Electrode cap end face dressing and debris control method and system

Abstract

This invention discloses a method and system for controlling chip adhesion during electrode cap end face grinding, relating to the field of resistance welding technology. The method involves acquiring the end face defect characteristics of the electrode cap under each working condition; based on the end face defect characteristics under the current working condition, combined with the reference mean and covariance matrix of the end face defect characteristics under stable grinding conditions, obtaining the Mahalanobis distance under the current working condition; based on the Mahalanobis distance and key end face defect characteristics, constructing a chip adhesion risk index, and obtaining a chip adhesion risk value after standardization; based on the chip adhesion risk value, statistical analysis is performed to obtain the mean and variance of the chip adhesion risk value, introducing a risk fluctuation coefficient and a consistency jump index to construct a comprehensive stability index; a nonlinear coupling model of rotational speed and pressure is established, combined with the comprehensive stability index, and by constraining data fluctuations, a stable processing window is obtained. Then, within the stable processing window, the optimal efficiency parameter is selected, enabling precise control of the grinding process, reducing chip adhesion defects, and improving grinding quality.

Description

Technical Field

[0001] This invention relates to the field of resistance welding technology, and in particular to a method and system for controlling chip adhesion during electrode cap end face grinding. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Electrode caps are used to apply pressure and conduct electricity during resistance spot welding. Wear on their end faces can affect welding quality, thus requiring periodic re-grinding. However, during electrode cap re-grinding, defects such as adhered chips, broken chip marks, and roughness degradation are easily generated on the electrode cap end face. Moreover, the chip adhesion defect has obvious probabilistic and fluctuating characteristics. That is, repeated re-grinding under the same working conditions results in significant variations in the end face quality, affecting the re-grinding quality and the service life of the electrode cap.

[0004] Existing grinding methods mainly rely on experience to adjust grinding speed and pressure, but they have the following problems: they are highly experience-based, parameter adjustment depends on the operator's experience, and the grinding effect is unstable; they do not fully consider the interaction of factors such as speed and pressure; they lack scientific assessment and control of the risk of chip adhesion, which makes it impossible to accurately control the grinding process; and it is difficult to determine a continuous and stable processing window area, making it easy to enter a high-risk area and cause chip adhesion. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a method and system for controlling chip adhesion during electrode cap end face grinding. The aim is to quantitatively model the risk of chip adhesion on the electrode cap end face during grinding and to construct a speed-pressure-coupling relationship model to achieve a continuous expression of the stable processing window.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0007] In a first aspect, the present invention provides a method for controlling debris adhesion during the grinding of the electrode cap end face, comprising:

[0008] Obtain the end face defect characteristics of the electrode cap under each working condition;

[0009] Based on the end face defect characteristics under the current working conditions, and combined with the reference mean and covariance matrix of the end face defect characteristics under the stable grinding state, the Mahalanobis distance under the current working conditions is obtained; based on the Mahalanobis distance and key end face defect characteristics, a chip adhesion risk index is constructed, and the chip adhesion risk value is obtained after standardizing the scale.

[0010] Based on the debris risk value, statistical analysis was performed to obtain the mean and variance of the debris risk value. A risk fluctuation coefficient and a consistency jump index were introduced to construct a comprehensive stability index.

[0011] A nonlinear coupling model of rotational speed and pressure is established. By combining the comprehensive stability index and constraining data fluctuations, a stable processing window is obtained, and then the optimal efficiency parameter is selected within the stable processing window.

[0012] A further technical solution is that the end face defect features include the proportion of end face debris area, the number of debris connected regions, the proportion of chip breakage groove density or length, and the end face roughness index. The key end face defect features include the proportion of end face debris area and the proportion of chip breakage groove density or length.

[0013] A further technical solution is that the Mahalanobis distance is used to measure the statistical deviation between the current end-face defect characteristics and the ideal stable state, and is expressed as:

[0014]

[0015] in, The Mahalanobis distance, This is the feature vector of the end face defect. This serves as a reference average for end-face defect characteristics under stable grinding conditions. The covariance matrix under stable grinding conditions, This is a transpose.

[0016] A further technical solution is that the debris risk index is expressed as:

[0017]

[0018] in, As the risk index for adhering debris, , , These are the weighting coefficients. The Mahalanobis distance, This represents the normalized percentage of the end-face debris area. This refers to the normalized density or length ratio of the chip fracture grooves.

[0019] A further technical solution is that the comprehensive stability index is expressed as:

[0020]

[0021] in, As a comprehensive stability index, , , These are the weighting coefficients. This represents the mean of the debris risk values. This is the risk volatility coefficient. This is a consistent jump indicator.

[0022] A further technical solution is to set dual thresholds based on the comprehensive stability index to classify the working conditions into different levels: when the comprehensive stability index is less than or equal to the stability threshold, it is determined to be a stable processing condition; when the comprehensive stability index is greater than the stability threshold but less than or equal to the risk threshold, it is determined to be a transitional working condition; and when the comprehensive stability index is greater than the risk threshold, it is determined to be a chip-adhesion risk working condition.

[0023] A further technical solution is that the stable processing window is a set of parameters that satisfy the comprehensive stability threshold and limit the risk volatility coefficient, expressed as:

[0024]

[0025] in, To stabilize the processing window, For grinding speed, For grinding pressure, As a comprehensive stability index, To stabilize the threshold, This is the risk volatility coefficient. To preset the upper limit of fluctuation, As a risk threshold, It is a nonlinear coupling model of rotational speed and pressure.

[0026] Secondly, the present invention provides a control system for the grinding and chip adhesion of electrode cap end faces, comprising:

[0027] The data acquisition module is configured to acquire the end face defect features of the electrode cap under each working condition.

[0028] The risk modeling module is configured to: obtain the Mahalanobis distance under the current working conditions based on the end face defect characteristics under the current working conditions, combined with the reference mean and covariance matrix of the end face defect characteristics under the stable grinding state; construct the chip adhesion risk index based on the Mahalanobis distance and key end face defect characteristics, and obtain the chip adhesion risk value after unifying the scale.

[0029] The coupling analysis module is configured to: based on the debris risk value, statistically analyze the mean and variance of the debris risk value, introduce the risk fluctuation coefficient and the consistency jump index, and construct a comprehensive stability index;

[0030] The stability window determination module is configured to: establish a nonlinear coupling model of rotational speed and pressure, combine it with a comprehensive stability index, obtain a stable processing window by constraining data fluctuations, and then select the parameter with the optimal efficiency within the stable processing window.

[0031] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the electrode cap end face grinding chip control method as described in the first aspect.

[0032] Fourthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the electrode cap end face grinding chip control method as described in the first aspect.

[0033] The above one or more technical solutions have the following beneficial effects:

[0034] Existing processes mainly rely on experience to select rotation speed and pressure parameters, lacking quantitative assessment of chip adhesion risk and scientific definition of stable processing areas, as well as systematic modeling of the coupling relationship between rotation speed and pressure, making it difficult to determine a continuous and stable processing window area. This invention proposes an optimization method based on multi-factor coupling, which can accurately control the grinding process, reduce chip adhesion defects, and improve grinding quality.

[0035] This invention solves the problems of traditional methods relying on empirical parameter tuning and difficulty in quantifying chip adhesion risk by using multi-index fusion risk modeling, speed-pressure coupled response surface analysis, and fluctuation stability determination, thus achieving quantifiable control of chip adhesion during end-face grinding. By introducing a comprehensive stability index, stable processing areas can be accurately identified, and chip adhesion defects caused by process instability can be avoided.

[0036] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0037] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0038] Figure 1 This is a flowchart of a method for controlling chip adhesion during electrode cap end face grinding according to an embodiment of the present invention;

[0039] Figure 2 This is a diagram showing the experimental results of electrode cap grinding in an embodiment of the present invention;

[0040] Figure 3 These are before and after comparison images of the electrode cap before and after grinding defects according to an embodiment of the present invention, wherein (a) is a chip adhesion image at the center point and (b) is a surface finishing image. Detailed Implementation

[0041] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0043] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0044] Example 1

[0045] like Figure 1 As shown, this embodiment discloses a method for controlling chip adhesion during electrode cap end face grinding, which includes the following steps:

[0046] S1: Obtain the end face defect characteristics of the electrode cap under each working condition, and statistically analyze the mean value of the end face defect characteristics under each working condition;

[0047] In this embodiment, different grinding speeds are used. With grinding pressure Conduct cross-combination tests, with each set of operating conditions repeated at least 5 times, such as... Figure 2 As shown. For the first In this experiment, vectors were extracted from the end-face defect features. Used to quantitatively characterize the degree of debris adhesion and defects on the end face:

[0048]

[0049] in, For the first The grinding speed in this test With grinding pressure The percentage of the end face area with adhering debris under working conditions reflects the percentage of the area with adhering debris during the grinding process. For the first The grinding speed in this test With grinding pressure The number of connected regions of adhesive debris under operating conditions represents the density distribution of adhesive debris on the end face; For the first The grinding speed in this test With grinding pressure The density or length ratio of chip breakage grooves under working conditions reflects the characteristics of grooves formed due to grinding or material fracture; For the first The grinding speed in this test With grinding pressure The end face roughness index under working conditions indicates the surface smoothness after grinding.

[0050] Statistical analysis was performed on the repeated experimental data for each working condition, and the characteristic mean vector for that working condition was calculated to represent the average level of end-face defects under that parameter combination.

[0051]

[0052] in, Grinding speed With grinding pressure Mean value of end face defect characteristics under operating conditions This represents the number of tests conducted under this operating condition.

[0053] Through the aforementioned technical features, the subjective and vague concept of severe debris adhesion is transformed into a multi-dimensional objective indicator consisting of the proportion of debris area, the number of connected regions, the groove density, and the roughness. These four indicators characterize the end face state after grinding from four dimensions: the scale, distribution pattern, damage characteristics, and surface quality of the debris, providing rich and comprehensive information.

[0054] S2: Based on the end face defect characteristics under the current working conditions, combined with the reference mean and covariance matrix of the end face defect characteristics under the stable grinding state, the Mahalanobis distance under the current working conditions is obtained; based on the Mahalanobis distance and key end face defect characteristics, a chip adhesion risk index is constructed, and the chip adhesion risk value is obtained after unifying the scale.

[0055] In this embodiment, the covariance matrix is ​​calculated based on the end-face defect characteristics and the mean of the end-face defect characteristics to reflect the coupling and fluctuation relationship between various defect indicators and to reveal the correlation between different defect indicators under different operating conditions.

[0056]

[0057] in, Let be the covariance matrix.

[0058] To measure the deviation between the current operating condition and the stable processing state, the Mahalanobis distance is introduced. This distance is used to measure the statistical deviation between the current defect characteristics and the ideal stable state.

[0059]

[0060] in, The Mahalanobis distance, This serves as a reference average for end-face defect characteristics under stable grinding conditions. The covariance matrix under stable grinding conditions, This is a transpose.

[0061] By combining Mahalanobis distance with key defect indicators, a debris adhesion risk index is constructed. This is used to measure the risk of chip adhesion during the grinding process.

[0062]

[0063] in, , , These are weighting coefficients, summing to 1, determined by experimental data, and used to adjust the importance of different features in risk calculation. This represents the normalized percentage of the end-face debris area. This refers to the normalized density or length ratio of the chip fracture grooves.

[0064] By further configuring the compression function, the risk index is mapped to a uniform scale, resulting in a normalized debris risk value. :

[0065]

[0066] in, The higher the value, the higher the risk of chip adhesion, and the more likely chip adhesion defects are to occur during the grinding process.

[0067] By employing the aforementioned technical features, not only are the numerical values ​​of each characteristic indicator considered, but the covariance matrix also eliminates the correlation effects caused by coupled fluctuations between indicators (such as debris area and roughness). Therefore, it can more accurately measure the overall degree of deviation from the ideal stable state of the current operating condition, rather than simply the exceeding of a single indicator. By combining the normalized key defect features with Mahalanobis distance using weighting coefficients, a risk index is constructed, which considers both the overall deviation and enhances the sensitivity to the two most critical defect indicators: debris area and groove density.

[0068] S3: Based on the debris risk value, statistical analysis is performed to obtain the mean and variance of the debris risk value. A risk fluctuation coefficient and consistency jump index are introduced to construct a comprehensive stability index.

[0069] In this embodiment, the chip adhesion risk value for each operating condition combination is... Perform statistical analysis and calculate its mean. and variance :

[0070]

[0071]

[0072] To maintain stability, a risk volatility coefficient is introduced because certain operating conditions may fluctuate.

[0073]

[0074] in, This is the risk volatility coefficient. The value is preset to a positive number. The risk volatility coefficient is used to measure the dispersion of chip adhesion risk under the same working condition, preventing misjudgment of stable processing windows. Additionally, to characterize the volatility characteristics between repeated trials, a consistency jump index is introduced:

[0075]

[0076] in, This is a consistency jump indicator. For the first The grinding speed in this test With grinding pressure Risk value of debris adhesion under operating conditions. When A higher value indicates that the risk of chip adhesion is inconsistent across different tests and exhibits random fluctuations, making it unsuitable as a stable processing condition.

[0077] To simultaneously consider risk level, volatility stability, and consistent jumps, a comprehensive stability index is constructed. , is represented as:

[0078]

[0079] in, , , These are the weighting coefficients. .

[0080] By incorporating the aforementioned technical features, introducing risk volatility coefficients and consistency jump indicators to eliminate unstable factors, the comprehensive stability index can effectively screen out truly stable operating conditions.

[0081] S4: Establish a nonlinear coupling model of rotational speed and pressure, combine it with a comprehensive stability index, obtain a stable processing window by constraining data fluctuations, and then select the parameter with the best efficiency within the stable processing window.

[0082] Combined with risk volatility coefficient constraints

[0083] In this embodiment, to avoid subjective setting of the threshold, a comprehensive stability index set based on all operating conditions is used. The threshold is determined using the quantile method:

[0084]

[0085] in, Quantile threshold, It is a quantile function. This is the quantile ratio coefficient.

[0086] Set dual thresholds Forming a hierarchical determination, in which Corresponding to the stability threshold, Corresponding risk threshold.

[0087] Based on this, the working conditions are stratified and determined: when When it is determined to be a stable processing condition; when When it is determined to be a transitional operating condition; when The condition was identified as a risky condition for debris adhesion.

[0088] Based on all experimental data, a nonlinear coupling model of rotational speed and pressure was established. This was achieved by introducing an interaction term (…). (Describing the coupling relationship between rotational speed and pressure) and quadratic terms ( and (Describing the nonlinear effects of rotational speed and pressure variables on debris risk), demonstrating the nonlinear coupling effect of rotational speed and pressure on debris risk:

[0089]

[0090] in, This is a constant term, reflecting the system's basic risk level or baseline state; This is the linear influence coefficient of rotational speed, used to characterize the direct impact of rotational speed changes on the risk of chip adhesion. The linear influence coefficient of pressure is used to characterize the direct impact of changes in regrinding pressure on the risk of chip adhesion. The coefficient representing the interaction between rotational speed and pressure describes the combined impact of their coupling effect on the risk of chip adhesion. The coefficient of the quadratic term of the rotational speed is used to characterize the nonlinear effect of rotational speed changes; The coefficient of the quadratic term representing pressure is used to characterize the nonlinear effect of changes in grinding pressure. For rotational speed, For pressure. These coefficients can be obtained by parameter estimation using the least squares method on experimental data.

[0091] Based on the nonlinear coupling model and hierarchical determination, and by constraining the volatility of experimental data, a stable processing window is defined as a set of parameters that satisfy the comprehensive stability threshold and have limited volatility coefficients. :

[0092]

[0093] in, The upper limit of fluctuation is preset to exclude unstable operating conditions with "low average risk but large fluctuation".

[0094] Since a stable processing window should be continuous to avoid "discrete isolated points" that make it difficult to reproduce the process in the field, a neighborhood consistency constraint is further applied to the candidate window points:

[0095]

[0096] in, Grinding speed With grinding pressure The overall stability index under operating conditions Grinding speed With grinding pressure The overall stability index under operating conditions This sets a preset threshold for neighborhood variation. If a point meets the threshold but differs too much from its neighborhood, it is considered an isolated point and removed from the stable processing window, thereby improving the continuity and transferability of the stable window.

[0097] Select the parameter with the optimal efficiency within the stable processing window, where For the efficiency function:

[0098]

[0099] in, The optimal grinding speed. The optimal grinding pressure is achieved.

[0100] Current working conditions When the stability window condition is not met, the operating condition is adjusted back to the stable region using the minimum cost projection method:

[0101]

[0102] in, The adjusted grinding speed. The adjusted grinding pressure; , This is a weighting coefficient used to balance the adjustment costs of speed and pressure. In this adjustment formula... , It is a combination of parameters obtained by discretization in the experimental conditions. The determination of the parameter combination needs to meet the comprehensive stability index, fluctuation coefficient and debris risk index, and the parameter combination that meets the threshold conditions is selected.

[0103] like Figure 3 As shown, a comparison image of the electrode cap before and after grinding defects is given. (a) is the chip adhesion defect that occurs during the grinding process, and (b) is the surface finish image after being controlled by the present invention. The present invention can accurately control the grinding process, reduce chip adhesion defects, and improve the grinding quality.

[0104] Through the aforementioned technical features, the speed-pressure nonlinear coupling model accurately captures the interaction between speed and pressure, as well as the nonlinear impact on chip adhesion risk, revealing the nonlinear interaction mechanism. By employing a triple screening process involving a comprehensive threshold, upper limit of fluctuation, and neighborhood consistency constraints, the resulting stable processing window exhibits continuity and robustness, ensuring that process parameters remain within a stable region even under minor fluctuations in actual production conditions, significantly enhancing the method's engineering practical value. Within the stable processing window, positive optimization Z is achieved by solving the efficiency function; when current operating conditions are unfavorable, negative correction is achieved through the minimum-cost projection method, bringing the process back on track with minimal adjustment cost, forming a complete decision-making and control closed loop oriented towards actual production processes.

[0105] In summary, this invention overcomes the limitations of traditional grinding control that relies on a single threshold or manual experience. By constructing a multi-dimensional end-face defect feature vector that includes debris area, number of connected regions, groove density, and roughness, and introducing Mahalanobis distance to measure the statistical deviation between the current operating condition and the steady state, it can comprehensively and objectively capture subtle changes in grinding quality. Based on this, a debris-adhesion risk index constructed by combining key defect features achieves precise quantification of debris-adhesion risk during grinding, providing a reliable data foundation for subsequent process control.

[0106] This invention not only focuses on the average level of risk, but also introduces a risk fluctuation coefficient (reflecting the dispersion of risk) and a consistency jump index (reflecting the repeatability of the process) through statistical analysis, and integrates the three to construct a comprehensive stability index. This index can effectively distinguish different operating conditions, avoid occasional chip adhesion defects caused by process fluctuations, and significantly improve the robustness and reliability of process evaluation.

[0107] This invention reveals the nonlinear interaction between rotational speed and pressure on the risk of chip adhesion by establishing a nonlinear coupling model. Based on this, it combines a comprehensive stability index threshold and volatility constraints, and innovatively introduces a neighborhood consistency constraint to eliminate discrete operating conditions, thereby defining a continuous, smooth, and easily reproducible stable processing window in actual production. This solves the problem of ambiguous stable region boundaries and difficulty in practical application in traditional methods.

[0108] While ensuring grinding quality (within the stable window), this invention automatically selects the most efficient process parameters by solving an efficiency function, achieving a balance between high quality and high efficiency. Simultaneously, for situations where the current operating condition deviates from the stable window, a minimum-cost projection method is designed. This method, considering the weighting of speed and pressure adjustment costs, can bring the operating condition back to the stable region with minimal adjustment cost. This provides a clear guiding strategy for closed-loop feedback control and adaptive adjustment in the production process, effectively ensuring the continuity and stability of welding production.

[0109] In some implementations, in addition to using a quadratic response surface model, a multinomial regression model, a piecewise function model, or an interpolated surface model can also be used to fit the coupling relationship between rotational speed and pressure in the construction of the risk function.

[0110] In some implementations, in addition to using the fixed threshold method, the stable processing window can also be determined by quantile value determination, confidence interval determination, or risk gradient change determination. At the same time, the stable window can be locally widened or narrowed according to actual production needs.

[0111] Example 2

[0112] This embodiment discloses a chip adhesion control system for grinding the end face of an electrode cap, including:

[0113] The data acquisition module is configured to acquire the end face defect features of the electrode cap under each working condition.

[0114] The risk modeling module is configured to: obtain the Mahalanobis distance under the current working conditions based on the end face defect characteristics under the current working conditions, combined with the reference mean and covariance matrix of the end face defect characteristics under the stable grinding state; construct the chip adhesion risk index based on the Mahalanobis distance and key end face defect characteristics, and obtain the chip adhesion risk value after unifying the scale.

[0115] The coupling analysis module is configured to: based on the debris risk value, statistically analyze the mean and variance of the debris risk value, introduce the risk fluctuation coefficient and the consistency jump index, and construct a comprehensive stability index;

[0116] The stability window determination module is configured to: establish a nonlinear coupling model of rotational speed and pressure, combine it with a comprehensive stability index, obtain a stable processing window by constraining data fluctuations, and then select the parameter with the optimal efficiency within the stable processing window.

[0117] Example 3

[0118] The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method of Embodiment 1.

[0119] Example 4

[0120] The purpose of this embodiment is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method of Embodiment 1.

[0121] The steps and methods involved in the apparatuses of Embodiments 3 and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0122] Those skilled in the art will understand that the various steps and modules described in this invention can be implemented using general-purpose computer devices, or optionally, using computer-executable program code. Therefore, they can be stored in a storage device for execution by a computer, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This invention is not limited to any specific combination of hardware and software.

[0123] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0124] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for controlling chip adhesion during electrode cap end face grinding, characterized in that, include: Obtain the end face defect characteristics of the electrode cap under each working condition; Based on the end face defect characteristics under the current working conditions, and combined with the reference mean and covariance matrix of the end face defect characteristics under the stable grinding state, the Mahalanobis distance under the current working conditions is obtained; based on the Mahalanobis distance and key end face defect characteristics, a chip adhesion risk index is constructed, and the chip adhesion risk value is obtained after standardizing the scale. The end face defect features include the proportion of end face debris area, the number of connected debris areas, the proportion of chip breakage groove density or length, and the end face roughness index. The key end face defect features include the proportion of end face debris area and the proportion of chip breakage groove density or length. Based on the debris risk value, statistical analysis was performed to obtain the mean and variance of the debris risk value. A risk fluctuation coefficient and a consistency jump index were introduced to construct a comprehensive stability index. The risk fluctuation coefficient is used to measure the dispersion of chip adhesion risk under the same working condition, preventing misjudgment of stable processing windows. The calculation formula is as follows: in, This is the risk volatility coefficient. As a preset positive number, This represents the variance of the risk value of chip adhesion under the same operating conditions. This represents the average value of the risk of chip adhesion under the same working condition. The consistency jump index is used to characterize the fluctuation characteristics of debris risk between repeated trials, and the calculation formula is as follows: in, This is a consistency jump indicator. The number of tests under the same working conditions. For the first The grinding speed in this test With grinding pressure Risk value of debris adhesion under operating conditions; The comprehensive stability index is expressed as: in, As a comprehensive stability index, , , These are the weighting coefficients. This represents the mean of the debris risk values. This is the risk volatility coefficient. This is a consistency jump indicator; A nonlinear coupling model of rotational speed and pressure is established. By combining the comprehensive stability index and constraining data fluctuations, a stable processing window is obtained, and then the optimal efficiency parameter is selected within the stable processing window.

2. The method of claim 1, wherein the method further comprises: The Mahalanobis distance is used to measure the statistical deviation of the current end-face defect characteristics from the ideal steady state, and is expressed as: in, The Mahalanobis distance, This is the feature vector of the end face defect. This serves as a reference average for end-face defect characteristics under stable grinding conditions. The covariance matrix under stable grinding conditions, This is a transpose.

3. The method of claim 1, wherein the method further comprises: The debris risk index is expressed as follows: in, As the risk index for adhering debris, , , These are the weighting coefficients. The Mahalanobis distance, This represents the normalized percentage of the end-face debris area. This refers to the normalized density or length ratio of the chip fracture grooves.

4. The method of claim 1, wherein the method further comprises: Based on the comprehensive stability index, a dual threshold is set to classify the working conditions into different levels: when the comprehensive stability index is less than or equal to the stability threshold, it is determined to be a stable processing condition; when the comprehensive stability index is greater than the stability threshold but less than or equal to the risk threshold, it is determined to be a transitional working condition; and when the comprehensive stability index is greater than the risk threshold, it is determined to be a chip-adhesion risk working condition.

5. The method of claim 1, wherein the method further comprises: The stable processing window is a set of parameters that satisfy the comprehensive stability threshold and have a limited risk volatility coefficient, expressed as: in, To stabilize the processing window, For grinding speed, For grinding pressure, As a comprehensive stability index, To stabilize the threshold, This is the risk volatility coefficient. To preset the upper limit of fluctuation, As a risk threshold, It is a nonlinear coupling model of rotational speed and pressure.

6. A chip / debris control system for grinding the end face of an electrode cap, employing the chip / debris control method for grinding the end face of an electrode cap as described in any one of claims 1-5, characterized in that, include: The data acquisition module is configured to acquire the end face defect features of the electrode cap under each working condition. The risk modeling module is configured to: obtain the Mahalanobis distance under the current working conditions based on the end face defect characteristics under the current working conditions, combined with the reference mean and covariance matrix of the end face defect characteristics under the stable grinding state; construct the chip adhesion risk index based on the Mahalanobis distance and key end face defect characteristics, and obtain the chip adhesion risk value after unifying the scale. The coupling analysis module is configured to: based on the debris risk value, statistically analyze the mean and variance of the debris risk value, introduce the risk fluctuation coefficient and the consistency jump index, and construct a comprehensive stability index; The stability window determination module is configured to: establish a nonlinear coupling model of rotational speed and pressure, combine it with a comprehensive stability index, obtain a stable processing window by constraining data fluctuations, and then select the parameter with the optimal efficiency within the stable processing window.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that When the program is executed by the processor, it implements the steps in the electrode cap end face grinding chip control method as described in any one of claims 1-5.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the electrode cap end face grinding chip control method as described in any one of claims 1-5.

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