibration amplitude. Specifically, this involves performing RMS calculation on the extracted envelope within a certain time window. The RMS value effectively reflects the effective value or average energy of the signal. For non-sinusoidal or complex waveform vibration signals, the RMS value is a reliable indicator of their vibration intensity or amplitude. The vibration amplitude obtained in this way can more accurately characterize the actual micro-vibration of the metal sheet.

[0042] This application's solution addresses the problems of noise interference and insufficient accuracy in directly extracting vibration amplitude in complex industrial environments using traditional methods. Specifically, by introducing steps such as spectrum analysis, adaptive filtering, envelope extraction, and root mean square (RMS) calculation, it solves these issues. First, spectrum analysis accurately locates the characteristic frequency range of the micro-vibrations in the sheet metal caused by aeroacoustic excitation, providing a basis for subsequent filtering. Second, based on the identified characteristic frequency range, an adaptive bandpass filter is designed and applied. Its center frequency and bandwidth dynamically track the dominant frequency and spectral width of the sheet metal micro-vibrations, ensuring effective filtering of irrelevant noise and interference under different operating conditions, thus obtaining a pure vibration signal. Next, envelope extraction is performed on the filtered signal, stripping away the carrier component and focusing on changes in vibration amplitude. Finally, the RMS value of the envelope is calculated, providing a stable and physically meaningful quantitative indicator of vibration amplitude. This series of processes makes the vibration amplitude extracted from the high-frequency vibration signal more accurate and robust, providing a reliable input for subsequent calculation of DC deviations caused by high-frequency vibration, thereby improving the overall accuracy of distance control between the nozzle and the metal sheet.

[0043] In some preferred embodiments, this application is implemented as follows: Assuming that during fiber laser cutting, the original capacitance value acquired by the capacitive sensing loop is separated to obtain a high-frequency vibration signal. This high-frequency vibration signal is first sent to a Fast Fourier Transform module for spectral analysis. Analysis of the spectrum reveals that aeroacoustic excitation caused by the interaction between the auxiliary gas flow field and the cutting gap results in significant micro-vibration characteristics in the metal sheet within a frequency range of approximately 500Hz to 1500Hz, with the dominant frequency likely around 800Hz and a spectral width of approximately 200Hz. Based on this identified characteristic frequency range, a digital adaptive bandpass filter is designed. This filter can be implemented using a Kalman filter or a least mean square algorithm, with its center frequency initialized to 800Hz and bandwidth set to 200Hz. During the cutting process, if changes occur in the sheet material, thickness, or auxiliary gas pressure, causing the dominant frequency of the sheet's micro-vibration to drift to 900Hz and the spectral width to become 250Hz, the adaptive bandpass filter can adjust its center frequency to 900Hz in real time and adjust its bandwidth to 250Hz to ensure effective capture of the target vibration signal. Subsequently, the signal processed by the adaptive bandpass filter is subjected to envelope extraction via a Hilbert transform module to obtain an envelope reflecting the changes in vibration amplitude. Finally, the root mean square (RMS) value of this envelope is calculated every 10 milliseconds. For example, if the calculated RMS value is 0.05 pF, this value is used as the vibration amplitude at the current moment for subsequent DC deviation calculation. In this way, even in complex cutting environments, the micro-vibration amplitude of the sheet metal can be accurately and robustly extracted.

[0044] This application further proposes a step for dynamically adjusting the dielectric constant parameter in the nonlinear relationship between the capacitance value of a capacitive sensing loop and distance, based on the pressure fluctuation amplitude and frequency: Monitor the geometric morphology of waste accumulation areas; Based on the geometric morphology of the waste accumulation area, the auxiliary gas pressure, and the distance between the nozzle and the metal plate, the geometric parameters of the confined cavity are calculated. Based on the geometric parameters of the confined cavity, the pressure fluctuation amplitude, and the pressure fluctuation frequency, the dielectric constant parameter in the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance is dynamically adjusted.

[0045] Specifically, "monitoring the geometric morphology of the waste accumulation area" refers to acquiring data such as the distribution, height, volume, or shape of waste (e.g., slag, oxides) within the cutting area in real-time or near real-time using visual sensors, laser scanners, or other non-contact measuring devices. The purpose is to quantify the impact of the waste on the physical boundary of the auxiliary gas flow field. "Calculating the geometric parameters of the confined cavity" can be understood as calculating key geometric parameters such as the effective volume, cross-sectional area, and flow channel length of the confined cavity formed between the nozzle and the metal plate, based on the monitored geometric morphology of the waste accumulation area, the current auxiliary gas pressure, and the actual distance between the nozzle and the metal plate, using a fluid dynamics model or empirical model. For example, waste accumulation may form an irregular cavity, and calculating its geometric parameters helps to more accurately describe the actual space of gas flow. In practical applications, "dynamically adjusting the dielectric constant parameter in the nonlinear relationship between the capacitance value and distance of the capacitive sensing ring" refers to combining the calculated geometric parameters of the confined cavity with the real-time monitored pressure fluctuation amplitude and frequency. This is achieved through a pre-set lookup table, neural network model, or physical model to correct the dielectric constant parameter in the nonlinear relationship model between the capacitance value and distance of the capacitive sensing ring. The aim is to ensure that the dielectric constant parameter more accurately reflects the true physical state of the auxiliary gas flow field during the cutting process, including flow field changes caused by waste accumulation.

[0046] This application's solution incorporates monitoring of the geometric morphology of the waste accumulation area and combines this with the auxiliary gas pressure and the distance between the nozzle and the metal plate to calculate the geometric parameters of the confined cavity, thereby enabling a more comprehensive understanding of the actual physical environment of the auxiliary gas flow field. Since waste accumulation alters the effective space for gas flow, thus affecting the gas dielectric constant and the propagation characteristics of pressure fluctuations, the geometric parameters of these confined cavities are included in the dielectric constant parameter adjustment process. This ensures that the dynamic adjustment of the dielectric constant parameter no longer relies solely on pressure fluctuations but comprehensively considers the physical boundary conditions of the flow field. Consequently, the dielectric constant parameter in the nonlinear relationship model between the capacitance value of the capacitive sensing ring and distance can more accurately reflect the true dielectric characteristics of the auxiliary gas flow field below the cutting head, thereby improving the accuracy of the DC deviation calculation caused by high-frequency vibration.

[0047] In some preferred embodiments, it is assumed that during the fiber laser cutting of thick metal sheets, slag gradually accumulates below the cutting kerf due to the high cutting speed or material properties. Traditional methods may adjust the dielectric constant solely by monitoring pressure fluctuations in the auxiliary gas flow field. However, since the slag accumulation alters the effective gas flow path, even if the pressure fluctuation signal is detected, the adjustment of the dielectric constant may not be accurate enough. The solution of this application uses a vision sensor or laser ranging module installed near the cutting head to monitor the geometric morphology of the waste accumulation area below the cutting kerf in real time. For example, it identifies the height and width of the slag accumulation and digitizes them as geometric parameters. Simultaneously, by combining the current auxiliary gas pressure (e.g., obtained through a pressure sensor) and the distance between the nozzle and the metal sheet (e.g., initially estimated using the average capacitance value of a capacitive sensing ring), the system calculates the effective volume and flow channel cross-sectional area of ​​the confined cavity below the nozzle. For example, when slag buildup reduces the flow channel cross-sectional area by 20%, the system dynamically adjusts the dielectric constant parameter in the nonlinear relationship between the capacitance of the capacitive sensing ring and distance, based on this information and in conjunction with real-time monitored pressure fluctuation amplitude (e.g., 5 kPa) and pressure fluctuation frequency (e.g., 200 Hz). Specifically, the system may fine-tune the dielectric constant parameter based on a pre-established flow field model or experimental data to more accurately reflect changes in gas density and composition caused by slag buildup. For example, if the calculated confined cavity geometry indicates an increased gas velocity, the dielectric constant may be lowered accordingly. In this way, even under complex flow field changes caused by slag buildup, the distance measurement model of the capacitive sensing ring maintains high accuracy, ensuring precise nozzle height control during laser cutting and preventing decreased cutting quality or nozzle damage due to distance deviations.

[0048] Based on the nonlinear relationship between the capacitance of the capacitive sensing ring and distance, the vibration amplitude, and the average capacitance value, the steps for calculating the DC deviation caused by high-frequency vibration include: Continuously monitor the internal temperature of the capacitive sensing ring, the ambient temperature, and the cumulative operating time; The degree of drift in the nonlinear response characteristics of the capacitive sensing ring is evaluated based on the internal temperature, ambient temperature, and cumulative operating time of the capacitive sensing ring. Based on the degree of drift of the nonlinear response characteristics of the capacitive sensing loop, the coefficient parameters in the nonlinear relationship model between the capacitance value and distance of the capacitive sensing loop are corrected to obtain the corrected nonlinear relationship model. Based on the corrected nonlinear relationship model, vibration amplitude, and average capacitance value, the DC deviation caused by high-frequency vibration is calculated.

[0049] Specifically, continuously monitoring the internal temperature, ambient temperature, and cumulative operating time of the capacitive sensing loop involves installing a temperature sensor inside the capacitive sensing loop and using an ambient temperature sensor to obtain the external ambient temperature, while simultaneously recording the device's operating time. This process aims to obtain key parameters affecting the performance of the capacitive sensing loop. The purpose is to provide the necessary data foundation for subsequent evaluation of nonlinear response characteristic drift.

[0050] The assessment of the drift in the nonlinear response characteristics of the capacitive sensing ring, based on its internal temperature, ambient temperature, and cumulative operating time, can be understood as using a pre-established drift model or empirical data to analyze the influence of these parameters on the nonlinear relationship between the capacitance value and distance of the capacitive sensing ring. For example, increased temperature may cause material expansion, altering the spacing between capacitor plates or the dielectric constant; long-term operation may lead to material aging, changing its dielectric properties. By comprehensively analyzing these factors, the degree of deviation in the nonlinear response characteristics can be quantified.

[0051] In practical applications, based on the degree of drift in the nonlinear response characteristics of the capacitive sensing loop, the coefficient parameters in the nonlinear relationship model between the capacitance value and distance of the capacitive sensing loop are corrected to obtain a corrected nonlinear relationship model. This involves using the assessed degree of drift as a correction factor to adjust the correlation coefficients (such as intercept, slope, and higher-order term coefficients) in the original nonlinear relationship model. For example, if the assessment results show that the overall capacitance value is too high, the constant term in the model can be adjusted; if the degree of nonlinearity changes, the higher-order term coefficients can be adjusted. The aim is to make the nonlinear relationship model more accurately reflect the actual performance of the capacitive sensing loop under current operating conditions.

[0052] Therefore, based on the corrected nonlinear relationship model, vibration amplitude, and average capacitance value, the DC deviation caused by high-frequency vibration can be calculated. This means that a more accurate DC deviation can be calculated by using a corrected nonlinear relationship model that better reflects reality, combined with real-time acquired vibration amplitude and average capacitance value. This ensures that the measurement and control of the distance between the nozzle and the metal plate can maintain high precision even in the presence of high-frequency vibration.

[0053] This application's solution, by introducing continuous monitoring of the internal temperature of the capacitive sensing loop, ambient temperature, and cumulative operating time, can comprehensively grasp the key factors affecting the nonlinear response characteristics of the capacitive sensing loop. It is precisely because these factors cause the nonlinear relationship between the capacitance value of the capacitive sensing loop and distance to drift that direct calculation of DC deviation using a fixed model introduces errors. By evaluating these parameters, the degree of drift in the nonlinear response characteristics can be quantified, and the coefficient parameters in the nonlinear relationship model between the capacitance value of the capacitive sensing loop and distance can be dynamically corrected accordingly. This corrected nonlinear relationship model can more accurately reflect the true performance of the capacitive sensing loop under current operating conditions. Therefore, when calculating DC deviation caused by high-frequency vibration, it can eliminate or significantly reduce the errors caused by the drift of the sensor's own characteristics, ensuring the accuracy of DC deviation calculation.

[0054] In some preferred embodiments, it is assumed that during the laser cutting process, the internal temperature of the capacitive sensing ring rises from 25°C to 40°C, and the equipment has accumulated 1000 hours of operation. First, this data is acquired using a built-in temperature sensor and a system timer. Then, based on a preset temperature-drift curve and aging-drift model, it is assessed that the nonlinear response characteristics of the capacitive sensing ring have experienced an overall drift of 0.5% at the current temperature and accumulated operating time, and its nonlinear coefficient has also changed slightly. Based on this assessment result, the constant and quadratic terms in the original nonlinear relationship model between capacitance and distance are adjusted accordingly. For example, if the original model is C=aD^2+bD+c, it is corrected to C'=a'D^2+b'D+c', where a', b', and c' are new coefficients adjusted according to the degree of drift. Finally, using this corrected nonlinear relationship model, combined with the real-time acquired vibration amplitude and average capacitance value, a more accurate DC deviation is calculated, thereby achieving precise control of the distance between the nozzle and the metal sheet, maintaining high cutting accuracy even under conditions of temperature changes or equipment aging.

[0055] The steps for evaluating the degree of drift in the nonlinear response characteristics of the capacitive sensing loop also include: Set up multiple discrete temperature monitoring points; Based on the location information of each temperature monitoring point and the temperature value monitored by the temperature monitoring point, combined with the material thermal conductivity characteristics and structural geometry information of the capacitive sensing ring, the temperature field distribution inside the capacitive sensing ring is calculated. Based on the temperature field distribution inside the capacitive sensing ring, identify the local hot spot region and the average temperature region inside the capacitive sensing ring. The degree of drift in the nonlinear response characteristics of the capacitive sensing loop is evaluated based on the temperature values ​​of local hot spots, the temperature values ​​of the average temperature area, and the cumulative working time.

[0056] Specifically, setting up multiple discrete temperature monitoring points refers to placing miniature temperature sensors at different key locations within the capacitive sensing ring, such as its sensitive element, support structure, and connection points. These sensors can be thermistors, thermocouples, or infrared temperature sensors, and their purpose is to obtain real-time temperature data for different regions inside the capacitive sensing ring.

[0057] Based on the location information of each temperature monitoring point and the temperature values ​​monitored, combined with the material thermal conductivity characteristics and structural geometry of the capacitive sensing ring, the calculation of the internal temperature field distribution of the capacitive sensing ring can be understood as using numerical simulation methods (such as finite element analysis) or interpolation algorithms based on physical models to extend discrete temperature measurement data to the entire internal space of the capacitive sensing ring. This requires considering the thermophysical parameters of the material used in the capacitive sensing ring, such as thermal conductivity and specific heat capacity, as well as its specific geometry, size, and boundary conditions (such as convective heat transfer and radiative heat transfer with the environment), thereby constructing a continuous temperature distribution map. The purpose is to obtain more comprehensive and detailed temperature information inside the capacitive sensing ring.

[0058] In practical applications, identifying local hotspot regions and average temperature regions within a capacitive sensing ring based on its internal temperature field distribution involves analyzing and calculating the temperature field distribution to determine local areas with significantly higher temperatures than the surrounding areas (i.e., local hotspot regions) and the average temperature region representing the overall temperature level. This can be achieved by setting temperature thresholds, using gradient analysis, or clustering algorithms. For example, a temperature threshold can be set, and any region exceeding this threshold is identified as a local hotspot region. Simultaneously, the average temperature of the entire or most of the region can be calculated as a representative value for the average temperature region. The purpose is to distinguish regions with different thermal states within the capacitive sensing ring, as these regions may have varying effects on the drift of nonlinear response characteristics.

[0059] Furthermore, assessing the degree of drift in the nonlinear response characteristics of the capacitive sensing loop, based on the temperature values ​​of local hotspot areas, the temperature values ​​of the average temperature area, and the cumulative operating time, involves inputting the highest or average temperature of the identified local hotspot areas, the average temperature of the average temperature area, and the cumulative operating time of the capacitive sensing loop into a pre-established drift assessment model. This model can be an empirical formula, a lookup table, or a machine learning-based model, used to quantify the potential deviation of the nonlinear relationship between the capacitance value and distance of the capacitive sensing loop under different thermal states and operating durations. The aim is to provide a more accurate and representative assessment result of the nonlinear response characteristic drift.

[0060] This application's solution, by setting multiple discrete temperature monitoring points and combining material thermal conductivity characteristics and structural geometry information, calculates the internal temperature field distribution of the capacitive sensing ring. This allows for the acquisition of more comprehensive and detailed internal thermal state information than a single temperature point or ambient temperature. The detailed temperature field distribution makes it possible to identify local hotspot regions and average temperature regions. Local hotspot regions are often areas of material stress concentration, accelerated aging, or more significant performance degradation; their temperature values ​​have a more direct and significant impact on the drift of nonlinear response characteristics. By combining the temperature values ​​of these key regions with the overall average temperature and cumulative operating time, the variation law of the nonlinear response characteristics of the capacitive sensing ring under long-term operation and complex thermal loads can be captured more accurately, thus providing a more reliable input for subsequent DC deviation calculations.

[0061] In some preferred embodiments, the evaluation process of the capacitive sensing ring is implemented as follows: First, eight miniature thermistors are set as temperature monitoring points on key parts of the capacitive sensing ring, such as the ceramic substrate, metal electrodes, and encapsulation materials. The location information of these thermistors is accurately recorded. During the cutting process, the temperature values ​​of these eight monitoring points are collected in real time. Subsequently, using a pre-established three-dimensional finite element heat conduction model of the capacitive sensing ring, these eight discrete temperature values ​​are used as boundary conditions or internal constraints. Combined with parameters such as thermal conductivity and specific heat capacity of materials such as ceramics and metals, the continuous temperature field distribution inside the entire capacitive sensing ring is calculated numerically. Next, the calculated temperature field is analyzed to identify areas with temperatures higher than a certain preset threshold (e.g., 10°C higher than the overall average temperature) as local hot spots, and the average temperature of these areas is calculated. At the same time, the average temperature of the entire capacitive sensing ring is calculated as a representative value of the average temperature region. Finally, the average temperature of the local hot spots, the overall average temperature, and the cumulative working time of the capacitive sensing ring are input into a drift evaluation model based on neural network training. This model outputs a quantified degree of drift in the nonlinear response characteristics. For example, when the temperature in a local hotspot area is high and the cumulative working time is long, the assessed drift will increase accordingly, thereby guiding the subsequent correction of the nonlinear relationship model and ensuring the accuracy of DC deviation estimation.

[0062] The steps for identifying local hot spots and average temperature regions inside a capacitive sensing ring based on the temperature field distribution inside the ring include: Based on the distribution information of the thermal expansion coefficient of the material of the capacitive sensing ring, the temperature field distribution inside the capacitive sensing ring is divided into regions. Calculate the local deformation caused by temperature changes within the divided regions based on the material's thermal expansion coefficient. Based on local deformation, the recognition threshold and recognition boundary of the local hot spot area and average temperature area inside the capacitive sensing ring are dynamically adjusted. Based on the dynamically adjusted recognition threshold and recognition boundary, the local hot spot area and average temperature area inside the capacitive sensing ring are identified.

[0063] Specifically, the distribution information of the thermal expansion coefficient of the capacitive sensing ring refers to the thermal expansion coefficient data corresponding to different physical locations or regions composed of different materials. This data can be obtained through materials science experiments, supplier datasheets, or finite element analysis. Dividing the temperature field distribution inside the capacitive sensing ring into regions aims to subdivide the entire ring's temperature field into several sub-regions with similar material properties or geometric features, allowing for more precise analysis of the thermal response of each region.

[0064] The calculation of local deformation caused by temperature changes within a defined region, based on the material's thermal expansion coefficient, can be understood as using thermodynamics and solid mechanics principles to predict the size or shape changes of that region under thermal stress, based on the temperature change and its corresponding material's thermal expansion coefficient. For example, linear deformation can be calculated using the linear expansion formula ΔL=α*L0*ΔT, where α is the thermal expansion coefficient, L0 is the initial length, and ΔT is the temperature change. The purpose is to quantify the impact of temperature changes on the physical structure of the capacitive sensing ring, providing a physical basis for subsequent identification threshold and boundary adjustments.

[0065] In practical applications, dynamically adjusting the identification thresholds and boundaries of local hot spots and average temperature regions within the capacitive sensing loop based on local deformation means revising the temperature thresholds used to distinguish between local hot spots and average temperature regions, as well as the geometric boundaries of these regions, in real time based on calculated local deformation data. For example, if a region undergoes significant deformation due to thermal expansion, its physical boundaries may expand outward, or its internal temperature gradient distribution may change. In this case, it is necessary to adjust the upper and lower limits of the temperature range or the spatial coordinate range for identifying that region accordingly. The purpose is to ensure that the identified local hot spots and average temperature regions accurately reflect the true physical state of the capacitive sensing loop under its current thermal deformation state.

[0066] This application's solution first divides the internal temperature field distribution of the capacitive sensing ring into regions by introducing the distribution information of the material's thermal expansion coefficient, thus enabling independent analysis of the characteristics of different material or structural regions. Based on this, the local deformation caused by temperature changes is calculated according to the material's thermal expansion coefficient and temperature change in each divided region. This local deformation directly reflects the physical structural changes of the capacitive sensing ring under thermal stress. Because this physical structural change affects the nonlinear relationship between the capacitance value and distance of the capacitive sensing ring, and consequently affects the actual location and range of local hot spots and average temperature regions, this application further dynamically adjusts the identification thresholds and boundaries used to identify local hot spots and average temperature regions based on these local deformations. This dynamic adjustment ensures that even when the capacitive sensing ring undergoes thermal deformation, the identified local hot spots and average temperature regions still accurately correspond to their true physical state, thus providing more reliable basic data for subsequent nonlinear response drift assessment.

[0067] In some preferred embodiments, it is assumed that the capacitive sensing ring is composed of two different materials, A and B, where material A has a lower coefficient of thermal expansion and material B has a higher coefficient of thermal expansion. When the capacitive sensing ring operates, its internal temperature field distribution is calculated. First, based on the distribution information of materials A and B and their respective coefficients of thermal expansion, the internal temperature field distribution of the capacitive sensing ring is divided into material A region and material B region. Next, for each temperature change in material A region and material B region, the resulting local deformation is calculated. For example, material B region, due to its higher coefficient of thermal expansion, may experience greater deformation under the same temperature change. Subsequently, based on these calculated local deformations, the temperature thresholds and spatial boundaries for identifying local hotspot regions and average temperature regions are dynamically adjusted. Specifically, if the physical boundary of material B region expands outward due to deformation, the boundary conditions for identifying this region are correspondingly widened; if the deformation causes the temperature gradient in a certain region to become steeper, the temperature threshold for identifying hotspot regions may be adjusted to more sensitively capture these changes. Ultimately, by utilizing these dynamically adjusted recognition thresholds and boundaries, the local hot spots and average temperature regions inside the capacitive sensing ring are accurately identified.

[0068] This application further proposes steps for evaluating the degree of drift in the nonlinear response characteristics of a capacitive induction loop, including: Independent temperature sensitivity coefficients and aging sensitivity coefficients are set for different material regions of the capacitive sensing ring; Based on the temperature values ​​of local hot spots, the temperature values ​​of average temperature areas, and the cumulative working time, combined with independent temperature sensitivity coefficients and aging sensitivity coefficients, the drift component of the nonlinear response characteristics of each material region is calculated. The degree of nonlinear response drift of the capacitive sensing loop is obtained by weighting and superimposing the drift components of the nonlinear response characteristics of each material region and the weight of each material region on the overall nonlinear response drift.

[0069] Specifically, setting independent temperature sensitivity coefficients and aging sensitivity coefficients for different material regions of the capacitive sensing ring means dividing the ring into several material regions with different physicochemical properties based on its structural design and material composition. For example, the capacitive sensing ring may include dielectric material regions, electrode material regions, and support structure material regions. For each divided material region, independent temperature sensitivity coefficients and aging sensitivity coefficients are pre-set or experimentally calibrated according to its specific material properties and function within the capacitive sensing ring. The temperature sensitivity coefficient characterizes the sensitivity of the electrical or geometric properties of the material region to temperature changes, while the aging sensitivity coefficient characterizes the sensitivity of the electrical or geometric properties of the material region to changes with cumulative operating time (i.e., degree of aging). These coefficients can be constants or functions that vary with temperature, time, or other environmental factors.

[0070] Furthermore, based on the temperature values ​​of local hotspot areas, the temperature values ​​of average temperature areas, and the cumulative operating time, combined with independent temperature sensitivity coefficients and aging sensitivity coefficients, the nonlinear response characteristic drift component of each material region is calculated. This means that for the local hotspot areas and average temperature areas identified within the capacitive sensing loop, their temperature values ​​are used to drive the temperature sensitivity coefficients of each material region, while the cumulative operating time is used to drive the aging sensitivity coefficients. By performing mathematical operations on these temperature values, cumulative operating time, and the independent sensitivity coefficients of the corresponding material regions (e.g., through a preset drift model or empirical formula), the nonlinear response characteristic drift component of each material region due to temperature and aging effects can be calculated separately. Each component represents the independent contribution of that material region to the overall nonlinear response drift.

[0071] The degree of nonlinear response drift of the capacitive sensing loop is obtained by weighting and summing the drift components of the nonlinear response characteristics of each material region and their respective weights on the overall nonlinear response drift. Specifically, the influence of each material region on the overall nonlinear response drift of the capacitive sensing loop may differ, therefore a weight needs to be assigned to each material region. This weight can be determined based on factors such as the proportion of the material region's contribution to the total capacitance of the capacitive sensing loop, its criticality to distance measurement accuracy, or the significance of its drift on overall performance. By multiplying the drift component of each material region by its corresponding weight and summing all weighted components, a more comprehensive and accurate degree of nonlinear response drift of the capacitive sensing loop can be obtained.

[0072] This application's solution establishes independent temperature and aging sensitivity coefficients for different material regions of the capacitive sensing ring. Based on these coefficients, the drift components for each material region are calculated and then weighted and superimposed. This allows for a more precise capture of the nonlinear response drift caused by material heterogeneity within the capacitive sensing ring. This method considers the differentiated responses of different materials to temperature and aging, making the assessment of the overall drift level no longer a single, coarse figure, but a precise summary based on the contributions of each component. Therefore, it can more accurately reflect the true performance changes of the capacitive sensing ring under complex operating conditions, providing a more reliable basis for subsequent DC deviation calculations and distance control.

[0073] In some preferred embodiments, it is assumed that the capacitive sensing ring consists of a dielectric layer, upper and lower electrode layers, and an external encapsulation layer. The dielectric layer mainly affects the capacitance value, and its dielectric constant is sensitive to temperature and aging; the electrode layer mainly affects the effective area, and its coefficient of thermal expansion is sensitive to temperature; the encapsulation layer provides structural support and environmental isolation, and its aging may affect the overall stability.

[0074] First, independent temperature sensitivity coefficients and aging sensitivity coefficients are set for the three different material regions: dielectric layer, electrode layer, and encapsulation layer. For example, the dielectric layer may have a higher temperature sensitivity coefficient (reflecting the change of dielectric constant with temperature), while the electrode layer may have a higher aging sensitivity coefficient (reflecting material fatigue or oxidation).

[0075] Secondly, after monitoring the temperature values ​​of local hotspot areas, the temperature values ​​of average temperature areas, and the cumulative operating time, this data is input into a preset drift model. This model, combined with the independent sensitivity coefficients of each material region, calculates the drift components of the nonlinear response characteristics of the dielectric layer, electrode layer, and encapsulation layer, respectively. For example, the drift component of the dielectric layer may be mainly caused by temperature, while the drift component of the electrode layer may be mainly caused by the cumulative operating time.

[0076] Finally, weights are assigned based on the influence of each material region on the overall performance of the capacitive sensing ring. For example, the dielectric layer has the greatest impact on the capacitance value, so its drift component may be given a higher weight; the electrode layer follows; and the encapsulation layer has a relatively lower weight. By superimposing these weighted drift components, the overall nonlinear response drift of the capacitive sensing ring can be obtained. In this way, the drift of the capacitive sensing ring can be evaluated more comprehensively and accurately, providing reliable distance control for high-precision laser cutting.

[0077] This application further proposes the following steps for calculating the nonlinear response characteristic drift component of each material region based on the temperature values ​​of local hot spots, the temperature values ​​of the average temperature region, and the cumulative working time, combined with independent temperature sensitivity coefficients and aging sensitivity coefficients: Continuously monitor the internal temperature of the capacitive sensing ring, the ambient temperature, and the cumulative operating time; Based on the internal temperature of the capacitive sensing ring, the ambient temperature, and the cumulative working time, the temperature gradient change rate and the cumulative working time change rate are calculated in real time. Based on the rate of change of temperature gradient and the rate of change of cumulative working time, the independent temperature sensitivity coefficient and aging sensitivity coefficient are dynamically adjusted. Based on the dynamically adjusted independent temperature sensitivity coefficient, the dynamically adjusted independent aging sensitivity coefficient, the temperature value of the local hot spot area, the temperature value of the average temperature area, and the cumulative working time, the drift component of the nonlinear response characteristics of each material region is calculated.

[0078] Specifically, the internal temperature, ambient temperature, and cumulative operating time of the capacitive sensing ring are continuously monitored to obtain the current thermal state and operational history information of the sensing ring. The internal temperature is acquired in real time by multiple temperature sensors installed inside the sensing ring, the ambient temperature is obtained through external environmental sensors, and the cumulative operating time is recorded by a system timer. This data forms the basis for subsequent calculations of the temperature gradient change rate and the cumulative operating time change rate.

[0079] The real-time calculation of the temperature gradient change rate and the cumulative operating time change rate can be understood as a quantification of the rate of change of thermal stress and the aging process of the induction ring. The temperature gradient change rate refers to the spatial trend or temporal rate of temperature change per unit time, which can be obtained, for example, by differentiating or fitting the derivative of continuously acquired temperature data. The cumulative operating time change rate reflects the cumulative rate of equipment operation time and can be used to assess the aging rate of materials within a specific time period. These change rate parameters can more precisely capture the instantaneous response characteristics of the induction ring under dynamic operating conditions.

[0080] In practical applications, independent temperature sensitivity coefficients and aging sensitivity coefficients are dynamically adjusted based on the rate of change of temperature gradient and the rate of change of cumulative operating time. For example, a lookup table or function relationship based on experimental data or a simulation model can be pre-established. This lookup table or function relationship takes the rate of change of temperature gradient and the rate of change of cumulative operating time as input and outputs the corresponding corrected temperature sensitivity coefficients and aging sensitivity coefficients. The purpose is to enable these coefficients to adapt to the actual operating state of the sensing loop in real time, thereby more accurately reflecting the material's true sensitivity to temperature and aging.

[0081] Therefore, based on the dynamically adjusted independent temperature sensitivity coefficient, the dynamically adjusted independent aging sensitivity coefficient, the temperature value of the local hot spot area, the temperature value of the average temperature area, and the cumulative working time, the drift component of the nonlinear response characteristics of each material region is calculated. This means that the sensitivity coefficient used in calculating the drift component is no longer a fixed value, but is corrected according to the real-time dynamic changes of the sensing loop, thereby improving the accuracy of the drift component calculation.

[0082] This application's solution addresses the problem that fixed coefficients cannot accurately reflect the true response characteristics of the sensing ring under dynamic conditions by introducing real-time calculations of the temperature gradient change rate and the cumulative operating time change rate, and adjusting the temperature sensitivity coefficient and aging sensitivity coefficient based on these dynamic parameters. Specifically, when the temperature of the sensing ring changes rapidly, the temperature gradient change rate increases accordingly, and the system dynamically adjusts the temperature sensitivity coefficient to better reflect the material's behavior under rapid thermal stress. Similarly, as the cumulative operating time continues to increase, especially when the aging rate may accelerate under specific operating conditions, the cumulative operating time change rate is used to dynamically adjust the aging sensitivity coefficient to more accurately assess the long-term performance degradation of the material. Through this dynamic adjustment mechanism, the drift component of the nonlinear response characteristics of each material region can be calculated more accurately, thus making the assessment of the degree of drift in the nonlinear response characteristics of the capacitive sensing ring more accurate, and providing a more reliable basis for subtracting the DC deviation from the average capacitance value.

[0083] In some preferred embodiments, it is assumed that during fiber laser cutting, a sudden adjustment in cutting power or auxiliary gas flow causes the internal temperature of the capacitive sensing ring to rise rapidly from 25°C to 40°C within a short period. Traditional solutions might still use a preset temperature sensitivity coefficient for drift assessment. However, in the solution of this application, the system continuously monitors the rapid rise in internal temperature and calculates a higher temperature gradient change rate in real time. Simultaneously, if the equipment has accumulated a long working time, the system also calculates the corresponding cumulative working time change rate. Based on these real-time calculated change rates, a pre-stored lookup table or algorithm model is invoked to dynamically adjust the temperature sensitivity coefficient and aging sensitivity coefficient upwards or downwards to more accurately reflect the material's true sensitivity under the current rapid heating and aging conditions. For example, if the temperature gradient change rate is high, the temperature sensitivity coefficient may be dynamically increased to better capture the material's nonlinear response under thermal shock. Subsequently, using these dynamically adjusted sensitivity coefficients, combined with the temperature values ​​of local hotspot areas, the temperature values ​​of the average temperature area, and the cumulative working time, a more accurate drift component of the nonlinear response characteristics is calculated. Ultimately, this more accurate drift component will be used to correct the nonlinear relationship model between capacitance and distance, thereby ensuring that the distance between the nozzle and the metal sheet can be controlled with high precision even under dynamically changing working conditions, avoiding a decrease in cutting quality due to drift errors.

[0084] refer to Figure 2 This application proposes a high-precision fiber laser cutting system for metal sheets, applied to the aforementioned high-precision fiber laser cutting method for metal sheets. The system comprises: The module acquires the original capacitance value of the capacitive sensing loop. The separation module separates the original capacitance value of the capacitive sensing loop into an average capacitance value and a high-frequency vibration signal; The extraction module extracts the vibration amplitude from the high-frequency vibration signal; The calculation module calculates the DC deviation caused by high-frequency vibration based on the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance, the vibration amplitude, and the average capacitance value. The correction module subtracts the DC deviation from the average capacitance value to obtain the corrected average capacitance value. The control module controls the distance between the nozzle and the metal sheet based on the corrected average capacitance value.

[0085] Specifically, the acquisition module can be a data acquisition unit configured to receive signals from the capacitive sensing loop and convert them into raw capacitance values ​​that can be processed by the system. For example, this module may include an analog-to-digital converter and corresponding signal conditioning circuitry to convert the analog capacitance signal output from the capacitive sensing loop into a digital signal and transmit it to the subsequent processing module. This module ensures accurate and real-time acquisition of the raw capacitance values.

[0086] The separation module can be understood as a signal processor, configured to process the raw capacitance values ​​acquired by the acquisition module, decomposing them into an average capacitance value representing the average distance between the nozzle and the metal plate, and an instantaneous high-frequency vibration signal reflecting high-frequency vibration. For example, this module can use digital filters (e.g., high-pass and low-pass filters) or frequency domain analysis methods based on Fourier transform to effectively distinguish signals with different frequency components for subsequent processing.

[0087] In practical applications, the extraction module is specifically a unit used to analyze high-frequency vibration signals. It is configured to accurately quantify the vibration amplitude from the high-frequency vibration signal output by the separation module. For example, this module can use techniques such as peak detection algorithms, root mean square calculation, or envelope extraction to obtain key parameters characterizing the vibration intensity.

[0088] Furthermore, the calculation module can be a computational unit that internally stores a nonlinear relationship model between the capacitance value of the capacitive sensing loop and the distance. This module is configured to receive the vibration amplitude provided by the extraction module and the average capacitance value provided by the separation module, and, in conjunction with the preset nonlinear relationship model, calculate the DC deviation caused by high-frequency vibration through numerical calculation or table lookup. Its purpose is to quantify the systematic error caused by vibration in the average distance measurement.

[0089] Furthermore, the correction module can be an arithmetic logic unit configured to receive the DC deviation output from the calculation module and the average capacitance value output from the separation module, and perform a subtraction operation to eliminate the DC deviation from the average capacitance value, thereby obtaining a more accurate corrected average capacitance value. Its purpose is to provide a true average distance representation unaffected by high-frequency vibrations.

[0090] Finally, the control module can be a closed-loop control system configured to receive the corrected average capacitance value output by the correction module and compare it with a preset target distance. Based on the comparison result, the module generates corresponding control commands to drive the actuator (e.g., a Z-axis servo motor) to adjust the distance between the nozzle and the metal sheet to maintain a constant cutting gap. The aim is to achieve precise, dynamic control of the cutting gap.

[0091] This application's system, through modular design, concretizes each step of the high-precision fiber laser cutting method for metal sheets into functionally independent modules, thereby achieving precise control of the distance between the nozzle and the metal sheet during the cutting process. Specifically, the acquisition module is responsible for real-time and accurate acquisition of the original capacitance value of the capacitive sensing loop, providing basic data for subsequent processing. Subsequently, the separation module decomposes the original capacitance value into an average capacitance value and a high-frequency vibration signal, effectively decoupling static distance information from dynamic vibration information. The extraction module focuses on quantifying the vibration amplitude from the high-frequency vibration signal, providing key parameters for assessing the impact of vibration. The calculation module, based on the nonlinear relationship between capacitance value and distance, vibration amplitude, and average capacitance value, accurately calculates the DC deviation caused by high-frequency vibration. This is the core of this scheme, revealing the nonlinear impact of vibration on distance measurement. The correction module effectively eliminates the measurement error caused by vibration by subtracting this DC deviation from the average capacitance value, thus obtaining more realistic distance information between the nozzle and the metal sheet. Finally, the control module uses this corrected average capacitance value to adjust the nozzle position in real time, ensuring the stability and accuracy of the cutting gap. It is precisely because of this refined division of labor and collaborative work among modules that the entire system can efficiently and accurately execute high-precision cutting methods, overcoming the measurement errors and control instability problems caused by vibration in traditional methods.

[0092] Through the above technical solution, this application provides a high-precision fiber laser cutting system for metal sheets with a clear structure and well-defined functions. This system significantly improves the efficiency and reliability of the method by decomposing complex steps into independent, collaborative modules. Compared to solutions that only describe the steps, this system automates and integrates data acquisition, signal processing, error calculation, and distance control, reducing manual intervention and operational complexity. Furthermore, the modular design facilitates maintenance, upgrades, and fault diagnosis, enhancing the overall availability of the equipment. Through the close cooperation of each module, the system can compensate for DC deviations caused by high-frequency vibrations in real time and accurately, ensuring that the distance between the nozzle and the metal sheet remains optimal. This greatly improves the precision and stability of laser cutting, especially in high-speed or complex contour cutting scenarios, where its advantages are even more pronounced.

[0093] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A method of high-precision fiber laser cutting of a metal sheet material, characterized in that, The method includes the following steps: Obtain the original capacitance value of the capacitive sensing loop; The original capacitance value of the capacitive sensing loop is separated into the average capacitance value and the high-frequency vibration signal. Extracting vibration amplitude from high-frequency vibration signals; Based on the nonlinear relationship between the capacitance value and distance of the capacitive sensing ring, the vibration amplitude, and the average capacitance value, the DC deviation caused by high-frequency vibration is calculated, where the distance refers to the spacing between the nozzle and the metal plate. The corrected average capacitance value is obtained by subtracting the DC deviation from the average capacitance value. The distance between the nozzle and the metal sheet is controlled based on the corrected average capacitance value.

2. The method of claim 1, wherein the high-precision fiber laser cutting of the metal sheet is performed by a laser cutting machine. Based on the nonlinear relationship between the capacitance of the capacitive sensing ring and distance, the vibration amplitude, and the average capacitance value, the steps for calculating the DC deviation caused by high-frequency vibration include: Real-time monitoring of pressure fluctuations in the auxiliary gas flow field, and extraction of pressure fluctuation amplitude and frequency; The dielectric constant parameter in the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance is dynamically adjusted based on the pressure fluctuation amplitude and frequency. Based on the dynamically adjusted dielectric constant parameter, vibration amplitude, and average capacitance value, the DC deviation caused by high-frequency vibration is calculated.

3. The method of claim 1, wherein the high-precision fiber laser cutting of a metal sheet is performed by using a laser beam having a wavelength of 1,064 nm. The steps for extracting vibration amplitude from high-frequency vibration signals include: Spectral analysis of high-frequency vibration signals identifies the characteristic frequency range of micro-vibrations in the sheet metal caused by aeroacoustic excitation. Based on the characteristic frequency range of the micro-vibration of the board, an adaptive bandpass filter is designed. The center frequency and bandwidth of the adaptive bandpass filter are used to track the dominant frequency and spectral width of the micro-vibration of the board. The high-frequency vibration signal is filtered by an adaptive bandpass filter to obtain the filtered signal. The envelope of the filtered signal is extracted to obtain the envelope. Calculate the root mean square value of the envelope as the amplitude of the vibration.

4. The method for high-precision fiber laser cutting of metal sheets as described in claim 2, characterized in that, The steps for dynamically adjusting the dielectric constant parameter in the nonlinear relationship between the capacitance value of the capacitive sensing loop and the distance, based on the pressure fluctuation amplitude and frequency, include: Monitor the geometric morphology of waste accumulation areas; Based on the geometric morphology of the waste accumulation area, the auxiliary gas pressure, and the distance between the nozzle and the metal plate, the geometric parameters of the confined cavity are calculated. Based on the geometric parameters of the confined cavity, the pressure fluctuation amplitude, and the pressure fluctuation frequency, the dielectric constant parameter in the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance is dynamically adjusted.

5. The method for high-precision fiber laser cutting of metal sheets as described in claim 1, characterized in that, Based on the nonlinear relationship between the capacitance of the capacitive sensing ring and distance, the vibration amplitude, and the average capacitance value, the steps for calculating the DC deviation caused by high-frequency vibration include: Continuously monitor the internal temperature of the capacitive sensing ring, the ambient temperature, and the cumulative operating time; The degree of drift in the nonlinear response characteristics of the capacitive sensing ring is evaluated based on the internal temperature, ambient temperature, and cumulative operating time of the capacitive sensing ring. Based on the degree of drift of the nonlinear response characteristics of the capacitive sensing loop, the coefficient parameters in the nonlinear relationship model between the capacitance value and distance of the capacitive sensing loop are corrected to obtain the corrected nonlinear relationship model. Based on the corrected nonlinear relationship model, vibration amplitude, and average capacitance value, the DC deviation caused by high-frequency vibration is calculated.

6. The method for high-precision fiber laser cutting of metal sheets as described in claim 5, characterized in that, The evaluation of the drift in the nonlinear response characteristics of a capacitive sensing loop also includes the following steps: Set up multiple discrete temperature monitoring points; Based on the location information of each temperature monitoring point and the temperature value monitored by the temperature monitoring point, combined with the material thermal conductivity characteristics and structural geometry information of the capacitive sensing ring, the temperature field distribution inside the capacitive sensing ring is calculated. Based on the temperature field distribution inside the capacitive sensing ring, identify the local hot spot region and the average temperature region inside the capacitive sensing ring. The degree of drift in the nonlinear response characteristics of the capacitive sensing loop is evaluated based on the temperature values ​​of local hot spots, the temperature values ​​of the average temperature area, and the cumulative working time.

7. The method for high-precision fiber laser cutting of metal sheets as described in claim 6, characterized in that, The steps for identifying local hot spots and average temperature regions inside a capacitive sensing ring based on the temperature field distribution inside the ring include: Based on the distribution information of the thermal expansion coefficient of the material of the capacitive sensing ring, the temperature field distribution inside the capacitive sensing ring is divided into regions. Calculate the local deformation caused by temperature changes within the divided regions based on the material's thermal expansion coefficient. Based on local deformation, the recognition threshold and recognition boundary of the local hot spot area and average temperature area inside the capacitive sensing ring are dynamically adjusted. Based on the dynamically adjusted recognition threshold and recognition boundary, the local hot spot area and average temperature area inside the capacitive sensing ring are identified.

8. The method for high-precision fiber laser cutting of metal sheets as described in claim 6, characterized in that, The evaluation of the drift in the nonlinear response characteristics of a capacitive sensing loop also includes the following steps: Independent temperature sensitivity coefficients and aging sensitivity coefficients are set for different material regions of the capacitive sensing ring; Based on the temperature values ​​of local hotspot areas, the temperature values ​​of average temperature areas, and the cumulative working time, combined with independent... Temperature sensitivity coefficient and aging sensitivity coefficient are used to calculate the drift component of the nonlinear response characteristics of each material region; The degree of nonlinear response drift of the capacitive sensing loop is obtained by weighting and superimposing the drift components of the nonlinear response characteristics of each material region and the weight of each material region on the overall nonlinear response drift.

9. The method for high-precision fiber laser cutting of metal sheets as described in claim 8, characterized in that, The steps for calculating the drift component of the nonlinear response characteristics of each material region, based on the temperature values ​​of local hot spots, the temperature values ​​of the average temperature region, and the cumulative working time, combined with independent temperature sensitivity coefficients and aging sensitivity coefficients, include: Continuously monitor the internal temperature of the capacitive sensing ring, the ambient temperature, and the cumulative operating time; Based on the internal temperature of the capacitive sensing ring, the ambient temperature, and the cumulative working time, the temperature gradient change rate and the cumulative working time change rate are calculated in real time. Based on the rate of change of temperature gradient and the rate of change of cumulative working time, the independent temperature sensitivity coefficient and aging sensitivity coefficient are dynamically adjusted. Based on the dynamically adjusted independent temperature sensitivity coefficient, the dynamically adjusted independent aging sensitivity coefficient, the temperature value of the local hot spot area, the temperature value of the average temperature area, and the cumulative working time, the drift component of the nonlinear response characteristics of each material region is calculated.

10. A high-precision fiber laser cutting system for metal sheets, applied to the high-precision fiber laser cutting method for metal sheets as described in claim 1, characterized in that, The system includes: The module acquires the original capacitance value of the capacitive sensing loop. The separation module separates the original capacitance value of the capacitive sensing loop into an average capacitance value and a high-frequency vibration signal; The extraction module extracts the vibration amplitude from the high-frequency vibration signal; The calculation module calculates the DC deviation caused by high-frequency vibration based on the nonlinear relationship between the capacitance value of the capacitive sensing ring and the distance, the vibration amplitude, and the average capacitance value. The distance refers to the spacing between the nozzle and the metal plate. The correction module subtracts the DC deviation from the average capacitance value to obtain the corrected average capacitance value. The control module controls the distance between the nozzle and the metal sheet based on the corrected average capacitance value.

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