Electrochemical combined machining cooperative control method for complex inner cavity structure of pump body

Abstract

The invention discloses an electrochemical combined machining cooperative control method for a complex inner cavity structure of a pump body, and belongs to the technical field of electrochemical combined machining. A machining area is divided into a plurality of cavity sections through a three-dimensional CAD model, machining current, interelectrode voltage and in-cavity back pressure signals are collected, and a circulation state characterization value and a smoothness reference interval are constructed. And calculating a static geometric complexity coefficient, coupling the static geometric complexity coefficient with a real-time circulation deviation amount to obtain a comprehensive processing difficulty coefficient, and carrying out grading marking according to a difficulty threshold value. Screening corresponding electrolyte from an electrolyte database according to a difficulty label, calculating a deviation value in combination with a dissolution rate and a byproduct generation amount, determining a preferable electrolyte type of a cavity section, setting parameters such as initial current, voltage and flow velocity according to the parameters, and constructing a cooperative control rule through relative deviation with a real-time signal; self-adaptive adjustment, fine difficulty identification, electrolyte accurate matching and multi-parameter cooperative stable control in the machining process are achieved, and the machining uniformity and the overall machining efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of electrochemical composite processing technology, specifically to a collaborative control method for electrochemical composite processing of complex internal structures of pump bodies. Background Technology

[0002] With the improvement of material properties and the increasing demand for microstructures, traditional mechanical cutting methods are gradually showing limitations when dealing with deep cavities, variable cross-section channels, and abrupt surface changes. Therefore, electrochemical machining has attracted widespread attention due to its characteristics such as no tool wear and applicability to difficult-to-machine materials. In recent years, composite machining strategies have been increasingly used for complex internal cavity structures. By incorporating multiple factors such as electrolyte flow status, curvature distribution, and the degree of geometric tapering of cavity segments into the analysis, differentiated machining control based on microstructural features can be achieved. However, existing composite machining methods are still relatively crude in terms of the coordinated control of multiple internal cavity segments. They often rely solely on experience to select electrolyte types or process parameters, failing to establish a systematic judgment mechanism that combines multi-source data such as current signals, inter-electrode voltage, back pressure changes, and the degree of geometric abrupt changes. This results in insufficient assurance of local machining accuracy, stability, and uniformity in complex internal cavity regions.

[0003] Current electrochemical machining control technologies mostly focus on adjustment strategies for single cavity sections or single signal parameters, lacking the ability to accurately identify complexities such as curvature abrupt changes, rapidly contracting spatial sections, mismatched inlet and outlet cross-sectional areas, and turbulent flow fields within the pump body. Furthermore, existing methods generally lack a time-series identification mechanism for dynamic flow states, making it difficult to translate the changing trends of data such as current, inter-electrode voltage, and back pressure into real-time judgments of processing deterioration. They also lack processing logic that couples static geometric complexity with dynamic flow changes. Regarding electrolyte selection, existing technologies typically lack quantitative ratio models based on dissolution rate, byproduct generation, and cavity characteristics, failing to provide differentiated and refined process matching strategies for the complex multi-point coupling processing challenges within complex cavities. Summary of the Invention

[0004] The purpose of this invention is to provide a method for coordinated control of electrochemical composite processing for complex internal structures of pump bodies, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A collaborative control method for electrochemical composite processing of complex internal structures of pump bodies is proposed. This method includes the following steps: Step S1: Obtain the processing area of ​​the pump body. Based on the 3D CAD model of the pump body, divide the processing area into several cavity segments and collect processing current signal data, inter-electrode voltage signal data, and cavity back pressure signal data; calculate the flow state characterization value of the cavity segments and set a slackness benchmark interval; Step S2: Obtain the inner wall area and inner wall curvature of the cavity segments and calculate the curvature severity index and space contraction degree index, normalize and weighted sum to obtain the static geometric complexity coefficient of the cavity segments; Step S3: Based on the flow state characterization value, calculate the dynamic flow deterioration degree of the cavity segments; based on the static geometric complexity coefficient and... The dynamic flow deterioration degree is calculated to determine the real-time comprehensive processing difficulty coefficient of the cavity segment; a preset processing difficulty threshold range is defined, and a difficulty label is attached to the cavity segment; Step S4: Based on the difficulty label, the corresponding electrolyte is selected from the electrolyte database to construct a set of candidate electrolyte types for the cavity segment, and the dissolution rate and by-product generation amount of each electrolyte are retrieved to calculate the deviation between the electrolyte and the cavity segment; Step S5: The electrolyte with the smallest deviation is selected as the preferred processing electrolyte type for the cavity segment; the initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate are calculated; the processing current signal data and inter-electrode voltage signal data of the cavity segment are compared with the initial processing current and initial inter-electrode voltage to calculate the relative deviation; and collaborative control rules are set.

[0006] As a preferred embodiment of the electrochemical composite machining collaborative control method for complex internal structures of pump bodies described in this invention, the machining area of ​​the pump body is obtained, and a preset time acquisition sequence is denoted as... ,in, This represents the t-th acquisition time point, where T represents the total number of acquisition time points. At each acquisition time point, the processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data of the processing area of ​​the pump body are acquired sequentially. The acquisition time points are then recorded. The processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data collected below are denoted as follows: , and ; A 3D CAD model of the pump body was obtained from the pump body design database. Based on this 3D CAD model, the processing area of ​​the pump body was divided into several cavity segments. The data acquisition time points were... The processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data of the k-th cavity segment acquired below are denoted as follows: , and .

[0007] As a preferred embodiment of the electrochemical composite processing and collaborative control method for complex internal structures of pumps described in this invention, based on the data acquisition time point... The processing current signal data of the kth cavity segment collected below Inter-electrode voltage signal data and intracavitary back pressure signal data The flow state characterization value of the k-th cavity segment is calculated using the following formula: ; in, Indicates the time point of data collection The flow state characterization value of the kth cavity segment; Obtain the flow state characterization values ​​of the k-th cavity segment at all acquisition time points, construct the flow state characterization value sequence of the k-th cavity segment, and calculate the mean and standard deviation of the flow state characterization value sequence, denoted as . and ; Set the patency baseline interval for the k-th cavity segment, denoted as . ,in, This represents the preset standard deviation influence factor; the patency benchmark interval for each cavity segment is obtained, and a patency benchmark interval table is constructed.

[0008] As a preferred embodiment of the electrochemical composite processing collaborative control method for complex internal cavity structures of pumps described in this invention, based on the three-dimensional CAD model of the pump body, the inner wall area and inner wall surface of the k-th cavity segment are obtained, denoted as follows: and ; Calculate the curvature intensity index of the k-th cavity segment using the following formula: ; in, The index represents the curvature intensity of the k-th cavity segment, and p represents the inner wall surface curvature of the k-th cavity segment. The p-th point on, Indicates the inner wall curved surface The average curvature at the p-th point on the curve; The spatial contraction index of the k-th cavity segment is calculated using the following formula: ; in, This represents the spatial contraction index of the k-th cavity segment. This represents the inlet cross-sectional area of ​​the k-th cavity segment. This represents the exit cross-sectional area of ​​the k-th cavity segment. Represents the cross-sectional area gradient. This represents the average cross-sectional area along the friction path of the k-th cavity segment; Curvature intensity index of the k-th cavity segment and spatial contraction index After normalization and weighted summation, the static geometric complexity coefficient of the k-th cavity segment is obtained, denoted as . .

[0009] As a preferred embodiment of the electrochemical composite processing and collaborative control method for complex internal structures of pumps described in this invention, based on the data acquisition time point... The flow state characterization value of the kth cavity segment Calculate the data collection time point The dynamic flow deterioration of the k-th cavity segment is calculated using the following formula: ,in, Indicates the time point of data collection The dynamic flow deterioration degree of the k-th cavity segment. This indicates the preset adjustment coefficient; Based on the static geometric complexity coefficient of the k-th cavity segment and the dynamic flow deterioration of the k-th cavity segment Calculate the time of acquisition for the k-th cavity segment. The real-time integrated processing difficulty coefficient is calculated using the following formula: ,in, This indicates that the k-th cavity segment was at the acquisition time point. The real-time integrated processing difficulty coefficient is as follows. This represents the preset coupling gain coefficient.

[0010] As a preferred embodiment of the electrochemical composite processing collaborative control method for complex internal structures of pumps described in this invention, a preset processing difficulty threshold range is defined. If the k-th cavity segment is at the acquisition time point... If the real-time comprehensive processing difficulty coefficient is greater than the maximum value within the processing difficulty threshold range, then the k-th cavity segment is determined to be at the acquisition time point. The following are the high-difficulty machining sections, labeled with "high difficulty". If the k-th cavity segment is at the acquisition time point If the real-time integrated processing difficulty coefficient is within the processing difficulty threshold range, then the k-th cavity segment is determined to be within the range of the acquisition time point. The following are the machining sections of medium difficulty, labeled with a medium difficulty level. If the k-th cavity segment is at the acquisition time point If the real-time comprehensive processing difficulty coefficient is less than the minimum value within the processing difficulty threshold range, then the k-th cavity segment is determined to be at the acquisition time point. The following are the low-difficulty machining sections, labeled as low-difficulty.

[0011] As a preferred embodiment of the electrochemical composite processing and collaborative control method for complex internal structures of pumps described in this invention, the k-th cavity segment is obtained at the acquisition time point. The difficulty label is set, and based on the difficulty label, the corresponding electrolyte is selected from the electrolyte database, and the k-th cavity is constructed at the acquisition time point. The following is a set of alternative electrolyte types; Based on the set of candidate electrolyte types, the dissolution rate and by-product generation amount of each electrolyte are retrieved. Based on the dissolution rate and by-product generation amount, the deviation between the m-th electrolyte and the k-th cavity is calculated using the following formula: ,in, This represents the deviation between the m-th electrolyte and the k-th cavity segment. This represents the dissolution rate of the m-th electrolyte. This represents the amount of byproducts generated by the m-th electrolyte. This represents the preset factor influencing the amount of by-products generated. This indicates that the preset dissolution rate matches the target value.

[0012] As a preferred embodiment of the electrochemical composite processing collaborative control method for complex internal cavity structures of pumps described in this invention, the deviation between all electrolytes and the k-th cavity segment is obtained. If the deviation between the m-th electrolyte and the k-th cavity segment... To minimize the deviation, the m-th electrolyte is used as the sample collection time point for the k-th chamber segment. Preferred electrolyte type for processing; Based on the k-th cavity segment at the acquisition time point Real-time integrated processing difficulty coefficient The dissolution rate of the m-th electrolyte The amount of byproducts generated by the m-th electrolyte The initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate are calculated using the following formulas: ; in, , and These represent the k-th cavity segment at the acquisition time point. The initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate are used. , and These represent the reference current, reference voltage, and reference current velocity, respectively. and These represent the average values ​​of dissolution rate and byproduct formation in the electrolyte database, respectively. , and These represent the preset adjustment coefficients.

[0013] As a preferred embodiment of the electrochemical composite processing collaborative control method for complex internal structures of pumps described in this invention, the data acquisition time points are... The processing current signal data and inter-electrode voltage signal data of the k-th cavity segment collected below are compared with those of the k-th cavity segment at the acquisition time point. The initial processing current and initial inter-electrode voltage are compared, and the relative deviations are calculated. The relative deviations of current and voltage are denoted as follows: and ; Based on the patency benchmark interval of the k-th cavity segment and collection time point The flow state characterization value of the kth cavity segment Configure the collaborative control rules as follows: like ,and If the current deviation exceeds the preset threshold, the data acquisition time point is determined. If the flow in the kth cavity is obstructed and the current is too high, then reduce the processing current and increase the electrolyte flow rate. like ,and If the current deviation is less than the preset threshold, the data acquisition time point is determined. If the flow in the kth cavity is obstructed, increase the electrolyte flow rate. like ,and If the voltage deviation exceeds the preset threshold, the data acquisition time point is determined. If the flow rate in the kth cavity is too fast and the voltage is too high, then the voltage and electrolyte flow rate should be reduced. like ,and If the voltage deviation is less than the preset threshold, then the data acquisition time point is determined. If the flow rate in the kth cavity is fast, then the electrolyte flow rate should be reduced. The system acquires processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data at each acquisition time point in real time, updates the flow status characterization value and the slackness benchmark interval table in real time, and performs dynamic management and control.

[0014] This system is designed for a collaborative control system for electrochemical composite processing of complex internal structures of pump bodies. It includes: a data acquisition and interval construction module, an index and coefficient calculation module, a difficulty coefficient calculation and label attachment module, an electrolyte selection and deviation calculation module, and an initial data calculation and rule setting module. The data acquisition and interval construction module: acquires the processing area of ​​the pump body, divides the processing area into several cavity segments based on the three-dimensional CAD model of the pump body, and acquires processing current signal data, inter-electrode voltage signal data and cavity back pressure signal data; calculates the flow state characterization value of the cavity segment, and sets the smoothness benchmark interval; The index and coefficient calculation module: obtains the inner wall area and inner wall surface of the cavity segment, calculates the curvature severity index and the spatial contraction index, normalizes and weights the sum to obtain the static geometric complexity coefficient of the cavity segment; The difficulty coefficient calculation and labeling module calculates the dynamic flow deterioration degree of the cavity segment based on the flow state characterization value; calculates the real-time comprehensive processing difficulty coefficient of the cavity segment based on the static geometric complexity coefficient and the dynamic flow deterioration degree; presets the processing difficulty threshold range and adds difficulty labels to the cavity segment. The electrolyte selection and deviation calculation module: Based on the difficulty label, selects the corresponding electrolyte from the electrolyte database, constructs a set of candidate electrolyte types for the cavity segment, retrieves the dissolution rate and by-product generation amount of each electrolyte, and calculates the deviation between the electrolyte and the cavity segment. The initial data calculation and rule setting module: selects the electrolyte with the smallest deviation as the preferred processing electrolyte type for the cavity segment; calculates the initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate; compares the processing current signal data and inter-electrode voltage signal data of the cavity segment with the initial processing current and initial inter-electrode voltage to calculate the relative deviation; and sets collaborative control rules.

[0015] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The electrochemical composite processing collaborative control method for complex internal structures of pump bodies provided by this invention divides the processing area into multiple cavity segments based on a three-dimensional CAD model and collects processing current, inter-electrode voltage, and cavity back pressure signals within a preset time series. This constructs flow state characterization values ​​and generates a flowability benchmark interval for each cavity segment, thereby achieving segmented and time-sequential characterization of the processing state and providing a reliable reference for subsequent judgment of flow anomalies. Subsequently, by extracting the curvature changes and cross-sectional contraction characteristics of the cavity segments, the curvature severity index and spatial contraction degree index are calculated and weighted to obtain a static geometric complexity coefficient, enabling the quantitative identification of difficult-to-process areas at the structural level. Furthermore, the static... By combining geometric complexity with real-time flow state deviation, a dynamic-static integrated real-time comprehensive processing difficulty coefficient is formed. Based on a difficulty threshold, high, medium, and low difficulty labels are attached to cavity segments to achieve a categorized expression of processing risks. Based on the difficulty labels, corresponding electrolyte types are matched from an electrolyte database. The deviation between the electrolyte and the cavity segment is calculated by combining the dissolution rate and by-product generation, constructing a suitability ranking, transforming electrolyte selection from empirical to degree-matching. Finally, the electrolyte with the smallest deviation is selected as the preferred type for that cavity segment. The initial processing current, voltage, and flow rate are calculated based on its characteristics, and the relative deviation is compared with the real-time acquired signals to form a collaborative control rule, enabling the processing process to adaptively adjust under multi-parameter coupling. Through the above-mentioned multi-dimensional linkage mechanism of structure-flow-electrochemistry, this method can achieve more refined state identification, more accurate electrolyte matching, and more stable collaborative control in complex internal cavity environments, ultimately improving processing uniformity, reducing local abnormal corrosion, and significantly improving overall processing stability and efficiency. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0017] Figure 1 This is a schematic diagram of the steps of the electrochemical composite processing and collaborative control method for complex internal cavity structures of pumps according to the present invention; Figure 2 This is a schematic diagram of the electrochemical composite processing collaborative control system for complex internal structures of pump bodies, as described in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 In this first embodiment: a method for coordinated control of electrochemical composite processing for complex internal structures of pump bodies is provided, which includes the following steps: Step S1: Obtain the processing area of ​​the pump body. Based on the three-dimensional CAD model of the pump body, divide the processing area into several cavity segments and collect processing current signal data, inter-electrode voltage signal data and cavity back pressure signal data; calculate the flow state characterization value of the cavity segment and set the smoothness benchmark interval.

[0020] Specifically, the processing area of ​​the pump body is acquired, and a preset time acquisition sequence is set, denoted as... ,in, This represents the t-th acquisition time point, where T represents the total number of acquisition time points. At each acquisition time point, the processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data of the processing area of ​​the pump body are acquired sequentially. The acquisition time points are then recorded. The processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data collected below are denoted as follows: , and ; A 3D CAD model of the pump body was obtained from the pump body design database. Based on this 3D CAD model, the processing area of ​​the pump body was divided into several cavity segments. The data acquisition time points were... The processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data of the k-th cavity segment acquired below are denoted as follows: , and .

[0021] Furthermore, based on the data collection time point The processing current signal data of the kth cavity segment collected below Inter-electrode voltage signal data and intracavitary back pressure signal data The flow state characterization value of the k-th cavity segment is calculated using the following formula: ; in, Indicates the time point of data collection The flow state characterization value of the kth cavity segment; For example, assuming the data collection time point The first cavity segment of the data was collected. Inter-electrode voltage signal data and intracavitary back pressure signal data Given 105A, 11.5V, and 150000Pa respectively, substituting these values ​​into the calculation, we can obtain the flow state characterization values ​​for the first cavity segment. Among them, the circulation status characterization value The unit is This measures the intracavity flow resistance generated per unit input electrical power. The product of current (A) and voltage (V) (W) represents the energy input rate of electrochemical processing, and the back pressure (Pa) represents the degree of resistance encountered by the electrolyte during intracavity flow. If the flow state characterization value... A lower value indicates less flow resistance per unit of electrical power, suggesting smooth electrolyte flow and efficient energy utilization in processing; if the flow state characterization value A higher value means that a unit of electrical power generates greater flow resistance, indicating that the flow is obstructed (such as blockage or eddies), and a large amount of energy is consumed in overcoming the flow resistance, resulting in reduced processing efficiency.

[0022] It should be noted that the quantification of cavity flow status based on the coupling relationship of current, voltage, and back pressure is crucial. During processing, the smoothness of electrolyte flow directly affects the electrochemical reaction efficiency: current and voltage reflect the energy input of electrochemical processing, while back pressure reflects the flow resistance of the electrolyte within the cavity. The ratio of these three directly reflects the matching degree between energy input and flow resistance. In practical scenarios, if electrolyte flow is obstructed in a cavity due to its narrow structure, the back pressure will increase, while the current may decrease due to impaired ion transport, ultimately leading to... Increased capacity allows for more accurate identification of circulation disruptions.

[0023] By coupling the three core signals of processing current (energy input), inter-electrode voltage (reaction intensity), and cavity back pressure (flow resistance) into a single characterization value, the confusion of multi-parameter scattered analysis is avoided, and the smoothness of flow is transformed from a qualitative judgment to a quantitative value. In actual processing, abnormalities can be quickly identified directly by the magnitude of the value.

[0024] The efficiency and stability of electrochemical machining are highly dependent on electrolyte flow—increased back pressure hinders ion transport, while the current / voltage ratio reflects the intensity of the reaction. The ratio of these three factors precisely matches the coupling law of flow-reaction, such as in deep cavity machining. A sudden increase can be directly identified as electrolyte blockage, which is more comprehensive than simply monitoring back pressure.

[0025] Obtain the flow state characterization values ​​of the k-th cavity segment at all acquisition time points, construct the flow state characterization value sequence of the k-th cavity segment, and calculate the mean and standard deviation of the flow state characterization value sequence, denoted as . and ; Set the patency baseline interval for the k-th cavity segment, denoted as . ,in, This represents the preset standard deviation influence factor; the patency benchmark interval for each cavity segment is obtained, and a patency benchmark interval table is constructed.

[0026] For example, suppose the mean of the sequence of flow state characterization values ​​is... Standard deviation of the circulation state characterization value sequence Standard deviation influence factor If the value is 1.5, then substituting it into the formula, we can find that the baseline range for smooth traffic flow is... .

[0027] In this invention, a flow state characterization value is obtained by combining multi-dimensional signals, which enables the characterization of whether the fluid flow in the cavity is smooth, whether there is blockage or local pressure abnormality, so that the processing process no longer relies on a single indicator to judge the flow status; a flow rate benchmark interval is constructed by using the mean and standard deviation of the time series, which realizes the statistical and adaptive reference of the normal state, rather than relying on an ideal model, so that the subsequent flow deterioration detection is objective; and a flow rate benchmark interval table is constructed by cavity segment, which provides an independent benchmark for subsequent personalized difficulty analysis and process selection, avoiding the one-size-fits-all treatment of complex cavities.

[0028] Step S2: Obtain the inner wall area and inner wall surface of the cavity segment, calculate the curvature intensity index and spatial contraction index, normalize and weighted sum to obtain the static geometric complexity coefficient of the cavity segment.

[0029] Specifically, based on the 3D CAD model of the pump body, the inner wall area and inner wall surface of the k-th cavity segment are obtained, denoted as follows: and ; Calculate the curvature intensity index of the k-th cavity segment using the following formula: ; in, The index represents the curvature intensity of the k-th cavity segment, and p represents the inner wall surface curvature of the k-th cavity segment. The p-th point on, Indicates the inner wall curved surface The average curvature at the p-th point on the curve; For example, suppose the inner wall area of ​​the first cavity segment is... If the integral value of the surface curvature is 450, then the curvature intensity index of the first cavity segment is... .

[0030] It should be noted that the degree of unevenness of the cavity wall is quantified by the integral mean of the surface's average curvature. Average curvature This reflects the degree of curvature at a point on the surface; the absolute value is integrated and then divided by the area of ​​the inner wall. This formula yields the average curvature intensity of the entire cavity segment. It closely reflects the actual structure of the pump's internal cavity, particularly in areas with abrupt changes in curvature and corners within the pump's flow channel, where the absolute value of the average curvature is large. The value will increase significantly, accurately marking geometrically difficult-to-machine areas.

[0031] By integrating the absolute value of the average curvature of all points on the inner wall surface and then dividing by the inner wall area, the severity of the average curvature over the entire domain is obtained, rather than focusing only on local inflection points, such as the abrupt change zone of the continuous surface of the pump body flow channel. Traditional methods may miss some difficult points, but this formula can cover them all.

[0032] The abrupt changes in curvature and corner areas of the complex internal cavity of the pump body are the most difficult parts to ensure machining accuracy, with an average curvature of... It directly reflects the difficulty of surface processing (the greater the curvature, the more difficult it is for the electrode to fit with the inner wall). The formula quantifies this difficulty through integral average, providing a precise basis for subsequent differentiated control.

[0033] The spatial contraction index of the k-th cavity segment is calculated using the following formula: ; in, This represents the spatial contraction index of the k-th cavity segment. This represents the inlet cross-sectional area of ​​the k-th cavity segment. This represents the exit cross-sectional area of ​​the k-th cavity segment. Represents the cross-sectional area gradient. This represents the average cross-sectional area along the friction path of the k-th cavity segment; For example, suppose the inlet cross-sectional area of ​​the first cavity segment is... Export cross-sectional area , , Substituting into the formula, we can find that... .

[0034] It should be noted that the spatial contraction characteristics of the cavity section are quantified from two dimensions: the difference in inlet and outlet cross-sectional areas and the rate of change of cross-sectional area along the process. The former reflects the degree of abrupt change in cross-sectional area by the ratio of the difference between the inlet and outlet cross-sectional areas to the minimum value; the latter reflects the smoothness of contraction / expansion along the process by the ratio of the cross-sectional area gradient to the average cross-sectional area. The maximum value of the two is taken as the final index. In actual processing, cavities with large differences in inlet and outlet cross-sectional areas (such as conical cavities) or abrupt changes in cross-sectional area along the process (such as stepped holes) are prone to electrolyte eddies, resulting in poor processing uniformity. The difficulty of this type of spatial structure can be accurately quantified.

[0035] To address the two common shrinkage issues in pump internal cavities—mismatch between inlet and outlet cross-sectional areas and gradual changes in cross-sectional area along the flow path—two sub-formulas are used to quantify the issues separately, and then the maximum value is taken to ensure that the most severe shrinkage problems are identified first (for example, if the difference between the inlet and outlet of a certain cavity section is small but the shrinkage along the flow path is severe, it can still be marked as high shrinkage).

[0036] Curvature intensity index of the k-th cavity segment and spatial contraction index After normalization and weighted summation, the static geometric complexity coefficient of the k-th cavity segment is obtained, denoted as . .

[0037] Step S3: Based on the flow state characterization value, calculate the dynamic flow deterioration degree of the cavity segment; based on the static geometric complexity coefficient and the dynamic flow deterioration degree, calculate the real-time comprehensive processing difficulty coefficient of the cavity segment; preset the processing difficulty threshold range, and add a difficulty label to the cavity segment.

[0038] Specifically, based on the data collection time point The flow state characterization value of the kth cavity segment Calculate the data collection time point The dynamic flow deterioration of the k-th cavity segment is calculated using the following formula: ,in, Indicates the time point of data collection The dynamic flow deterioration degree of the k-th cavity segment. This indicates the preset adjustment coefficient; It should be noted that the deviation between the real-time circulation status and the normal status is quantified based on statistical methods. It is the average of historical circulation status values ​​(the benchmark for normal circulation). It is the standard deviation (normal fluctuation range). This is the adjustment coefficient (sensitivity for adapting to different cavity segments). The numerator is the absolute deviation between the real-time value and the reference value, and the denominator is the allowable fluctuation range. The larger the ratio, the more severe the flow deterioration. This formula solves the problem of identifying dynamic fluctuations in actual processing: for example, changes in viscosity due to increased electrolyte temperature during processing, or byproduct accumulation clogging the flow channel, will all affect the flow state characterization value. Deviation from the mean This allows for the quantification of the degree of deterioration, preventing normal fluctuations from being misjudged as abnormalities.

[0039] Based on the static geometric complexity coefficient of the k-th cavity segment and the dynamic flow deterioration of the k-th cavity segment Calculate the time of acquisition for the k-th cavity segment. The real-time integrated processing difficulty coefficient is calculated using the following formula: ,in, This indicates that the k-th cavity segment was at the acquisition time point. The real-time integrated processing difficulty coefficient is as follows. This represents the preset coupling gain coefficient.

[0040] It should be noted that by coupling static geometric difficulty with dynamic flow difficulty, a comprehensive processing risk assessment value is obtained. It is the static geometric complexity coefficient (the inherent difficulty of the structure). It is the dynamic deterioration of circulation (the dynamic difficulty in the processing). It is the coupling gain coefficient (adjusting the weight of the dynamic difficulty on the overall difficulty), which achieves the organic integration of the two through "static value × (1 + dynamic influence factor)".

[0041] Furthermore, a preset processing difficulty threshold range is defined; if the k-th cavity segment is at the acquisition time point... If the real-time comprehensive processing difficulty coefficient is greater than the maximum value within the processing difficulty threshold range, then the k-th cavity segment is determined to be at the acquisition time point. The following are the high-difficulty machining sections, labeled with "high difficulty". If the k-th cavity segment is at the acquisition time point If the real-time integrated processing difficulty coefficient is within the processing difficulty threshold range, then the k-th cavity segment is determined to be within the range of the acquisition time point. The following are the machining sections of medium difficulty, labeled with a medium difficulty level. If the k-th cavity segment is at the acquisition time point If the real-time comprehensive processing difficulty coefficient is less than the minimum value within the processing difficulty threshold range, then the k-th cavity segment is determined to be at the acquisition time point. The following are the low-difficulty machining sections, labeled as low-difficulty.

[0042] Step S4: Based on the difficulty label, select the corresponding electrolyte from the electrolyte database, construct a set of candidate electrolyte types for the cavity segment, retrieve the dissolution rate and by-product generation amount of each electrolyte, and calculate the deviation between the electrolyte and the cavity segment.

[0043] Specifically, the k-th cavity segment is obtained at the acquisition time point. The difficulty label is set, and based on the difficulty label, the corresponding electrolyte is selected from the electrolyte database, and the k-th cavity is constructed at the acquisition time point. The set of candidate electrolyte types is as follows: if the difficulty label is high, electrolytes with a dissolution rate greater than or equal to the preset high difficulty dissolution rate threshold, high conductivity, and good thermal stability are selected; if the difficulty label is medium, electrolytes with a dissolution rate in the preset medium difficulty dissolution rate range, low by-product generation, and good rheological properties are selected; if the difficulty label is low, electrolytes with by-product generation less than or equal to the preset low difficulty by-product threshold, weak corrosivity, and low viscosity are selected. Based on the set of candidate electrolyte types, the dissolution rate and by-product generation amount of each electrolyte are retrieved. Based on the dissolution rate and by-product generation amount, the deviation between the m-th electrolyte and the k-th cavity is calculated using the following formula: ,in, This represents the deviation between the m-th electrolyte and the k-th cavity segment. This represents the dissolution rate of the m-th electrolyte. This represents the amount of byproducts generated by the m-th electrolyte. This represents the preset factor influencing the amount of by-products generated. This indicates that the preset dissolution rate matches the target value.

[0044] For example, assuming the dissolution rate of the first electrolyte... Byproduct generation , , , , , , ,but: ; The second solution will be used as the first chamber segment at the collection time point. The preferred electrolyte type for processing.

[0045] It should be noted that the key to quantifying the compatibility between the electrolyte and the cavity is matching the dissolution rate and controlling by-products. It is the dissolution rate of the electrolyte. It is the average value of the flow state of the cavity segment (indirectly reflecting the range of dissolution rate required for processing), and the absolute value of the difference between the two reflects the degree of matching of the dissolution rate; It is the amount of electrolyte byproducts generated. This is the byproduct impact factor (emphasizing the weight of byproduct interference with flow); the smaller the sum of the two, the better the electrolyte compatibility. In actual processing, high-difficulty cavities (such as areas with abrupt curvature changes) require electrolytes with moderate dissolution rates (to avoid excessive corrosion or insufficient processing), and the amount of byproducts generated should be low (to prevent blockage of flow channels). This formula can screen out the optimal solution with "suitable dissolution rate and few byproducts" from a large number of electrolytes, replacing traditional empirical selection.

[0046] Step S5: Select the electrolyte with the smallest deviation as the preferred processing electrolyte type for the cavity segment; calculate the initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate; compare the processing current signal data and inter-electrode voltage signal data of the cavity segment with the initial processing current and initial inter-electrode voltage to calculate the relative deviation; set the collaborative control rules.

[0047] Specifically, obtain the deviation between all electrolytes and the k-th cavity segment. If the deviation between the m-th electrolyte and the k-th cavity segment... To minimize the deviation, the m-th electrolyte is used as the sample collection time point for the k-th chamber segment. Preferred electrolyte type for processing; Based on the k-th cavity segment at the acquisition time point Real-time integrated processing difficulty coefficient The dissolution rate of the m-th electrolyte The amount of byproducts generated by the m-th electrolyte The initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate are calculated using the following formulas: ; in, , and These represent the k-th cavity segment at the acquisition time point. The initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate are used. , and These represent the reference current, reference voltage, and reference current velocity, respectively. and These represent the average values ​​of dissolution rate and byproduct formation in the electrolyte database, respectively. , and These represent the preset adjustment coefficients.

[0048] For example, suppose , , , , , , , , Substituting into the formula, we can see that: ; It should be noted that the current is linked to the overall processing difficulty: the higher the difficulty ( (For larger quantities), the current needs to be increased appropriately to ensure processing efficiency. It is the current adjustment coefficient; the voltage is linked to the electrolyte dissolution rate: the higher the dissolution rate ( (For large areas), the voltage needs to be adjusted appropriately to control the etching precision. It is the voltage adjustment coefficient; the flow rate is linked to the amount of electrolyte byproducts generated: the more byproducts ( (For larger quantities), the flow rate needs to be increased to remove byproducts. It is the flow rate adjustment coefficient; Furthermore, the collection time point The processing current signal data and inter-electrode voltage signal data of the k-th cavity segment collected below are compared with those of the k-th cavity segment at the acquisition time point. The initial processing current and initial inter-electrode voltage are compared, and the relative deviations are calculated. The relative deviations of current and voltage are denoted as follows: and ; Based on the patency benchmark interval of the k-th cavity segment and collection time point The flow state characterization value of the kth cavity segment Configure the collaborative control rules as follows: like ,and If the current deviation exceeds the preset threshold, the data acquisition time point is determined. If the flow in the kth cavity is obstructed and the current is too high, then reduce the processing current and increase the electrolyte flow rate. like ,and If the current deviation is less than the preset threshold, the data acquisition time point is determined. If the flow in the kth cavity is obstructed, increase the electrolyte flow rate. like ,and If the voltage deviation exceeds the preset threshold, the data acquisition time point is determined. If the flow rate in the kth cavity is too fast and the voltage is too high, then the voltage and electrolyte flow rate should be reduced. like ,and If the voltage deviation is less than the preset threshold, then the data acquisition time point is determined. If the flow rate in the kth cavity is fast, then the electrolyte flow rate should be reduced. The system acquires processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data at each acquisition time point in real time, updates the flow status characterization value and the slackness benchmark interval table in real time, and performs dynamic management and control.

[0049] In this embodiment, eight preset factors are involved, including: a preset standard deviation influence factor. Preset adjustment coefficient Preset coupling gain coefficient Preset factors affecting the amount of by-products generated Preset adjustment coefficient , and Preset dissolution rate matching target value ,in: Standard deviation influence factor The width used to define the "smoothness reference range", that is, the allowable fluctuation range of normal flow state, is usually based on the statistical analysis of historical processing data, such as the fluctuation of the flow state characterization value of similar cavity sections in the past when processing is stable. It can also be set according to the process tolerance. For example, in precision processing, a smaller value (such as 1.0 to 1.5) can be taken, while in general processing, it can be appropriately relaxed (such as 2.0 to 3.0). Adjustment coefficient Adjusting the weight of the standard deviation in the formula for calculating dynamic flow deterioration affects the sensitivity to deviations in flow conditions. This sensitivity is related to the geometric structure of the cavity segment, such as segments with high curvature and drastic cross-sectional changes. The value can be appropriately reduced to enhance the response to fluctuations. This can be observed through simulation or experimentation. To optimize the accuracy and response speed of abnormal flow state detection, a value that ensures both sensitivity and stability of the system is selected. Coupling gain coefficient Control the degree of influence of dynamic flow deterioration on the overall processing difficulty coefficient, and achieve the coupling of static geometric difficulty and dynamic flow state. If the dynamic flow state has a significant impact on processing quality (such as easily blocked areas). Take the larger value; if the geometric structure itself is the main challenge, then... The weights can be appropriately reduced, and the impact weights of the two factors on the processing results can be assessed based on historical processing data or expert experience. Factors affecting byproduct formation The weight of by-product generation in the deviation calculation should be controlled; if the cavity is prone to blockage or the by-products have a significant impact on surface quality, the weight should be increased. If there are few or easily discharged electrolyte byproducts, the dosage should be appropriately reduced. By experimentally testing the accumulation of byproducts in typical cavity sections with different electrolytes, and combining this with processing quality data, the determination can be made. The range of values ​​for; Adjustment coefficient , and The degree to which current, voltage, and flow rate are affected by processing difficulty, dissolution rate, and by-product formation in the initial parameter calculations are controlled, respectively. It is related to material removal rate and processing efficiency, and is usually based on electrochemical processing theory (such as Faraday's law) and experimental calibration; It is related to the electrolyte conductivity and inter-electrode gap, and needs to be combined with the voltage-dissolution rate relationship curve; Related to flow field characteristics and by-product discharge capacity, it is often based on fluid dynamics simulation or experimental data. In short, the processing effect under different parameter combinations can be systematically tested through orthogonal experiments, response surface methodology, and other methods, and the optimized values ​​of each coefficient can be obtained through regression analysis. Dissolution rate matches target value The target dissolution rate can be set directly based on the difficulty label (high, medium, low) of the cavity segment. Through processing experiments, the processing effect under various dissolution rates can be tested on cavities with different difficulty characteristics. The evaluation indicators include surface roughness, dimensional accuracy, processing time, etc.

[0050] Please see Figure 2In this second embodiment: an electrochemical composite processing collaborative control system for complex internal cavity structures of pump bodies is provided. The system includes: a data acquisition and interval construction module, an index and coefficient calculation module, a difficulty coefficient calculation and label attachment module, an electrolyte selection and deviation calculation module, and an initial data calculation and rule setting module. The data acquisition and interval construction module: acquires the processing area of ​​the pump body, divides the processing area into several cavity segments based on the three-dimensional CAD model of the pump body, and acquires processing current signal data, inter-electrode voltage signal data and cavity back pressure signal data; calculates the flow state characterization value of the cavity segment, and sets the smoothness benchmark interval; The index and coefficient calculation module: obtains the inner wall area and inner wall surface of the cavity segment, calculates the curvature severity index and the spatial contraction index, normalizes and weights the sum to obtain the static geometric complexity coefficient of the cavity segment; The difficulty coefficient calculation and labeling module calculates the dynamic flow deterioration degree of the cavity segment based on the flow state characterization value; calculates the real-time comprehensive processing difficulty coefficient of the cavity segment based on the static geometric complexity coefficient and the dynamic flow deterioration degree; presets the processing difficulty threshold range and adds difficulty labels to the cavity segment. The electrolyte selection and deviation calculation module: Based on the difficulty label, selects the corresponding electrolyte from the electrolyte database, constructs a set of candidate electrolyte types for the cavity segment, retrieves the dissolution rate and by-product generation amount of each electrolyte, and calculates the deviation between the electrolyte and the cavity segment. The initial data calculation and rule setting module: selects the electrolyte with the smallest deviation as the preferred processing electrolyte type for the cavity segment; calculates the initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate; compares the processing current signal data and inter-electrode voltage signal data of the cavity segment with the initial processing current and initial inter-electrode voltage to calculate the relative deviation; and sets collaborative control rules.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0052] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for coordinated control of electrochemical composite processing for complex internal structures of pump bodies, characterized in that, The method includes the following steps: Step S1: Obtain the processing area of ​​the pump body. Based on the three-dimensional CAD model of the pump body, divide the processing area into several cavity segments and collect processing current signal data, inter-electrode voltage signal data and cavity back pressure signal data; calculate the flow state characterization value of the cavity segment and set the smoothness benchmark interval. Step S2: Obtain the inner wall area and inner wall surface of the cavity segment, calculate the curvature severity index and spatial contraction index, normalize and weighted sum to obtain the static geometric complexity coefficient of the cavity segment; Step S3: Based on the flow state characterization value, calculate the dynamic flow deterioration degree of the cavity segment; based on the static geometric complexity coefficient and the dynamic flow deterioration degree, calculate the real-time comprehensive processing difficulty coefficient of the cavity segment; preset the processing difficulty threshold range and add a difficulty label to the cavity segment; Step S4: Based on the difficulty tag, select the corresponding electrolyte from the electrolyte database, construct a set of candidate electrolyte types for the cavity segment, retrieve the dissolution rate and by-product generation amount of each electrolyte, and calculate the deviation between the electrolyte and the cavity segment. Step S5: Select the electrolyte with the smallest deviation as the preferred processing electrolyte type for the cavity segment; calculate the initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate; compare the processing current signal data and inter-electrode voltage signal data of the cavity segment with the initial processing current and initial inter-electrode voltage to calculate the relative deviation; set the collaborative control rules.

2. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 1, characterized in that, The specific implementation process of step S1 includes: Acquire the processing area of ​​the pump body, preset the time acquisition sequence, and denot it as... ,in, This represents the t-th acquisition time point, where T represents the total number of acquisition time points. At each acquisition time point, the processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data of the processing area of ​​the pump body are acquired sequentially. The acquisition time points are then recorded. The processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data acquired below are denoted as follows: , and ; A 3D CAD model of the pump body was obtained from the pump body design database. Based on this 3D CAD model, the processing area of ​​the pump body was divided into several cavity segments. The data acquisition time points were... The processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data of the k-th cavity segment acquired below are denoted as follows: , and .

3. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 2, characterized in that, The specific implementation process of step S1 also includes: Based on the data collection time point The processing current signal data of the kth cavity segment acquired below Inter-electrode voltage signal data and intracavitary back pressure signal data The flow state characterization value of the k-th cavity segment is calculated using the following formula: ； in, Indicates the time point of data collection The flow state characterization value of the kth cavity segment; Obtain the flow state characterization values ​​of the k-th cavity segment at all acquisition time points, construct the flow state characterization value sequence of the k-th cavity segment, and calculate the mean and standard deviation of the flow state characterization value sequence, denoted as . and ; Set the patency baseline interval for the k-th cavity segment, denoted as . ,in, This represents the preset standard deviation influence factor; the patency benchmark interval for each cavity segment is obtained, and a patency benchmark interval table is constructed.

4. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 3, characterized in that, The specific implementation process of step S2 includes: Based on the 3D CAD model of the pump body, the inner wall area and inner wall surface of the k-th cavity segment are obtained, denoted as […]. and ; Calculate the curvature intensity index of the k-th cavity segment using the following formula: ； in, The index represents the curvature intensity of the k-th cavity segment, and p represents the inner wall surface curvature of the k-th cavity segment. The p-th point on, Indicates the inner wall curved surface The average curvature at the p-th point on the curve; The spatial contraction index of the k-th cavity segment is calculated using the following formula: ； in, This represents the spatial contraction index of the k-th cavity segment. This represents the inlet cross-sectional area of ​​the k-th cavity segment. This represents the exit cross-sectional area of ​​the k-th cavity segment. Represents the cross-sectional area gradient. This represents the average cross-sectional area along the friction path of the k-th cavity segment; Curvature intensity index of the k-th cavity segment and spatial contraction index After normalization and weighted summation, the static geometric complexity coefficient of the k-th cavity segment is obtained, denoted as . .

5. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 4, characterized in that, The specific implementation process of step S3 includes: Based on the data collection time point The flow state characterization value of the kth cavity segment Calculate the data collection time point The dynamic flow deterioration of the k-th cavity segment is calculated using the following formula: ,in, Indicates the time point of data collection The dynamic flow deterioration degree of the k-th cavity segment. This indicates the preset adjustment coefficient; Based on the static geometric complexity coefficient of the k-th cavity segment and the dynamic flow deterioration of the k-th cavity segment Calculate the time of acquisition for the k-th cavity segment. The real-time integrated processing difficulty coefficient is calculated using the following formula: ,in, This indicates that the k-th cavity segment was at the acquisition time point. The real-time integrated processing difficulty coefficient is as follows. This represents the preset coupling gain coefficient.

6. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 5, characterized in that, The specific implementation process of step S3 also includes: The preset processing difficulty threshold range, if the k-th cavity segment is at the acquisition time point If the real-time comprehensive processing difficulty coefficient is greater than the maximum value within the processing difficulty threshold range, then the k-th cavity segment is determined to be at the acquisition time point. The following are the high-difficulty machining sections, labeled with "high difficulty". If the k-th cavity segment is at the acquisition time point If the real-time integrated processing difficulty coefficient is within the processing difficulty threshold range, then the k-th cavity segment is determined to be within the range of the acquisition time point. The following are the machining sections of medium difficulty, labeled with a medium difficulty level. If the k-th cavity segment is at the acquisition time point If the real-time comprehensive processing difficulty coefficient is less than the minimum value within the processing difficulty threshold range, then the k-th cavity segment is determined to be at the acquisition time point. The following are the low-difficulty machining sections, labeled as low-difficulty.

7. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 6, characterized in that, The specific implementation process of step S4 includes: Obtain the k-th cavity segment at the acquisition time point The difficulty label is set, and based on the difficulty label, the corresponding electrolyte is selected from the electrolyte database, and the k-th cavity is constructed at the acquisition time point. The following is a set of alternative electrolyte types; Based on the set of candidate electrolyte types, the dissolution rate and by-product generation amount of each electrolyte are retrieved. Based on the dissolution rate and by-product generation amount, the deviation between the m-th electrolyte and the k-th cavity is calculated using the following formula: ,in, This represents the deviation between the m-th electrolyte and the k-th cavity segment. This represents the dissolution rate of the m-th electrolyte. This represents the amount of byproducts generated by the m-th electrolyte. This represents the preset factor influencing the amount of by-products generated. This indicates that the preset dissolution rate matches the target value.

8. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 7, characterized in that, The specific implementation process of step S5 includes: Obtain the deviation of all electrolytes from the k-th cavity segment. If the deviation of the m-th electrolyte from the k-th cavity segment... To minimize the deviation, the m-th electrolyte is used as the sample collection time point for the k-th chamber segment. Preferred electrolyte type for processing; Based on the k-th cavity segment at the acquisition time point Real-time integrated processing difficulty coefficient The dissolution rate of the m-th electrolyte The amount of byproducts generated by the m-th electrolyte The initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate are calculated using the following formulas: ； in, , and These represent the k-th cavity segment at the acquisition time point. The initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate are used. , and These represent the reference current, reference voltage, and reference current velocity, respectively. and These represent the average values ​​of dissolution rate and byproduct formation in the electrolyte database, respectively. , and These represent the preset adjustment coefficients.

9. The electrochemical composite machining collaborative control method for complex internal cavity structures of pump bodies according to claim 8, characterized in that, The specific implementation process of step S5 also includes: Collection time point The processing current signal data and inter-electrode voltage signal data of the k-th cavity segment collected below are compared with those of the k-th cavity segment at the acquisition time point. The initial processing current and initial inter-electrode voltage are compared, and the relative deviations are calculated. The relative deviations of current and voltage are denoted as follows: and ; Based on the patency benchmark interval of the k-th cavity segment and collection time point The flow state characterization value of the kth cavity segment Configure the collaborative control rules as follows: like ,and If the current deviation exceeds the preset threshold, the data acquisition time point is determined. If the flow in the kth cavity is obstructed and the current is too high, then reduce the processing current and increase the electrolyte flow rate. like ,and If the current deviation is less than the preset threshold, the data acquisition time point is determined. If the flow in the kth cavity is obstructed, increase the electrolyte flow rate. like ,and If the voltage deviation exceeds the preset threshold, the data acquisition time point is determined. If the flow rate in the kth cavity is too fast and the voltage is too high, then the voltage and electrolyte flow rate should be reduced. like ,and If the voltage deviation is less than the preset threshold, then the data acquisition time point is determined. If the flow rate in the kth cavity is fast, then the electrolyte flow rate should be reduced. The system acquires processing current signal data, inter-electrode voltage signal data, and intracavity back pressure signal data at each acquisition time point in real time, updates the flow status characterization value and the slackness benchmark interval table in real time, and performs dynamic management and control.

10. The collaborative control system of the electrochemical composite machining collaborative control method for complex internal cavity structures of pumps according to claim 1, characterized in that, The collaborative control system includes: a data acquisition and interval construction module, an index and coefficient calculation module, a difficulty coefficient calculation and label attachment module, an electrolyte selection and deviation calculation module, and an initial data calculation and rule setting module. The data acquisition and interval construction module: acquires the processing area of ​​the pump body, divides the processing area into several cavity segments based on the three-dimensional CAD model of the pump body, and acquires processing current signal data, inter-electrode voltage signal data and cavity back pressure signal data; calculates the flow state characterization value of the cavity segment, and sets the smoothness benchmark interval; The index and coefficient calculation module: obtains the inner wall area and inner wall surface of the cavity segment, calculates the curvature severity index and the spatial contraction index, normalizes and weights the sum to obtain the static geometric complexity coefficient of the cavity segment; The difficulty coefficient calculation and labeling module calculates the dynamic flow deterioration degree of the cavity segment based on the flow state characterization value; calculates the real-time comprehensive processing difficulty coefficient of the cavity segment based on the static geometric complexity coefficient and the dynamic flow deterioration degree; presets the processing difficulty threshold range and adds difficulty labels to the cavity segment. The electrolyte selection and deviation calculation module: Based on the difficulty label, selects the corresponding electrolyte from the electrolyte database, constructs a set of candidate electrolyte types for the cavity segment, retrieves the dissolution rate and by-product generation amount of each electrolyte, and calculates the deviation between the electrolyte and the cavity segment. The initial data calculation and rule setting module: selects the electrolyte with the smallest deviation as the preferred processing electrolyte type for the cavity segment; calculates the initial processing current, initial inter-electrode voltage, and initial electrolyte flow rate; compares the processing current signal data and inter-electrode voltage signal data of the cavity segment with the initial processing current and initial inter-electrode voltage to calculate the relative deviation; and sets collaborative control rules.

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