Special material machining control method and system for electric spark machine tool

Abstract

The invention discloses an electric spark machine tool special material machining control method and system, and relates to the technical field of linkage control, and the method comprises the steps: carrying out the multi-axis linkage discharge parameter optimization of an electric spark linear cutting machine tool, and obtaining the multi-axis linkage discharge parameter optimization data; according to the multi-axis linkage discharge parameter optimization data, performing multi-axis cooperative motion trajectory planning to obtain multi-axis cooperative motion trajectory data; carrying out real-time discharge energy dynamic adjustment on the wire cut electrical discharge machining tool according to the multi-axis cooperative motion track data and the material discharge coefficient matching data to obtain discharge energy adjustment data, and carrying out multi-axis linkage precision compensation control based on the discharge energy adjustment data to obtain multi-axis linkage precision compensation data; and building a machining process stability evaluation model based on the multi-axis linkage precision compensation data to obtain the machining process stability evaluation model, and integrating the machining process stability evaluation model to the wire cut electrical discharge machining machine tool to execute multi-axis linkage control. The method has the effect of improving the linkage control efficiency.

Description

Technical Field

[0001] This application relates to the field of linkage control technology, and in particular to a method and system for controlling the processing of special materials using an electrical discharge machine tool. Background Technology

[0002] Wire EDM machines, as precision machining equipment, are widely used in mold manufacturing, precision parts processing, and other fields. Their multi-axis linkage function is the core for achieving complex contours and high-precision machining. As machining demands extend to high-hardness and complex curved materials, the requirements for the accuracy and stability of multi-axis (such as X, Y, Z, U, V axes) collaborative control have significantly increased. In existing technologies, multi-axis linkage control mostly relies on preset fixed discharge parameters. Parameter settings are often based on operator experience or a single material type. Although basic sensors are equipped to collect workpiece surface data, they are only used for simple state monitoring and do not achieve deep adaptation between material properties and discharge parameters. At the same time, trajectory planning often considers the motion of each axis independently, with insufficient analysis of the motion coupling relationship between axes and error transmission laws. Accuracy compensation is also mostly limited to single-position correction. The overall control mode lacks the ability to respond to dynamic changes in the machining process in real time, and can only meet the needs of low-to-medium precision machining, making it difficult to adapt to stable machining in high-demand scenarios.

[0003] In related technologies, the existing multi-axis linkage control of wire EDM machines has the following obvious technical limitations: 1. The wire EDM machine has poor parameter adaptability. Fixed discharge parameters cannot be dynamically adjusted according to the characteristics of the workpiece material composition, surface hardness, etc. It is easy to cause low discharge efficiency or workpiece surface burn due to the mismatch between parameters and material discharge breakdown threshold. Second, the multi-axis coordination accuracy of wire EDM machines is insufficient. The effects of inter-axis motion coupling and error transmission are ignored. The trajectory planning and parameter optimization are disconnected, which easily leads to inter-axis motion interference and trajectory deviation. Moreover, the backlash error is amplified when the load changes, further reducing the machining accuracy. Third, the dynamic response of wire EDM machines is lagging, and the discharge energy cannot be adjusted in real time when the processing gap changes, which can easily lead to discharge interruption or short circuit. In addition, there is a lack of processing stability assessment mechanism based on data such as vibration signals, relying on manual judgment of stability. The lagging adjustment leads to large fluctuations in the processing process. Fourth, the lack of adaptive capability of the wire EDM machine tool means that it cannot optimize parameters in real time according to the processing status, resulting in a high scrap rate for high-hardness and complex contour workpieces, which makes it difficult to meet the needs of precision manufacturing. This reduces the efficiency of multi-axis linkage control of the wire EDM machine tool, and there are areas for improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a method and system for controlling the machining of special materials using an electrical discharge machine tool.

[0005] Firstly, this application provides a method for controlling the machining of special materials using an electrical discharge machine tool, comprising the following steps: Step S1: Collect material property data of the workpiece through the multi-sensor network of the wire EDM machine tool to obtain workpiece material property data, and perform adaptive matching of material discharge coefficient based on workpiece material property data to obtain material discharge coefficient matching data; Step S2: Optimize the multi-axis linkage discharge parameters of the wire EDM machine based on the material discharge coefficient matching data to obtain multi-axis linkage discharge parameter optimization data. Based on the multi-axis linkage discharge parameter optimization data, plan the multi-axis cooperative motion trajectory to obtain multi-axis cooperative motion trajectory data. Step S3: Based on the multi-axis coordinated motion trajectory data and material discharge coefficient matching data, the discharge energy of the wire EDM machine tool is dynamically adjusted in real time to obtain discharge energy adjustment data. Based on the discharge energy adjustment data, multi-axis linkage accuracy compensation control is performed to obtain multi-axis linkage accuracy compensation data. Step S4: Construct a machining process stability assessment model based on multi-axis linkage accuracy compensation data to obtain the machining process stability assessment model; integrate the machining process stability assessment model into the wire EDM machine tool to execute multi-axis linkage control.

[0006] Preferably, step S1 includes the following steps: Step S11: Perform spectral analysis of the material composition of the workpiece using the multi-sensor network of the wire EDM machine tool to obtain the spectral data of the workpiece material composition; perform ultrasonic testing on the surface hardness of the workpiece to obtain the surface hardness data of the workpiece. Step S12: Calculate the electrical conductivity coefficient of the workpiece material based on the spectral data of the workpiece material composition and the surface hardness data of the workpiece, and obtain the electrical conductivity coefficient data of the material. Based on the electrical conductivity coefficient data of the material, analyze the thermal conductivity characteristics of the material to obtain the thermal conductivity characteristics data of the material. Step S13: Based on the material conductivity coefficient data and material thermal conductivity data, predict the material discharge breakdown threshold to obtain the material discharge breakdown threshold data; Step S14: Perform adaptive matching of material discharge coefficients based on material discharge breakdown threshold data to obtain material discharge coefficient matching data.

[0007] Preferably, step S2 includes the following steps: Step S21: Based on the material discharge coefficient matching data, perform multi-axis motion coupling relationship analysis on the X-axis, Y-axis, Z-axis, U-axis, and V-axis of the wire EDM machine tool to obtain multi-axis motion coupling relationship data; Step S22: Model the axial motion error propagation based on the multi-axis motion coupling relationship data to obtain the axial motion error propagation model; Step S23: Based on the axial motion error propagation model, optimize the multi-axis linkage discharge parameters of the material discharge coefficient matching data to obtain the optimized multi-axis linkage discharge parameter data; Step S24: Based on the multi-axis linkage discharge parameter optimization data, perform multi-axis cooperative motion trajectory planning to obtain multi-axis cooperative motion trajectory data.

[0008] Preferably, step S23 includes the following steps: Step S231: Perform axial dynamic response characteristic analysis on the multi-axis motion coupling relationship data to obtain axial dynamic response characteristic data; Step S232: Based on the axial dynamic response characteristic data, perform a correlation analysis between the discharge pulse and shaft motion delay on the material discharge coefficient matching data to obtain the correlation data between the discharge pulse and shaft motion delay; Step S233: Optimize the discharge energy distribution of each axis based on the correlation data between the discharge pulse and the axis motion delay to obtain the optimized discharge energy distribution data for each axis; Step S234: Based on the optimized data of discharge energy distribution of each axis, perform multi-axis linkage discharge timing synchronization adjustment to obtain multi-axis linkage discharge timing synchronization data; Step S235: Optimize the multi-axis linkage discharge parameters based on the multi-axis linkage discharge timing synchronization data to obtain optimized multi-axis linkage discharge parameter data.

[0009] Preferably, step S3 includes the following steps: Step S31: Real-time monitoring of machining clearance is performed based on multi-axis coordinated motion trajectory data to obtain real-time monitoring data of machining clearance; Step S32: Based on the real-time monitoring data of the processing gap, dynamically adjust the discharge energy of the material discharge coefficient matching data to obtain the discharge energy adjustment data; Step S33: Perform multi-axis motion position compensation calculation based on the discharge energy adjustment data to obtain multi-axis motion position compensation data; Step S34: Perform multi-axis linkage accuracy compensation control based on multi-axis motion position compensation data to obtain multi-axis linkage accuracy compensation data.

[0010] Preferably, step S34 includes the following steps: Step S341: Analyze the backlash error of each axis in the multi-axis motion position compensation data to obtain the backlash error data of each axis; Step S342: Based on the backlash error data of each shaft, perform axial load change compensation on the discharge energy adjustment data to obtain axial load change compensation data; Step S343: Perform multi-axis linkage acceleration smoothing processing based on the axial load change compensation data to obtain multi-axis linkage acceleration smoothing data; Step S344: Perform multi-axis linkage accuracy compensation control based on multi-axis linkage acceleration smoothing data to obtain multi-axis linkage accuracy compensation data.

[0011] Preferably, step S4 includes the following steps: Step S41: Acquire vibration signals during the machining process based on multi-axis linkage accuracy compensation data to obtain vibration signal data during the machining process; Step S42: Extract spectral features from the vibration signal data during the processing to obtain spectral feature data of the processing process; Step S43: Calculate the processing stability evaluation index based on the spectral characteristic data of the processing process to obtain the processing stability evaluation index data; Step S44: Construct a process stability assessment model based on the processing stability assessment index data to obtain the processing stability assessment model; Step S45: Integrate the machining process stability assessment model into the wire EDM machine tool to achieve multi-axis linkage adaptive control.

[0012] Preferably, step S44 includes the following steps: Step S441: Perform multi-axis linkage control stability boundary analysis on the machining stability evaluation index data to obtain multi-axis linkage control stability boundary data; Step S442: Optimize adaptive control parameters based on the stability boundary data of multi-axis linkage control to obtain optimized adaptive control parameter data; Step S443: Build a process stability assessment model based on the adaptive control parameter optimization data to obtain the process stability assessment model.

[0013] Secondly, this application provides a special material machining control system for an electrical discharge machine tool, comprising: The data acquisition module is used to acquire material property data of the workpiece through the multi-sensor network of the wire EDM machine tool, obtain workpiece material property data, and perform adaptive matching of material discharge coefficient based on workpiece material property data to obtain material discharge coefficient matching data. The optimization module is used to optimize the multi-axis linkage discharge parameters of the wire EDM machine tool based on the material discharge coefficient matching data, obtain the multi-axis linkage discharge parameter optimization data, and plan the multi-axis cooperative motion trajectory based on the multi-axis linkage discharge parameter optimization data to obtain the multi-axis cooperative motion trajectory data. The analysis and processing module is used to dynamically adjust the discharge energy of the wire EDM machine tool in real time based on the multi-axis coordinated motion trajectory data and the material discharge coefficient matching data to obtain discharge energy adjustment data. Based on the discharge energy adjustment data, multi-axis linkage accuracy compensation control is performed to obtain multi-axis linkage accuracy compensation data. The control module is used to construct a machining process stability assessment model based on multi-axis linkage accuracy compensation data, and then integrate the machining process stability assessment model into the wire EDM machine tool to execute multi-axis linkage control.

[0014] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the special material processing control method for an electrical discharge machine tool as described in any of the above claims.

[0015] In summary, this application includes at least one of the following beneficial technical effects: This application provides a special material machining control method for EDM machines. It collects workpiece material composition and surface hardness data through a multi-sensor network to achieve adaptive matching of the material discharge coefficient, avoiding low discharge efficiency or workpiece burn-out caused by mismatch between fixed parameters and material properties. Based on multi-axis motion coupling relationships and error propagation modeling, it optimizes discharge parameters and trajectory planning, combining backlash error analysis, axial load compensation, and acceleration smoothing to significantly reduce inter-axis motion interference and trajectory deviation, improving multi-axis collaborative accuracy. It monitors machining clearance in real time and dynamically adjusts discharge energy, combined with a stability evaluation model constructed from vibration signal analysis during machining, enabling real-time monitoring and adaptive control of the machining status, avoiding discharge interruptions or short circuits, and reducing reliance on manual intervention. Ultimately, it can reduce the scrap rate of high-hardness, complex-contour workpieces, meeting precision manufacturing needs, while extending equipment lifespan, thereby effectively improving the multi-axis linkage control efficiency of wire EDM machines. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for controlling the machining of special materials using an electrical discharge machine tool, according to an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the system for controlling the special material processing of an electrical discharge machine tool according to an embodiment of this application. Detailed Implementation

[0019] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0020] Example 1 This application discloses a method for controlling the processing of special materials using an electrical discharge machine tool.

[0021] Reference Figure 1 A method for controlling the machining of special materials using an electrical discharge machine tool includes the following steps: Step S1: Collect material property data of the workpiece through the multi-sensor network of the wire EDM machine tool to obtain workpiece material property data, and perform adaptive matching of material discharge coefficient based on workpiece material property data to obtain material discharge coefficient matching data; Step S2: Optimize the multi-axis linkage discharge parameters of the wire EDM machine based on the material discharge coefficient matching data to obtain multi-axis linkage discharge parameter optimization data. Based on the multi-axis linkage discharge parameter optimization data, plan the multi-axis cooperative motion trajectory to obtain multi-axis cooperative motion trajectory data. Step S3: Based on the multi-axis coordinated motion trajectory data and material discharge coefficient matching data, the discharge energy of the wire EDM machine tool is dynamically adjusted in real time to obtain discharge energy adjustment data. Based on the discharge energy adjustment data, multi-axis linkage accuracy compensation control is performed to obtain multi-axis linkage accuracy compensation data. Step S4: Construct a machining process stability assessment model based on multi-axis linkage accuracy compensation data to obtain the machining process stability assessment model; integrate the machining process stability assessment model into the wire EDM machine tool to execute multi-axis linkage control.

[0022] It should be noted that step S1 includes the following steps: Step S11: Perform spectral analysis of the material composition of the workpiece using the multi-sensor network of the wire EDM machine tool to obtain the spectral data of the workpiece material composition; perform ultrasonic testing on the surface hardness of the workpiece to obtain the surface hardness data of the workpiece. Step S12: Calculate the electrical conductivity coefficient of the workpiece material based on the spectral data of the workpiece material composition and the surface hardness data of the workpiece, and obtain the electrical conductivity coefficient data of the material. Based on the electrical conductivity coefficient data of the material, analyze the thermal conductivity characteristics of the material to obtain the thermal conductivity characteristics data of the material. Step S13: Based on the material conductivity coefficient data and material thermal conductivity data, predict the material discharge breakdown threshold to obtain the material discharge breakdown threshold data; Step S14: Perform adaptive matching of material discharge coefficient based on material discharge breakdown threshold data to obtain material discharge coefficient matching data. The material discharge coefficient matching data includes matching parameters for pulse width, pulse interval, and discharge current.

[0023] Specifically, the spectral sensors in the multi-sensor network of the wire EDM machine tool are used to perform spectral analysis of the material composition of the workpiece. This multi-sensor network integrates high-precision detection equipment such as spectral sensors and ultrasonic sensors. The spectral sensors emit light beams of specific wavelengths to illuminate the workpiece surface. After the light beams interact with the elements inside the workpiece material, characteristic spectra are generated. By detecting the wavelength and intensity of the characteristic spectra, the elemental composition and content ratio of each element in the workpiece material can be accurately analyzed, thus obtaining the spectral data of the workpiece material composition. Simultaneously, the ultrasonic testing equipment in the multi-sensor network is used to perform ultrasonic testing on the surface hardness of the workpiece. The ultrasonic testing equipment emits light beams of specific wavelengths to illuminate the workpiece surface. High-frequency ultrasonic waves are emitted from the surface. As the ultrasonic waves propagate within the workpiece, their propagation speed and reflection coefficient vary depending on the workpiece's surface hardness. By detecting the propagation time and reflected signal intensity of the ultrasonic waves, and combining this with a pre-defined hardness calculation method, the hardness values ​​of different areas on the workpiece surface can be obtained, forming workpiece surface hardness data. Next, the material conductivity coefficient is calculated using the acquired workpiece material composition spectral data and workpiece surface hardness data. Based on the proportion of conductive elements in the material composition, the bonding state between elements, and the influence of surface hardness on the internal crystal structure of the material, a calculation method derived from electromagnetic theory is used to convert the workpiece material composition spectral data and workpiece surface hardness data into energy. Conductivity coefficient data is sufficient to reflect the material's electrical conductivity. Based on this, the thermal conductivity characteristics of the material are analyzed. Since there is a certain correlation between the electrical conductivity and thermal conductivity of a material, the thermal conductivity coefficient, thermal diffusivity, and other parameters of the material are calculated using thermal conductivity theory, taking into account the characteristics of thermally conductive elements in the material composition and the influence of crystal structure on thermal conductivity. Then, based on the calculated electrical conductivity coefficient and thermal conductivity data, the material's discharge breakdown threshold is predicted. The discharge breakdown threshold refers to the minimum voltage or energy required for a material to be broken down by an electric spark to form a discharge channel. The better the material's conductivity, the easier it is for charge to accumulate, and the higher the breakdown threshold. The lower the energy required, the better the thermal conductivity, and the easier it is for the heat generated during the discharge process to dissipate, which will also affect the breakdown threshold. By establishing a correlation model between conductivity, thermal conductivity and breakdown threshold, and substituting specific data, the discharge breakdown threshold data of the workpiece material can be predicted. The correlation model between conductivity, thermal conductivity and breakdown threshold can be obtained by fitting historical data. Finally, based on the predicted material discharge breakdown threshold data, the material discharge coefficient is adaptively matched. The discharge coefficient mainly includes pulse width, pulse interval and discharge current. When the breakdown threshold is low, the pulse width and discharge current can be appropriately reduced to avoid over-discharge damage to the workpiece, while the pulse interval is adjusted to ensure discharge stability.When the breakdown threshold is high, the pulse width and discharge current need to be increased to ensure effective breakdown of the material and formation of a discharge channel. Through the above adaptive adjustments, material discharge coefficient matching data containing specific values ​​for pulse width, pulse interval, and discharge current are finally obtained.

[0024] The above-mentioned technical solution solves the problem in traditional wire EDM machining where fixed discharge parameters are selected based solely on the workpiece material type, ignoring the differences in characteristics between different batches and regions of the same material. By using spectral analysis and ultrasonic testing, the material composition and surface hardness are accurately obtained, providing a precise data foundation for subsequent parameter matching. The calculation of the material's conductivity and thermal conductivity further reveals the electrical and thermal behavior of the material during discharge, avoiding the limitations of relying solely on experience to judge material properties. The prediction of the discharge breakdown threshold provides a clear basis for matching the discharge coefficients, ensuring that the matched pulse width, pulse interval, and discharge current accurately match the material's breakdown characteristics. This avoids both insufficient discharge energy leading to low processing efficiency and ineffective material cutting, and excessive discharge energy causing workpiece surface burns and decreased processing accuracy. It significantly improves the matching accuracy between discharge parameters and material properties, laying a solid foundation for high-quality multi-axis linkage machining, and is particularly suitable for workpiece machining scenarios that are sensitive to material properties and require high processing accuracy.

[0025] It should be noted that step S2 includes the following steps: Step S21: Based on the material discharge coefficient matching data, perform multi-axis motion coupling relationship analysis on the X-axis, Y-axis, Z-axis, U-axis, and V-axis of the wire EDM machine tool to obtain multi-axis motion coupling relationship data; Step S22: Model the axial motion error propagation based on the multi-axis motion coupling relationship data to obtain the axial motion error propagation model; Step S23: Based on the axial motion error propagation model, optimize the multi-axis linkage discharge parameters of the material discharge coefficient matching data to obtain the optimized multi-axis linkage discharge parameter data; Step S24: Based on the multi-axis linkage discharge parameter optimization data, perform multi-axis cooperative motion trajectory planning to obtain multi-axis cooperative motion trajectory data.

[0026] Specifically, firstly, based on material discharge coefficient matching data, a multi-axis motion coupling relationship analysis is performed on the X, Y, Z, U, and V axes of the wire EDM machine. Multi-axis motion coupling refers to the impact of changes in the motion state of one axis on the motion of other axes. For example, rapid movement of the X-axis may cause slight vibrations in the Y-axis due to overall machine tool rigidity issues; the coordinated adjustment of the U and V axes may affect the positioning accuracy of the Z-axis. Then, based on the motion parameters of each axis and the discharge energy requirements in the material discharge coefficient matching data, the interaction of each axis under different motion states is simulated, analyzing the degree of motion interference and motion response delay between axes, thus obtaining multi-axis motion coupling relationship data. Next, based on the obtained multi-axis motion coupling relationship data, axial motion error propagation modeling is performed. Axial motion error propagation refers to the transmission of motion errors from one axis to other axes during multi-axis linkage, thus affecting overall machining accuracy. For example, the positioning error of the X-axis may be transmitted to the machining trajectory when linked with the Y-axis, leading to… To address trajectory deviation, based on multi-axis motion coupling data, the error propagation path and propagation coefficient are identified. An axial motion error propagation model is constructed using error theory and statistical analysis. Then, based on this model, multi-axis linkage discharge parameters are optimized using material discharge coefficient matching data. The axial motion error propagation model can predict the propagation of motion errors along each axis under different discharge parameters. For example, when the discharge current is large, increased load on each axis may lead to increased motion errors, and the error propagation effect will be more pronounced. By substituting the initial discharge parameters from the material discharge coefficient matching data into the axial motion error propagation model, the error propagation results under different parameter combinations are simulated. Combined with machining accuracy requirements, the discharge parameters are adjusted to reduce the negative impact of error propagation. For example, in parameter ranges where error propagation is significant, the discharge current is appropriately reduced and the pulse interval is adjusted to ensure that while meeting discharge requirements, error propagation is controlled within acceptable limits. Finally, optimized multi-axis linkage discharge parameter data is obtained. Finally, based on the optimized multi-axis linkage discharge parameter optimization data, multi-axis collaborative motion trajectory planning is performed. During the trajectory planning process, the workpiece machining contour requirements, as well as the discharge energy distribution and motion load limitations of each axis in the multi-axis linkage discharge parameter optimization data, are combined. A trajectory interpolation algorithm is used to plan the motion path and motion sequence of each axis while ensuring that the motion of each axis meets the discharge parameter requirements. This allows the multi-axis to respond synchronously to the discharge demand during the motion process, avoiding the disconnect between the motion of a certain axis and the discharge process due to unreasonable trajectory planning. For example, it avoids insufficient discharge due to excessively fast motion of a certain axis, or excessive local discharge due to excessively slow motion. Finally, multi-axis collaborative motion trajectory data is obtained.

[0027] The above technical solution solves the problem of neglecting the multi-axis motion coupling relationship and error propagation effect in traditional multi-axis linkage control. Traditional methods usually treat each axis as an independent motion unit when planning the trajectory and optimizing parameters, without considering the motion interference and error propagation between axes. This leads to large trajectory deviations and reduced accuracy in actual processing. However, this step accurately identifies the mutual influence between axes and the error propagation law through multi-axis motion coupling relationship analysis and error propagation modeling, providing a scientific basis for parameter optimization. The discharge parameter optimization based on the error propagation model can ensure that the optimized parameters not only adapt to the material properties, but also effectively suppress the negative impact of error propagation, improving the overall adaptability of the parameters. Furthermore, the multi-axis collaborative motion trajectory planning combined with the optimized parameters achieves deep coordination between the motion trajectory and the discharge parameters, avoiding the problem of trajectory and parameter decoupling. This ensures that during multi-axis linkage, the motion of each axis not only meets the processing contour requirements, but also accurately matches the discharge process, significantly reducing the motion error and trajectory deviation of multi-axis linkage, and improving the processing accuracy and trajectory consistency. It is especially suitable for high-precision workpieces with complex curved surfaces and multi-dimensional linkage processing, and can effectively guarantee the processing contour accuracy and surface quality of such workpieces.

[0028] Furthermore, step S23 includes the following steps: Step S231: Perform axial dynamic response characteristic analysis on the multi-axis motion coupling relationship data to obtain axial dynamic response characteristic data; Step S232: Based on the axial dynamic response characteristic data, perform a correlation analysis between the discharge pulse and shaft motion delay on the material discharge coefficient matching data to obtain the correlation data between the discharge pulse and shaft motion delay; Step S233: Optimize the discharge energy distribution of each axis based on the correlation data between the discharge pulse and the axis motion delay to obtain the optimized discharge energy distribution data for each axis; Step S234: Based on the optimized data of discharge energy distribution of each axis, perform multi-axis linkage discharge timing synchronization adjustment to obtain multi-axis linkage discharge timing synchronization data; Step S235: Optimize the multi-axis linkage discharge parameters based on the multi-axis linkage discharge timing synchronization data to obtain optimized multi-axis linkage discharge parameter data.

[0029] Specifically, firstly, the axial dynamic response characteristics of the multi-axis motion coupling relationship data are analyzed. Axial dynamic response characteristics refer to the speed and accuracy with which each axis follows the motion command after receiving it. For example, if the command requires the X-axis to move at a speed of 10 mm / s, the actual time it takes for the X-axis to reach that speed and the overshoot during the process are considered. Based on the motion interference and load changes of each axis in the multi-axis motion coupling relationship data, dynamic testing methods are used to detect the dynamic response indicators of each axis under different motion parameters and load conditions, such as response time, damping coefficient, and natural frequency, thereby obtaining axial dynamic response characteristic data. Next, based on the axial dynamic response characteristic data, a correlation analysis of discharge pulse and shaft motion delay was performed on the material discharge coefficient matching data. The discharge pulse is the basic unit of the discharge process, and the shaft motion delay refers to the time difference between the shaft receiving the motion command and the actual start of motion. Due to the different dynamic response characteristics of each shaft, their motion delay also varies. If the generation sequence of the discharge pulse does not match the shaft motion sequence, it will lead to a misalignment between the discharge position and the shaft motion position. For example, if the discharge pulse is generated before the X-axis has reached the specified position, it will cause over-discharge at that position. Based on the response time and delay parameters in the axial dynamic response characteristic data, combined with the pulse frequency and pulse sequence in the material discharge coefficient matching data, the time difference between the discharge pulse generation time and the actual arrival time of each shaft at the specified motion position was analyzed to identify the delay correlation law between the two and obtain the correlation data between discharge pulse and shaft motion delay. Then, based on the obtained correlation data between discharge pulses and axis motion delays, the discharge energy distribution of each axis is optimized. Different axes have different motion delays, which leads to different matching degrees between their actual discharge positions and processing requirements under the same discharge pulse timing. For example, if an axis with a large delay is discharged with a uniform energy distribution, the position lag may lead to wasted discharge energy or insufficient processing. Based on the delay correlation data, the effective discharge time and position matching degree of each axis in different processing stages are calculated. According to the principle of appropriately increasing energy for small delays and optimizing energy timing and adjusting energy magnitude for large delays, the discharge energy distribution ratio of each axis is adjusted to ensure that each axis obtains appropriate discharge energy within its effective motion range, avoiding local processing quality differences caused by uneven energy distribution, and obtaining the optimized discharge energy distribution data for each axis. Subsequently, based on the optimized discharge energy distribution data of each axis, the multi-axis linkage discharge timing synchronization is adjusted. Discharge timing synchronization means ensuring that the generation time of the discharge pulse corresponding to each axis is precisely aligned with the actual time when the axis reaches the designated machining position. According to the motion delay data of each axis and the energy distribution optimization requirements, a timing calibration algorithm is used to adjust the generation timing of the discharge pulse of each axis. For example, for axes with large delays, the discharge pulse is triggered in advance, and for axes with small delays, the original timing or a slightly adjusted timing is used to trigger the discharge pulse. This ensures that during the linkage process, the discharge pulse of each axis can be accurately generated when it reaches the designated machining position, thereby achieving synchronization between the discharge timing and the axis movement and obtaining multi-axis linkage discharge timing synchronization data.Finally, the multi-axis linkage discharge parameters are optimized based on the multi-axis linkage discharge timing synchronization data. The timing synchronization data is integrated with the energy distribution optimization data of each axis, and the core parameters such as pulse width, pulse interval, and discharge current in the material discharge coefficient matching data are adjusted to ensure that the optimized parameters meet both the energy distribution requirements of each axis and the timing synchronization requirements. For example, the pulse interval parameter is adjusted according to the pulse trigger interval after timing synchronization, and the discharge current is fine-tuned according to the energy distribution requirements to finally obtain the multi-axis linkage discharge parameter optimization data.

[0030] The above technical solution precisely solves the key problems of asynchronous discharge pulses and axis movements, and uneven energy distribution among axes in traditional multi-axis linkage discharge control. Traditional methods typically use a uniform discharge timing and energy distribution, without considering the motion delay caused by differences in the dynamic response of each axis. This results in misalignment between the discharge pulses and axis movements, with some areas experiencing insufficient discharge and others excessive discharge, severely affecting machining quality. This step, through axial dynamic response analysis and delay correlation analysis, accurately captures the correlation between the motion delay characteristics of each axis and the discharge pulses, providing a precise basis for energy distribution and timing synchronization. Optimization of energy distribution among axes ensures that each axis receives appropriate energy within its machining range, avoiding energy waste and uneven machining. Synchronous adjustment of the multi-axis linkage discharge timing achieves precise alignment between the discharge pulses and axis movements, fundamentally solving the problem of misalignment between discharge and movement. The final optimized multi-axis linkage discharge parameters combine energy adaptability and timing synchronization, ensuring accurate and stable discharge at each machining position during multi-axis linkage, significantly improving surface quality and dimensional accuracy, and reducing workpiece scrap rates due to uneven discharge.

[0031] It should be noted that step S3 includes the following steps: Step S31: Real-time monitoring of machining clearance is performed based on multi-axis coordinated motion trajectory data to obtain real-time monitoring data of machining clearance; Step S32: Based on the real-time monitoring data of the processing gap, dynamically adjust the discharge energy of the material discharge coefficient matching data to obtain the discharge energy adjustment data; Step S33: Perform multi-axis motion position compensation calculation based on the discharge energy adjustment data to obtain multi-axis motion position compensation data; Step S34: Perform multi-axis linkage accuracy compensation control based on multi-axis motion position compensation data to obtain multi-axis linkage accuracy compensation data.

[0032] Specifically, firstly, real-time monitoring of the machining gap is performed based on multi-axis collaborative motion trajectory data. The machining gap refers to the distance between the electrode wire and the workpiece during wire EDM. Its size directly affects the discharge stability and machining quality. If the gap is too large, it may lead to the inability to form an effective discharge, while if the gap is too small, it may easily cause a short circuit between the electrode wire and the workpiece. Based on the multi-axis collaborative motion trajectory data, the theoretical relative position of the electrode wire and the workpiece at each moment can be known. By installing a gap monitoring device (such as a capacitive gap sensor) in the machining area of ​​the machine tool, the capacitive sensor will determine the actual gap size based on the change in capacitance value between the electrode wire and the workpiece. Since the capacitance value is inversely proportional to the distance between the two plates, by collecting the capacitance signal in real time and converting it into a gap value, the difference between the theoretical gap and the actual gap is compared to obtain real-time monitoring data of the machining gap. This data can reflect the dynamic changes in the gap during the machining process, such as whether the gap increases or decreases due to workpiece deformation or electrode wire wear. Next, based on the real-time monitoring data of the machining gap, the discharge energy is dynamically adjusted according to the material discharge coefficient matching data. Changes in the machining gap directly affect the discharge effect. When the machining gap is detected to increase, the actual discharge distance increases. If the original discharge energy is maintained, it may be insufficient to break down the gap and form a discharge channel, leading to discharge interruption or decreased processing efficiency. In this case, the discharge energy needs to be appropriately increased, such as by increasing the discharge current or extending the pulse width. When the machining gap is detected to decrease, the discharge distance shortens. If the original discharge energy is maintained, it is easy to cause the discharge to be too strong, resulting in accelerated electrode wire wear and workpiece surface burn. In this case, the discharge energy needs to be appropriately reduced, such as by reducing the discharge current, shortening the pulse width, or increasing the pulse interval. Through real-time adjustment, it is ensured that the discharge energy is always adapted to the current machining gap, and the discharge energy adjustment data is obtained. Then, multi-axis motion position compensation calculations are performed based on the obtained discharge energy adjustment data. The adjustment of discharge energy may affect the motion load and motion accuracy of each axis. For example, when the discharge energy increases, the machining reaction force on each axis increases, which may cause deviations between the actual motion position and the theoretical position. Based on the discharge energy adjustment data, the influence of different energy adjustment amounts on the motion of each axis is analyzed. For example, when the energy increases by 10%, the X-axis may have a position deviation of 0.001mm. Combining the theoretical position parameters in the multi-axis cooperative motion trajectory data, an error compensation algorithm is used to calculate the position compensation amount that each axis needs to adjust under the current discharge energy conditions in order to correct the deviation between the actual motion position and the theoretical position, and obtain multi-axis motion position compensation data.Finally, based on the obtained multi-axis motion position compensation data, multi-axis linkage accuracy compensation control is performed. The position compensation amount of each axis is converted into specific motion control commands and sent to the drive system of each axis. For example, a compensation command of "increase displacement by 0.001mm" is issued to the X-axis and a compensation command of "decrease displacement by 0.0008mm" is issued to the Y-axis. This ensures that each axis can correct its position deviation in real time according to the compensation command during the motion process. At the same time, combined with the coordination requirements of multi-axis linkage, the motion sequence and speed of each axis are adjusted to avoid the multi-axis linkage trajectory disorder caused by compensating only one axis. Ultimately, the accuracy of multi-axis linkage is improved, and multi-axis linkage accuracy compensation data is obtained.

[0033] The above technical solution achieves closed-loop management of gap monitoring, energy adjustment, position compensation, and precision control during the machining process. This solves the problem in traditional methods where discharge energy and motion position cannot be matched in a timely manner after changes in the machining gap. Traditional methods typically set fixed discharge energy and motion parameters, which cannot be adjusted promptly when the machining gap changes due to workpiece deformation, electrode wire wear, or other factors, leading to unstable discharge and decreased machining accuracy. This step, through real-time monitoring of the machining gap, can capture dynamic changes in the gap immediately, providing a basis for subsequent adjustments. Dynamic adjustment of discharge energy based on gap changes ensures a consistently stable and effective discharge process, avoiding discharge interruptions caused by excessively large gaps or machining damage caused by excessively small gaps. Multi-axis motion position compensation calculation and precision compensation control correct for motion position deviations caused by energy adjustment and gap changes, ensuring that the actual motion trajectory of multi-axis linkage always conforms to the theoretical trajectory, significantly improving machining accuracy and process stability.

[0034] Furthermore, step S34 includes the following steps: Step S341: Analyze the backlash error of each axis in the multi-axis motion position compensation data to obtain the backlash error data of each axis; Step S342: Based on the backlash error data of each shaft, perform axial load change compensation on the discharge energy adjustment data to obtain axial load change compensation data; Step S343: Perform multi-axis linkage acceleration smoothing processing based on the axial load change compensation data to obtain multi-axis linkage acceleration smoothing data; Step S344: Perform multi-axis linkage accuracy compensation control based on multi-axis linkage acceleration smoothing data to obtain multi-axis linkage accuracy compensation data.

[0035] Specifically, firstly, backlash error analysis is performed on the multi-axis motion position compensation data for each axis. Backlash error refers to the deviation between the actual and theoretical positions of each axis of the machine tool when the motion direction changes due to the clearance between the transmission mechanisms. For example, when the X-axis switches from forward motion to reverse motion, the clearance between the lead screw and the nut will cause the X-axis to "go empty" in the early stage of reverse motion, and it will not be able to generate displacement immediately. Based on the multi-axis motion position compensation data, the position compensation change of each axis at the moment of motion direction switching is extracted, and the difference between the theoretical position and the actual position at that moment is compared. Combined with the design parameters of the machine tool transmission mechanism, the magnitude and variation law of backlash error of each axis when switching different motion directions are analyzed. For example, the backlash error of the X-axis when rotating from forward to reverse is 0.002mm, and the error of the Y-axis when rotating from reverse to forward is 0.0015mm, thus obtaining the backlash error data of each axis. Next, axial load variation compensation is performed on the discharge energy adjustment data based on the backlash error data of each axis. Axial load variation refers to the change in the machining load on each axis due to the change in discharge energy during the discharge energy adjustment process. For example, when the discharge energy increases, the cutting force of the electrode wire on the workpiece increases, and each axis needs to bear a greater load. The backlash error will show different degrees of influence when the load changes. The increase in load may make the actual impact of the backlash error more obvious. Based on the backlash error data of each axis, the influence of backlash error on machining accuracy under different load conditions (corresponding to different discharge energies) is analyzed. For example, when the discharge energy increases by 20%, the position deviation caused by the X-axis backlash error increases from 0.002mm to 0.0025mm. According to the above influence law, the energy distribution corresponding to each axis in the discharge energy adjustment data is adjusted. At the same time, the additional position compensation caused by the load change is calculated, and the original discharge energy adjustment data is corrected to ensure that the influence of backlash error is controlled within the allowable range when the load changes, thus obtaining the axial load variation compensation data. Then, multi-axis linkage acceleration smoothing is performed based on the axial load change compensation data. Sudden acceleration changes can cause machine tool vibration, which in turn affects machining accuracy. Especially in multi-axis linkage, a sudden acceleration change in one axis may be transmitted to other axes through the overall structure of the machine tool, resulting in a decrease in multi-axis motion coordination. Based on the axial load change compensation data, the acceleration requirements of each axis after load change are analyzed. For example, if the original acceleration is maintained when the load increases, it may cause motor overload or increased vibration. A smoothing algorithm is used to adjust the acceleration change curve of each axis so that the acceleration smoothly transitions from the initial value to the target value, avoiding sudden increases or decreases in acceleration. For example, the acceleration of the X-axis is smoothly increased from 500 mm / s² to 600 mm / s², rather than directly jumping up, thus obtaining multi-axis linkage acceleration smoothing data.Finally, multi-axis linkage accuracy compensation control is performed based on multi-axis linkage acceleration smoothing data. The position compensation amount in the axial load change compensation data is integrated with the motion parameters in the acceleration smoothing data to form a complete multi-axis linkage control command, which is sent to each axis drive system. This ensures that each axis can compensate for the impact of backlash error according to load changes during movement, and can move with smooth acceleration to avoid vibration interference. At the same time, it maintains the motion coordination between multiple axes. For example, when the X-axis is performing position compensation, the Y-axis adjusts its motion speed according to the acceleration smoothing data to ensure that the linkage trajectory of the two is accurate. Ultimately, accurate compensation of multi-axis linkage accuracy is achieved, and multi-axis linkage accuracy compensation data is obtained.

[0036] It should be noted that step S4 includes the following steps: Step S41: Acquire vibration signals during the machining process based on multi-axis linkage accuracy compensation data to obtain vibration signal data during the machining process; Step S42: Extract spectral features from the vibration signal data during the processing to obtain spectral feature data of the processing process; Step S43: Calculate the processing stability evaluation index based on the spectral characteristic data of the processing process to obtain the processing stability evaluation index data; Step S44: Construct a process stability assessment model based on the processing stability assessment index data to obtain the processing stability assessment model; Step S45: Integrate the machining process stability assessment model into the wire EDM machine tool to achieve multi-axis linkage adaptive control.

[0037] Specifically, firstly, vibration signals during the machining process are collected based on multi-axis linkage accuracy compensation data. Vibration during machining is an important indicator of machining stability. Excessive vibration can cause electrode wire vibration and workpiece position displacement, thereby affecting machining accuracy and surface quality. Based on the multi-axis linkage accuracy compensation data, the motion state and compensation status of each axis can be known. Vibration sensors are installed at key positions of the machine tool. Vibration sensors can convert mechanical vibration into electrical signals. During multi-axis linkage machining, vibration signals at each key position at different machining stages are collected in real time. Combined with the motion parameters in the multi-axis linkage accuracy compensation data, the changes in vibration signals under different motion and compensation states are recorded to obtain machining process vibration signal data. This data includes key information such as vibration amplitude, vibration frequency, and vibration duration. Next, spectral features are extracted from the vibration signal data during the machining process. Vibration signals are usually a superposition of multiple frequency components. Different frequencies of vibration correspond to different sources of interference. For example, low-frequency vibration may come from an unstable machine tool foundation, while high-frequency vibration may come from high-frequency vibration of the electrode wire. Signal processing technology is used to decompose the vibration signal data, converting the vibration signal in the time domain into a spectrum in the frequency domain. From the spectrum, the main vibration frequencies, the amplitude ratio of each frequency component, and the vibration intensity corresponding to the characteristic frequencies are identified. The above spectral features can accurately reflect the source and degree of influence of vibration. For example, if the vibration amplitude of a certain specific frequency is too high, it may correspond to a fault in the transmission mechanism of a certain axis. Through the above extraction process, the spectral feature data of the machining process is obtained. Then, processing stability assessment indicators are calculated based on the spectral characteristic data of the processing process. The processing stability assessment indicators are quantitative standards for measuring whether the processing process is stable. Based on the spectral characteristic data, key parameters that can reflect stability are selected, such as the maximum vibration amplitude, the duration of characteristic frequency vibration, and the concentration of spectral energy distribution. Combined with the preset processing accuracy requirements and equipment operation standards, the threshold range of each indicator is set. Statistical analysis and weighted calculation methods are used to convert the spectral characteristic data into specific assessment indicator values. For example, the maximum vibration amplitude is 0.01 mm (threshold is 0.02 mm) and the duration of characteristic frequency vibration is 2 s (threshold is 5 s). The stability of the current processing process is judged by these values, and the processing stability assessment indicator data is obtained.Subsequently, a processing stability assessment model is constructed based on the processing stability assessment index data. The processing stability assessment model needs to have the ability to determine whether the processing process is stable in real time. Using the processing stability assessment index data as samples, combined with the corresponding processing results, machine learning algorithms or statistical modeling methods are used to train the processing stability assessment model to learn the correlation between the assessment index and processing stability. For example, when the maximum vibration amplitude exceeds the threshold by 80% and the duration of characteristic frequency vibration exceeds the threshold by 50%, the processing stability assessment model judges the processing process to be in a "meta-stable" state. When all indicators are within the threshold range, it is judged to be in a "stable" state. Through multiple sample training and model optimization, it is ensured that the processing stability assessment model can accurately judge the processing stability based on the real-time assessment index data, thus obtaining the processing stability assessment model. Finally, the machining process stability assessment model is integrated into the control system of the wire EDM machine. During multi-axis linkage machining, the control system will collect vibration signals, calculate spectrum characteristics and evaluation indicators in real time, and input them into the machining process stability assessment model. The machining process stability assessment model outputs the current machining stability state. If it is judged to be "unstable" or "meta-stable", the control system will automatically adjust the multi-axis linkage parameters. If it is judged to be "stable", it will maintain the current parameters and continue machining, thereby realizing multi-axis linkage adaptive control and ensuring that the entire machining process is always in a stable state.

[0038] The above technical solution enables real-time monitoring, evaluation, and adaptive control of machining process stability, overcoming the limitations of traditional machining methods that rely on operator experience to judge stability. Traditional methods require operators to subjectively assess stability by observing spark patterns and listening to machining sounds, resulting in delayed and inaccurate judgments that can lead to persistent instability and negatively impact machining quality. This step, through vibration signal acquisition and spectral feature extraction, accurately captures stability-related information during machining, avoiding errors from subjective judgment. The calculation of machining stability evaluation indicators transforms abstract vibration signals into quantitative evaluation standards, making stability assessment more objective and scientific. The construction and integration of the machining process stability evaluation model automates and enables real-time stability assessment, allowing the model to quickly respond to changes in machining status and issue timely adjustment commands. Finally, adaptive control ensures timely correction when instability occurs, preventing workpiece scrap, electrode wire breakage, and other malfunctions caused by stability issues, significantly improving the reliability and consistency of the machining process while reducing reliance on operator experience.

[0039] Furthermore, step S44 includes the following steps: Step S441: Perform multi-axis linkage control stability boundary analysis on the machining stability evaluation index data to obtain multi-axis linkage control stability boundary data; Step S442: Optimize adaptive control parameters based on the stability boundary data of multi-axis linkage control to obtain optimized adaptive control parameter data; Step S443: Build a process stability assessment model based on the adaptive control parameter optimization data to obtain the process stability assessment model.

[0040] Specifically, firstly, a stability boundary analysis of multi-axis linkage control is performed on the machining stability evaluation index data. The stability boundary of multi-axis linkage control refers to the critical index value at which the machining process transitions from a stable state to an unstable state. Exceeding this boundary value, the machining process will enter an unstable state. Based on the machining stability evaluation index data, a large number of evaluation index values ​​and corresponding machining stability states under different machining conditions are collected. A boundary identification algorithm is used to analyze the above data to find the critical values ​​of each parameter in the evaluation index. For example, the critical value of the maximum vibration amplitude is 0.02 mm; exceeding this value, the machining process is unstable. The critical value of the characteristic frequency vibration duration is 5 seconds; exceeding this value, the machining process is unstable. At the same time, the interaction between various evaluation indices on the stability boundary is analyzed. For example, when the vibration amplitude approaches the critical value, the critical value of the characteristic frequency vibration duration decreases. Through analysis, the stability boundary range and boundary change law of multi-axis linkage control under different machining conditions are clarified, and multi-axis linkage control stability boundary data are obtained. Next, adaptive control parameters are optimized based on the stability boundary data of multi-axis linkage control. Adaptive control parameters refer to the parameters that the control system uses to adjust multi-axis linkage parameters, such as parameter adjustment range, adjustment response speed, and adjustment trigger threshold. The stability boundary data clarifies the critical range of machining stability. Based on this data, the optimization objective of the adaptive control parameters is set: to ensure that the control system can adjust the multi-axis linkage parameters in a timely and appropriate manner when the machining state approaches the stability boundary, pulling the machining state back to the stable range, while avoiding excessive adjustment that leads to parameter fluctuations. For example, when the vibration amplitude reaches 80% of the stability boundary, parameter adjustment is triggered, and the adjustment range is set to 5% to avoid excessive adjustment leading to new instability. According to the changing pattern of the stability boundary, the adjustment response speed is optimized. For example, in the early stage of machining, the stability boundary range is relatively wide, and the adjustment response speed can be appropriately slowed down. In the critical stage of machining, the stability boundary range is relatively narrow, and the adjustment response speed needs to be accelerated. Through optimization, adaptive control parameter optimization data that can adapt to the stability boundary requirements is obtained.Finally, a machining process stability assessment model is constructed based on the adaptive control parameter optimization data. The adaptive control parameter optimization data is integrated with the previous machining stability assessment index data and multi-axis linkage control stability boundary data as the model's input and constraints. On the basis of the original model framework (such as machine learning model, statistical model), the adjustment logic of adaptive control parameters is added, so that the model can not only judge the machining stability state, but also output specific parameter adjustment suggestions based on the stability boundary data and the adaptive control parameter optimization data. For example, when the model judges that the machining state is close to the stability boundary, it will output the adjustment instruction of "reducing the X-axis speed by 5% and reducing the discharge current by 3%" based on the adaptive control parameter optimization data. At the same time, the model will continuously learn the stability changes after parameter adjustment through the feedback mechanism, further optimize its own judgment and adjustment logic, and ensure that the model can accurately judge the stability and give reasonable adjustment suggestions under different machining conditions, thus obtaining the machining process stability assessment model.

[0041] Example 2 This application also discloses a special material processing control system for electrical discharge machine tools.

[0042] Reference Figure 2 A special material processing control system for an electrical discharge machine tool, comprising: The data acquisition module is used to acquire material property data of the workpiece through the multi-sensor network of the wire EDM machine tool, obtain workpiece material property data, and perform adaptive matching of material discharge coefficient based on workpiece material property data to obtain material discharge coefficient matching data. The optimization module is used to optimize the multi-axis linkage discharge parameters of the wire EDM machine tool based on the material discharge coefficient matching data, obtain the multi-axis linkage discharge parameter optimization data, and plan the multi-axis cooperative motion trajectory based on the multi-axis linkage discharge parameter optimization data to obtain the multi-axis cooperative motion trajectory data. The analysis and processing module is used to dynamically adjust the discharge energy of the wire EDM machine tool in real time based on the multi-axis coordinated motion trajectory data and the material discharge coefficient matching data to obtain discharge energy adjustment data. Based on the discharge energy adjustment data, multi-axis linkage accuracy compensation control is performed to obtain multi-axis linkage accuracy compensation data. The control module is used to construct a machining process stability assessment model based on multi-axis linkage accuracy compensation data, and then integrate the machining process stability assessment model into the control system of the wire EDM machine tool to execute multi-axis linkage control.

[0043] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0044] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0045] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for controlling the machining of special materials using an electrical discharge machine tool, characterized in that, Includes the following steps: Step S1: Collect material property data of the workpiece through the multi-sensor network of the wire EDM machine tool to obtain workpiece material property data, and perform adaptive matching of material discharge coefficient based on workpiece material property data to obtain material discharge coefficient matching data; Step S2: Optimize the multi-axis linkage discharge parameters of the wire EDM machine based on the material discharge coefficient matching data to obtain multi-axis linkage discharge parameter optimization data. Based on the multi-axis linkage discharge parameter optimization data, plan the multi-axis cooperative motion trajectory to obtain multi-axis cooperative motion trajectory data. Step S3: Based on the multi-axis coordinated motion trajectory data and material discharge coefficient matching data, the discharge energy of the wire EDM machine tool is dynamically adjusted in real time to obtain discharge energy adjustment data. Based on the discharge energy adjustment data, multi-axis linkage accuracy compensation control is performed to obtain multi-axis linkage accuracy compensation data. Step S4: Construct a machining process stability assessment model based on multi-axis linkage accuracy compensation data to obtain the machining process stability assessment model. Integrate the machining process stability assessment model into the wire EDM machine tool to execute multi-axis linkage control.

2. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Perform material composition spectral analysis on the workpiece using the multi-sensor network of the wire EDM machine tool to obtain the workpiece material composition spectral data; perform ultrasonic testing on the surface hardness of the workpiece to obtain the workpiece surface hardness data; Step S12: Calculate the electrical conductivity coefficient of the workpiece material based on the spectral data of the workpiece material composition and the surface hardness data of the workpiece to obtain the electrical conductivity coefficient data. Analyze the thermal conductivity characteristics of the material based on the electrical conductivity coefficient data to obtain the thermal conductivity characteristics data of the material. Step S13: Based on the material conductivity coefficient data and material thermal conductivity data, predict the material discharge breakdown threshold to obtain the material discharge breakdown threshold data; Step S14: Perform adaptive matching of material discharge coefficients based on material discharge breakdown threshold data to obtain material discharge coefficient matching data.

3. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 2, characterized in that, Step S2 includes the following steps: Step S21: Based on the material discharge coefficient matching data, perform multi-axis motion coupling relationship analysis on the X-axis, Y-axis, Z-axis, U-axis, and V-axis of the wire EDM machine tool to obtain multi-axis motion coupling relationship data; Step S22: Model the axial motion error propagation based on the multi-axis motion coupling relationship data to obtain the axial motion error propagation model; Step S23: Based on the axial motion error propagation model, optimize the multi-axis linkage discharge parameters of the material discharge coefficient matching data to obtain the optimized multi-axis linkage discharge parameter data; Step S24: Based on the multi-axis linkage discharge parameter optimization data, perform multi-axis cooperative motion trajectory planning to obtain multi-axis cooperative motion trajectory data.

4. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 3, characterized in that, Step S23 includes the following steps: Step S231: Perform axial dynamic response characteristic analysis on the multi-axis motion coupling relationship data to obtain axial dynamic response characteristic data; Step S232: Based on the axial dynamic response characteristic data, perform a correlation analysis between the discharge pulse and shaft motion delay on the material discharge coefficient matching data to obtain the correlation data between the discharge pulse and shaft motion delay; Step S233: Optimize the discharge energy distribution of each axis based on the correlation data between the discharge pulse and the axis motion delay to obtain the optimized discharge energy distribution data for each axis; Step S234: Based on the optimized data of discharge energy distribution of each axis, perform multi-axis linkage discharge timing synchronization adjustment to obtain multi-axis linkage discharge timing synchronization data; Step S235: Optimize the multi-axis linkage discharge parameters based on the multi-axis linkage discharge timing synchronization data to obtain optimized multi-axis linkage discharge parameter data.

5. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Real-time monitoring of machining clearance is performed based on multi-axis coordinated motion trajectory data to obtain real-time monitoring data of machining clearance; Step S32: Based on the real-time monitoring data of the processing gap, dynamically adjust the discharge energy of the material discharge coefficient matching data to obtain the discharge energy adjustment data; Step S33: Perform multi-axis motion position compensation calculation based on the discharge energy adjustment data to obtain multi-axis motion position compensation data; Step S34: Perform multi-axis linkage accuracy compensation control based on multi-axis motion position compensation data to obtain multi-axis linkage accuracy compensation data.

6. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 5, characterized in that, Step S34 includes the following steps: Step S341: Analyze the backlash error of each axis in the multi-axis motion position compensation data to obtain the backlash error data of each axis; Step S342: Based on the backlash error data of each shaft, perform axial load change compensation on the discharge energy adjustment data to obtain axial load change compensation data; Step S343: Perform multi-axis linkage acceleration smoothing processing based on the axial load change compensation data to obtain multi-axis linkage acceleration smoothing data; Step S344: Perform multi-axis linkage accuracy compensation control based on multi-axis linkage acceleration smoothing data to obtain multi-axis linkage accuracy compensation data.

7. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Acquire vibration signals during the machining process based on multi-axis linkage accuracy compensation data to obtain vibration signal data during the machining process; Step S42: Extract spectral features from the vibration signal data during the processing to obtain spectral feature data of the processing process; Step S43: Calculate the processing stability evaluation index based on the spectral characteristic data of the processing process to obtain the processing stability evaluation index data; Step S44: Construct a process stability assessment model based on the processing stability assessment index data to obtain the processing stability assessment model; Step S45: Integrate the machining process stability assessment model into the wire EDM machine tool to achieve multi-axis linkage adaptive control.

8. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 7, characterized in that, Step S44 includes the following steps: Step S441: Perform multi-axis linkage control stability boundary analysis on the machining stability evaluation index data to obtain multi-axis linkage control stability boundary data; Step S442: Optimize adaptive control parameters based on the stability boundary data of multi-axis linkage control to obtain optimized adaptive control parameter data; Step S443: Build a process stability assessment model based on the adaptive control parameter optimization data to obtain the process stability assessment model.

9. A special material machining control system for an electrical discharge machine tool, applied to the special material machining control method for an electrical discharge machine tool as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire material property data of the workpiece through the multi-sensor network of the wire EDM machine tool, obtain workpiece material property data, and perform adaptive matching of material discharge coefficient based on workpiece material property data to obtain material discharge coefficient matching data. The optimization module is used to optimize the multi-axis linkage discharge parameters of the wire EDM machine based on the material discharge coefficient matching data, obtain the multi-axis linkage discharge parameter optimization data, and plan the multi-axis cooperative motion trajectory based on the multi-axis linkage discharge parameter optimization data to obtain the multi-axis cooperative motion trajectory data. The analysis and processing module is used to dynamically adjust the discharge energy of the wire EDM machine tool in real time based on the multi-axis coordinated motion trajectory data and the material discharge coefficient matching data to obtain discharge energy adjustment data. Based on the discharge energy adjustment data, multi-axis linkage accuracy compensation control is performed to obtain multi-axis linkage accuracy compensation data. The control module is used to construct a machining process stability assessment model based on multi-axis linkage accuracy compensation data, thereby obtaining the machining process stability assessment model; the machining process stability assessment model is then integrated into the wire EDM machine tool to execute multi-axis linkage control.

10. A computer-readable storage medium, characterized in that: The device stores instructions that, when executed on a computer, cause the computer to perform a special material machining control method for an electrical discharge machine tool as described in any one of claims 1 to 8.

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