A special material processing control method and system for an electric spark machine tool

Abstract

The application discloses a special material processing control method and system for an electric spark machine tool, and relates to the technical field of linkage control.The method comprises the following steps: performing multi-axis linkage discharge parameter optimization on an electric spark wire cutting machine tool to obtain multi-axis linkage discharge parameter optimization data; performing multi-axis collaborative motion trajectory planning according to the multi-axis linkage discharge parameter optimization data to obtain multi-axis collaborative motion trajectory data; performing real-time discharge energy dynamic adjustment on the electric spark wire cutting machine tool according to the multi-axis collaborative motion trajectory data and material discharge coefficient matching data to obtain discharge energy adjustment data; performing multi-axis linkage precision compensation control based on the discharge energy adjustment data to obtain multi-axis linkage precision compensation data; and performing processing process stability evaluation model construction based on the multi-axis linkage precision compensation data to obtain a processing process stability evaluation model and integrating the model into the electric spark wire cutting machine tool to perform multi-axis linkage control.The application has the effect of improving linkage control efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of linkage control, in particular to a special material processing control method and system for an electric spark machine tool. BACKGROUND

[0002] As a precision machining equipment, the wire-cut electric discharge machine is widely used in mold manufacturing, precision part machining and other fields. Its multi-axis linkage function is the core to realize complex contour and high-precision machining. With the extension of machining demand to high hardness and complex curved surface materials, the precision and stability of multi-axis (such as X, Y, Z, U, V axis) cooperative control are significantly improved. In the prior art, multi-axis linkage control is mostly dependent on preset fixed discharge parameters, and the parameter setting is usually based on the experience of operators 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 realize deep adaptation of material characteristics and discharge parameters. At the same time, the trajectory planning mostly considers the motion of each axis independently, and the analysis of the coupling relationship between axes and the error transmission law is insufficient. The precision compensation is mostly limited to single position correction, and the overall control mode lacks real-time response ability to dynamic changes in the machining process, which can only meet the needs of medium and low precision machining and is difficult to adapt to stable machining in high requirement scenarios.

[0003] In related technologies, the multi-axis linkage control of the existing wire-cut electric discharge machine has the following obvious technical limitations:

[0004] 1. Poor parameter adaptability of the wire-cut electric discharge machine. The fixed discharge parameters cannot be dynamically adjusted according to the material composition, surface hardness and other characteristics of the workpiece, which easily leads to low discharge efficiency or workpiece surface burn due to the mismatch between the parameters and the material discharge breakdown threshold.

[0005] 2. Insufficient multi-axis cooperative precision of the wire-cut electric discharge machine. The coupling between axes and the error transmission effect are ignored, the trajectory planning and parameter optimization are disconnected, and the motion interference between axes and trajectory deviation are easily caused. Moreover, the reverse gap error is amplified when the load changes, further reducing the machining precision.

[0006] 3. The dynamic response of the wire-cut electric discharge machine is lagging. When the machining gap changes, the discharge energy cannot be adjusted in real time, which easily causes discharge interruption or short circuit. Moreover, there is a lack of machining stability evaluation mechanism based on vibration signals and other data, and the stability is judged by manual operation. The adjustment lag leads to large fluctuations in the machining process.

[0007] 4. The wire-cut electric discharge machine lacks adaptive ability. It cannot optimize the parameters in real time according to the machining state, which leads to high rejection rate of high hardness and complex contour workpieces, and is difficult to meet the needs of precision manufacturing. Thus, the multi-axis linkage control efficiency of the wire-cut electric discharge machine is reduced, and there is room for improvement. SUMMARY

[0008] Aiming at the deficiencies of the prior art, the application provides a special material processing control method and system for an electric spark machine tool.

[0009] In a first aspect, the application provides a special material processing control method for an electric spark machine tool, comprising the following steps:

[0010] Step S1: collecting material characteristic data of a workpiece by a multi-sensor network of an electric spark wire cutting machine tool to obtain workpiece material characteristic data, and performing adaptive matching of a material discharge coefficient according to the workpiece material characteristic data to obtain material discharge coefficient matching data;

[0011] Step S2: optimizing multi-axis linkage discharge parameters of the electric spark wire cutting machine tool based on the material discharge coefficient matching data to obtain multi-axis linkage discharge parameter optimization data, and performing multi-axis collaborative motion trajectory planning according to the multi-axis linkage discharge parameter optimization data to obtain multi-axis collaborative motion trajectory data;

[0012] Step S3: performing real-time discharge energy dynamic adjustment of the electric spark wire cutting machine tool according to the multi-axis collaborative motion trajectory data and the material discharge coefficient matching data to obtain discharge energy adjustment data, and performing multi-axis linkage precision compensation control based on the discharge energy adjustment data to obtain multi-axis linkage precision compensation data;

[0013] Step S4: constructing a processing process stability evaluation model based on the multi-axis linkage precision compensation data to obtain the processing process stability evaluation model; and integrating the processing process stability evaluation model into the electric spark wire cutting machine tool to perform multi-axis linkage control.

[0014] Preferably, the step S1 comprises the following steps:

[0015] Step S11: performing material composition spectrum analysis on the workpiece by the multi-sensor network of the electric spark wire cutting machine tool to obtain workpiece material composition spectrum data; and performing surface hardness ultrasonic detection on the workpiece to obtain workpiece surface hardness data;

[0016] Step S12: performing material conductivity coefficient calculation on the workpiece material composition spectrum data and the workpiece surface hardness data to obtain material conductivity coefficient data, and performing material heat conduction characteristic analysis based on the material conductivity coefficient data to obtain material heat conduction characteristic data;

[0017] Step S13: performing material discharge breakdown threshold prediction according to the material conductivity coefficient data and the material heat conduction characteristic data to obtain material discharge breakdown threshold data;

[0018] Step S14: performing adaptive matching of the material discharge coefficient based on the material discharge breakdown threshold data to obtain material discharge coefficient matching data.

[0019] Preferably, the step S2 comprises the following steps:

[0020] Step S21: Based on the material discharge coefficient matching data, the X-axis, Y-axis, Z-axis, U-axis and V-axis of the wire-cut electrical discharge machine are analyzed for multi-axis motion coupling relationship, and multi-axis motion coupling relationship data are obtained.

[0021] Step S22: According to the multi-axis motion coupling relationship data, an axial motion error transmission model is established.

[0022] Step S23: Based on the axial motion error transmission model, the multi-axis linkage discharge parameter optimization data are obtained by optimizing the material discharge coefficient matching data.

[0023] Step S24: According to the multi-axis linkage discharge parameter optimization data, multi-axis cooperative motion trajectory data are obtained by planning the multi-axis cooperative motion trajectory.

[0024] Preferably, the step S23 comprises the following steps:

[0025] Step S231: The multi-axis motion coupling relationship data are analyzed for axial dynamic response characteristics, and axial dynamic response characteristic data are obtained.

[0026] Step S232: Based on the axial dynamic response characteristic data, the discharge pulse and axial motion delay correlation data are obtained by analyzing the discharge pulse and axial motion delay correlation of the material discharge coefficient matching data.

[0027] Step S233: According to the discharge pulse and axial motion delay correlation data, the multi-axis linkage discharge timing synchronization data are obtained by optimizing the discharge energy distribution of each axis.

[0028] Step S234: Based on the multi-axis linkage discharge timing synchronization data, the multi-axis linkage discharge timing synchronization data are obtained by optimizing the multi-axis linkage discharge timing synchronization data.

[0029] Step S235: According to the multi-axis linkage discharge timing synchronization data, the multi-axis linkage discharge parameter optimization data are obtained by optimizing the multi-axis linkage discharge parameter.

[0030] Preferably, the step S3 comprises the following steps:

[0031] Step S31: According to the multi-axis cooperative motion trajectory data, the machining gap real-time monitoring data are obtained by monitoring the machining gap in real time.

[0032] Step S32: Based on the machining gap real-time monitoring data, the discharge energy dynamic adjustment data are obtained by dynamically adjusting the discharge energy of the material discharge coefficient matching data.

[0033] Step S33: Multi-axis motion position compensation calculation is performed according to the discharge energy adjustment data, and multi-axis motion position compensation data is obtained;

[0034] Step S34: Multi-axis linkage precision compensation control is performed based on the multi-axis motion position compensation data, and multi-axis linkage precision compensation data is obtained.

[0035] Preferably, the step S34 comprises the following steps:

[0036] Step S341: Each-axis reverse gap error analysis is performed on the multi-axis motion position compensation data, and each-axis reverse gap error data is obtained;

[0037] Step S342: Axial load change compensation is performed on the discharge energy adjustment data based on the each-axis reverse gap error data, and axial load change compensation data is obtained;

[0038] Step S343: Multi-axis linkage acceleration smoothing processing is performed according to the axial load change compensation data, and multi-axis linkage acceleration smoothing data is obtained;

[0039] Step S344: Multi-axis linkage precision compensation control is performed based on the multi-axis linkage acceleration smoothing data, and multi-axis linkage precision compensation data is obtained.

[0040] Preferably, the step S4 comprises the following steps:

[0041] Step S41: Machining process vibration signal acquisition is performed based on the multi-axis linkage precision compensation data, and machining process vibration signal data is obtained;

[0042] Step S42: Frequency spectrum feature extraction is performed on the machining process vibration signal data, and machining process frequency spectrum feature data is obtained;

[0043] Step S43: Machining stability evaluation index calculation is performed according to the machining process frequency spectrum feature data, and machining stability evaluation index data is obtained;

[0044] Step S44: Machining process stability evaluation model construction is performed based on the machining stability evaluation index data, and a machining process stability evaluation model is obtained.

[0045] Step S45: The machining process stability evaluation model is integrated into the wire cut electrical discharge machine tool, and multi-axis linkage adaptive control is realized.

[0046] Preferably, the step S44 comprises the following steps:

[0047] Step S441: Multi-axis linkage control stability boundary analysis is performed on the machining stability evaluation index data, and multi-axis linkage control stability boundary data is obtained;

[0048] Step S442: adaptive control parameter optimization based on the multi-axis linkage control stability boundary data, to obtain adaptive control parameter optimization data;

[0049] Step S443: processing process stability evaluation model construction according to the adaptive control parameter optimization data, to obtain the processing process stability evaluation model.

[0050] In a second aspect, the present application provides a special material processing control system of an electric spark machine tool, comprising:

[0051] A data acquisition module is configured to acquire material characteristic data of a workpiece by a multi-sensor network of the electric spark wire cutting machine tool, to obtain workpiece material characteristic data, to perform adaptive matching of a material discharge coefficient based on the workpiece material characteristic data, and to obtain material discharge coefficient matching data.

[0052] An optimization module is configured to perform multi-axis linkage discharge parameter optimization of the electric spark wire cutting machine tool based on the material discharge coefficient matching data, to obtain multi-axis linkage discharge parameter optimization data, to perform multi-axis collaborative motion trajectory planning based on the multi-axis linkage discharge parameter optimization data, and to obtain multi-axis collaborative motion trajectory data.

[0053] An analysis and processing module is configured to perform real-time discharge energy dynamic adjustment of the electric spark wire cutting machine tool based on the multi-axis collaborative motion trajectory data and the material discharge coefficient matching data, to obtain discharge energy adjustment data, to perform multi-axis linkage precision compensation control based on the discharge energy adjustment data, and to obtain multi-axis linkage precision compensation data.

[0054] A control module is configured to perform processing process stability evaluation model construction based on the multi-axis linkage precision compensation data, to obtain the processing process stability evaluation model, and to integrate the processing process stability evaluation model into the electric spark wire cutting machine tool to perform multi-axis linkage control.

[0055] In a third aspect, the present application provides a computer readable storage medium storing instructions, which, when executed on a computer, cause the computer to perform the special material processing control method of the electric spark machine tool.

[0056] In summary, the present application includes at least one of the following beneficial technical effects:

[0057] The application provides a special material processing control method of an electric spark machine tool. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0059] Figure 1 is a method flow chart of the special material processing control of the electric spark machine tool in the embodiment of the present application.

[0060] Figure 2 is a system schematic diagram of the special material processing control of the electric spark machine tool in the embodiment of the present application. DETAILED DESCRIPTION

[0061] The following will be described in combination with the drawings. Figures 1-2 The present application will be further described in detail.

[0062] Embodiment 1

[0063] The embodiment of the present application discloses a special material processing control method of an electric spark machine tool.

[0064] Referring to Figure 1 A special material processing control method of an electric spark machine tool comprises the following steps:

[0065] Step S1: material characteristic data of a workpiece is collected by a multi-sensor network of an electric spark wire cutting machine tool, workpiece material characteristic data is obtained, material discharge coefficient adaptive matching is performed according to the workpiece material characteristic data, and material discharge coefficient matching data is obtained;

[0066] Step S2: based on the material discharge coefficient matching data, the multi-axis linkage discharge parameter optimization of the wire-cut electrical discharge machine is carried out, and multi-axis linkage discharge parameter optimization data is obtained; according to the multi-axis linkage discharge parameter optimization data, multi-axis cooperative motion trajectory planning is carried out, and multi-axis cooperative motion trajectory data is obtained;

[0067] Step S3: according to the multi-axis cooperative motion trajectory data and the material discharge coefficient matching data, the real-time discharge energy dynamic adjustment of the wire-cut electrical discharge machine is carried out, and discharge energy adjustment data is obtained; based on the discharge energy adjustment data, multi-axis linkage precision compensation control is carried out, and multi-axis linkage precision compensation data is obtained;

[0068] Step S4: based on the multi-axis linkage precision compensation data, a machining process stability evaluation model is constructed, and a machining process stability evaluation model is obtained; the machining process stability evaluation model is integrated into the wire-cut electrical discharge machine to perform multi-axis linkage control.

[0069] It should be noted that the step S1 includes the following steps:

[0070] Step S11: through the multi-sensor network of the wire-cut electrical discharge machine, material composition spectrum analysis of the workpiece is carried out, and workpiece material composition spectrum data is obtained; surface hardness ultrasonic detection of the workpiece is carried out, and workpiece surface hardness data is obtained;

[0071] Step S12: material conductivity coefficient calculation is carried out on the workpiece material composition spectrum data and the workpiece surface hardness data, and material conductivity coefficient data is obtained; based on the material conductivity coefficient data, material thermal conduction characteristic analysis is carried out, and material thermal conduction characteristic data is obtained;

[0072] Step S13: according to the material conductivity coefficient data and the material thermal conduction characteristic data, material discharge breakdown threshold prediction is carried out, and material discharge breakdown threshold data is obtained;

[0073] Step S14: based on the material discharge breakdown threshold data, material discharge coefficient adaptive matching is carried out, and material discharge coefficient matching data is obtained, which includes matching parameters of pulse width, pulse interval and discharge current.

[0074] Specifically, the material composition spectrum data of the workpiece is obtained by performing material composition spectrum analysis on the workpiece by a spectrum sensor in a multi-sensor network of an electric spark wire cutting machine tool, the multi-sensor network integrating high-precision detection devices such as the spectrum sensor and an ultrasonic sensor; the spectrum sensor emits a light beam of a specific wavelength to irradiate the surface of the workpiece, and the light beam and the internal elements of the workpiece material interact to generate characteristic spectrum; by detecting the wavelength and intensity of the characteristic spectrum, the element composition and content ratio of each element of the workpiece material can be accurately analyzed, thereby obtaining the material composition spectrum data of the workpiece; meanwhile, the surface hardness of the workpiece is detected by an ultrasonic detection device in the multi-sensor network; the ultrasonic detection device emits high-frequency ultrasonic waves to the surface of the workpiece; the propagation speed and reflection coefficient of the ultrasonic waves change due to the difference in surface hardness of the workpiece when the ultrasonic waves propagate in the workpiece; by detecting the propagation time and reflection signal intensity of the ultrasonic waves, and combining a preset hardness calculation method, the hardness values of different regions of the surface of the workpiece can be obtained, thereby forming the surface hardness data of the workpiece; then, the material conductivity coefficient is calculated based on the obtained material composition spectrum data and surface hardness data of the workpiece; according to the content ratio of the conductive elements in the material composition, the bonding state between the elements, and the influence of the surface hardness on the internal crystal structure of the material, the material composition spectrum data and the surface hardness data of the workpiece are converted into conductivity coefficient data that can reflect the conductivity of the material by using a calculation method derived from electromagnetism theory; on this basis, the material thermal conduction characteristics are analyzed based on the material conductivity coefficient data; since the conductivity and thermal conductivity of the material are related, the thermal conductivity coefficient and thermal diffusivity of the material are calculated by combining the characteristics of the heat-conducting elements in the material composition and the influence of the crystal structure on heat conduction, thereby obtaining the material thermal conduction characteristic data; then, the material discharge breakdown threshold is predicted based on the calculated material conductivity coefficient data and material thermal conduction characteristic data; the discharge breakdown threshold refers to the minimum voltage or energy required for the material to be broken down by electric sparks to form a discharge channel; the better the conductivity of the material, the easier the charge accumulation, and the lower the energy required for breakdown; the better the thermal conductivity, the easier the heat generated during the discharge process is dissipated, which also affects the breakdown threshold; by establishing a correlation model of 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 of conductivity, thermal conductivity and breakdown threshold can be obtained by fitting historical data; finally, the material discharge coefficient is adaptively matched based on the predicted material discharge breakdown threshold data; 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 excessive discharge damage to the workpiece, and the pulse interval is adjusted to ensure stable discharge.When the breakdown threshold is high, the pulse width and the discharge current need to be increased to ensure that the material can be effectively broken down to form a discharge channel. Through the above adaptive adjustment, the material discharge coefficient matching data containing the specific values of the pulse width, pulse interval and discharge current are finally obtained.

[0075] The above technical solutions solve the problem in traditional wire electrical discharge machining that only fixed discharge parameters are selected according to the type of workpiece material, and the characteristics of different batches and different regions of the same material are ignored. Through spectral analysis and ultrasonic detection, the material composition and surface hardness are accurately obtained, providing an accurate data basis for subsequent parameter matching. The calculation of the material conductivity coefficient and the thermal conductivity characteristics further reveals the electrical and thermal behavior of the material during the discharge process, avoiding the limitations of relying only on experience to judge the material characteristics. The prediction of the discharge breakdown threshold provides a clear basis for the matching of the discharge coefficient, ensuring that the matched pulse width, pulse interval and discharge current can accurately adapt to the breakdown characteristics of the material, neither leading to low processing efficiency and ineffective material cutting due to insufficient discharge energy, nor causing workpiece surface burning and processing precision decline due to excessive discharge energy. The matching precision of the discharge parameters and the material characteristics is significantly improved, laying a solid foundation for the subsequent high-quality multi-axis linkage machining, especially suitable for workpiece machining scenarios that are sensitive to material characteristics and require high processing precision.

[0076] It should be noted that the step S2 includes the following steps:

[0077] Step S21: Based on the material discharge coefficient matching data, analyze the multi-axis motion coupling relationship of the X-axis, Y-axis, Z-axis, U-axis and V-axis of the wire electrical discharge machine tool, and obtain multi-axis motion coupling relationship data;

[0078] Step S22: According to the multi-axis motion coupling relationship data, model the axial motion error transmission, and obtain the axial motion error transmission model;

[0079] Step S23: Based on the axial motion error transmission model, optimize the multi-axis linkage discharge parameters of the material discharge coefficient matching data, and obtain multi-axis linkage discharge parameter optimization data;

[0080] Step S24: According to the multi-axis linkage discharge parameter optimization data, plan the multi-axis cooperative motion trajectory, and obtain multi-axis cooperative motion trajectory data.

[0081] Specifically, first, based on the material discharge coefficient matching data, the X-axis, Y-axis, Z-axis, U-axis and V-axis of the wire-cut electrical discharge machine are analyzed for multi-axis motion coupling relationship. The multi-axis motion coupling relationship refers to the influence of the motion state change of a certain axis on the motion of other axes. For example, the rapid movement of the X-axis may cause slight vibration of the Y-axis due to the overall rigidity problem of the machine tool. The linkage adjustment of the U-axis and the V-axis may affect the positioning accuracy of the Z-axis. Then, according to the motion parameters of each axis and the discharge energy requirement in the material discharge coefficient matching data, the interaction of each axis in different motion states is simulated, and the motion interference degree and motion response delay between each axis are analyzed, so as to obtain the multi-axis motion coupling relationship data. Then, the multi-axis motion coupling relationship data is used to model the axial motion error transmission. The axial motion error transmission refers to the transmission of the motion error of a certain axis to other axes in the multi-axis linkage process, which further affects the 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, resulting in trajectory deviation. Based on the multi-axis motion coupling relationship data, the error transmission path and transmission coefficient are identified. The error theory and statistical analysis method are used to construct the axial motion error transmission model. Then, the material discharge coefficient matching data is optimized for multi-axis linkage discharge parameters based on the constructed axial motion error transmission model. The axial motion error transmission model can predict the transmission of the motion error of each axis under different discharge parameters. For example, when the discharge current is large, the increased load of each axis may cause the motion error to increase, and the error transmission effect is more obvious. By substituting the initial discharge parameters in the material discharge coefficient matching data into the axial motion error transmission model, the error transmission results under different parameter combinations are simulated. The discharge parameters are adjusted to reduce the negative effects of error transmission within the allowable range according to the machining accuracy requirements. For example, in the parameter interval where the error transmission is obvious, the discharge current is appropriately reduced and the pulse interval is adjusted to ensure that the error transmission is controlled within the allowable range while meeting the discharge requirements. Finally, the multi-axis linkage discharge parameter optimization data is obtained. Finally, the multi-axis linkage discharge parameter optimization data is used for multi-axis coordinated motion trajectory planning. In the trajectory planning process, the machining profile requirements of the workpiece and the discharge energy distribution and the motion load limit of each axis in the multi-axis linkage discharge parameter optimization data are combined. The trajectory interpolation algorithm is used to plan the motion path and motion timing of each axis under the premise that the motion of each axis meets the discharge parameter requirements, so that the multi-axis can respond to the discharge demand synchronously in the motion process, avoiding the disconnection between the motion of a certain axis and the discharge process caused by unreasonable trajectory planning. For example, it is avoided that a certain axis moves too fast to cause insufficient discharge, or moves too slowly to cause local over-discharge. Finally, the multi-axis coordinated motion trajectory data is obtained.

[0082] The technical scheme solves the problem of ignoring the multi-axis motion coupling relationship and error transmission effect in traditional multi-axis linkage control, and the traditional method usually regards each axis as an independent motion unit when planning a trajectory and optimizing parameters, without considering the motion interference and error transmission between axes, resulting in a large trajectory deviation and a decline in precision in actual processing, while the step accurately identifies the mutual influence and error transmission law between axes through multi-axis motion coupling relationship analysis and error transmission modeling, providing a scientific basis for parameter optimization; the discharge parameter optimization based on the error transmission model can ensure that the optimized parameters not only adapt to the material characteristics, but also effectively suppress the negative effects of error transmission, improving the comprehensive adaptability of the parameters; and the multi-axis coordinated motion trajectory planning combined with the optimized parameters realizes the deep coordination of the motion trajectory and the discharge parameters, avoids the problem of disconnection between the trajectory and the parameters, ensures that the motion of each axis in the multi-axis linkage process meets the processing contour requirements and accurately matches the discharge process, significantly reduces the motion error and trajectory deviation of multi-axis linkage, improves the processing precision and trajectory consistency, and is especially suitable for high-precision workpieces with complex curved surfaces and multi-dimensional linkage processing, which can effectively ensure the processing contour precision and surface quality of such workpieces.

[0083] Further, the step S23 comprises the following steps:

[0084] Step S231: Perform axial dynamic response characteristic analysis on the multi-axis motion coupling relationship data to obtain axial dynamic response characteristic data;

[0085] Step S232: Perform discharge pulse and axis motion delay correlation analysis on the material discharge coefficient matching data based on the axial dynamic response characteristic data to obtain discharge pulse and axis motion delay correlation data;

[0086] Step S233: Perform each-axis discharge energy distribution optimization according to the discharge pulse and axis motion delay correlation data to obtain each-axis discharge energy distribution optimization data;

[0087] Step S234: Perform multi-axis linkage discharge timing synchronization adjustment based on the each-axis discharge energy distribution optimization data to obtain multi-axis linkage discharge timing synchronization data;

[0088] Step S235: Perform multi-axis linkage discharge parameter optimization according to the multi-axis linkage discharge timing synchronization data to obtain multi-axis linkage discharge parameter optimization data.

[0089] Specifically, first, the multi-axis motion coupling relationship data is analyzed for axial dynamic response characteristics. The axial dynamic response characteristics refer to the speed and accuracy of the actual motion state of each axis following the motion instruction, for example, the instruction requires the X-axis to move at a speed of 10 mm / s, the time for the X-axis to actually reach the speed, the overshoot during the process, etc. Based on the motion interference and load changes of each axis in the multi-axis motion coupling relationship data, a dynamic test method is used to detect the dynamic response indicators of each axis under different motion parameters and load conditions, such as response time, damping coefficient, natural frequency, etc., thereby obtaining the axial dynamic response characteristic data. Next, based on the axial dynamic response characteristic data, the discharge pulse and axis motion delay correlation analysis is performed on the material discharge coefficient matching data. The discharge pulse is the basic unit of the discharge process, and the axis motion delay refers to the time difference between the axis receiving the motion instruction and the actual start of motion. Due to the different dynamic response characteristics of each axis, there are differences in motion delay. If the generation timing of the discharge pulse does not match the timing of the axis motion, it will cause the discharge position and the axis motion position to be misaligned, for example, the discharge pulse is generated when the X-axis has not yet reached the specified position, which will cause excessive 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 timing in the material discharge coefficient matching data, the time difference between the generation time of the discharge pulse and the time when each axis actually reaches the specified motion position is analyzed, the delay correlation between the two is identified, and the discharge pulse and axis motion delay correlation data is obtained. Then, according to the obtained discharge pulse and axis motion delay correlation data, the discharge energy distribution of each axis is optimized. Different axes have different motion delays, which leads to different matching degrees of the actual discharge position and the processing requirement under the same discharge pulse timing, for example, if a large delay axis is discharged with uniform energy distribution, it may cause energy waste or insufficient processing due to position lag. Based on the delay correlation data, the effective discharge time and position matching degree of each axis in different processing stages are calculated, and the energy distribution proportion of each axis is adjusted according to the principle of appropriately increasing energy for small delay and optimizing energy timing and adjusting energy size for large delay, to ensure that each axis obtains adaptive discharge energy within its effective motion interval and avoid local processing quality differences caused by uneven energy distribution, thereby obtaining the discharge energy distribution optimization data of each axis. Subsequently, based on the discharge energy distribution optimization data of each axis, the multi-axis linkage discharge timing synchronization is adjusted. Discharge timing synchronization refers to ensuring that the generation time of the discharge pulse of each axis is accurately aligned with the time when the axis actually reaches the specified processing position. According to the motion delay data and energy distribution optimization requirements of each axis, a timing calibration algorithm is used to adjust the generation timing of the discharge pulse of each axis, for example, the discharge pulse of a large delay axis is triggered in advance, and the discharge pulse of a small delay axis is triggered according to the original timing or slightly adjusted timing. This ensures that the discharge pulse of each axis can be accurately generated when it reaches the specified processing position during multi-axis linkage, realizes the synchronization of discharge timing and axis motion, and obtains the multi-axis linkage discharge timing synchronization data.Finally, according to the multi-axis linkage discharge timing synchronization data, the multi-axis linkage discharge parameter optimization is carried out, the timing synchronization data and the energy distribution optimization data of each axis are integrated, the core parameters such as pulse width, pulse interval and discharge current in the material discharge coefficient matching data are adjusted, so that the optimized parameters not only meet the energy distribution requirements of each axis, but also meet the timing synchronization requirements, for example, according to the pulse trigger interval after timing synchronization, the pulse interval parameter is adjusted, and according to the energy distribution requirements, the discharge current size is fine-tuned, and finally the multi-axis linkage discharge parameter optimization data is obtained.

[0090] The above technical scheme accurately solves the key problems of asynchronous discharge pulse and axis movement and uneven energy distribution of each axis in traditional multi-axis linkage discharge control. The traditional method usually uses unified discharge timing and energy distribution, without considering the movement delay caused by the dynamic response difference of each axis, which causes the discharge pulse and the axis movement position to be misaligned, part of the area to be under-discharged and part of the area to be over-discharged, seriously affecting the processing quality. In this step, the movement delay characteristics of each axis and the correlation law of the discharge pulse are accurately captured through the analysis of the dynamic response of the axis and the correlation analysis of the delay, which provides accurate basis for energy distribution and timing synchronization. The optimization of energy distribution of each axis ensures that each axis can obtain adaptive energy in its processing interval, avoiding energy waste and uneven processing. The synchronous adjustment of multi-axis linkage discharge timing realizes the accurate alignment of the discharge pulse and the axis movement position, and fundamentally solves the problem of misalignment of discharge and movement. The finally optimized multi-axis linkage discharge parameters have energy adaptability and timing synchronization, which can ensure that the discharge at each processing position is accurate and stable during multi-axis linkage, significantly improving the processing surface quality and size accuracy, and reducing the scrap rate caused by uneven discharge.

[0091] It should be noted that the step S3 includes the following steps:

[0092] Step S31: Real-time monitoring of processing gap according to multi-axis cooperative motion trajectory data, to obtain real-time monitoring data of processing gap;

[0093] Step S32: Dynamic adjustment of discharge energy of material discharge coefficient matching data based on real-time monitoring data of processing gap, to obtain discharge energy adjustment data;

[0094] Step S33: Multi-axis movement position compensation calculation according to discharge energy adjustment data, to obtain multi-axis movement position compensation data;

[0095] Step S34: Multi-axis linkage precision compensation control based on multi-axis movement position compensation data, to obtain multi-axis linkage precision compensation data.

[0096] Specifically, first, the machining gap is monitored in real time according to the multi-axis cooperative motion trajectory data. The machining gap refers to the distance between the electrode wire and the workpiece in the wire electrical discharge machining process, which directly affects the discharge stability and machining quality. If the gap is too large, it may not form effective discharge, and if the gap is too small, it may cause short circuit between the electrode wire and the workpiece. Based on the multi-axis cooperative motion trajectory data, the theoretical relative position of the electrode wire and the workpiece at each time 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 can determine the actual gap size according to the change of the capacitance value between the electrode wire and the workpiece. Because the capacitance value is inversely proportional to the distance between the two electrodes, 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 machining gap monitoring data. This data can reflect the dynamic changes of the gap during machining, such as whether the gap increases or decreases due to workpiece deformation or electrode wire wear. Next, based on the real-time machining gap monitoring data, the discharge energy dynamic adjustment is performed on the material discharge coefficient matching data. The change of the machining gap will directly affect the discharge effect. When the machining gap is monitored to increase, the actual discharge distance increases. If the original discharge energy is maintained, it may not be enough to form a discharge channel due to insufficient energy, resulting in discharge interruption or reduced machining efficiency. At this time, the discharge energy needs to be appropriately increased, such as increasing the discharge current, extending the pulse width, etc. When the machining gap is monitored to decrease, the discharge distance shortens. If the original discharge energy is still maintained, it may easily lead to excessive discharge, causing accelerated electrode wire wear and workpiece surface burn. At this time, the discharge energy needs to be appropriately reduced, such as reducing the discharge current, shortening the pulse width, or increasing the pulse interval, etc. Through real-time adjustment, the discharge energy is always adapted to the current machining gap to obtain discharge energy adjustment data. Then, according to the obtained discharge energy adjustment data, the multi-axis motion position compensation calculation is performed. The adjustment of the discharge energy may affect the motion load and motion accuracy of each axis. For example, when the discharge energy increases, the machining reaction force received by each axis increases, which may cause a deviation between the actual motion position and the theoretical position. Based on the discharge energy adjustment data, the influence degree of different energy adjustment amounts on the motion of each axis is analyzed, such as when the energy increases by 10%, the X-axis may produce a position deviation of 0.001 mm. Combined with the theoretical position parameters in the multi-axis cooperative motion trajectory data, an error compensation algorithm is used to calculate the position compensation amount required by each axis under the current discharge energy condition 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 precision compensation control is performed, the position compensation amount of each axis is converted into specific motion control instructions, and the driving system of each axis is sent, for example, the compensation instruction of "increasing 0.001 mm displacement" is issued to the X axis, and the compensation instruction of "decreasing 0.0008 mm displacement" is issued to the Y axis, so that the position deviation can be corrected in real time during the movement of each axis according to the compensation instruction, and the movement time sequence and speed of each axis are adjusted in combination with the cooperation demand of multi-axis linkage, so that the multi-axis linkage trajectory is not disordered due to the compensation of a single axis, and finally the multi-axis linkage precision is improved, and the multi-axis linkage precision compensation data is obtained.

[0097] The above technical scheme realizes closed-loop management of gap monitoring-energy adjustment-position compensation-precision control in the machining process, solves the problem that the discharge energy and the movement position cannot be timely adapted after the machining gap changes in the traditional method, and the traditional method usually sets fixed discharge energy and movement parameters, which cannot be timely adjusted when the machining gap changes due to factors such as workpiece deformation and electrode wire loss, resulting in unstable discharge and reduced machining precision; the step can capture the dynamic change of the gap in the first time through real-time monitoring of the machining gap, and provide a basis for subsequent adjustment; the discharge energy is dynamically adjusted based on the gap change, so that the discharge process is always stable and effective, and the discharge interruption caused by too large gap or the machining damage caused by too small gap is avoided; the multi-axis motion position compensation calculation and precision compensation control correct the movement position deviation caused by energy adjustment and gap change, so that the actual movement trajectory of multi-axis linkage always matches the theoretical trajectory, and the machining precision and process stability are significantly improved.

[0098] Further, the step S34 comprises the following steps:

[0099] Step S341: Each axis reverse gap error analysis is performed on the multi-axis motion position compensation data, and each axis reverse gap error data is obtained;

[0100] Step S342: Based on the each axis reverse gap error data, the axial load change compensation data is obtained by performing axial load change compensation on the discharge energy adjustment data;

[0101] Step S343: Multi-axis linkage acceleration smoothing processing is performed according to the axial load change compensation data, and multi-axis linkage acceleration smoothing data is obtained;

[0102] Step S344: Multi-axis linkage precision compensation control is performed based on the multi-axis linkage acceleration smoothing data, and multi-axis linkage precision compensation data is obtained.

[0103] Specifically, first, the multi-axis motion position compensation data is analyzed for each axis reverse gap error. The reverse gap error refers to the deviation between the actual position and the theoretical position of each axis of the machine tool when the movement direction is changed due to the gap between the transmission mechanisms. For example, when the X-axis switches from forward motion to reverse motion, the gap between the screw and the nut will cause the X-axis to "walk empty" at the beginning of reverse motion, and cannot immediately produce displacement. Based on the multi-axis motion position compensation data, the position compensation amount change of each axis at the time of movement direction switching is extracted, the difference between the theoretical position and the actual position at that time is compared, and the design parameters of the machine tool transmission mechanism are combined to analyze the size and error change rule of the reverse gap error of each axis at different movement direction switching times. For example, the reverse gap error of the X-axis when switching from forward to reverse is 0.002 mm, and the error when the Y-axis switches from reverse to forward is 0.0015 mm, thereby obtaining the reverse gap error data of each axis. Then, based on the reverse gap error data of each axis, the discharge energy adjustment data is compensated for axial load changes. Axial load change refers to the change in machining load of each axis due to the change in discharge energy during discharge energy adjustment. For example, when the discharge energy increases, the cutting force of the electrode wire on the workpiece increases, and each axis needs to bear greater load. The reverse gap error will show different degrees of influence when the load changes. An increase in load may cause the actual influence of the reverse gap error to be more obvious. Based on the reverse gap error data of each axis, the influence of the reverse gap error on machining precision 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 reverse gap error of the X-axis increases from 0.002 mm to 0.0025 mm. According to the above influence rule, the energy distribution corresponding to each axis in the discharge energy adjustment data is adjusted, and the additional position compensation amount caused by the change in load is calculated to modify the original discharge energy adjustment data, ensuring that the influence of the reverse gap error is controlled within the allowed range when the load changes, thereby obtaining the axial load change compensation data. Then, the multi-axis linkage acceleration smoothing is performed according to the axial load change compensation data. Acceleration mutation will cause the machine tool to vibrate, thereby affecting the machining precision. Especially in the multi-axis linkage process, the acceleration mutation of a certain axis may be transmitted to other axes through the overall structure of the machine tool, causing a decrease in multi-axis motion coordination. Based on the axial load change compensation data, the acceleration demand of each axis after the load change is analyzed. For example, when the load increases, if the original acceleration is maintained, the motor may be overloaded or the vibration may be intensified. A smoothing algorithm is used to adjust the acceleration change curve of each axis to smoothly transition the acceleration 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 instead of being directly increased, thereby obtaining the multi-axis linkage acceleration smoothing data.Finally, based on the multi-axis linkage acceleration smoothing data, the multi-axis linkage precision compensation control is carried out, the position compensation amount in the axial load change compensation data is integrated with the motion parameters in the acceleration smoothing data, a complete multi-axis linkage control instruction is formed, and is sent to each axis driving system, so that in the movement process of each axis, the influence of the reverse gap error caused by the load change compensation can be compensated, smooth acceleration movement can be realized to avoid vibration interference, and the movement coordination between the multiple axes can be maintained, for example, when the X axis is performing position compensation, the Y axis adjusts the movement speed according to the acceleration smoothing data, so that the linkage trajectory of the two is accurate, the multi-axis linkage precision is finally accurately compensated, and multi-axis linkage precision compensation data is obtained.

[0104] It should be noted that the step S4 comprises the following steps:

[0105] Step S41: Collecting machining process vibration signals based on multi-axis linkage precision compensation data to obtain machining process vibration signal data;

[0106] Step S42: Extracting frequency spectrum features from the machining process vibration signal data to obtain machining process frequency spectrum feature data;

[0107] Step S43: Calculating machining stability evaluation indexes according to the machining process frequency spectrum feature data to obtain machining stability evaluation index data;

[0108] Step S44: Constructing a machining process stability evaluation model based on the machining stability evaluation index data to obtain the machining process stability evaluation model;

[0109] Step S45: Integrating the machining process stability evaluation model into the wire cut electrical discharge machine tool to realize multi-axis linkage adaptive control.

[0110] Specifically, first, based on multi-axis linkage precision compensation data, the vibration signal of the machining process is collected. The vibration in the machining process is an important indicator reflecting the stability of the machining. Excessive vibration will cause electrode wire shaking and workpiece position deviation, and further affect the machining precision and surface quality. According to the multi-axis linkage precision compensation data, the motion state and compensation of each axis can be known. The vibration sensor is installed at the key position of the machine tool. The vibration sensor can convert mechanical vibration into electrical signal. In the multi-axis linkage machining process, the vibration signals of each key position in different machining stages are collected in real time. Combined with the motion parameters in the multi-axis linkage precision compensation data, the vibration signal changes under different motion and compensation states are recorded. The vibration signal data of the machining process is obtained, which contains the key information such as vibration amplitude, vibration frequency and vibration duration. Then, the frequency spectrum feature of the vibration signal data of the machining process is extracted. The vibration signal is usually the superposition of multiple frequency components. Different frequency vibrations correspond to different interference sources. For example, low-frequency vibration may come from unstable machine tool foundation, and high-frequency vibration may come from electrode wire high-frequency vibration. The signal processing technology is used to decompose the vibration signal data, and the vibration signal in the time domain is converted into the frequency spectrum in the frequency domain. The main vibration frequency, the amplitude ratio of each frequency component, the vibration intensity corresponding to the characteristic frequency and other information are identified from the frequency spectrum. The above frequency spectrum features can accurately reflect the source and influence degree of vibration. For example, the vibration amplitude of a certain specific frequency is too high, which may correspond to the fault of the transmission mechanism of a certain axis. Through the above extraction process, the frequency spectrum feature data of the machining process is obtained. Then, the machining stability evaluation index is calculated according to the frequency spectrum feature data of the machining process. The machining stability evaluation index is a quantitative standard for measuring whether the machining process is stable. Based on the frequency spectrum feature data, the key parameters reflecting stability are selected, such as the maximum vibration amplitude, the characteristic frequency vibration duration, the spectral energy distribution concentration degree, etc. Combined with the preset machining precision requirement and equipment operation standard, the threshold range of each index is set. The statistical analysis and weighted calculation method is used to convert the frequency spectrum feature data into specific evaluation index values. For example, the maximum vibration amplitude is 0.01mm (threshold is 0.02mm), and the characteristic frequency vibration duration is 2s (threshold is 5s). The stability degree of the current machining process is judged through these values, and the machining stability evaluation index data is obtained.Afterwards, the machining process stability evaluation model is constructed based on the machining stability evaluation index data. The machining process stability evaluation model needs to have the ability to judge whether the machining process is stable in real time. The machining stability evaluation index data is taken as a sample, combined with the corresponding machining result, and a machine learning algorithm or a statistical modeling method is used to train the machining process stability evaluation model to learn the correlation between the evaluation index and the machining stability. For example, when the maximum value of the vibration amplitude exceeds 80% of the threshold value and the characteristic frequency vibration duration exceeds 50% of the threshold value, the machining process stability evaluation model judges that the machining process is in a "sub-stable" state. When all the indexes are within the threshold value range, it is judged to be in a "stable" state. Through multiple sample training and model optimization, it is ensured that the machining process stability evaluation model can accurately judge the machining stability according to the real-time evaluation index data, and the machining process stability evaluation model is obtained. Finally, the machining process stability evaluation model is integrated into the control system of the wire cut electrical discharge machine. During the multi-axis linkage machining process, the control system will collect vibration signals, calculate spectral features and evaluation indexes in real time, input them into the machining process stability evaluation model, and the machining process stability evaluation model outputs the current machining stability state. If it is judged to be "unstable" or "sub-stable", the control system will automatically adjust the multi-axis linkage parameters. If it is judged to be "stable", the current parameters are maintained for continuous machining, so as to realize multi-axis linkage adaptive control and ensure that the entire machining process is always in a stable state.

[0111] The above technical solution realizes real-time monitoring, evaluation and adaptive control of the machining process stability, solves the limitations of relying on the experience of operators to judge stability in traditional machining, and avoids the subjective judgment errors. The calculation of the machining stability evaluation index converts the abstract vibration signal into a quantitative evaluation standard, making the stability judgment more objective and scientific. The construction and integration of the machining process stability evaluation model realize the automation and real-time of the stability judgment. The model can quickly respond to changes in the machining state and issue adjustment instructions in time. The final adaptive control ensures that the machining process can be corrected in time when an unstable trend occurs, avoiding workpiece scrap, electrode wire breakage and other faults caused by stability problems, significantly improving the reliability and consistency of the machining process, and reducing the dependence on the experience of operators.

[0112] Further, the step S44 includes the following steps:

[0113] Step S441: Multi-axis linkage control stability boundary analysis is performed on the machining stability evaluation index data to obtain multi-axis linkage control stability boundary data.

[0114] Step S442: adaptive control parameter optimization based on the multi-axis linkage control stability boundary data, to obtain adaptive control parameter optimization data;

[0115] Step S443: processing process stability evaluation model construction according to the adaptive control parameter optimization data, to obtain the processing process stability evaluation model.

[0116] Specifically, first, the multi-axis linkage control stability boundary of the processing stability evaluation index data is analyzed. The multi-axis linkage control stability boundary refers to the critical index value of the processing process from stable state to unstable state. If the boundary value is exceeded, the processing process will enter an unstable state. Based on the processing stability evaluation index data, a large number of evaluation index values under different processing conditions and corresponding processing stability states 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, and the processing process is unstable when the value exceeds this value. The critical value of the characteristic frequency vibration duration is 5 s, and the processing process is unstable when the value exceeds this value. The mutual influence of each evaluation index on the stability boundary is also analyzed. For example, when the vibration amplitude approaches the critical value, the critical value of the characteristic frequency vibration duration will decrease. Through analysis, the stability boundary range and boundary variation law of multi-axis linkage control under different processing conditions are determined, and the multi-axis linkage control stability boundary data is obtained. Then, adaptive control parameter optimization is performed based on the multi-axis linkage control stability boundary data. The adaptive control parameter refers to the parameter that the control system relies on when adjusting the multi-axis linkage parameter, such as parameter adjustment amplitude, adjustment response speed, adjustment trigger threshold, etc. The stability boundary data determines the critical range of processing stability. Based on this data, the optimization goal of adaptive control parameters is set: to ensure that the control system can timely and moderately adjust the multi-axis linkage parameter when the processing state approaches the stability boundary, and pull the processing state back to the stable interval, 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 amplitude is set to 5% to avoid excessive adjustment that leads to new instability. According to the variation law of the stability boundary, the adjustment response speed is optimized. For example, in the early stage of processing, the stability boundary range is wide, and the adjustment response speed can be appropriately slowed down. In the key stage of processing, the stability boundary range is narrow, and the adjustment response speed needs to be accelerated. Through optimization, adaptive control parameter optimization data that can adapt to the requirements of the stability boundary is obtained.Finally, according to the adaptive control parameter optimization data, the processing process stability evaluation model is constructed, the adaptive control parameter optimization data is integrated with the previous processing stability evaluation index data and the multi-axis linkage control stability boundary data as the input and constraint conditions of the model, on the basis of the original model framework (such as machine learning model, statistical model), the adjustment logic of the adaptive control parameter is added, so that the model can not only judge the processing stability state, but also output specific parameter adjustment suggestions according to the stability boundary data and the adaptive control parameter optimization data, for example, when the model judges that the processing 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, and at the same time, the model will continuously learn the stability change after parameter adjustment through the feedback mechanism, further optimize the judgment and adjustment logic of the model, and ensure that the model can accurately judge the stability and give reasonable adjustment suggestions under different processing conditions, and finally obtain the processing process stability evaluation model.

[0117] Embodiment 2

[0118] The embodiment of the application also discloses a special material processing control system of an electric spark machine tool.

[0119] With reference to Figure 2 A special material processing control system of an electric spark machine tool comprises:

[0120] A data acquisition module is configured to acquire material characteristic data of a workpiece by a multi-sensor network of the electric spark wire cutting machine tool, to perform adaptive matching of a material discharge coefficient based on the material characteristic data of the workpiece, and to obtain material discharge coefficient matching data.

[0121] An optimization module is configured to optimize discharge parameters of multi-axis linkage of the electric spark wire cutting machine tool based on the material discharge coefficient matching data, to obtain multi-axis linkage discharge parameter optimization data, to plan a multi-axis collaborative motion trajectory based on the multi-axis linkage discharge parameter optimization data, and to obtain multi-axis collaborative motion trajectory data.

[0122] An analysis and processing module is configured to perform real-time discharge energy dynamic adjustment of the electric spark wire cutting machine tool based on the multi-axis collaborative motion trajectory data and the material discharge coefficient matching data, to obtain discharge energy adjustment data, to perform multi-axis linkage precision compensation control based on the discharge energy adjustment data, and to obtain multi-axis linkage precision compensation data.

[0123] A control module is configured to construct a processing process stability evaluation model based on the multi-axis linkage precision compensation data, to obtain the processing process stability evaluation model, and to integrate the processing process stability evaluation model into a control system of the electric spark wire cutting machine tool to perform multi-axis linkage control.

[0124] The above merely illustrates and describes the concept of the present application, and those skilled in the art can make various modifications or supplements to the specific embodiments described or adopt similar ways to replace, as long as the concept of the present application is not deviated, which shall belong to the protection scope of the present application.

[0125] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0126] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details and limit the present application to the specific embodiments. Obviously, many modifications and changes can be made according to the contents of the present specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application.

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 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; 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 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; 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, and 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 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.

3. 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.

4. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 3, 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.

5. 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.

6. The method for controlling the machining of special materials using an electrical discharge machine tool according to claim 5, 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.

7. 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 1-6, 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.

8. 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 6.

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