Method and system for self-compensation of thin-walled cabin inner cavity

Abstract

The application provides a kind of thin-walled cabin inner cavity self-compensation processing method and system, comprising: (1) preparing metal blank with uniform physical properties everywhere; (2) reasonable installation and positioning of thin-walled cabin on equipment; (3) processing zero point setting and tool length compensation; (4) pre-optimization of tool position path; (5) automatic induction of wall thickness of cooling liquid containing carbon nanotubes; (6) real-time ball tool angle self-induction of thin-walled metal wall thickness of cooling liquid containing carbon nanotubes; (7) error calculation of product induction wall thickness and theoretical wall thickness; (8) automatic compensation and correction of pre-planned tool position data; (9) thin-walled inner cavity traversal processing inner surface. The application can realize real-time and effective processing of each point of thin-walled inner cavity, and provides technical support for obtaining qualified thin-walled cabin through one-time processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machining, in particular to a self-compensation machining method and system for a thin-walled cabin inner cavity. BACKGROUND

[0002] In the fields of aerospace, weapon equipment design and manufacturing, reducing the structure weight is a very important design and manufacturing concept for improving the space and aviation carrying capacity and the flight striking distance of weapon equipment, and has become a development trend of modern advanced manufacturing technology. Among them, the thin-walled cabin (wall thickness 1-3 mm) as an important shape part on strategic weapon missiles and carrier rockets has the characteristics of high precision requirement (generally wall thickness tolerance needs to meet ±0.05) and long generation cycle.

[0003] The traditional thin-walled inner cavity mainly relies on the means of installing an angular milling head by traditional equipment, and through the mode of rough machining→semi-finish machining→finish machining, gradually measuring the product wall thickness manually, and gradually adjusting the product wall thickness size manually by adjusting the length of the tool to make the cabin wall thickness finally reach the qualified level. However, the traditional means has the problems of local over-difference of product wall thickness, serious time consumption and low actual production efficiency.

[0004] Patent document CN116117597A provides a space vehicle thin-walled cabin lightening groove online measurement and compensation machining method, which comprises the following steps: installing a thickness measuring head; setting measurement points on the surface of each to-be-machined lightening groove and obtaining the theoretical thickness and outer contour position; measuring the actual thickness of each measurement point and obtaining the actual outer contour position; calculating the compensation value of each measurement point according to the theoretical value and the measured value of the thickness and the outer contour; judging whether the compensation requirement is met according to the compensation value, if met, compensating the tool length based on the comprehensive compensation value for machining, if not met, reconstructing the machining surface based on the actual thickness and the outer contour position, and machining based on the reconstructed machining surface using the initial tool length. However, the precision still needs to be improved.

[0005] In view of the technical problems in the prior art, the present application provides a thin-walled cabin inner cavity self-compensation machining method and system. The thin-walled cabin inner cavity high-efficiency, high-precision, self-compensation machining method can realize real-time and effective machining of each point of the thin-walled inner cavity, and provides technical support for obtaining a qualified thin-walled cabin through one-time machining. SUMMARY

[0006] In view of the defects in the prior art, the purpose of the present application is to provide a thin-walled cabin inner cavity self-compensation machining method and system.

[0007] According to the thin-walled cabin inner cavity self-compensation machining method provided by the present application, the method comprises the following steps:

[0008] Step S1: preparing a metal blank;

[0009] Step S2: positioning the thin-walled cabin on the equipment;

[0010] Step S3: setting the zero point and compensating the tool length;

[0011] Step S4: prepositioning and optimizing the tool path;

[0012] Step S5: automatically sensing and calibrating the wall thickness of the cooling liquid containing carbon nanotubes;

[0013] Step S6: automatically sensing and calibrating the wall thickness of the cooling liquid containing carbon nanotubes in real time;

[0014] Step S7: calculating the error between the sensed wall thickness and the theoretical wall thickness;

[0015] Step S8: automatically compensating and correcting the prepositioned tool path data;

[0016] Step S9: traversing the inner surface of the thin-walled inner cavity; wherein the thin-walled refers to a wall thickness of 1-3 mm.

[0017] Preferably, the step S1 comprises: selecting a metal cabin requiring inner cavity machining as the original blank, confirming that the spatial positions requiring machining have the same physical properties, and the error of the physical properties at different positions is not more than 5%;

[0018] The step S2 comprises: installing and positioning through tooling, gluing, pressing, and positioning;

[0019] The step S3 comprises: setting the product machining coordinate system according to the characteristics of the thin-walled cabin, installing the inner-cooled ball head machining tool, and compensating the tool length into the equipment numerical control system; the inner cavity machining tool is an inner-cooled ball head tool, and the inner cooling hole position is at the center of the ball head tool.

[0020] Preferably, the step S4 comprises: planning the optimal inner cavity machining tool path according to the characteristics of the inner cavity of the thin-walled cabin, improving the continuity, efficiency, and stability of the subsequent tool path data self-compensation and cutting process; the optimized tool path has the characteristics of tool path smoothing, less tool retraction times, and uniform cutting amount at different positions;

[0021] The step S5 comprises: realizing real-time measurement of the metal thin wall according to the inner surface of the thin-walled cabin, calibrating the wall thickness induced by the cooling liquid containing carbon nanotubes by uniformly taking a point in each set area to measure the product wall thickness through ultrasonic wave, and ensuring the coordination and consistency of the wall thickness at different positions; when the cutting fluid containing carbon nanotubes is sprayed at the inner cooling hole of the ball head tool, due to the conductivity of carbon nanotubes and the electromagnetic physical properties of the metal wall thickness, the cutting fluid containing carbon nanotubes can measure the wall thickness data in real time and induce the wall thickness, and then realize real-time measurement of the product wall thickness by following the cutting fluid.

[0022] Preferably, in step S5: the carbon nanotube cutting fluid is conductive. By fixing electrodes anode and cathode on the chamber and tool holder respectively, an electric field Ea of a certain intensity is generated between the chamber and the tool holder. According to the different electric field intensity characteristics of different cutting materials, the electric field intensity characteristics of different materials are measured by calibration. There is a fixed contact point a between the cutting area and the tool. The contact point has conductive characteristics in a certain area S. Different wall thickness areas form different potential energies. The magnitude of the potential energy reflects the corresponding wall thickness change of the chamber.

[0023] Preferably, step S6 includes: during the actual machining of the thin-walled chamber cavity, the optimal tool path planned in step S4 is run, and the real-time wall thickness data at that point is collected in real time by the coolant containing carbon nanotubes. Different wall thicknesses and different cutting angles jointly determine the final potential difference of the product. By calibrating the influence of different angles on the potential difference, the real-time wall thickness data h of each machining position of the product can be obtained.

[0024] Preferably, in step S6:

[0025] (1) When the angle between the ball end mill and the thin-walled inner cavity is 90°, the carbon nanotubes in the cutting fluid are uniformly distributed at the tip of the ball end mill, and the distribution area is S1 with an electric field strength of E(90). When the angle between the ball end mill and the thin-walled inner cavity is 90°, it is considered that the state at this time is the standard state, and the wall thickness measured at this time is h0.

[0026] (2) When the angle between the ball end mill and the thin-walled inner cavity is >90°, the distribution area of ​​carbon nanotubes in the cutting fluid at the ball end is f*S1 / b, where f is the carbon nanotube material coefficient and b is the angle influence coefficient. At this time, the electric field strength E(θ) = E(90)*b / (f*S1); the wall thickness is h(δ) = h0*μ, where μ is the angle constant and is positively correlated with E(θ).

[0027] (3) When the angle between the ball end mill and the thin-walled inner cavity is <90°, the distribution area of ​​carbon nanotubes in the cutting fluid at the ball end is f*S1 / a, where f is the carbon nanotube material coefficient and a is the angle influence coefficient. At this time, the electric field strength E(θ)=E(90)*a / (f*S1); the wall thickness is h(δ)=h0*μ, where μ is the angle constant and is positively correlated with E(θ).

[0028] S1 is the area of ​​the distribution region of carbon nanotubes in the cutting fluid uniformly distributed at the tip of the ball end mill, E(90) is the electric field strength when the angle between the ball end mill and the thin-walled inner cavity is 90°, and the wall thickness measured when the angle between the ball end mill and the thin-walled inner cavity is 90° is h0.

[0029] Preferably, step S7 includes: comparing the wall thickness data of the spatial point collected in real time by the cooling liquid containing carbon nanotubes with the processed theoretical wall thickness data of the spatial point to obtain the actual error of the wall thickness data of the spatial point;

[0030] The method for obtaining the actual error of the wall thickness data at spatial points is as follows: by measuring the actual processed wall thickness h(1) of the product in real time, the theoretical wall thickness of the product is determined according to the product's design specifications, which is h(0), and the actual processing error λ = h(1) - h(0);

[0031] The actual error refers to the specific actual value of undercut or overcut that occurs when the product is deformed during processing, even though it is processed according to the theoretical contour.

[0032] Preferably, step S8 includes: real-time compensation and correction of the point data at the spatial point optimized in step four, thereby correcting the actual tool path at that point, realizing real-time self-compensation and self-correction of the tool path, and ensuring that the final wall thickness reaches the theoretical wall thickness h(0) of the product; the self-compensation standard procedure in step S8 is: based on the real-time obtained machining error λ, automatically correcting the X, Y, and Z of the CNC machining program to ensure:

[0033]

[0034] Complete the real-time correction of the product tool path to ensure that the final wall thickness reaches the theoretical wall thickness h(0);

[0035] Step S9 includes: traversing all machining tool paths in step eight to achieve compensation machining of the thin-walled cabin cavity.

[0036] The present invention provides a self-compensating machining system for thin-walled cabin cavities, which is processed using the aforementioned self-compensating machining method for thin-walled cabin cavities.

[0037] The thin-walled cabin cavity provided by the present invention is obtained by processing using the aforementioned thin-walled cabin cavity self-compensation processing method, or by processing using the aforementioned thin-walled cabin cavity self-compensation processing system.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. This invention achieves the electrical conductivity of cutting fluid by using a cutting fluid containing carbon nanotubes, thereby enabling real-time measurement of the wall thickness of thin-walled metal chambers.

[0040] 2. This invention obtains the real-time machining error of the thin-walled metal cabin wall thickness by measuring the wall thickness of the thin-walled metal cavity in real time using a cutting fluid containing carbon nanotubes.

[0041] 3. This invention achieves real-time online correction and compensation of the product by real-time compensation and correction of the tool positions at various locations in the planned space, thereby achieving uniform wall thickness throughout the thin-walled cabin cavity and obtaining efficient, high-precision, and self-compensating machining of thin-walled metal cavities. Attached Figure Description

[0042] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0043] Figure 1 The diagram shown is a schematic flowchart of the processing method of the present invention.

[0044] Figure 2 The diagram shown is a schematic diagram of a 90° compensation method for the internal cavity of a thin-walled cabin provided by an embodiment of the present invention.

[0045] Figure 3 The diagram shown is a schematic diagram of a compensation method for a thin-walled cabin cavity >90° provided by an embodiment of the present invention.

[0046] Figure 4 The diagram shown is a schematic diagram of a compensation method for a thin-walled cabin cavity <90° provided by an embodiment of the present invention.

[0047] The diagram shows:

[0048] Metal hull 1

[0049] Ball end mill with internal cooling hole in the ball center 2

[0050] Coolant containing carbon nanotubes 3

[0051] Equipment side milling head 4

[0052] Carbon nanotube wall thickness sensing system 5

[0053] Data Processing System 6

[0054] Point Feedback Correction System 7 Detailed Implementation

[0055] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0056] This invention aims to solve the technical problems in existing thin-walled chamber internal cavity machining processes, such as the inability to perform real-time online measurement, compensation, and measurement of product wall thickness, leading to uneven wall thickness, severe local deviations, and low production efficiency. This invention mainly builds upon traditional angle milling heads by installing a ball-end mill with internal cooling, a carbon nanotube wall thickness sensing system, a data processing system, and a point-feedback correction system. The wall thickness sensing system, containing carbon nanotube coolant, senses the thin-walled chamber wall thickness in real time. The data processing system calculates the error between the theoretical wall thickness at each point and the real-time sensed wall thickness data. The point-feedback and correction system corrects the pre-optimized tool trajectory point data in real time, enabling real-time and effective machining of all points within the thin-walled internal cavity. This provides technical support for obtaining qualified thin-walled chambers in a single machining operation.

[0057] This invention provides a high-efficiency, high-precision, and self-compensating machining method for the internal cavity of an irregularly shaped thin-walled chamber. The machining method includes the following steps: (1) preparing a metal billet with uniform physical properties; (2) properly installing and positioning the thin-walled chamber on the equipment; (3) setting the machining zero point and compensating for the tool length; (4) optimizing the tool path in advance; (5) automatically calibrating the wall thickness using a coolant containing carbon nanotubes; (6) calibrating the thin-walled metal wall thickness using a real-time ball end mill angle calibrated with a coolant containing carbon nanotubes; (7) calculating the error between the product's sensed wall thickness and the theoretical wall thickness; (8) automatically compensating and correcting the pre-planned tool path data; and (9) machining the inner surface of the thin-walled internal cavity. Here, "thin-walled" refers to a wall thickness of 1-3 mm.

[0058] The present invention will now be described in more detail.

[0059] The processing method includes the following steps:

[0060] Step 1: Select any type of metal chamber that requires internal cavity machining as the raw material, such as... Figure 2 As shown, the spatial locations within the inner cavity of the metal blank that need to be processed have the same physical properties, and the error in physical properties at each location does not exceed 5%, in order to reduce the compensation error caused by subsequent automatic compensation due to blank material issues.

[0061] Step 2: Using conventional installation and positioning methods such as tooling, gluing, and pressure plates, the thin-walled chamber is reliably installed on the equipment to prevent product vibration;

[0062] Step 3: Set up the product machining coordinate system according to the characteristics of the thin-walled cabin, install the ball end mill with internal cooling, and compensate the tool length to the CNC system of the equipment; the internal cavity machining tool is a ball end mill with internal cooling, and the internal cooling opening is located at the center of the ball end mill;

[0063] Step 4: Based on the characteristics of the thin-walled cabin cavity, plan the optimal tool path for machining the internal cavity to improve the continuity, efficiency, and stability of subsequent tool path data self-compensation and the cutting process. The optimized tool path has the characteristics of smooth tool path, fewer tool entry and exit times, and uniform cutting amount at all points. In the preferred example, there is only one entry and exit. When the ball end mill is machining the thin-walled cabin cavity, the cutting amount is uniform and consistent, ensuring consistent tool cutting state and stable cutting process.

[0064] Step 5: Based on the inner surface of the thin-walled compartment, such as... Figure 2 As shown, real-time measurement of thin-walled metal is achieved within a set area, for example, 25cm. 2 Within the area, a point is uniformly selected and the wall thickness of the carbon nanotubes is calibrated by measuring the product wall thickness using ultrasonic waves, ensuring that the wall thickness is consistent throughout.

[0065] In this process, when the cutting fluid containing carbon nanotubes is sprayed out from the cooling orifice inside the ball end mill, the conductivity of the carbon nanotubes allows for real-time measurement of the wall thickness based on the electromagnetic physical properties of metal wall thickness. This enables the cutting fluid to follow the flow of the cutting fluid and measure the product wall thickness in real time. Specifically, the carbon nanotube cutting fluid is conductive. By fixing electrodes (anode and cathode) on the chamber and tool holder respectively, an electric field Ea of a certain intensity is generated between the chamber and the tool holder. Based on the different electric field strength characteristics of different cutting materials, the electric field strength characteristics of different materials are measured through calibration. A fixed contact point a exists between the cutting area and the tool. Within a certain region S of this contact point, conductivity is achieved. Different wall thickness regions generate different potential energies, and the magnitude of these potential energies reflects the corresponding wall thickness changes within the chamber.

[0066] Step Six: During the actual machining of the thin-walled chamber cavity, the optimal tool path planned in Step Four is executed. Real-time wall thickness data at that point is collected in real time using a coolant containing carbon nanotubes. Different wall thicknesses and different cutting angles jointly determine the final potential difference of the product. By calibrating the influence of different angles on the potential difference, the real-time wall thickness data h of each machining position of the product can be obtained; for example... Figure 2 The image shows the machining state when the ball-end tool forms a 90° angle with the thin-walled inner cavity. Figure 3 This refers to machining conditions where the angle between the ball-end tool and the thin-walled inner cavity is greater than 90°. Figure 4 This refers to the machining state where the angle between the ball-end tool and the thin-walled inner cavity is <90°; corresponding to the implementation of step six in three different embodiments:

[0067] (1) When the angle between the ball end mill and the thin-walled inner cavity is 90°, the carbon nanotubes in the cutting fluid are uniformly distributed at the tip of the ball end mill, and the distribution area is S1 with an electric field strength of E(90). When the angle between the ball end mill and the thin-walled inner cavity is 90°, it is considered that the state at this time is the standard state, and the wall thickness measured at this time is h0.

[0068] (2) When the angle between the ball end mill and the thin-walled inner cavity is >90°, the distribution area of ​​carbon nanotubes in the cutting fluid at the ball end is f*S1 / b, where f is the carbon nanotube material coefficient and b is the angle influence coefficient. At this time, the electric field strength E(θ)=E(90)*b / (f*S1); the wall thickness is h(δ)=h0*μ, where μ is the angle constant and is positively correlated with E(θ).

[0069] (3) When the angle between the ball end mill and the thin-walled inner cavity is <90°, the distribution area of ​​carbon nanotubes in the cutting fluid at the ball end is f*S1 / a, where f is the carbon nanotube material coefficient and a is the angle influence coefficient. At this time, the electric field strength E(θ)=E(90)*a / (f*S1); the wall thickness is h(δ)=h0*μ, where μ is the angle constant and is positively correlated with E(θ).

[0070] During the actual machining process of the internal cavity of the cabin, the inclination angle θ of the tool can be known in real time at any machining moment, and the wall thickness of the product can be measured in real time based on the above different relationships.

[0071] Step 7, as follows Figure 2 As shown, the data processing system compares the wall thickness data of the spatial point collected in real time by the cooling liquid containing carbon nanotubes with the theoretical wall thickness data of the spatial point to obtain the actual error of the wall thickness data of the spatial point. Specifically, the method to obtain the actual error of the wall thickness data of the spatial point is: by measuring the actual processed wall thickness h(1) of the product in real time. The theoretical wall thickness of the product is determined according to the design specifications of the product, which is generally h(0). The actual processing error λ = h(1) - h(0).

[0072] The actual error refers to the specific actual value of undercut or overcut that occurs when the product is deformed during processing, even though it is processed according to the theoretical contour.

[0073] Step 8, as Figure 2 As shown, the point-of-care feedback correction system compensates and corrects the point data at the spatial point optimized in step four in real time, thereby correcting the actual tool path at that point. This achieves real-time self-compensation and self-correction of the tool path, ensuring that the final wall thickness reaches the theoretical wall thickness h(0) of the product. That is, based on the real-time machining error λ, the X, Y, and Z axes of the CNC machining program are automatically corrected to ensure:

[0074]

[0075] This enables real-time correction of the product's tool path, ensuring that the final wall thickness reaches the product's theoretical wall thickness h(0).

[0076] Finally, step nine involves traversing all machining toolpaths from step eight to achieve efficient, high-precision, and self-compensating machining of the thin-walled cabin cavity.

[0077] The present invention provides a self-compensating machining system for thin-walled cabin cavities, which is processed using the aforementioned self-compensating machining method for thin-walled cabin cavities.

[0078] The thin-walled cabin cavity provided by the present invention is obtained by processing using the aforementioned thin-walled cabin cavity self-compensation processing method, or by processing using the aforementioned thin-walled cabin cavity self-compensation processing system.

[0079] In summary, this invention provides a highly efficient, high-precision, and self-compensating machining method and system for thin-walled cabin cavities. It utilizes a coolant containing carbon nanotubes to automatically sense the metal thin-wall thickness through the internal cooling holes of a ball-end tool. A data processing system obtains the error between the actual and theoretical thin-walled cabin thickness in real time. Furthermore, through self-compensation and self-correction of pre-planned point data, it achieves accurate cutting based on actual positioning data in real time, thereby realizing highly efficient and high-precision machining of thin-walled cabin cavities.

[0080] In the description of this application, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "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 application 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 limitations on this application.

[0081] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for self-compensating machining of a thin-walled cabin cavity, characterized in that, include: Step S1: Prepare the metal billet; Step S2: Install and position the thin-walled cabin on the equipment; Step S3: Setting the machining zero point and compensating for tool length; Step S4: Optimize the toolpath before positioning; Step S5: Automatic sensing calibration of the wall thickness of the coolant containing carbon nanotubes; Step S6: Real-time self-sensing calibration of thin-walled metal wall thickness using carbon nanotube-containing coolant ball end mill angle; Step S7: Calculate the error between the product's sensed wall thickness and the theoretical wall thickness; Step S8: Automatic compensation and correction of the pre-planned tool position data; Step S9: Machining the inner surface of the thin-walled cavity; where thin-walled refers to a wall thickness of 1-3 mm; Step S5 includes: Real-time measurement of the thin metal wall is achieved based on the inner surface of the thin-walled chamber. The wall thickness induced by the coolant containing carbon nanotubes is calibrated by uniformly selecting a point within each set area and measuring the product wall thickness using ultrasonic waves, ensuring consistent wall thickness across all locations. When the cutting fluid containing carbon nanotubes is sprayed from the cooling hole inside the ball end mill, due to the conductivity of the carbon nanotubes, and based on the electromagnetic physical characteristics of the metal wall thickness, the cutting fluid containing carbon nanotubes can measure the wall thickness data in real time and sense the wall thickness, thereby achieving real-time measurement of the product wall thickness by following the cutting fluid. The carbon nanotube cutting fluid is conductive. By fixing electrodes anode and cathode on the chamber and tool holder respectively, an electric field Ea of a certain intensity is generated between the chamber and the tool holder. Based on the different electric field strength characteristics of different cutting materials, the electric field strength characteristics of different materials are measured through calibration. A fixed contact point a exists between the cutting area and the tool. Within a certain area S of this contact point, there is conductivity. Different wall thickness areas form different potential energies, and the magnitude of the potential energy reflects the corresponding wall thickness change of the chamber. Step S6 includes: during the actual machining of the thin-walled chamber cavity, the optimal tool path planned in step S4 is run, and the real-time wall thickness data at that point is collected in real time by the coolant containing carbon nanotubes. Different wall thicknesses and different cutting angles jointly determine the final potential difference of the product. By calibrating the influence of different angles on the potential difference, the real-time wall thickness data h of each machining position of the product can be obtained.

2. The self-compensating machining method for thin-walled cabin cavities according to claim 1, characterized in that, Step S1 includes: selecting a metal cabin that needs to be machined internally as the original blank, and confirming that all spatial locations to be machined have the same physical properties, with the error in physical properties not exceeding 5%; Step S2 includes: installation and positioning via tooling, adhesive bonding, pressure plate installation; Step S3 includes: setting the product processing coordinate system according to the characteristics of the thin-walled cabin, installing a ball end mill with internal cooling, and compensating the tool length to the CNC system of the equipment; the internal cavity processing tool is a ball end mill with internal cooling, and the internal cooling opening is located at the center of the ball end mill.

3. The self-compensating machining method for thin-walled cabin cavities according to claim 1, characterized in that, Step S4 includes: planning the optimal internal cavity machining tool path based on the characteristics of the thin-walled cabin cavity, improving the continuity, efficiency and stability of subsequent tool path data self-compensation; the optimized tool path has the characteristics of smooth tool path, fewer tool entry and exit times and uniform cutting amount at all points.

4. The self-compensating machining method for thin-walled cabin cavities according to claim 1, characterized in that, In step S6: (1) When the angle between the ball end mill and the thin-walled inner cavity is 90°, the carbon nanotubes in the cutting fluid are uniformly distributed at the tip of the ball end mill, and the distribution area is S1 with an electric field strength of E (90). When the angle between the ball end mill and the thin-walled inner cavity is 90°, it is considered that the state at this time is the standard state, and the wall thickness measured at this time is h0. (2) When the angle between the ball end mill and the thin-walled inner cavity is >90°, the distribution area of ​​carbon nanotubes in the cutting fluid at the ball end is f*S1 / b, where f is the carbon nanotube material coefficient and b is the angle influence coefficient. At this time, the electric field strength E(θ) = E(90)*b / (f*S1); the wall thickness is h(δ) = h0*μ, where μ is the angle constant and is positively correlated with E(θ). (3) When the angle between the ball end mill and the thin-walled inner cavity is <90°, the distribution area of ​​carbon nanotubes in the cutting fluid at the ball end is f*S1 / a, where f is the carbon nanotube material coefficient and a is the angle influence coefficient. At this time, the electric field strength E(θ) = E(90)*a / (f*S1); the wall thickness is h(δ) = h0*μ, where μ is the angle constant and is positively correlated with E(θ). S1 is the area of ​​the distribution region of carbon nanotubes uniformly distributed in the cutting fluid at the tip of the ball end mill, E (90) is the electric field strength when the angle between the ball end mill and the thin-walled inner cavity is 90°, and the wall thickness measured when the angle between the ball end mill and the thin-walled inner cavity is 90° is h0.

5. The self-compensating machining method for thin-walled cabin cavities according to claim 1, characterized in that, Step S7 includes: comparing the wall thickness data of the spatial point collected in real time based on the cooling liquid containing carbon nanotubes with the processed theoretical wall thickness data of the spatial point to obtain the actual error of the wall thickness data of the spatial point; The method for obtaining the actual error of the wall thickness data of spatial points is: by measuring the actual processed wall thickness h(1) of the product in real time. The theoretical wall thickness of the product is determined according to the product's design specifications, which is h(0). The actual processing error λ = h(1) - h(0). The actual error refers to the specific actual value of undercut or overcut that occurs when the product is deformed during processing, even though it is processed according to the theoretical contour.

6. The self-compensating machining method for thin-walled cabin cavities according to claim 1, characterized in that, Step S8 includes: real-time compensation and correction of the point data at the spatial point optimized in step S4, thereby correcting the actual tool path at that point, realizing real-time self-compensation and self-correction of the tool path, and ensuring that the final wall thickness reaches the theoretical wall thickness h(0) of the product; the self-compensation standard procedure in step S8 is: based on the real-time obtained machining error λ, automatically correcting the X, Y, and Z of the CNC machining program to ensure: λ= Complete the real-time correction of the product tool path to ensure that the final wall thickness reaches the theoretical wall thickness h(0) of the product. Step S9 includes: traversing all machining tool paths in step S8 to achieve compensation machining of the thin-walled cabin cavity.

7. A self-compensating machining system for thin-walled cabin cavities, characterized in that, The thin-walled cabin cavity self-compensation machining method according to any one of claims 1 to 6 is used for machining.

8. A thin-walled cabin cavity, characterized in that, It is processed by the self-compensating machining method for the thin-walled cabin cavity as described in any one of claims 1 to 6, or processed by the self-compensating machining system for the thin-walled cabin cavity as described in claim 7.

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