A clamping force optimization and instability early warning method and system based on edge computing

Abstract

This invention relates to the field of clamping mechanics monitoring, optimization, and instability early warning technology in the machining process of aerospace structural components, and particularly to a clamping force optimization and instability early warning method and system based on edge computing. The method of this invention calculates a reasonable clamping force range through a clamping force optimization method based on a clamping mechanics model, realizing online early warning of clamping instability during machining. It effectively solves the problems of lacking a foundation for quantitative research on clamping mechanics, lacking quantifiable positive input control methods for clamping mechanics, and lacking effective clamping mechanics monitoring measures during machining.

Description

Technical Field

[0001] This invention relates to the field of clamping force monitoring, optimization, and instability early warning technology in the machining process of aerospace structural components, and particularly to a clamping force optimization and instability early warning method and system based on edge computing. Background Technology

[0002] Controlling the mechanical stability of the clamping assembly is a crucial aspect of risk control for typical aerospace structural components. While online monitoring technology is widely used in process quality control, the following problems still exist:

[0003] (1) There is a lack of foundational research on the quantitative analysis of clamping mechanics, which is still largely based on empirical judgment. The machining of aerospace parts has its own characteristics, with complex processes, clamping methods, and mechanical states, which makes it very difficult to analyze the mechanical changes during machining and the mechanical state of clamping. Therefore, for a long time, the clamping mechanics control of large aerospace structural parts has been mainly based on empirical trial and error, lacking reliable basic data, control methods, and judgment standards.

[0004] (2) Lack of quantifiable positive input control methods for clamping mechanics. For a long time, whether in the design of clamping tooling schemes or in the operation of CNC machining processes, there has been a lack of reliable mechanical basis data and standards, and therefore a lack of quantifiable positive input control methods for clamping mechanics.

[0005] (3) In the processing of key products, there is a lack of effective clamping mechanical monitoring measures. CNC machining is a complex mechanical change process, which will generate corresponding clamping mechanical fluctuations. When the clamping mechanical fluctuations exceed a certain range, clamping instability is likely to occur, leading to quality accidents. Typical cases include: loose bolt pressure plates, shaking of process bosses, and movement of the part body, which often result in part damage, tool damage, or even equipment accidents. Summary of the Invention

[0006] Purpose of the invention: To provide a clamping force optimization and instability early warning method and system based on edge computing to solve the problems existing in the prior art. It can realize the quantitative analysis of clamping mechanics in the machining of aerospace structural parts, provide stable clamping evaluation criteria, and realize online early warning of clamping instability during the machining process through clamping force optimization of the clamping model, thereby improving the reliability, stability and machining accuracy of parts.

[0007] Technical solution:

[0008] A clamping force optimization and instability early warning method based on edge computing includes:

[0009] Step 1: Establish a cutting force coefficient model and a dynamic cutting force model, and calculate the cutting force coefficient based on the cutting force coefficient model; use the cutting force coefficient to calculate the cutting force under different cutting parameters based on the dynamic cutting force model;

[0010] Step 2: Establish a clamping mechanics model for the workpiece-bolt assembly clamping system, and calculate the theoretical clamping force for each set of cutting parameters based on the cutting force under different cutting parameters.

[0011] Step 3: Determine the initial clamping force based on the theoretical clamping force under each set of cutting parameters;

[0012] The clamping force stability range under each set of cutting parameters is calculated based on the clamping mechanics model; the maximum clamping force is calculated based on the tensile strength condition of each bolt; the minimum clamping force is calculated based on the non-slip condition; and finally, the maximum and minimum clamping forces in the clamping system are determined.

[0013] Step 4: Predict clamping instability based on edge computing monitoring system: The edge computing monitoring system measures the actual clamping force in real time. If the clamping force is not within the stable range of the clamping force under the current cutting parameters, it is judged as clamping instability.

[0014] Furthermore, in step 1, a dynamic cutting force model is established, specifically as follows:

[0015] Based on a mathematical model of a two-degree-of-freedom cutting system, the dynamic cutting force F(t) and the tangential cutting force coefficient K are derived. t The function expression: in,

[0016] a is the cutting depth, K t Let [A0] be the milling force coefficient, [A0] be the average directional coefficient, and {Δ(t)} be the time variation. The tangential cutting force coefficient K is obtained through the cutting force coefficient model and cutting experiments. t The value is then used to obtain the mathematical expression for the dynamic cutting force model.

[0017] Furthermore, in step two, a clamping mechanics model is established, specifically: mathematical modeling is performed based on the bolt-pressure plate model to derive the theoretical calculation formula for the actual clamping force.

[0018] Furthermore, in step two, a clamping mechanics model is established, and an actual clamping force measurement experiment is conducted. Regression analysis is performed based on the measured data, and the results are compared with the formula. If the difference is too large, the actual regression formula is used as the clamping mechanics model for calculating the actual clamping force.

[0019] Furthermore, in step three, the tensile condition is satisfied: Where σ caF1 is the bolt tensile strength, F2 is the total tensile force on a single bolt, d1 is the bolt mean diameter, and [σ] is the bolt allowable stress.

[0020] Furthermore, in step three, the non-slip condition is satisfied: Where μ is the coefficient of contact friction between the bolt and the workpiece, F i F″ represents the frictional force exerted by the bolt against lateral loads. i K is the frictional force that the bolt uses to resist the rotational torque. f For the safety factor, F h is the clamping force, and T is the clamping torque.

[0021] Furthermore, step three also includes: re-optimizing and determining the maximum and minimum clamping forces based on fixed-point optimization or global optimization.

[0022] Furthermore, in step three, the clamping force is optimized at a fixed point, specifically as follows:

[0023] Considering the direction of the horizontal cutting force, the direction is divided into several, and each case is calculated separately. For a bolt, n sets of maximum and minimum bolt preload values ​​will be obtained. The minimum value F(θ)max among the n maximum values ​​and the maximum value F(θ)min among the n minimum values ​​are recorded as fixed-point optimization values.

[0024] Furthermore, in step three, the clamping force is optimized globally, specifically as follows:

[0025] The workpiece is divided into several grids with m points in the horizontal position. By fixed-point analysis, the optimized values ​​of the maximum and minimum bolt preload of the workpiece-bolt group system under the action of horizontal cutting force at each point are known, namely F(θ)max and F(θ)min. Therefore, the m points also correspond to the optimized values ​​of the maximum and minimum bolt preload of m groups. The minimum value F(i)max among the maximum values ​​of m groups and the maximum value F(i)min among the minimum values ​​of m groups are recorded as the global optimized values.

[0026] A clamping force optimization and instability early warning system based on edge computing includes:

[0027] The first modeling module is used to establish a cutting force coefficient model and a dynamic cutting force model, and to calculate the cutting force coefficient based on the cutting force coefficient model; and to calculate the cutting force under different cutting parameters based on the dynamic cutting force model using the cutting force coefficient.

[0028] The second modeling module is used to establish the clamping mechanics model of the workpiece-bolt assembly clamping system and calculate the theoretical clamping force under each set of cutting parameters based on the cutting force under different cutting parameters.

[0029] The clamping force determination module is used to determine the initial clamping force based on the theoretical clamping force under each set of cutting parameters.

[0030] The clamping force stability range under each set of cutting parameters is calculated based on the clamping mechanics model; the maximum clamping force is calculated based on the tensile strength condition of each bolt; the minimum clamping force is calculated based on the non-slip condition; and finally, the maximum and minimum clamping forces in the clamping system are determined.

[0031] The instability early warning module is used to provide early warning of clamping instability based on the edge computing monitoring system. Specifically, it measures the actual clamping force in real time. If the clamping force is not within the stable range of the clamping force under the current cutting parameters, it is judged as clamping instability.

[0032] Beneficial effects:

[0033] This invention utilizes an edge computing-based clamping mechanics monitoring system to provide quantitative analysis of clamping mechanics during the machining of aerospace structural components and to offer stable clamping evaluation criteria. By employing a clamping force optimization method based on a clamping mechanics model to calculate a reasonable clamping force range, this invention enables online early warning of clamping instability during machining. It effectively addresses issues such as the lack of a foundation for quantitative research on clamping mechanics, the lack of quantifiable positive input control methods for clamping mechanics, and the lack of effective clamping mechanics monitoring measures during machining. Attached Figure Description

[0034] Figure 1 This is a flowchart of a clamping force optimization and instability early warning method based on edge computing according to the present invention;

[0035] Figure 2 A mechanical analysis diagram of a bolt-plate clamping system provided for an embodiment;

[0036] Figure 3 A schematic diagram of the clamping point position of a clamping system provided in an embodiment;

[0037] Figure 4 The example provides a fixed-point analysis diagram for clamping force optimization calculation based on the clamping mechanics model;

[0038] Figure 5 The global optimization diagram provided for the embodiment is a calculation of clamping force optimization based on the clamping mechanics model. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are only some, not all, of the embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0040] In the description of this invention, it should be understood that the terms "center", "axial", "vertical", "upper", "lower", "upper end", "bottom end", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this invention.

[0041] like Figures 1-5 The present invention provides a clamping force optimization and instability early warning method based on edge computing, comprising the following steps:

[0042] Step 1: Establish a cutting force coefficient model and a dynamic cutting force model, and calculate the cutting force coefficient based on the cutting force coefficient model; use the cutting force coefficient to calculate the cutting force under different cutting parameters based on the dynamic cutting force model;

[0043] Step 2: Establish a clamping mechanics model for the workpiece-bolt assembly clamping system, and calculate the theoretical clamping force for each set of cutting parameters based on the cutting force under different cutting parameters.

[0044] Step 3: In order to ensure the stability of the clamping system, determine the clamping force for the initial clamping based on the theoretical clamping force under each set of cutting parameters.

[0045] The clamping force stability range under each set of cutting parameters is calculated based on the clamping mechanics model; the maximum clamping force is calculated based on the tensile strength condition of each bolt; the minimum clamping force is calculated based on the non-slip condition; and finally, the maximum and minimum clamping forces in the clamping system are determined.

[0046] Step 4: Predict clamping instability based on edge computing monitoring system: The edge computing monitoring system measures the actual clamping force in real time. If the clamping force is not within the stable range of the clamping force under the current cutting parameters, it is judged as clamping instability.

[0047] Preferably, the established cutting force coefficient model is as follows:

[0048]

[0049] K rc The milling force coefficient in the x-direction;

[0050] K re The x-axis cutting edge force coefficient;

[0051] K tc The milling force coefficient in the y-direction;

[0052] K te The cutting edge force coefficient in the y-direction;

[0053] K ac The milling force coefficient in the z-axis direction;

[0054] K ae Z is the cutting edge force coefficient;

[0055] N is the number of teeth on the cutting tool, which is known.

[0056] a is the depth of cut, which is known;

[0057] c is the feed per tooth, which is known;

[0058] The average milling force in the x-direction was measured experimentally.

[0059] F y The average milling force in the y-direction was measured experimentally.

[0060] The average milling force in the z-direction is measured experimentally.

[0061] Preferably, the dynamic cutting force modeling is based on mathematical modeling of a two-degree-of-freedom cutting system to derive the dynamic cutting force F(t) and the tangential cutting force coefficient K. t The function expression: in,

[0062] {F(t)} represents the two-degree-of-freedom dynamic milling force, namely the milling forces Fx, Fy, and Fz at time t;

[0063] a is the depth of cut;

[0064] K t This is the milling force coefficient;

[0065] [A0] is the average directionality coefficient, which is known;

[0066] {Δ(t)} represents the change over time;

[0067] The tangential cutting force coefficient K was obtained through a cutting force coefficient model and cutting experiments. t The value is then used to obtain the mathematical expression for the dynamic cutting force.

[0068] Preferably, the clamping mechanics model is established by mathematical modeling based on the bolt-plate model to derive the theoretical calculation formula for the actual clamping force. In order to ensure the accuracy of the theoretical calculation formula, an actual machining clamping force measurement experiment is conducted. Regression analysis is performed based on the measured data and compared with the formula. If the difference is too large, the actual regression formula is used as the clamping mechanics model for calculating the actual clamping force.

[0069] Preferably, the tensile condition and the non-slip condition satisfy the following formulas respectively:

[0070] Tensile conditions: No-slip condition:

[0071] Where σ ca F is the bolt tensile strength, F2 is the total tensile force on a single bolt, d1 is the bolt pitch diameter, [σ] is the bolt allowable stress, μ is the coefficient of contact friction between the bolt and the workpiece, and F i F″ represents the frictional force exerted by the bolt against lateral loads. i K is the frictional force that the bolt uses to resist the rotational torque. f For the safety factor, F h is the clamping force, and T is the clamping torque.

[0072] Step three also includes: re-optimizing and determining the maximum and minimum clamping forces based on fixed-point optimization or global optimization.

[0073] Preferably, the clamping force fixed-point optimization takes into account the direction of the horizontal cutting force, divides the direction into several, calculates each case separately, and obtains n sets of maximum and minimum bolt preload values ​​for a bolt. The minimum value F(θ)max among the n sets of maximum values ​​and the maximum value F(θ)min among the n sets of minimum values ​​are recorded as the fixed-point optimization value.

[0074] Preferably, the global optimization of clamping force involves dividing the workpiece into several grids with m points at the horizontal position. Through fixed-point analysis, the optimized values ​​of the maximum and minimum bolt preload of the workpiece-bolt group system under the action of the horizontal cutting force at each point are known, namely F(θ)max and F(θ)min. Therefore, the m points also correspond to the optimized values ​​of the maximum and minimum bolt preload of m groups. The minimum value F(i)max among the m maximum values ​​and the maximum value F(i)min among the m minimum values ​​are recorded as the global optimization value. This optimization value is the global optimal value of the bolt preload in the clamping system. The optimization method for the remaining bolt preloads is the same as above.

[0075] 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.

[0076] The purpose of this invention is to provide a clamping force optimization and instability early warning method and monitoring system based on edge computing to solve the problems existing in the prior art. It can meet the quantitative analysis of clamping mechanics in the machining of aerospace structural parts, provide stable clamping evaluation criteria, and realize online early warning of clamping instability during the machining process through clamping force optimization of clamping mechanics model, thereby improving the reliability, stability and machining accuracy of parts.

[0077] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0078] Example 1

[0079] Figure 1 This is a flowchart of a clamping force optimization and instability early warning method based on edge computing according to the present invention. This embodiment provides a clamping force optimization and instability early warning method based on edge computing, which includes the following steps:

[0080] Step 1, cutting force coefficient modeling and dynamic cutting force modeling: Calculate the cutting force coefficient based on the cutting force coefficient model, and use the cutting force coefficient to calculate the cutting force in real time based on the dynamic cutting force model.

[0081] In this embodiment, the dynamic cutting force expression of the two-degree-of-freedom cutting system is shown in Equation 1, and the average cutting force coefficient model is shown in Equation 2:

[0082]

[0083] {F(t)} represents the two-degree-of-freedom dynamic milling force, namely the milling forces Fx, Fy, and Fz at time t;

[0084] a is the depth of cut;

[0085] K t This is the milling force coefficient;

[0086] [A0] is the average directionality coefficient, which is known;

[0087] {Δ(t)} represents the change over time;

[0088] K rc The milling force coefficient in the x-direction;

[0089] K re The x-axis cutting edge force coefficient;

[0090] K tc The milling force coefficient in the y-direction;

[0091] K te The cutting edge force coefficient in the y-direction;

[0092] K ac The milling force coefficient in the z-axis direction;

[0093] K ae Z is the cutting edge force coefficient;

[0094] N is the number of teeth on the cutting tool, which is known.

[0095] a is the depth of cut, which is known;

[0096] c is the feed per tooth, which is known;

[0097] The average milling force in the x-direction was measured experimentally.

[0098] F y The average milling force in the y-direction was measured experimentally.

[0099] The average milling force in the z-axis was measured experimentally.

[0100] N sets of cutting force data (N≥14) were obtained through cutting tests. The cutting force coefficient K corresponding to N sets was calculated using Formulas 1 and 2. t The functional relationship between the average cutting force coefficient and four factors—cutting speed, depth of cut, width of cut, and feed per tooth—is established, as shown in Formula 3:

[0101]

[0102] K t This is the tangential milling force coefficient;

[0103] X1 is the cutting speed variable, and the experimental data is known.

[0104] X2 is the cutting depth variable, and the experimental data is known.

[0105] X3 is the cutting width variable, and the experimental data is known.

[0106] X4 is the feed per tooth variable, which is known data from the experiment;

[0107] The unknown coefficients a0~a1 in formula 3 can be obtained by inversely solving the N sets of Kt values ​​obtained from the above cutting test. 14By doing so, the functional relationship between the cutting force coefficient and the cutting parameters can be obtained. Substituting these parameters into Formula 1 allows for the calculation of the cutting force coefficient based on the cutting force coefficient model. Finally, the cutting force coefficient is used to perform real-time cutting force calculation based on the dynamic cutting force model.

[0108] Step 2: Establishing a clamping mechanics model based on the workpiece-bolt assembly clamping system: Perform mechanical analysis on the workpiece-bolt assembly clamping system and establish a mathematical model of clamping force, and calculate the actual clamping force based on the cutting force.

[0109] Figure 2 For a bolt-plate clamping system, a mechanical analysis of the clamping system yields the following mathematical model of clamping force: Where F0 is the bolt preload, F1 is the residual preload, i.e. the actual clamping force, F′ is the bolt axial working load, and in machining, F is the vertical cutting force and the component of the workpiece weight distributed on each bolt, F′=(Fz+G) / 4;

[0110] C m The stiffness of the connected materials is known.

[0111] C b The bolt stiffness is known.

[0112] Step 3: Optimize clamping force calculations based on the clamping mechanics model: To ensure the stability of the clamping system and determine the initial clamping preload, the stable range of the clamping force needs to be calculated. The maximum clamping force is calculated based on the tensile strength of each bolt, and the minimum clamping force is calculated based on the non-slip condition. Ultimately, the maximum and minimum preload values ​​in the clamping system can be determined. Then, the optimal range of the clamping force is determined based on fixed-point optimization and global optimization.

[0113] In this embodiment, as Figure 3 The diagram shows the clamping point positions of a clamping system. The maximum preload is calculated based on the tensile strength conditions of each bolt, and the bolt tensile strength conditions satisfy the following formula:

[0114] Formula 4:

[0115]

[0116] Therefore, the maximum value of F2 is:

[0117]

[0118] The preload of each bolt satisfies Formula 6:

[0119]

[0120] Where F2 is the total tensile force on each bolt, F0 is the bolt preload, and F is the axial working load of the bolt considering the overturning moment. F = (M / l) + F' (the axial working load affected by the overturning moment is different for each bolt, so each bolt must be analyzed separately). Therefore, the maximum value of F0 is:

[0121]

[0122] The maximum value of the preload of each bolt under each condition can be obtained by formulas 5 and 7.

[0123] The minimum preload is calculated based on the no-slip condition. Since the bolt group is subjected to lateral load, rotational torque, axial load, and overturning moment, under no-slip conditions, the residual preload provided by the bolt group should resist the force displacement generated by the lateral load and the torque generated by the force on the centroid of the bolt group. The residual preload of each bolt in the bolt group is divided into two parts: ① Fi' resisting the lateral load, ② Fi” resisting the rotational torque, satisfying formula 8:

[0124]

[0125] According to the force analysis of bolt groups in mechanical design, the transverse load and rotational torque have the same effect on each bolt in the bolt group. Therefore, each bolt should satisfy Formula 9:

[0126]

[0127] Therefore, the residual preload Fi of each bolt resisting lateral load and rotational torque satisfies Equation 10:

[0128] F i =F′ i +F″ i ≥(K f F h / 4μ)+(K f T / 4μ) (10)

[0129] Therefore, the minimum value of Fi is:

[0130]

[0131] Because the lateral load and rotational torque resisted by the frictional force generated by the total residual preload are in different directions, the frictional force generated by the total residual preload should be the vector sum of the two frictional forces. The total residual preload of each bolt must be analyzed separately.

[0132] The preload of each bolt satisfies Formula 12:

[0133]

[0134] Therefore, the minimum value of F0 is:

[0135]

[0136] The minimum preload of each bolt under each condition can be obtained using formulas 11 and 13.

[0137] In fixed-point analysis, such as Figure 4 As shown, considering the direction of the horizontal cutting force, the direction is divided into several, and each case is calculated separately. For a bolt, n sets of maximum and minimum bolt preload values ​​will be obtained. The minimum value F(θ)max among the n sets of maximum values ​​and the maximum value F(θ)min among the n sets of minimum values ​​are recorded as fixed-point optimization values.

[0138] During global optimization, such as Figure 5 As shown, the workpiece is divided into several grids with m points in the horizontal position. By fixed-point analysis, the optimized values ​​of the maximum and minimum bolt preload of the workpiece-bolt group system under the action of horizontal cutting force at each point are known, namely F(θ)max and F(θ)min. Therefore, the m points also correspond to the optimized values ​​of the maximum and minimum bolt preload of m groups. The minimum value F(i)max among the m maximum values ​​and the maximum value F(i)min among the m minimum values ​​are recorded as the global optimized value. This optimized value is the global optimal value of the bolt preload in the clamping system. The optimization method for the other bolt preloads is the same as above.

[0139] Step 4: Predict clamping instability based on edge computing monitoring system: The edge computing monitoring system measures the actual clamping force in real time and calculates the triaxial cutting force using the dynamic cutting force model. Based on the clamping mechanics model, the tensile and non-slip conditions of the workpiece-bolt group system are compared using inequalities. If the clamping force is not within the range of real-time stable clamping force, it is judged as clamping instability.

[0140] In this embodiment, as Figure 3 As shown, the clamping system uses four clamping points. The edge computing monitoring system obtains the actual clamping force data of the four clamping points. Combined with the calculation results of the clamping mechanics model, the real-time measured clamping force is substituted into the instability discrimination formula (i.e., the tensile and non-slip conditions are not met).

[0141] When the formula μF′1+μF′2+μF′3+μF′4 <K f F h ,μF″1+μF″2+μF″3+μF″4 <K f If T satisfies any one of these conditions, it is determined to be unstable, and an early warning shutdown is initiated.

[0142] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for clamping force optimization and instability early warning based on edge computing, characterized in that, include: Step 1: Establish a cutting force coefficient model and a dynamic cutting force model, and calculate the cutting force coefficient based on the cutting force coefficient model; use the cutting force coefficient to calculate the cutting force under different cutting parameters based on the dynamic cutting force model, and establish the dynamic cutting force model, specifically as follows: Dynamic cutting force is derived by mathematical modeling a two-degree-of-freedom cutting system. F(t) With tangential cutting force coefficient K t The function expression: ,in, 'a' represents the depth of cut. K t [A0] is the tangential milling force coefficient, and [A0] is the average directional coefficient. The tangential cutting force coefficient K is a time-varying quantity, obtained through a cutting force coefficient model and cutting experiments. t The value is then used to obtain the mathematical expression for the dynamic cutting force model; The cutting force coefficient model is as follows: K rc The milling force coefficient in the x-direction; K re The x-axis cutting edge force coefficient; K tc The milling force coefficient in the y-direction; K te The cutting edge force coefficient in the y-direction; K ac The milling force coefficient in the z-axis direction; K ae Z is the cutting edge force coefficient; N is the number of teeth on the cutting tool; a is the depth of cut; c is the feed per tooth; The average milling force in the x-direction; The average milling force in the y-direction; The average milling force in the z-direction; Step two: Establish the clamping mechanics model of the workpiece-bolt assembly clamping system, and calculate the theoretical clamping force for each set of cutting parameters based on the cutting force under different cutting parameters; the clamping mechanics model is as follows: ,in F 0 represents the bolt preload. F 1 represents the residual preload, i.e., the actual clamping force. For the axial working load of the bolt, in machining For vertical cutting force F z The component of the workpiece's weight G distributed on each bolt, ; C m The stiffness of the materials being connected; C b For bolt stiffness; Step 3: Determine the initial clamping force based on the theoretical clamping force under each set of cutting parameters; The clamping force stability range under each set of cutting parameters is calculated based on the clamping mechanics model; the maximum clamping force is calculated based on the tensile strength condition of each bolt; the minimum clamping force is calculated based on the non-slip condition; and finally, the maximum and minimum clamping forces in the clamping system are determined. The tensile strength condition for bolts is: ;in, Where F1 is the tensile strength of the bolt, F2 is the total tensile force on a single bolt, and d1 is the mean diameter of the bolt. This refers to the allowable stress of the bolt. The non-slip condition is: Where μ is the coefficient of contact friction between the bolt and the workpiece. To prevent the bolts from resisting the frictional force of lateral loads, The frictional force that the bolt uses to resist the rotational torque. For safety reasons, The clamping force is T, and the clamping torque is T. Step 4: Predict clamping instability based on edge computing monitoring system: The edge computing monitoring system measures the actual clamping force in real time. If the clamping force is not within the stable range of the clamping force under the current cutting parameters, it is judged as clamping instability.

2. The method according to claim 1, characterized in that, In step two, a clamping mechanics model is established, specifically as follows: Theoretical calculation formulas for actual clamping forces are derived by mathematical modeling based on the bolt-plate model.

3. The method according to claim 2, characterized in that, Step three also includes: The maximum and minimum clamping forces are determined by re-optimization based on fixed-point optimization or global optimization.

4. The method according to claim 3, characterized in that, In step three, the clamping force is optimized at a fixed point, specifically as follows: Considering the direction of the horizontal cutting force, the direction is divided into several, and each case is calculated separately. For a bolt, n sets of maximum and minimum bolt preload values ​​will be obtained. The minimum value F(θ)max among the n maximum values ​​and the maximum value F(θ)min among the n minimum values ​​are recorded as fixed-point optimization values.

5. The method according to claim 4, characterized in that, In step three, the clamping force is optimized globally, specifically as follows: The workpiece is divided into several grids with m points in the horizontal position. By fixed-point analysis, the optimized values ​​of the maximum and minimum bolt preload of the workpiece-bolt group system under the action of horizontal cutting force at each point are known, namely F(θ)max and F(θ)min. Therefore, the m points also correspond to the optimized values ​​of the maximum and minimum bolt preload of m groups. The minimum value F(i)max among the maximum values ​​of m groups and the maximum value F(i)min among the minimum values ​​of m groups are recorded as the global optimized values.

6. A clamping force optimization and instability early warning system based on edge computing, characterized in that, include: The first modeling module is used to establish a cutting force coefficient model and a dynamic cutting force model, and to calculate the cutting force coefficient based on the cutting force coefficient model; it also uses the cutting force coefficient to calculate the cutting force under different cutting parameters based on the dynamic cutting force model, thus establishing the dynamic cutting force model. Specifically: Dynamic cutting force is derived by mathematical modeling a two-degree-of-freedom cutting system. With tangential cutting force coefficient K t The function expression: ,in, 'a' represents the depth of cut. K t [A0] is the tangential milling force coefficient, and [A0] is the average directional coefficient. The tangential cutting force coefficient K is a time-varying quantity, obtained through a cutting force coefficient model and cutting experiments. t The value is then used to obtain the mathematical expression for the dynamic cutting force model; The cutting force coefficient model is as follows: K rc The milling force coefficient in the x-direction; K re The x-axis cutting edge force coefficient; K tc The milling force coefficient in the y-direction; K te The cutting edge force coefficient in the y-direction; K ac The milling force coefficient in the z-axis direction; K ae Z is the cutting edge force coefficient; N is the number of teeth on the cutting tool; a is the depth of cut; c is the feed per tooth; The average milling force in the x-direction; The average milling force in the y-direction; The average milling force in the z-direction; The second modeling module is used to establish the clamping mechanics model of the workpiece-bolt assembly clamping system and calculate the theoretical clamping force under each set of cutting parameters based on the cutting force under different cutting parameters. The clamping force determination module is used to determine the initial clamping force based on the theoretical clamping force under each set of cutting parameters. The clamping mechanics model is as follows: ,in F 0 represents the bolt preload. F 1 represents the residual preload, i.e., the actual clamping force. For the bolt's axial working load, F represents the vertical cutting force F during machining. z The component of the workpiece's weight G distributed on each bolt, ; C m The stiffness of the materials being connected; C b For bolt stiffness; The clamping force stability range under each set of cutting parameters is calculated based on the clamping mechanics model. The maximum clamping force is calculated based on the tensile strength condition of each bolt, and the minimum clamping force is calculated based on the non-slip condition. Finally, the maximum and minimum clamping forces in the clamping system are determined. The bolt tensile strength condition is as follows: ;in, Where F1 is the tensile strength of the bolt, F2 is the total tensile force on a single bolt, and d1 is the mean diameter of the bolt. This refers to the allowable stress of the bolt. The non-slip condition is: Where μ is the coefficient of contact friction between the bolt and the workpiece. To prevent the bolts from resisting the frictional force of lateral loads, The frictional force that the bolt uses to resist the rotational torque. For safety reasons, The clamping force is T, and the clamping torque is T. The instability early warning module is used to provide early warning of clamping instability based on the edge computing monitoring system. Specifically, it measures the actual clamping force in real time. If the clamping force is not within the stable range of the clamping force under the current cutting parameters, it is judged as clamping instability.

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