Method for determining pulse electrochemical machining nonlinear dynamic characteristic parameter mapping relation

Abstract

The invention discloses a method for determining a pulse electrochemical machining non-linear dynamic characteristic parameter mapping relation, which comprises the following steps: acquiring a current density time sequence of a workpiece in a pulse electrochemical machining process and surface topography data of the workpiece after the pulse electrochemical machining is completed, performing non-linear dynamic analysis on the surface topography data, and determining a pulse electrochemical machining non-linear dynamic characteristic parameter mapping relation. Obtaining a first workpiece surface space heterogeneous recursive entropy; performing nonlinear kinetic analysis on the current density time sequence to obtain a first current density heterogeneous recursive entropy; and establishing a linear regression relationship according to the first current density heterogeneous recursive entropy and the first workpiece surface space heterogeneous recursive entropy, and obtaining a corresponding relationship between the current density and the workpiece surface microstructure. Based on this, a linear regression relationship is established between the current density and the surface topography data, the surface quality of the workpiece after machining under the current parameters can be predicted in real time only according to the current density signal, and off-line measurement does not need to be carried out after machining is completed.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of electrolytic processing, and particularly relates to a method for determining a mapping relationship of nonlinear dynamic characteristic parameters of pulse electrolytic processing. BACKGROUND

[0002] As an advanced special processing technology, pulse electrolytic processing (PECM) removes materials by applying a pulse voltage between a tool electrode and a workpiece and using the principle of electrochemical anodic dissolution. It has been widely used in the fields of aerospace and precision mold, especially in the processing of difficult-to-cut materials such as titanium-aluminum alloy and nickel-based superalloy, due to its advantages such as no tool wear and no residual stress on the processed surface. PECM is essentially a highly complex, multi-physical field coupled nonlinear dynamic system. Current research and monitoring of the PECM process mostly stays at the macro level, making it difficult to fundamentally reveal the internal mechanism of the formation of the processing quality.

[0003] In the patent with publication number CN119304286A, a method for predicting the electrode feed speed of internal flow channel surface electrolytic finishing processing is disclosed. The initial peak height and the initial valley height of the actual internal surface are measured by a roughness measuring device. According to the electrolytic processing requirements, the micro-peak material dissolution speed, material removal amount, and actual processing gap at the observation point are calculated based on the processing voltage and the theoretical processing gap. The micro-valley material dissolution speed, material removal amount, and actual processing gap are also calculated. The micro-peak height and the valley depth at the next time point at the observation point are calculated based on the micro-peak material dissolution speed, material removal amount, and actual processing gap, as well as the micro-valley material dissolution speed, material removal amount, and actual processing gap. It is determined whether the sum of the micro-peak height and the valley depth meets the roughness requirement of the processing.

[0004] This method only reflects the relationship between the processing voltage and the surface roughness of the workpiece, and does not further explore the internal mechanism of the formation of the workpiece quality. SUMMARY

[0005] The embodiments of the present application provide a method for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrolytic processing, to realize the prediction of nonlinear behavior in the pulse electrolytic processing process.

[0006] According to a first aspect of the present application, the embodiments of the present application provide a method for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrolytic processing, which can include: Step A, obtaining the current density time series of the workpiece in the pulse electrolytic processing process, and the surface topography data of the workpiece after the pulse electrolytic processing is completed, and the process parameters of the pulse electrolytic processing are the first process parameter values; Step B, performing nonlinear dynamics analysis on the current density time series to obtain first current density heterogeneous recursive entropy; Step C, performing nonlinear dynamics analysis on the surface topography data to obtain first workpiece surface spatial heterogeneous recursive entropy; Step D, establishing a linear regression relationship according to the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy to obtain a corresponding relationship between the current density and the workpiece surface microtopography. Optionally, the nonlinear dynamics analysis on the current density time series to obtain the first current density heterogeneous recursive entropy comprises: segmenting the current density time series according to the number of current densities to obtain a plurality of current density data groups, the number of current density data in each current density data group being consistent; performing phase space reconstruction and heterogeneous recursive quantization analysis on each current density data group respectively to obtain a plurality of first current density heterogeneous recursive entropies.

[0007] Optionally, the phase space reconstruction and heterogeneous recursive quantization analysis on each current density data group respectively to obtain a plurality of first current density heterogeneous recursive entropies comprises: performing phase space reconstruction and heterogeneous recursive quantization analysis on each current density data group to obtain a preliminary first current density heterogeneous recursive entropy corresponding to each current density data group; sorting the plurality of preliminary first current density heterogeneous recursive entropies according to the arrangement order of each current density data group to obtain a heterogeneous recursive entropy time series; performing phase space reconstruction and heterogeneous recursive quantization analysis on the heterogeneous recursive entropy time series to obtain the first current density heterogeneous recursive entropy.

[0008] Optionally, the nonlinear dynamics analysis on the surface topography data to obtain the first workpiece surface spatial heterogeneous recursive entropy comprises: performing dimensionality reduction processing on the surface topography data to obtain one-dimensional state space data; performing phase space reconstruction on the one-dimensional state space data to obtain the first workpiece surface spatial heterogeneous recursive entropy.

[0009] Optionally, the dimensionality reduction processing on the surface topography data to obtain one-dimensional state space data comprises: performing denoising processing on the surface topography data to obtain actual surface topography data; performing dimensionality reduction processing on the actual surface topography data to obtain one-dimensional state space data.

[0010] Optionally, after the linear regression relationship is established according to the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy to obtain a corresponding relationship between the current density and the workpiece surface microtopography, the method further comprises: adjusting the process parameter in the pulse electrochemical machining process from a first process parameter value to a second process parameter value; repeating steps A-C according to the second process parameter value to obtain a second current density hetero-recursive entropy and a second workpiece surface spatial hetero-recursive entropy; establishing a linear regression relationship according to the first process parameter value, the first current density hetero-recursive entropy and the first workpiece surface spatial hetero-recursive entropy, and the second process parameter value, the second current density hetero-recursive entropy and the second workpiece surface spatial hetero-recursive entropy, to obtain a corresponding relationship between the process parameter, the current density and the micro-morphology of the workpiece surface.

[0011] Optionally, the process parameter comprises at least one of electrolyte pressure, machining voltage, pulse frequency and duty cycle.

[0012] According to a second aspect of the present application, an embodiment of the present application provides a device for determining a mapping relationship of a nonlinear dynamic characteristic parameter of pulse electrochemical machining, which can comprise: a pulse electrochemical machining machine tool; a current density acquisition module, which is connected with a pulse power source of the pulse electrochemical machining machine tool, and is configured to acquire a current density time sequence; a surface morphology acquisition module, configured to acquire surface morphology data of the workpiece; a nonlinear analysis module, an input end of the nonlinear analysis module is connected with an output end of the current density acquisition module and an output end of the surface morphology acquisition module, respectively, and the nonlinear analysis module is configured to perform nonlinear dynamic analysis on the current density time sequence to obtain a first current density hetero-recursive entropy, perform nonlinear dynamic analysis on the surface morphology data to obtain a first workpiece surface spatial hetero-recursive entropy, and establish a linear regression relationship according to the first current density hetero-recursive entropy and the first workpiece surface spatial hetero-recursive entropy to obtain a corresponding relationship between the current density and the micro-morphology of the workpiece surface.

[0013] Optionally, the surface morphology acquisition module is a laser confocal microscope.

[0014] Optionally, the current density acquisition module is a high-frequency oscilloscope.

[0015] The technical scheme provided by the embodiment of the present application at least brings the following beneficial effects: The embodiment of the present application provides a method for determining a mapping relationship of nonlinear dynamic characteristic parameters of pulse electrochemical machining, obtaining a current density time sequence of a workpiece in a pulse electrochemical machining process, and surface topography data of the workpiece after the pulse electrochemical machining is completed, performing nonlinear dynamic analysis on the current density time sequence to obtain first current density heterogeneous recursive entropy, performing nonlinear dynamic analysis on the surface topography data to obtain first workpiece surface space heterogeneous recursive entropy, establishing a linear regression relationship according to the first current density heterogeneous recursive entropy and the first workpiece surface space heterogeneous recursive entropy, and obtaining a corresponding relationship between the current density and the workpiece surface microtopography. Based on this, an accurate and quantifiable mathematical model (linear regression relationship) is established between the process electric signal (current density) and the surface topography data, the internal dynamic relationship is revealed, and thus in subsequent machining, only the current density signal needs to be monitored in real time and the current density heterogeneous recursive entropy thereof is calculated, so that the workpiece surface space heterogeneous recursive entropy (and then the surface quality) after machining is completed under the current parameters can be predicted in real time through the model, and offline measurement after machining is completed is not needed.

[0016] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings incorporated into the specification and constituting a part of the specification show embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application, and do not constitute undue limitations on the present application.

[0018] Figure 1 is a flowchart of a method for determining a mapping relationship of nonlinear dynamic characteristic parameters of pulse electrochemical machining according to an exemplary embodiment; Figure 2 is a structural schematic diagram of a device for determining a mapping relationship of nonlinear dynamic characteristic parameters of pulse electrochemical machining according to an exemplary embodiment; Figure 3 is a structural block diagram of an apparatus for determining a mapping relationship of nonlinear dynamic characteristic parameters of pulse electrochemical machining according to an exemplary embodiment. DETAILED DESCRIPTION

[0019] In order to more clearly understand the above-mentioned purposes, features and advantages of the present disclosure, the schemes of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0020] Many specific details are set forth in the following description in order to provide a thorough understanding of the present disclosure. However, the present disclosure can be practiced according to other embodiments that do not require some of the specific details described herein. It is understood that the present disclosure is well suited to implementing embodiments like those described herein without undue experimentation or further testing.

[0021] As described in the background, PECM is essentially a highly complex, multi-physical field coupling nonlinear dynamic system. Current research and monitoring of PECM process is mostly at the macro level, and it is difficult to fundamentally reveal the inherent mechanism of the formation of processing quality.

[0022] Based on this, the present application provides a method for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrochemical machining. First, the method for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrochemical machining provided by the embodiments of the present application is introduced.

[0023] As Figure 1 shown; Embodiment 1; It can include the following steps: S101, step A: obtaining the current density time series of the workpiece in the pulse electrochemical machining process, and the surface topography data of the workpiece after the pulse electrochemical machining is completed, and the process parameters of the pulse electrochemical machining are the first process parameter values; S102, step B: performing nonlinear dynamic analysis on the current density time series to obtain the first current density heterogeneous recurrence entropy; S103, step C: performing nonlinear dynamic analysis on the surface topography data to obtain the first workpiece surface space heterogeneous recurrence entropy; S104, step D: establishing a linear regression relationship according to the first current density heterogeneous recurrence entropy and the first workpiece surface space heterogeneous recurrence entropy to obtain the corresponding relationship between the current density and the workpiece surface microtopography. Based on the above embodiment, the current density time sequence of the workpiece in the pulse electrochemical machining process is obtained, and after the pulse electrochemical machining is completed, the surface topography data of the workpiece is obtained, the current density time sequence is analyzed by nonlinear dynamics, and the first current density heterogeneous recursive entropy is obtained; the surface topography data is analyzed by nonlinear dynamics, and the first workpiece surface space heterogeneous recursive entropy is obtained; a linear regression relationship is established according to the first current density heterogeneous recursive entropy and the first workpiece surface space heterogeneous recursive entropy, and the corresponding relationship between the current density and the workpiece surface microtopography is obtained. Based on this, an accurate and quantifiable mathematical model (linear regression relationship) is established between the process electric signal (current density) and the surface topography data, and the internal dynamic relationship is revealed. Therefore, in subsequent processing, only the current density signal needs to be monitored in real time and the current density heterogeneous recursive entropy is calculated, and the workpiece surface space heterogeneous recursive entropy (and the surface quality) under the current parameters can be predicted in real time through the model without waiting for the processing to be completed and then measuring offline.

[0024] In the above S101, the workpiece is processed by the pulse electrochemical machining machine tool, and the current density time sequence is collected by the current density collection module in the order of processing time.

[0025] After the electrochemical machining of the workpiece is completed, the surface topography of the workpiece is detected by the surface topography collection module to obtain the surface topography data of the workpiece.

[0026] It should be noted that the value of the process parameter of the pulse electrochemical machining machine tool in S101 is the first process parameter value, wherein the process parameter can include at least one of electrolyte pressure, machining voltage, pulse frequency, and duty cycle.

[0027] In the above S102, nonlinear dynamics analysis is an analysis method for studying the evolution law of the state of a system described by a nonlinear differential equation, whose behavior does not conform to the simple superposition principle.

[0028] The relationship between the current density and the time in the current density time sequence is analyzed to obtain the first current density heterogeneous recursive entropy.

[0029] In the above S103, the surface topography data is calculated in a similar manner to the above S102 to obtain the first workpiece surface space heterogeneous recursive entropy representing the complexity of the surface space structure of the workpiece.

[0030] In the above S104, the linear regression is a statistical analysis method for determining the quantitative relationship between two or more variables by using regression analysis in mathematical statistics. The obtained large number of first current density heterogeneous recursive entropy is subjected to linear regression analysis with the first workpiece surface spatial heterogeneous recursive entropy to establish the corresponding relationship between the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy. The first workpiece surface spatial heterogeneous recursive entropy is used to represent the surface spatial morphology of the workpiece. Therefore, the finally obtained corresponding relationship is the corresponding relationship between the current density and the surface microtopography of the workpiece.

[0031] Specifically, the first current density heterogeneous recursive entropy is taken as the independent variable, and the surface topography features of the first workpiece surface spatial heterogeneous recursive entropy are taken as the dependent variable for linear regression analysis. The results show that the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy both exhibit good linear relationships. This confirms the feasibility of predicting the complexity of the final surface topography by monitoring the nonlinear characteristics of the current density.

[0032] Optionally, in an example, the above S102 can include: S1021, segmenting the current density time sequence according to the number of current densities to obtain a plurality of current density data groups, the number of current density data in each current density data group being consistent; S1022, respectively performing phase space reconstruction and heterogeneous recursive quantization analysis on each current density data group to obtain first current density heterogeneous recursive entropy.

[0033] In the above S1021, the current density time sequence includes all current densities in the entire processing time period. This data is very large, and the calculation is very complex and time-consuming. In order to improve the calculation efficiency, the entire current density time sequence needs to be segmented to form a plurality of current density data groups, and the number of current density data in each current density data group is consistent. Specifically, the segmentation can be performed according to the structural features of the current density time sequence.

[0034] For example, assuming that the current density time sequence includes 30 million current densities, the first-10,000th current density, the 10,001-20,000th current density, the 20,001-30,000th current density, and the like are formed into 3000 current density data groups. In the present application, the number of current densities in the current density time sequence and the number of current densities in each current density data group are only examples and are not limited in any way. In other embodiments, they can also be replaced according to actual conditions.

[0035] In the above S1022, phase space reconstruction is a technique for reconstructing the phase space of a high-dimensional dynamical system from one-dimensional time series data, based on Takens' embedding theorem, for revealing the dynamic characteristics of complex systems (such as chaotic systems).

[0036] Heterogeneous recursive quantification analysis is an analysis method for quantitatively measuring and mathematically modeling complex systems composed of different types of elements with recursive or hierarchical structure.

[0037] The phase space reconstruction and heterogeneous recursive quantification analysis are performed on each current density data set respectively, and the first current density heterogeneous recursive entropy corresponding to each current density data set is calculated.

[0038] Specifically, in an example, the above S1022 can further include: S10221, performing phase space reconstruction and heterogeneous recursive quantification analysis on each current density data set to obtain a preliminary first current density heterogeneous recursive entropy corresponding to each current density data set; S10222, sorting the plurality of preliminary first current density heterogeneous recursive entropies in the order of arrangement of each current density data set to obtain a heterogeneous recursive entropy time series; S10223, performing phase space reconstruction and heterogeneous recursive quantification analysis on the heterogeneous recursive entropy time series to calculate the heterogeneous recursive entropy again to obtain the first current density heterogeneous recursive entropy.

[0039] In the above S10221, the phase space reconstruction and heterogeneous recursive quantification analysis are performed on each current density data set to obtain a preliminary first current density heterogeneous recursive entropy corresponding to each current density data set, and the first phase space reconstruction is to reveal the hidden dynamic rules in the current density time series.

[0040] In the above S10222, the preliminary first current density heterogeneous recursive entropies corresponding to each current density data set are sorted again in the order of arrangement of each current density data set to obtain a heterogeneous recursive entropy time series.

[0041] In the above S10223, the phase space reconstruction and heterogeneous recursive quantification analysis are performed again on the heterogeneous recursive entropy time series to calculate the first current density heterogeneous recursive entropy, and the second phase space reconstruction is to evaluate the evolution behavior of the complexity indicators generated by these rules.

[0042] Specifically, in an example, the method of S102 can be used for the following specific implementation for current density time series analysis: The current density time series under each pressure condition collected is processed. First, the long time series data is divided into multiple current density data groups (for example, 10000 current density data in each group). Phase space reconstruction is performed on each current density data group, the core of which is to determine the optimal embedding dimension m and time delay τ. In this embodiment, the mutual information method is preferably used to determine τ, and the pseudo-neighbor point method is used to determine m. For example, under the condition that the electrolyte pressure is 0.3 MPa, the optimal parameters determined are m=15 and τ=3. Based on the reconstructed phase space trajectory, the heterogeneous recurrence entropy of each data group is calculated, thereby obtaining a heterogeneous recurrence entropy time series. After noise reduction processing of the new sequence, phase space reconstruction is performed again, and the final current density heterogeneous recurrence entropy representing the dynamic complexity of the entire processing process is calculated. At the same time, a heterogeneous recurrence plot and an iterative function system plot can be generated for qualitative analysis.

[0043] Optionally, in an example, S103 can include: S1031, performing dimension reduction processing on the surface topography data to obtain one-dimensional state space data; S1032, performing phase space reconstruction on the one-dimensional state space data to obtain the first workpiece surface space heterogeneous recurrence entropy.

[0044] In S1031, the surface topography data collected is three-dimensional, and three-dimensional data is complex and difficult to calculate. In order to reduce the calculation difficulty, the three-dimensional surface topography data needs to be processed first, and then one-dimensional topography data is obtained. The one-dimensional topography data collectively constitutes one-dimensional state space data. Specifically, the surface data is traversed using Hilbert space curves to obtain the one-dimensional state space data.

[0045] In S1032, phase space reconstruction is performed similarly to the steps of S1022 to obtain the first workpiece surface space heterogeneous recurrence entropy.

[0046] Optionally, in an example, S1031 can include: S10311, performing noise reduction processing on the surface topography data to obtain actual surface topography data; S10312, performing dimension reduction processing on the actual surface topography data to obtain one-dimensional state space data.

[0047] In the above S10311, there may be some inaccurate data collection in the collected surface topography due to equipment reasons, and thus, in order to remove or suppress high-frequency noise, abnormal points (flying points) and smooth random fluctuations introduced in the measurement process, while maximizing the retention of the real characteristics of the surface, such as texture, scratches, peak and valley structures. The surface topography data needs to be denoised to remove those incorrect data, so as to obtain accurate actual surface topography data that can truly reflect the surface topography of the workpiece.

[0048] The denoising processing can include median filtering, Gaussian filtering, wavelet threshold denoising and the like.

[0049] In the above S10312, the three-dimensional actual surface topography information (one surface) is simplified or compressed into one-dimensional state space data capable of representing the main characteristics of the surface, so as to be applied to the time series-based nonlinear dynamics analysis method.

[0050] Specifically, the dimensionality reduction processing is based on the Hilbert transform idea, wherein the core idea of the Hilbert transform is: "using a line to traverse the entire three-dimensional space and convert it into a one-dimensional signal.

[0051] More specifically, the actual surface topography information is mapped into one-dimensional sequence data, and the one-dimensional sequence is sorted and partitioned, thereby constructing a discrete state space.

[0052] The surface topography data collected by the laser confocal microscope is subjected to dimensionality reduction processing. For nonlinear analysis, the three-dimensional surface data needs to be converted into one-dimensional sequence, and the Hilbert space filling curve method is preferably used to realize the conversion. Then, a nonlinear dynamics method similar to the current density analysis is used to analyze the one-dimensional state space data after conversion, to calculate the surface space heterogeneity recurrence entropy representing the complexity of the surface space structure, and to generate the corresponding surface HRP and surface IFS graphs. At the same time, the traditional surface roughness is calculated from the original three-dimensional surface topography data.

[0053] Optionally, in an example, after the above S104, the method can further include: S105, adjusting the process parameters in the pulse electrochemical machining process from the first process parameter value to the second process parameter value; S106, repeating steps A-C according to the second process parameter value to obtain a second current density heterogeneity recurrence entropy and a second workpiece surface space heterogeneity recurrence entropy; S107, linear regression relationship is established according to the first process parameter value, the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy, and the second process parameter value, the second current density heterogeneous recursive entropy and the second workpiece surface spatial heterogeneous recursive entropy, to obtain the corresponding relationship between the process parameters and the current density and the workpiece surface microstructure.

[0054] In the above S105, the process parameter during the electrolytic machining is the first process parameter value, and in order to study the relationship between the process parameter value and the current density and the workpiece surface morphology, it is necessary to re-adjust the value of the process parameter, and adjust the value of the process parameter from the first process parameter value to the second process parameter value.

[0055] In the above S106, the workpiece is electrolytically machined according to the second process parameter value, and the second current density heterogeneous recursive entropy and the second workpiece surface spatial heterogeneous recursive entropy calculated according to the same method as steps A-C are obtained again under the condition of the second process parameter.

[0056] In the above S107, the electrolytic machining is carried out according to the first process parameter value, and the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy are obtained, and the electrolytic machining is carried out according to the second process parameter value, and the second current density heterogeneous recursive entropy and the second workpiece surface spatial heterogeneous recursive entropy are obtained. Therefore, in order to study the influence of the process parameter value in the process parameter on the current density heterogeneous recursive entropy and the workpiece surface spatial heterogeneous recursive entropy, the first process parameter value, the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy; The second process parameter value, the second current density heterogeneous recursive entropy and the second workpiece surface spatial heterogeneous recursive entropy are analyzed, and their linear regression relationship is established, so as to determine the corresponding relationship between the process parameters and the current density and the workpiece surface microstructure.

[0057] Example 2: As Figure 2 shown, optionally, in an example, the present application also provides a device 200 for determining the mapping relationship of the nonlinear dynamic characteristic parameters of pulse electrolytic machining, which comprises: a pulse electrolytic machining machine tool 210; a current density acquisition module 220, the current density acquisition module 220 is connected with the pulse power supply 211 of the pulse electrolytic machining machine tool 210, and is used for acquiring the current density time sequence; a surface morphology acquisition module, used for acquiring the surface morphology data of the workpiece; The nonlinear analysis module is connected with the output end of the current density acquisition module 220 and the output end of the surface morphology acquisition module respectively, and is used for nonlinear dynamics analysis on the current density time sequence to obtain a first current density heterogeneous recursive entropy, nonlinear dynamics analysis on the surface morphology data to obtain a first workpiece surface space heterogeneous recursive entropy, and establishment of a linear regression relationship according to the first current density heterogeneous recursive entropy and the first workpiece surface space heterogeneous recursive entropy to obtain a corresponding relationship between the current density and the workpiece surface micro morphology.

[0058] In the above example, the current density acquisition module 220 can be a high-frequency oscilloscope. By connecting the high-frequency oscilloscope with the pulse power supply 211 of the pulse electrochemical machining machine tool 210, the current density in the power supply during the electrochemical machining process can be obtained, and thus the current density time sequence can be obtained.

[0059] The surface morphology acquisition module can be a laser confocal microscope. After the workpiece is electrochemically machined, the input end of the laser confocal microscope is aligned with the machined workpiece, and thus the surface morphology data of the workpiece can be obtained.

[0060] The nonlinear analysis module can be a computer with nonlinear analysis software. The nonlinear analysis module can perform the method as described in Embodiment 1 and achieve the same technical effects. To avoid repetition, details are not described here.

[0061] In the above embodiment 2, each method of the above embodiment 1 is included, and each process of the above embodiment 1 can be implemented, and the same technical effects can be achieved. To avoid repetition, details are not described here.

[0062] Embodiment 3: Figure 3 A hardware structure schematic diagram of a device for determining a nonlinear dynamics characteristic parameter mapping relationship of pulse electrochemical machining is shown.

[0063] The device for determining the nonlinear dynamics characteristic parameter mapping relationship of pulse electrochemical machining can include a processor 301 and a memory 302 having computer program instructions stored therein.

[0064] Specifically, the processor 301 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present application.

[0065] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 32 is non-volatile solid-state memory.

[0066] In certain embodiments, memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, typically, memory 302 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors 301), it is operable to perform the operations described with reference to one aspect of the invention.

[0067] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any of the methods described in the above embodiments for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrolytic machining.

[0068] In one example, the device for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrolytic machining may further include a communication interface 303 and a bus 304. As shown in the figure, the processor 301, memory 302, and communication interface 303 are connected through the bus 304 and complete communication with each other.

[0069] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of the present invention.

[0070] Bus 304 may include hardware, software, or both. For example, and not as a limitation, bus 304 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, a Wireless Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Control Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 304 may include one or more buses 304. Although specific bus 304s are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus 304 or interconnect.

[0071] The device for determining the mapping relationship of nonlinear dynamic characteristic parameters in pulse electrolytic machining can be based on the current method for determining the mapping relationship of nonlinear dynamic characteristic parameters in pulse electrolytic machining, thereby achieving a combination of... Figure 1 , 2 The method and apparatus 200 described herein are for determining the mapping relationship of nonlinear dynamic characteristic parameters in pulse electrolytic machining.

[0072] In addition, this embodiment of the invention also provides a computer program product, including computer program instructions, which, when executed by processor 301, can implement the steps and corresponding content of the aforementioned method embodiments.

[0073] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0074] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the required tasks. The programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0075] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0076] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable method, apparatus, and device for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrolytic machining, to produce a machine such that these instructions, executable via a computer or other programmable processor for determining the mapping relationship of nonlinear dynamic characteristic parameters of pulse electrolytic machining, enable the implementation of functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0077] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for determining the mapping relationship of nonlinear dynamic characteristic parameters in pulse electrolytic machining, characterized in that, The steps of the method are as follows: Step A: Obtain the current density time series of the workpiece during the pulse electrolytic machining process, and the surface morphology data of the workpiece after the pulse electrolytic machining is completed, wherein the process parameters of the pulse electrolytic machining are the first process parameter values; Step B: Perform nonlinear dynamic analysis on the current density time series to obtain the first current density heterogeneous recursive entropy; Step C: Perform nonlinear dynamic analysis on the surface morphology data to obtain the spatial heterogeneous recursive entropy of the first workpiece surface; Step D: Establish a linear regression relationship based on the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy to obtain the correspondence between current density and workpiece surface micromorphology.

2. The method as described in claim 1, characterized in that, The nonlinear dynamic analysis of the current density time series to obtain the first current density heterogeneous recursive entropy includes: The current density time series is segmented according to the number of current densities to obtain current density data groups, and the number of current density data in each current density data group is the same. Phase space reconstruction and heterogeneous recursive quantization analysis were performed on each current density data set to obtain multiple first current density heterogeneous recursive entropies.

3. The method as described in claim 2, characterized in that, The phase space reconstruction and heterogeneous recursive quantization analysis are performed on each current density data group to obtain multiple first current density heterogeneous recursive entropies, including: Phase space reconstruction and heterogeneous recursive quantization analysis are performed on each current density data group to obtain the preliminary first current density heterogeneous recursive entropy corresponding to each current density data group. According to the arrangement order of each current density data group, the multiple preliminary first current density heterogeneous recursive entropies are sorted to obtain the heterogeneous recursive entropy time series. Spatial reconstruction and heterogeneous recursive quantization analysis are performed on the heterogeneous recursive entropy time series to obtain the first current density heterogeneous recursive entropy.

4. The method as described in claim 1, characterized in that, The nonlinear dynamic analysis of the surface morphology data to obtain the spatial heterogeneous recursive entropy of the first workpiece surface includes: The surface morphology data is reduced in dimensionality to obtain one-dimensional state space data; Phase space reconstruction and heterogeneous recursive quantization analysis are performed on the one-dimensional state space data to obtain the heterogeneous recursive entropy of the first workpiece surface space.

5. The method as described in claim 4, characterized in that, The step of dimensionality reduction processing of the surface topography data to obtain one-dimensional state space data includes: The surface morphology data is denoised to obtain the actual surface morphology data; The actual surface morphology data is subjected to dimensionality reduction processing to obtain the one-dimensional state space data.

6. The method according to any one of claims 1-5, characterized in that, After establishing a linear regression relationship based on the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy to obtain the correspondence between current density and workpiece surface micromorphology, the method further includes: The process parameters in the pulse electrolytic machining process are adjusted from the first process parameter value to the second process parameter value; According to the second process parameter value, repeat steps A to C to obtain the second current density heterogeneous recursive entropy and the second workpiece surface spatial heterogeneous recursive entropy. A linear regression relationship is established based on the first process parameter value, the first current density heterogeneous recursive entropy, and the first workpiece surface spatial heterogeneous recursive entropy; and the second process parameter value, the second current density heterogeneous recursive entropy, and the second workpiece surface spatial heterogeneous recursive entropy, respectively, to obtain the correspondence between the process parameters, the current density, and the microstructure of the workpiece surface.

7. The method as described in claim 6, characterized in that, The process parameters include at least one of the following: electrolyte pressure, processing voltage, pulse frequency, and duty cycle.

8. A device for determining the mapping relationship of nonlinear dynamic characteristic parameters in pulse electrolytic machining, characterized in that, The device includes: Pulse electrolytic machining tool; A current density acquisition module is connected to the pulse power supply of the pulse electrolytic machining tool and is used to acquire a current density time series. The surface topography acquisition module is used to acquire the surface topography data of the workpiece. A nonlinear analysis module, whose input terminals are connected to the output terminals of the current density acquisition module and the surface morphology acquisition module, is used to perform nonlinear dynamic analysis on the current density time series to obtain a first current density heterogeneous recursive entropy; to perform nonlinear dynamic analysis on the surface morphology data to obtain a first workpiece surface spatial heterogeneous recursive entropy; and to establish a linear regression relationship based on the first current density heterogeneous recursive entropy and the first workpiece surface spatial heterogeneous recursive entropy to obtain the correspondence between current density and workpiece surface micromorphology.

9. The apparatus as claimed in claim 8, characterized in that, The surface morphology acquisition module is a laser confocal microscope.

10. The apparatus as claimed in claim 8, characterized in that, The current density acquisition module is a high-frequency oscilloscope.

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