A method and device for predicting and compensating surface assembly quality

Abstract

The present invention discloses a method and device for predicting and repairing and compensating the quality of curved surface assembly, and relates to the technical field of precision machining of numerically controlled machine tools. A specific implementation of the method includes: inputting a geometric error function and a thermal error function into a spatial error model to obtain a machining error prediction model; superimposing the acquired machining error on a theoretical surface of the assembly surface to obtain a predicted machining surface; calculating the shape error and assembly gap error of the assembly surface according to the assembly mid-plane determined by the assembly surface, and using the shape error and assembly gap error to predict the curved surface assembly quality of the part; when the curved surface assembly quality does not meet the preset assembly quality requirements, calculating the repair compensation amount of each assembly surface in the assembly surface based on the shape error, assembly gap error and machining error, and using the repair compensation amount to compensate the corresponding assembly surface. This implementation can ensure the assembly quality of the product.

Description

Technical Field

[0001] The invention relates to the technical field of precision machining of numerically controlled machine tools, and in particular to a method and device for predicting and repairing and compensating the quality of curved surface assembly. Background Art

[0002] As electromechanical products such as satellites, rockets, and aircraft develop towards complexity, lightness, and precision, the requirements for assembly accuracy and the difficulty of assembly and adjustment are increasing day by day, and it is becoming increasingly difficult to ensure product assembly performance. In such electromechanical products, a large number of key components are complex curved surfaces, and the assembly quality of these complex curved surface parts will have a significant impact on product performance.

[0003] In order to achieve high-quality assembly of complex curved surface parts, not only does it require very high geometric accuracy, but also multiple complex curved surface parts need to have good assembly accuracy and assembly performance. Complex curved surface parts are usually processed by five-axis CNC machine tools, and their assembly errors are mainly caused by the cumulative coupling of the machining errors of each part. The geometric and thermal errors of five-axis CNC machine tools account for 50% to 70% of the total machine tool errors, which is an important factor affecting the machining accuracy of complex curved surfaces. Assembly is one of the last links in product manufacturing, and the assembly quality largely determines the final quality of the product. However, the existing methods for improving assembly quality only focus on the fitting clearance, ignore the surface errors between the curved surfaces, and cannot guarantee the assembly quality of the final product. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and device for predicting and repairing and compensating the quality of curved surface assembly, which evaluates the curved surface assembly quality from two aspects: shape error and assembly gap error, and then calculates the optimal compensation amount of each assembly surface based on these two aspects, thereby ensuring the product assembly quality.

[0005] To achieve the above objective, according to one aspect of the present invention, a method for predicting and repairing and compensating surface assembly quality is provided.

[0006] The method for predicting and repairing and compensating the quality of curved surface assembly according to the embodiment of the present invention comprises: establishing a spatial error model of a machine tool; fitting the measured real geometric error data of the machine tool to form a geometric error function; fitting the measured real thermal error data of the machine tool to form a thermal error function; inputting the geometric error function and the thermal error function into the spatial error model to obtain a machining error prediction model of the machine tool; using the machining error prediction model to obtain the machining error of the assembly surface of the part, superimposing the obtained machining error on the theoretical surface of the assembly surface to obtain a predicted machining surface; determining the initial assembly position of the predicted machining surface, Based on predetermined surface information of the assembly surface, the relative position between the surfaces of the predicted machining surface is optimized to achieve assembly positioning of the part; for the part that achieves the assembly positioning, the shape error and assembly gap error of the assembly surface are calculated according to the assembly mid-plane determined by the assembly surface, and the shape error and the assembly gap error are used to predict the surface assembly quality of the part; when the surface assembly quality does not meet the preset assembly quality requirements, the repair compensation amount of each assembly surface in the assembly surface is calculated based on the shape error, the assembly gap error and the machining error, and the corresponding assembly surface is compensated using the repair compensation amount.

[0007] Optionally, the real thermal error data of the machine tool obtained by measurement is fitted to form a thermal error function, including: fitting the geometric part data in the real thermal error data into a polynomial function with coordinate values ​​as independent variables, fitting the thermal part data in the real thermal error data into a time-varying slope function with temperature as the independent variable, and superimposing the polynomial function and the time-varying slope function to obtain the thermal error function.

[0008] Optionally, the method further comprises: after obtaining the predicted machining surface, obtaining assembly constraint information of a real assembly scene, transforming the assembly constraint information into geometric information in the form of a transformation matrix, and combining the geometric information with the predicted machining surface to constrain the predicted machining surface.

[0009] Optionally, calculating the shape surface error includes: performing an averaging calculation on two assembly surfaces in the assembly surface to obtain the assembly median plane, discretizing the assembly median plane and the two assembly surfaces into multiple point cloud coordinates and normalizing the point cloud coordinates; matching the point cloud coordinates of the assembly median plane and the two assembly surfaces using a bulldozer distance algorithm; after the matching is completed, determining the minimum value of the sum of the Euclidean distances between each point on the assembly median plane and the corresponding points on the two assembly surfaces as the shape surface error.

[0010] Optionally, calculating the assembly gap error includes: performing an averaging calculation on two assembly surfaces in the assembly surface to obtain the assembly mid-plane, discretizing the assembly mid-plane and the two assembly surfaces into multiple point cloud coordinates and normalizing the point cloud coordinates; matching the point cloud coordinates of the assembly mid-plane and the two assembly surfaces using a bulldozer distance algorithm; after the matching is completed, determining the root mean square error between the coordinates of each point on the assembly mid-plane and the corresponding point on the two assembly surfaces as the assembly gap error.

[0011] Optionally, the assembly surface includes a pre-processed assembly surface and a post-processed assembly surface; the repair compensation amount Δa of the pre-processed assembly surface is calculated according to the following formula:

[0012] A0+a0+Δa=(B0+b0)·M

[0013] Wherein, A0 represents the coordinate data of the first-machined assembly surface, B0 represents the coordinate data of the post-machined assembly surface, a0 represents the machining error between the first-machined assembly surface and the corresponding theoretical surface, b0 represents the machining error between the post-machined assembly surface and the corresponding theoretical surface, and M represents the transformation matrix from the post-machined assembly surface coordinate system to the first-machined assembly surface coordinate system; and the method further includes: after compensating the first-machined assembly surface with the repair compensation amount Δa, judging whether the compensated first-machined assembly surface meets the first constraint condition determined by the surface error: if so, performing the machining operation; otherwise, setting b0 of the point that does not meet the first constraint condition to zero and recalculating the repair compensation amount Δa until the compensated first-machined assembly surface meets the first constraint condition; wherein, the first constraint condition is the tolerance zone added in the assembly median plane according to the surface error.

[0014] Optionally, the repair compensation amount Δb of the post-processing assembly surface is calculated according to the following formula:

[0015] B0+b0+Δb=A1·M'

[0016] Wherein, A1 represents the coordinate data of the first-processed assembly surface that meets the first constraint condition after compensation, M' represents the transformation matrix from the coordinate system of the first-processed assembly surface to the coordinate system of the post-processed assembly surface; and the method further includes: after compensating the post-processed assembly surface using the repair compensation amount Δb, judging whether the compensated post-processed assembly surface meets the second constraint condition determined by the assembly gap error: if so, executing the processing operation; otherwise, recalculating the repair compensation amount Δb until the compensated post-processed assembly surface meets the second constraint condition.

[0017] To achieve the above objective, according to another aspect of the present invention, a device for predicting and repairing and compensating surface assembly quality is provided.

[0018] The surface assembly quality prediction and repair compensation device of the embodiment of the present invention may include: a modeling unit, used to establish a spatial error model of a machine tool; fitting the measured real geometric error data of the machine tool to form a geometric error function; fitting the measured real thermal error data of the machine tool to form a thermal error function; inputting the geometric error function and the thermal error function into the spatial error model to obtain a machining error prediction model of the machine tool; an assembly positioning unit, used to obtain the machining error of the assembly surface of the part using the machining error prediction model, superimposing the obtained machining error on the theoretical surface of the assembly surface to obtain a predicted machining surface; determining an initial assembly position of the predicted machining surface based on The predetermined curved surface information of the assembly surface is used to optimize the relative position between the curved surfaces of the predicted machining surface to achieve the assembly positioning of the part; an assembly quality evaluation unit is used to: for the part that achieves the assembly positioning, calculate the shape error and assembly gap error of the assembly surface according to the assembly mid-plane determined by the assembly surface, and use the shape error and the assembly gap error to predict the curved surface assembly quality of the part; a repair compensation unit is used to: when the curved surface assembly quality does not meet the preset assembly quality requirements, calculate the repair compensation amount of each assembly surface in the assembly surface based on the shape error, the assembly gap error and the machining error, and use the repair compensation amount to compensate the corresponding assembly surface.

[0019] To achieve the above objective, according to another aspect of the present invention, an electronic device is provided.

[0020] An electronic device of the present invention includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the surface assembly quality prediction and repair compensation device method provided by the present invention.

[0021] To achieve the above objective, according to yet another aspect of the present invention, a computer-readable storage medium is provided.

[0022] A computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the surface assembly quality prediction and repair compensation device method provided by the present invention.

[0023] According to the technical solution of the present invention, the embodiments of the above invention have the following advantages or beneficial effects:

[0024] In the technical solution of the embodiment of the present invention, a spatial error model of a machine tool is first established, and the geometric error function and the thermal error function determined by the real error data are input into the spatial error model to obtain a machining error prediction model of the machine tool. Next, the machining error of the assembly surface of the part is obtained by using the machining error prediction model, and the obtained machining error is superimposed on the theoretical surface of the assembly surface to obtain the predicted machining surface, and the assembly positioning of the part is realized through the rough positioning and fine positioning steps. After that, the assembly median plane of the assembly surface is determined and the shape surface error and assembly clearance error of the assembly surface are calculated, and the calculated shape surface error and assembly clearance error are used to predict the surface assembly quality of the part. When the surface assembly quality does not meet the assembly quality requirements, the repair compensation amount of each assembly surface is calculated based on the shape surface error, the assembly clearance error and the machining error determined by the above machining error prediction model, and finally the corresponding assembly surface is compensated using the calculated repair compensation amount. Through the above steps, the surface assembly quality is comprehensively evaluated from the two aspects of the shape surface error and the assembly clearance error, and then the compensation amount of each assembly surface is accurately calculated from these two aspects, thereby greatly improving the assembly quality and assembly performance of the product.

[0025] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0027] Figure 1 It is a schematic diagram of the main steps of the method for predicting and repairing and compensating the surface assembly quality in an embodiment of the present invention;

[0028] Figure 2 is a schematic structural diagram of a five-axis machine tool according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of assembly surface prediction under the influence of geometric error and thermal error in an embodiment of the present invention;

[0030] Figure 4 It is a schematic diagram of coarse positioning and fine positioning of parts positioning in an embodiment of the present invention;

[0031] Figure 5 It is a schematic diagram of product assembly accuracy and performance analysis under the dual indicators of surface error and assembly clearance error in an embodiment of the present invention;

[0032] Figure 6 Schematic diagram of collaborative repair and compensation under the constraints of surface error and assembly gap error in an embodiment of the present invention;

[0033] Figure 7 It is a schematic diagram of the prediction of the shape error and assembly gap error of the assembly surface in an embodiment of the present invention;

[0034] Figure 8 is a schematic diagram of collaborative repair compensation according to an embodiment of the present invention;

[0035] Fig. 9 It is a schematic diagram of the components of the curved surface assembly quality prediction and repair compensation device in an embodiment of the present invention;

[0036] Fig.10 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;

[0037] Fig.11 It is a schematic diagram of the structure of an electronic device used to implement the method for predicting and repairing and compensating the curved surface assembly quality in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0039] It should be pointed out that the embodiments of the present invention and the technical features therein may be combined with each other without conflict.

[0040] Figure 1 It is a schematic diagram of the main steps of the method for predicting and repairing and compensating the surface assembly quality according to an embodiment of the present invention.

[0041] like Figure 1 As shown, the surface assembly quality prediction and repair compensation method of the embodiment of the present invention can be specifically performed according to the following steps:

[0042] Step S101: Establish a spatial error model of the machine tool; fit the measured real geometric error data of the machine tool to form a geometric error function; fit the measured real thermal error data of the machine tool to form a thermal error function; input the geometric error function and the thermal error function into the spatial error model to obtain a machining error prediction model of the machine tool.

[0043] Exemplarily, the above machine tool is a five-axis CNC machine tool. In this step, a spatial error model containing spatial position error and direction error can be pre-established, and a geometric error function and a thermal error function can be obtained by fitting the error data. In practical applications, the geometric part data in the real thermal error data can be fitted to a polynomial function with the coordinate value as the independent variable, and the thermal part data in the real thermal error data can be fitted to a time-varying slope function with the temperature as the independent variable, and finally the polynomial function and the time-varying slope function are superimposed to form a thermal error function.

[0044] Step S102: using the machining error prediction model to obtain the machining error of the assembly surface of the part, superimposing the obtained machining error on the theoretical surface of the assembly surface to obtain the predicted machining surface; determining the initial assembly position of the predicted machining surface, and optimizing the relative position between the surfaces of the predicted machining surface based on the predetermined surface information of the assembly surface to achieve assembly positioning of the part.

[0045] The purpose of this step is to simulate the real assembly state to realize the assembly positioning of parts. Specifically, the machining error prediction model determined in step S101 is first used to calculate the machining error of the assembly surface, and then the machining error is superimposed on the theoretical surface of the assembly surface to obtain the predicted machining surface. As a preferred scheme, the assembly constraint information of the real assembly scene can be obtained thereafter, the assembly constraint information can be transformed into geometric information in the form of a transformation matrix, and the geometric information can be combined with the predicted machining surface to constrain the predicted machining surface. Finally, the two-step positioning method of coarse positioning and fine positioning proposed in an embodiment of the present invention can be used to realize assembly positioning. Specifically, in the coarse positioning step, the initial assembly position of the predicted machining surface is first determined; in the fine positioning step, the relative position between the curved surfaces of the predicted machining surface is optimized based on the predetermined curved surface information of the assembly surface, thereby realizing the assembly positioning of parts.

[0046] Step S103: for the part to be assembled and positioned, the shape error and assembly clearance error of the assembly surface are calculated according to the assembly mid-plane determined by the assembly surface, and the shape error and assembly clearance error are used to predict the surface assembly quality of the part.

[0047] In this step, the concepts of shape error and assembly gap error are proposed. Shape error refers to the error formed from the difference in the two-dimensional shape of the assembly surface, and assembly gap error refers to the distance error formed from the angle of the assembly gap. In practical applications, the shape error can be calculated by the following steps: First, the two assembly surfaces in the assembly surface are averaged to obtain the assembly mid-plane, the assembly mid-plane and the two assembly surfaces are discretized into multiple point cloud coordinates and the point cloud coordinates are normalized; then, the bulldozer distance algorithm is used to match the point cloud coordinates of the assembly mid-plane and the two assembly surfaces; after the matching is completed, the minimum value of the sum of the Euclidean distances between each point on the assembly mid-plane and the corresponding points on the two assembly surfaces is determined as the shape error.

[0048] In specific applications, the assembly gap error can be calculated through the following steps: first, the two assembly surfaces in the assembly surface are averaged to obtain the assembly mid-plane, the assembly mid-plane and the two assembly surfaces are discretized into multiple point cloud coordinates and the point cloud coordinates are normalized; then, the bulldozer distance algorithm is used to match the point cloud coordinates of the assembly mid-plane and the two assembly surfaces; after the matching is completed, the root mean square error between the coordinates of each point on the assembly mid-plane and the corresponding point on the two assembly surfaces is determined as the assembly gap error.

[0049] Step S104: When the surface assembly quality does not meet the preset assembly quality requirements, the repair compensation amount of each assembly surface in the assembly surface is calculated based on the shape error, assembly gap error and processing error, and the repair compensation amount is used to compensate the corresponding assembly surface.

[0050] In this step, the compensation amount of each assembly surface can be calculated for the case where the assembly quality is unqualified, thereby improving the assembly quality. Specifically, the above assembly surface can include a pre-processed assembly surface and a post-processed assembly surface; the repair compensation amount Δa of the pre-processed assembly surface can be calculated according to the following formula:

[0051] A0+a0+Δa=(B0+b0)·M

[0052] Among them, A0 represents the coordinate data of the first-processed assembly surface, B0 represents the coordinate data of the second-processed assembly surface, a0 represents the machining error between the first-processed assembly surface and the corresponding theoretical surface, b0 represents the machining error between the second-processed assembly surface and the corresponding theoretical surface, and M represents the transformation matrix from the coordinate system of the second-processed assembly surface to the coordinate system of the first-processed assembly surface.

[0053] Preferably, after compensating the pre-machined assembly surface using the repair compensation Δa, it can be determined whether the compensated pre-machined assembly surface meets the first constraint condition determined by the surface error. Exemplarily, the first constraint condition can be a tolerance zone added to the assembly mid-plane according to the surface error. If the compensated pre-machined assembly surface meets the first constraint condition, the machining operation is performed; otherwise, the b0 of the point that does not meet the first constraint condition can be set to zero and the repair compensation Δa can be recalculated until the compensated pre-machined assembly surface meets the first constraint condition.

[0054] In an optional technical solution, the repair compensation amount Δb of the post-processing assembly surface can be calculated according to the following formula:

[0055] B0+b0+Δb=A1·M'

[0056] Wherein, A1 represents the coordinate data of the first-machined assembly surface that meets the first constraint condition after compensation, and M' represents the transformation matrix from the coordinate system of the first-machined assembly surface to the coordinate system of the later-machined assembly surface.

[0057] Similarly, after using the repair compensation amount Δb to compensate the post-processing assembly surface, it can be determined whether the compensated post-processing assembly surface meets the second constraint determined by the assembly clearance error: if so, perform the processing operation; otherwise, recalculate the repair compensation amount Δb until the compensated post-processing assembly surface meets the second constraint.

[0058] A specific embodiment of the present invention is described below.

[0059] The specific steps of this embodiment are as follows:

[0060] The first step is to predict the dimensional error of complex surfaces under the influence of geometric error and thermal error of five-axis machine tools.

[0061] There are 41 geometric errors in a five-axis CNC machine tool, 21 geometric errors in the three translation axes, 8 position-independent geometric errors (PIGEs) and 12 position-dependent geometric errors (PDGEs) in the two rotation axes. Here, the XACRYZ five-axis machine tool is taken as an example, and the definition of ISO 230-1 is followed. Its 41 geometric errors are shown in the following table.

[0062]

[0063] The kinematic chain structure of the five-axis CNC machine tool is as follows Figure 2 As shown, the X-axis, A-axis and C-axis are the workpiece kinematic chain, and the Y-axis and Z-axis are the tool kinematic chain. Based on the homogeneous coordinate transformation matrix method, the spatial error model of the five-axis machine tool can be established, in which the spatial position error ΔP and the direction error ΔO are shown as follows:

[0064]

[0065] in, is the actual motion matrix of the tool relative to the workpiece, is the ideal motion matrix of the tool relative to the workpiece.

[0066] After establishing the spatial error model of the five-axis machine tool, 41 geometric errors and thermal errors can be measured by laser interferometers, ballbars and other equipment. For geometric errors, the error curve can be fitted with a polynomial and described as a function of position (i.e., geometric error function); for thermal errors, the constant shape (geometric part) of the positioning error is fitted into a polynomial function with coordinate values ​​as independent variables, and the time-varying slope (thermal part) is fitted into a function of temperature, and finally a composite function (i.e., thermal error function) formed by the superposition of the constant shape and the time-varying slope is obtained. Substituting the geometric error function and the thermal error function into the spatial error model, a machining error prediction model is obtained, which can realize real-time prediction of machining errors on the workpiece tool path.

[0067] Step 2: Assembly positioning between assembly parts.

[0068] In the prediction and control of assembly accuracy, determining the assembly positioning of the part model is the primary issue. In practical applications, the assembly parts are first programmed to obtain the NC code used to control the machine tool. Then, the machining error of the assembly surface is predicted based on the above machining error prediction model, and finally the machining error is superimposed on the theoretical surface to obtain the predicted machining surface.

[0069] In the product design stage, the part model is theoretically assembled in the CAD software according to the assembly constraints. When it is exported in the form of STEP for real assembly simulation, only the final assembly state of the part (i.e., only the spatial position information) is often retained, and its assembly constraint information is lost. Specifically, in the embodiment of the present invention, the assembly constraints can be converted into geometric information, and the spatial position state of each part can be constrained in the form of a conversion matrix.

[0070] The assembly positioning of parts can be divided into two processes: rough positioning and fine positioning. In the rough positioning process, the initial position of the surface is determined by the ideal model positioning reference; in the fine positioning process, the relative position between the surfaces is optimized based on the surface information. The above steps can be seen in Figure 3 and Figure 4 , it needs to be explained that this article involves Figures 3 to 8 The relevant steps and processes are only shown as schematic diagrams. The specific contents and detailed values ​​of the formulas, charts, and coordinate systems involved in each figure do not need to be explained one by one. Figure 3 The process of forming a predicted machining surface by superimposing the theoretical assembly surface based on the NC code and the machining error is shown. Figure 4The assembly process simulating the real assembly state and the assembly method based on rough positioning and fine positioning are shown.

[0071] The third step is to predict the assembly accuracy based on the “shape-gap” index (i.e., the shape error index and the assembly gap error index).

[0072] The assembly gap between assembly surfaces is directly related to assembly accuracy and is one of the important characteristics reflecting product quality. It is often used for assembly accuracy analysis. However, complex curved surface products need to consider both assembly accuracy and assembly performance. Therefore, this embodiment uses the "shape-gap" dual assembly index to simultaneously analyze the assembly accuracy and performance of the product to ensure the qualified rate of electromechanical products.

[0073] like Figure 5 As shown in the figure, the "shape" index refers to the surface deviation between the assembly mid-plane and the theoretical assembly surface, which determines the assembly accuracy; the "gap" index corresponds to the gap between the two assembly surfaces, and its uniformity determines the assembly performance. The use of the "shape-gap" dual index can effectively improve the assembly quality. Shape surface error E shape The calculation formula is as follows:

[0074]

[0075] The calculation process is as follows: first, the two assembly surfaces are averaged to obtain the assembly median surface, and the assembly median surface and the theoretical assembly surface are discretized into point cloud coordinates (the corresponding points are P and Q, and N is the number of points on each surface). Next, the coordinates of the assembly median surface and the theoretical assembly surface are normalized. After that, the Earth Mover's Distance algorithm is used to match the points of the assembly median surface and the theoretical assembly surface. After the matching is completed, the Euclidean distance between the corresponding points and (d ij The minimum value of the Euclidean distance is the surface error.

[0076] The "gap" index refers to the assembly gap error E between the two assembly surfaces gap , its uniformity determines the assembly performance. The present invention adopts the root mean square error method to calculate the assembly gap error, and the calculation process is as follows:

[0077]

[0078] Among them, x i ,y i 、z i is the plane p on the assembly i The coordinate value of is the theoretical assembly surface q i The coordinate value of .

[0079] Step 4: Calculation of compensation for “shape-gap” collaborative repair

[0080] This step uses the "shape-gap" index as the optimization target, and improves the assembly quality of the product through coordinated repair and compensation. The first processed assembly surface is defined as surface A, and the later processed assembly surface is defined as surface B. The calculation method of the repair and compensation amount is described as follows.

[0081] First, the assembly mid-plane is obtained through two mating surfaces. According to the "shape" index requirements, a tolerance zone is added to the assembly mid-plane as a constraint condition for the repair calculation amount (the first constraint condition). In order to ensure the assembly quality, the repair compensation amount of surface A needs to comprehensively consider the machine tool processing error, the processing quality of surface B and the first constraint condition. The repair compensation amount Δa of surface A is calculated according to the following formula:

[0082] A0+a0+Δa=(B0+b0)·M

[0083] Among them, A0 represents the coordinate data of A, B0 represents the coordinate data of B surface, a0 represents the processing error between A surface and the corresponding theoretical surface, b0 represents the processing error between B surface and the corresponding theoretical surface, and M represents the transformation matrix from the coordinate system of A surface to the coordinate system of B surface.

[0084] After obtaining the repair compensation Δa of surface A through the above formula, the compensated assembly surface can be calculated based on the mirror method. After that, the performance of the compensated assembly surface A is evaluated to see if it meets the first constraint. If it fully meets the first constraint, processing can be started; if a point exceeds the performance tolerance band, b0=0 is taken at that point, the repair compensation Δa is recalculated and surface A is fitted. Processing can be started after the first constraint is met. Surface A is reconstructed based on Δa to obtain assembly surface A1.

[0085] After that, the compensation calculation is performed on the B surface. To ensure the assembly quality, the compensation amount of the B surface needs to comprehensively consider the predicted processing error, the predicted compensation amount of the A surface and the "gap" index constraint (i.e., the second constraint condition). The compensation amount Δb of the B surface is calculated according to the following formula:

[0086] B0+b0+Δb=A1·M'

[0087] A1 represents the coordinate data of the A surface that meets the first constraint condition after compensation, and M' represents the conversion matrix from the A surface coordinate system to the B surface coordinate system.

[0088] Finally, based on the optimized repair compensation Δb of the B surface, the mirror method can be used to obtain the compensated B1 surface, and the digital-driven assembly accuracy analysis model can be used to simulate the "form-gap" index under real assembly, thereby verifying the effectiveness of the "form-gap" collaborative repair compensation method. The above repair compensation calculation process can be found in Figure 6 .

[0089] Another specific embodiment of the present invention is described below.

[0090] In this embodiment, for Figure 7 (a) The complex surface assembly is analyzed for assembly accuracy. First, the tool path and NC code of the complex surface assembly are obtained by Surfmill and NX 12.0 software, and the geometric error and thermal error of the five-axis machine tool are predicted to obtain the actual machining surface, such as Figure 7 (b) As shown. By constructing the mapping parts and supplementing the assembly positioning information, the “shape-gap” error of the assembly is analyzed, as shown in Figure 7 (c) The results show that the shape error between the median surface of the complex curved surface assembly and the theoretical assembly surface reaches 10%, and the assembly clearance between the upper and lower assembly surfaces exceeds the theoretical value and is uneven, requiring compensation and repair to ensure assembly quality. Compared with the measured morphology, the prediction accuracy of this prediction method is over 90%.

[0091] like Figure 8 As shown in the figure, based on the results of assembly quality analysis, a preliminary experiment of collaborative repair and compensation was further conducted on complex curved surface mating parts. First, based on the predicted machine tool processing error, the processing quality of the mating surface and the "shape" index constraint, the repair compensation amount of the first processed assembly surface was calculated to obtain the compensated processing surface A. Then, based on the "gap" index constraint and the compensated surface A, the repair compensation amount of surface B was calculated. Finally, the collaborative repair of A and B was achieved, and the assembly accuracy after repair was improved by 32%.

[0092] In summary, the embodiment of the present invention provides a method for predicting the assembly quality of complex curved surfaces and "shape-gap" collaborative repair and compensation. The basic steps are: first, establish a machine tool geometric error and thermal error model, then simulate the real assembly state based on the "coarse positioning-fine positioning" method, and then construct a "shape-gap" dual index system to evaluate the assembly quality of complex curved surfaces, that is, by establishing an assembly mid-plane and calculating the shape surface error and matching clearance between the assembly surfaces to predict the assembly quality, and finally based on the optimal assembly accuracy target, collaboratively calculate the repair compensation amount of each assembly part, and perform real-time compensation on each assembly surface. In this way, compared with the prior art, the following beneficial effects can be achieved: in the complex curved surface assembly quality judgment scheme based on shape surface error and assembly clearance described above, the assembly mid-plane is established and the shape surface error and matching clearance between the assembly surfaces are calculated, which improves the prediction accuracy of the assembly quality, and further adopts the "shape-gap" dual index to evaluate the assembly quality, with the "shape-gap" assembly performance optimization as the goal, and the collaborative repair compensation amount of the assembly surface is calculated based on the constraint relationship between the assembly surfaces, and the assembly quality and assembly performance can be greatly improved after compensation.

[0093] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of the present invention are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0094] For the above-mentioned method embodiments, for the convenience of description, they are expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the order of the actions described, and some steps can actually be performed in other orders or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary to implement the present invention.

[0095] In order to better implement the above-mentioned solution of the embodiment of the present invention, relevant devices for implementing the above-mentioned solution are also provided below.

[0096] See also Fig. 9 As shown, the surface assembly quality prediction and repair compensation device 900 provided by the embodiment of the present invention may include: a modeling unit 901 , an assembly positioning unit 902 , an assembly quality evaluation unit 903 and a repair compensation unit 904 .

[0097] Among them, the modeling unit 901 can be used to establish a spatial error model of the machine tool; fit the measured real geometric error data of the machine tool to form a geometric error function; fit the measured real thermal error data of the machine tool to form a thermal error function; input the geometric error function and the thermal error function into the spatial error model to obtain a machining error prediction model of the machine tool; the assembly positioning unit 902 can be used to use the machining error prediction model to obtain the machining error of the assembly surface of the part, superimpose the obtained machining error on the theoretical surface of the assembly surface, and obtain a predicted machining surface; determine the initial assembly position of the predicted machining surface, based on the predetermined assembly surface The surface information of the predicted machining surface is used to optimize the relative position between the surfaces of the predicted machining surface to achieve the assembly positioning of the part; the assembly quality evaluation unit 903 can be used for: for the part that achieves the assembly positioning, calculating the shape error and the assembly gap error of the assembly surface according to the assembly mid-plane determined by the assembly surface, and using the shape error and the assembly gap error to predict the surface assembly quality of the part; the repair compensation unit 904 can be used for: when the surface assembly quality does not meet the preset assembly quality requirements, calculating the repair compensation amount of each assembly surface in the assembly surface based on the shape error, the assembly gap error and the machining error, and using the repair compensation amount to compensate the corresponding assembly surface.

[0098] In an embodiment of the present invention, the modeling unit 901 can be further used to: fit the geometric part data in the actual thermal error data into a polynomial function with the coordinate value as the independent variable, fit the thermal part data in the actual thermal error data into a time-varying slope function with the temperature as the independent variable, and superimpose the polynomial function and the time-varying slope function into the thermal error function.

[0099] As a preferred solution, the assembly positioning unit 902 can be further used to: after obtaining the predicted machining surface, obtain assembly constraint information of the actual assembly scene, transform the assembly constraint information into geometric information in the form of a transformation matrix, and combine the geometric information with the predicted machining surface to constrain the predicted machining surface.

[0100] Preferably, the assembly quality evaluation unit 903 can be further used to: perform an averaging calculation on the two assembly surfaces in the assembly surface to obtain the assembly median plane, discretize the assembly median plane and the two assembly surfaces into a plurality of point cloud coordinates and perform normalization on the point cloud coordinates; use a bulldozer distance algorithm to match the point cloud coordinates of the assembly median plane and the two assembly surfaces; after the matching is completed, determine the minimum value of the sum of the Euclidean distances between each point on the assembly median plane and the corresponding points on the two assembly surfaces as the surface error.

[0101] In practical applications, the assembly quality evaluation unit 903 can be further used to: perform an average calculation on the two assembly surfaces in the assembly surface to obtain the assembly median plane, discretize the assembly median plane and the two assembly surfaces into multiple point cloud coordinates and normalize the point cloud coordinates; use the bulldozer distance algorithm to match the point cloud coordinates of the assembly median plane and the two assembly surfaces; after the matching is completed, determine the root mean square error between the coordinates of each point on the assembly median plane and the corresponding point on the two assembly surfaces as the assembly gap error.

[0102] In a specific application, the assembly surface includes a pre-processed assembly surface and a post-processed assembly surface; the repair compensation unit 904 can be further used to calculate the repair compensation amount Δa of the pre-processed assembly surface by the following formula:

[0103] A0+a0+Δa=(B0+b0)·M

[0104] Wherein, A0 represents the coordinate data of the first-machined assembly surface, B0 represents the coordinate data of the post-machined assembly surface, a0 represents the machining error between the first-machined assembly surface and the corresponding theoretical surface, b0 represents the machining error between the post-machined assembly surface and the corresponding theoretical surface, and M represents the transformation matrix from the post-machined assembly surface coordinate system to the first-machined assembly surface coordinate system; the repair compensation unit 904 can be further used for: after compensating the first-machined assembly surface with the repair compensation amount Δa, judging whether the compensated first-machined assembly surface meets the first constraint condition determined by the surface error: if so, executing the machining operation; otherwise, setting b0 of the point that does not meet the first constraint condition to zero and recalculating the repair compensation amount Δa until the compensated first-machined assembly surface meets the first constraint condition; wherein, the first constraint condition is the tolerance zone added in the assembly midplane according to the surface error.

[0105] In an optional technical solution, the repair compensation unit 904 may be further configured to calculate the repair compensation amount Δb of the post-processing assembly surface according to the following formula:

[0106] B0+b0+Δb=A1·M'

[0107] Among them, A1 represents the coordinate data of the first-processed assembly surface that meets the first constraint condition after compensation, and M' represents the transformation matrix from the coordinate system of the first-processed assembly surface to the coordinate system of the post-processed assembly surface; the repair compensation unit 904 can be further used to: after compensating the post-processed assembly surface using the repair compensation amount Δb, determine whether the compensated post-processed assembly surface meets the second constraint condition determined by the assembly gap error: if so, perform the processing operation; otherwise recalculate the repair compensation amount Δb until the compensated post-processed assembly surface meets the second constraint condition.

[0108] According to the technical solution of the embodiment of the present invention, the surface assembly quality is comprehensively evaluated from two aspects: shape error and assembly gap error, and then the compensation amount of each assembly surface is accurately calculated from these two aspects, thereby greatly improving the assembly quality and assembly performance of the product.

[0109] Fig.10 An exemplary system architecture 1000 is shown to which the method for predicting and repairing and compensating the quality of curved surface assembly or the device for predicting and repairing and compensating the quality of curved surface assembly according to the embodiment of the present invention can be applied.

[0110] like Fig.10 As shown, the system architecture 1000 may include terminal devices 1001, 1002, 1003, a network 1004 and a server 1005 (this architecture is only an example, and the components included in the specific architecture may be adjusted according to the specific application). The network 1004 is used to provide a medium for a communication link between the terminal devices 1001, 1002, 1003 and the server 1005. The network 1004 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0111] Users can use terminal devices 1001, 1002, 1003 to interact with server 1005 through network 1004 to receive or send messages, etc. Various client applications can be installed on terminal devices 1001, 1002, 1003, such as assembly quality evaluation application (only as an example).

[0112] The terminal devices 1001 , 1002 , and 1003 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers, etc.

[0113] The server 1005 may be a server that provides various services, such as a background server (only an example) that provides support for the assembly quality evaluation application operated by the user using the terminal devices 1001, 1002, 1003. The background server may process the received quality evaluation request and feed back the processing result (such as the calculated assembly quality score - only an example) to the terminal devices 1001, 1002, 1003.

[0114] It should be noted that the curved surface assembly quality prediction and repair compensation method provided in the embodiment of the present invention can be executed by the server 1005 , and accordingly, the curved surface assembly quality prediction and repair compensation device can be set in the server 1005 .

[0115] It should be understood that Fig.10 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0116] The present invention also provides an electronic device. The electronic device of an embodiment of the present invention includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the surface assembly quality prediction and repair compensation method provided by the present invention.

[0117] Reference below Fig.11 , which shows a schematic diagram of the structure of a computer system 1100 of an electronic device suitable for implementing an embodiment of the present invention. Fig.11 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0118] like Fig.11 As shown, the computer system 1100 includes a central processing unit (CPU) 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage part 1108 into a random access memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the computer system 1100 are also stored. The CPU 1101, the ROM 1102, and the RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0119] The following components are connected to the I / O interface 1105: an input section 1106 including a keyboard, a mouse, etc.; an output section 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed so that a computer program read therefrom is installed into the storage section 1108 as needed.

[0120] In particular, according to the embodiments disclosed in the present invention, the process described in the main step diagram above can be implemented as a computer software program. For example, the embodiments of the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the main step diagram. In the above embodiment, the computer program can be downloaded and installed from the network through the communication part 1109, and / or installed from the removable medium 1111. When the computer program is executed by the central processing unit 1101, the above functions defined in the system of the present invention are executed.

[0121] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0122] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The units involved in the embodiments of the present invention may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes a modeling unit, an assembly positioning unit, an assembly quality evaluation unit, and a repair compensation unit. The names of these units do not constitute a limitation on the units themselves in some cases. For example, the modeling unit may also be described as "a unit that provides a machining error prediction model to the assembly positioning unit."

[0124] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by the device, the steps executed by the device include: establishing a spatial error model of the machine tool; fitting the measured real geometric error data of the machine tool to form a geometric error function; fitting the measured real thermal error data of the machine tool to form a thermal error function; inputting the geometric error function and the thermal error function into the spatial error model to obtain a machining error prediction model of the machine tool; using the machining error prediction model to obtain the machining error of the assembly surface of the part, superimposing the obtained machining error on the theoretical surface of the assembly surface to obtain a predicted machining surface; determining the Predict the initial assembly position of the machining surface, optimize the relative position between the curved surfaces of the predicted machining surface based on the predetermined curved surface information of the assembly surface, so as to achieve the assembly positioning of the part; for the part that has achieved the assembly positioning, calculate the shape error and assembly gap error of the assembly surface according to the assembly mid-plane determined by the assembly surface, and use the shape error and the assembly gap error to predict the curved surface assembly quality of the part; when the curved surface assembly quality does not meet the preset assembly quality requirements, calculate the repair compensation amount of each assembly surface in the assembly surface based on the shape error, the assembly gap error and the machining error, and use the repair compensation amount to compensate the corresponding assembly surface.

[0125] In the technical solution of the embodiment of the present invention, a spatial error model of a machine tool is first established, and the geometric error function and the thermal error function determined by the real error data are input into the spatial error model to obtain a machining error prediction model of the machine tool. Next, the machining error of the assembly surface of the part is obtained by using the machining error prediction model, and the obtained machining error is superimposed on the theoretical surface of the assembly surface to obtain the predicted machining surface, and the assembly positioning of the part is realized through the rough positioning and fine positioning steps. After that, the assembly median plane of the assembly surface is determined and the shape surface error and assembly clearance error of the assembly surface are calculated, and the calculated shape surface error and assembly clearance error are used to predict the surface assembly quality of the part. When the surface assembly quality does not meet the assembly quality requirements, the repair compensation amount of each assembly surface is calculated based on the shape surface error, the assembly clearance error and the machining error determined by the above machining error prediction model, and finally the corresponding assembly surface is compensated using the calculated repair compensation amount. Through the above steps, the surface assembly quality is comprehensively evaluated from the two aspects of the shape surface error and the assembly clearance error, and then the compensation amount of each assembly surface is accurately calculated from these two aspects, thereby greatly improving the assembly quality and assembly performance of the product.

[0126] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting and compensating the quality of curved surface assembly, characterized in that: include: Establish the spatial error model of machine tools; Fitting the measured real geometric error data of the machine tool to form a geometric error function; Fitting the measured real thermal error data of the machine tool to form a thermal error function; Inputting the geometric error function and the thermal error function into the spatial error model to obtain a machining error prediction model of the machine tool; The machining error of the assembly surface of the part is obtained by using the machining error prediction model, and the obtained machining error is superimposed on the theoretical surface of the assembly surface to obtain the predicted machining surface; the initial assembly position of the predicted machining surface is determined, and the relative position between the curved surfaces of the predicted machining surface is optimized based on the predetermined curved surface information of the assembly surface to achieve the assembly positioning of the part; For the part that realizes the assembly positioning, the shape error and the assembly gap error of the assembly surface are calculated according to the assembly median plane determined by the assembly surface, and the surface error and the assembly gap error are used to predict the surface assembly quality of the part; When the curved surface assembly quality does not meet the preset assembly quality requirements, the repair compensation amount of each assembly surface in the assembly surface is calculated based on the shape error, the assembly gap error and the processing error, and the repair compensation amount is used to compensate the corresponding assembly surface.

2. The method according to claim 1, characterized in that The step of fitting the measured real thermal error data of the machine tool to form a thermal error function comprises: The geometric part data in the real thermal error data is fitted into a polynomial function with coordinate values ​​as independent variables, the thermal part data in the real thermal error data is fitted into a time-varying slope function with temperature as independent variable, and the polynomial function and the time-varying slope function are superimposed to obtain the thermal error function.

3. The method according to claim 1, characterized in that The method further comprises: After obtaining the predicted machining surface, assembly constraint information of the real assembly scene is obtained, the assembly constraint information is transformed into geometric information in the form of a transformation matrix, and the geometric information is combined with the predicted machining surface to constrain the predicted machining surface.

4. The method according to claim 1, characterized in that: Calculating the surface error includes: Performing an average calculation on two assembly surfaces in the assembly surface to obtain the assembly median surface, discretizing the assembly median surface and the two assembly surfaces into a plurality of point cloud coordinates and performing normalization on the point cloud coordinates; The bulldozer distance algorithm is used to match the point cloud coordinates of the assembly median plane and the two assembly surfaces; after the matching is completed, the minimum value of the sum of the Euclidean distances between each point on the assembly median plane and the corresponding points on the two assembly surfaces is determined as the surface error.

5. The method according to claim 1, characterized in that Calculating the assembly gap error includes: Performing an average calculation on two assembly surfaces in the assembly surface to obtain the assembly median surface, discretizing the assembly median surface and the two assembly surfaces into a plurality of point cloud coordinates and performing normalization on the point cloud coordinates; The bulldozer distance algorithm is used to match the point cloud coordinates of the assembly median plane and the two assembly surfaces; after the matching is completed, the root mean square error between the coordinates of each point on the assembly median plane and the corresponding point on the two assembly surfaces is determined as the assembly gap error.

6. The method according to claim 1, characterized in that The assembly surface includes a pre-processed assembly surface and a post-processed assembly surface; the repair compensation amount Δa of the pre-processed assembly surface is calculated according to the following formula: A0+a0+Δa=(B0+b0)·M Wherein, A0 represents the coordinate data of the first-machined assembly surface, B0 represents the coordinate data of the second-machined assembly surface, a0 represents the machining error between the first-machined assembly surface and the corresponding theoretical surface, b0 represents the machining error between the second-machined assembly surface and the corresponding theoretical surface, and M represents the conversion matrix from the second-machined assembly surface coordinate system to the first-machined assembly surface coordinate system; and, The method further includes: after compensating the first-machined assembly surface with the repair compensation amount Δa, judging whether the compensated first-machined assembly surface meets the first constraint condition determined by the surface error: if so, performing the machining operation; otherwise, setting b0 of the points that do not meet the first constraint condition to zero and recalculating the repair compensation amount Δa until the compensated first-machined assembly surface meets the first constraint condition; wherein the first constraint condition is a tolerance zone added in the assembly median plane according to the surface error.

7. The method according to claim 6, characterized in that The repair compensation amount Δb of the post-processing assembly surface is calculated according to the following formula: B0+b0+Δb=A1·M' Wherein, A1 represents the coordinate data of the first-machined assembly surface that meets the first constraint condition after compensation, M' represents the transformation matrix from the coordinate system of the first-machined assembly surface to the coordinate system of the later-machined assembly surface; and, The method further includes: after compensating the post-processing assembly surface using the repair compensation amount Δb, judging whether the compensated post-processing assembly surface meets the second constraint condition determined by the assembly gap error: if so, performing the processing operation; otherwise, recalculating the repair compensation amount Δb until the compensated post-processing assembly surface meets the second constraint condition.

8. A curved surface assembly quality prediction and repair compensation device, characterized in that: include: A modeling unit, used to establish a spatial error model of the machine tool; Fitting the measured real geometric error data of the machine tool to form a geometric error function; Fitting the measured real thermal error data of the machine tool to form a thermal error function; Inputting the geometric error function and the thermal error function into the spatial error model to obtain a machining error prediction model of the machine tool; An assembly positioning unit is used to obtain the machining error of the assembly surface of the part by using the machining error prediction model, superimpose the obtained machining error on the theoretical surface of the assembly surface to obtain the predicted machining surface; determine the initial assembly position of the predicted machining surface, and optimize the relative position between the curved surfaces of the predicted machining surface based on the predetermined curved surface information of the assembly surface to achieve the assembly positioning of the part; An assembly quality evaluation unit is used to: for the part that realizes the assembly positioning, calculate the shape error and the assembly gap error of the assembly surface according to the assembly mid-plane determined by the assembly surface, and use the shape error and the assembly gap error to predict the surface assembly quality of the part; The repair and compensation unit is used to calculate the repair and compensation amount of each assembly surface in the assembly surface based on the shape error, the assembly gap error and the processing error when the surface assembly quality does not meet the preset assembly quality requirements, and use the repair and compensation amount to compensate the corresponding assembly surface.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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