An inverse method and system for optimizing process parameters of forced positioning and clamping of thin-walled structures made of aviation composite materials

Abstract

The present invention provides a method and system for inverse optimization of forced positioning and clamping process parameters for an aerospace composite thin-walled structure, including: S1: obtaining key positioning and clamping process parameters through sensitivity calculation; S2: constructing a forward mapping relationship mathematical model between assembly process parameters and the assembly quality of the composite thin-walled structure; S3: constructing a multi-objective / multi-constraint inverse optimization objective function, and determining the optimal process parameters for forced positioning and clamping by means of step-by-step iterative inverse derivation; S4: verifying the optimal forced positioning and clamping process parameters and feeding back the optimization effect of the inverse calculation of the process parameters. The present invention comprehensively considers the geometric and physical assembly performance states affecting the composite thin-walled structure and the allowable range of the process parameter constraints required therefor, optimizes the clamping process parameters through inverse calculation technology, inversely derives the optimal clamping scheme, precisely controls the distribution and magnitude of the clamping force, can effectively limit the deformation of the composite thin-walled structure during the assembly process, and prevent the occurrence of damage induced by assembly stress.

Description

[Technical field]

[0001] The invention relates to the technical field of composite material structure assembly, and in particular to an optimization inverse method and system for forced positioning and clamping process parameters of an aviation composite thin-wall structure. [Background technology]

[0002] Forced positioning clamping means that after the six degrees of freedom of a composite weak rigid workpiece are completely restricted, due to the incoordination between components caused by factors such as dimensional error, shape and position error, and assembly deformation, it is necessary to use additional clamping force to make the composite component displace or deform appropriately to adjust its surface shape or position, so as to reduce the local assembly gap and the overall assembly coordination deviation, and obtain the required geometric shape and physical properties. In the field of precision clamping and process parameter optimization technology in the field of aviation composite structure assembly, when dealing with small gap aviation composite thin-walled design structures with strict tolerance requirements, the rational use of forced positioning clamping operations can not only reduce the assembly gap and improve the geometric accuracy of the overall assembly structure, but also effectively control the excessive internal stress and damage caused during the assembly process, and enhance the mechanical stability of the composite thin-walled structure. Although forced assembly operations can reduce the assembly gap between structural parts to a certain extent, if they are used improperly, the key process parameters of forced clamping operations, such as the size of the clamping force, the location of the clamping force, and the sequence of the clamping force, will cause stress concentration inside the composite component. When the force is too large, it may even cause various assembly damages such as crushing, delamination, matrix damage, and fiber breakage inside the composite component, affecting the mechanical reliability and service life of the connection structure. Although this process is usually difficult to avoid and the internal assembly physical state of the structure is difficult to predict, from the perspective of reducing costs and improving assembly efficiency, the project allows the existence of an appropriate amount of assembly gap to directly carry out subsequent connection and other process operations. By accurately controlling the size of the clamping force, optimizing the location of the clamping point, and reasonably setting the clamping sequence, the stress distribution can be effectively guided and adjusted to avoid internal damage or deformation of the material caused by assembly stress concentration, thereby protecting the integrity and functionality of the composite material. In addition, considering that the forced positioning and clamping process parameters will directly affect the assembly geometry and physical properties of the composite thin-walled composite structure, in the process of process parameter optimization and regulation, the interaction between different types of parameters is complex and has two-way constraints, resulting in strong uncertainty in the control of assembly performance indicators. The inverse iterative control mechanism of the forced clamping process parameters for assembly performance indicators has not yet been formed, and it is impossible to effectively guide the on-site high-performance assembly operations of aviation composite thin-walled structures.

[0003] At the assembly site, the current adjustment method has the following disadvantages: ① Due to relying on manual operation and empirical judgment, it is difficult to meet the high-precision assembly requirements. The process of manual adjustment and repeated trial assembly takes a long time, increasing the production cycle and resulting in low efficiency. ② Multiple adjustments, rework, and waste generation during the assembly process directly increase the manufacturing cost. ③ There is a lack of systematic means for optimizing process parameters, insufficient identification of key factors in the assembly process, and it is difficult to achieve multi-objective optimization. ④ Facing the complex and variable thin-walled structure design, traditional methods are difficult to adjust flexibly, and the adaptability to new materials and complex structures is limited. ⑤ There is a lack of effective real-time feedback and iterative optimization mechanism, the discovery of assembly quality problems lags behind, and the implementation of improvement measures is slow, which is not conducive to continuous improvement and process upgrading.

[0004] In view of the deficiencies of the above traditional adjustment methods, for applying forced clamping to improve the assembly quality of aerospace composite thin-walled structures, how to combine automation and intelligent technologies, intelligent algorithm optimization, real-time monitoring and feedback systems, etc., to obtain a set of forced clamping process parameters that can not only maximize the reduction of assembly gaps and improve assembly quality, but also effectively control and reduce the internal stress and damage generated during the assembly process is the key.

[0005] Therefore, it is necessary to study an optimization inverse solution method and system for forced positioning and clamping process parameters of aerospace composite thin-walled structures to address the deficiencies of the existing technology and solve or mitigate one or more of the above problems.

Summary of the Invention

[0006] In view of this, the present invention provides an optimization inverse solution method and system for forced positioning and clamping process parameters of aerospace composite thin-walled structures. The key process parameters that have a greater impact on the assembly quality can be obtained by calculating the sensitivity. By constructing a forward and reverse mapping relationship mathematical model between the assembly process parameters and the assembly quality, and using intelligent algorithms and data learning and mining methods to solve, a set of reasonable forced positioning and clamping process parameters can be obtained, and it can be verified and feedback whether the obtained assembly process parameters meet the requirements, which can effectively limit the deformation of the composite thin-walled structure during the assembly process and prevent the occurrence of damage induced by assembly stress.

[0007] On the one hand, the present invention provides an optimization inverse solution method for forced positioning and clamping process parameters of aerospace composite thin-walled structures. The optimization inverse solution method for forced positioning and clamping process parameters of aerospace composite thin-walled structures includes the following steps:

[0008] S1: Obtain the key clamping process parameters by calculating the sensitivity values of the forced clamping process parameters that affect the assembly quality;

[0009] S2: Taking the allowable range of key clamping process parameters as the constraint condition, combined with the analysis of the expected value of the assembly quality of the aerospace composite thin-walled structure, and taking the minimization of the errors of assembly quality, assembly gap, assembly stress and damage expected value as the optimization objective, a mathematical model of the positive mapping relationship between assembly process parameters and the assembly quality of the composite thin wall is constructed;

[0010] S3: Construct a multi-objective / multi-constraint reverse optimization objective function, and through intelligent optimization algorithms and technical means of data learning and mining, formulate a reverse solution strategy for the mathematical model of the positive mapping relationship, and gradually iterate to reverse deduce the optimal forced positioning clamping process parameters;

[0011] S4: Carry out assembly operations according to the set of optimal forced positioning clamping process parameters, and verify and feedback the reverse optimization effect of the optimal forced positioning clamping process parameters.

[0012] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided. The assembly quality influence parameters in S1 include but are not limited to the magnitude and position distribution of the clamping force, the clamping sequence, the temperature control parameters, the pressure regulation parameters, the positioning accuracy parameters and the stability parameters.

[0013] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided. The methods for determining the assembly quality influence parameters in S1 include but are not limited to theoretical analytical calculation, finite element simulation test analysis and experimental data evaluation.

[0014] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided. The specific process of obtaining the key process parameters through sensitivity calculation in S1 specifically includes:

[0015] S11: Collect theoretical modeling data, finite element simulation data, and experimental detection data related to the forced positioning clamping process and quality effect to form a data analysis set;

[0016] S12: Combining the assembly process of the composite thin-walled combined structure, form a first set of all the initial forced clamping process parameters that affect the assembly quality from the data analysis set;

[0017] S13: Through the response degree of the assembly quality corresponding to the change of each assembly quality influence parameter in the first set, construct an assembly quality response matrix and convert the influence degree into quantitative data;

[0018] S14: Arrange the quantitative data results in descending order to obtain a set of process parameters with a greater influence on the assembly quality state, that is, obtain a second set containing a priority list;

[0019] S15: Based on the second set, determine the key process parameters to be inversely solved in the subsequent optimization inverse solution process.

[0020] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. Specifically, in S2, it includes:

[0021] S21: Establish specific expected indicators representing assembly quality based on the second set, and obtain an expected value set:

[0022] S22: Taking the allowable range of key process parameters and the condition that the thin-walled aerospace composite material is not damaged under the influence of key forced clamping process parameters as constraints, and taking the minimum difference between the actual value and the expected value of assembly quality as the optimization objective, analyze the complex coupling relationship between each process parameter and the ideal assembly quality index, and construct a mathematical model of the positive mapping relationship between assembly process parameters and the assembly quality of the composite thin wall.

[0023] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. Specifically, S3 is:

[0024] S31: Construct a multi-objective / multi-constraint reverse optimization objective function based on the mathematical model of the positive mapping relationship;

[0025] S32: Adopt an intelligent optimization solution algorithm, encode the key process parameters affecting the assembly quality of the thin-walled aerospace composite material as the individual genotypes in the genetic algorithm, and regard each set of parameter configurations as an independent individual to form an initial population;

[0026] S33: On the premise of ensuring the allowable range of key process parameters and the constraint that the thin-walled aerospace composite material is not damaged under the influence of key process parameters, perform iterative optimization on the initial process parameter population;

[0027] S34: Repeat S31 - S32 multiple times. Through fitness function evaluation, determine the optimal process number configuration that can maximize the assembly quality of the thin-walled aerospace composite material under the forced positioning clamping process. According to the assembly hierarchy relationship, inversely and gradually iterate and deduce to solve for the optimal process parameters, and obtain the third set of forced positioning clamping process parameters.

[0028] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. Specifically, in S4, it includes:

[0029] S41: Set up a flexible adjustment device, a thin-walled aerospace composite structure sample that meets the requirements of the desired process parameters according to the optimal process parameters in the third set, and establish an accurate stress measurement method and standard to design an experimental plan;

[0030] S42: Perform actual clamping operations on the thin-walled aerospace composite structure parts according to the optimal process parameters, measure the stress-strain distribution and damage conditions of the composite thin wall in the clamped state, and collect detailed on-site data records;

[0031] S43: Compare the data obtained from the test measurements with the preset expected values. For the optimal process parameters that do not meet the preset expected values, it is necessary to deeply analyze the data to identify the possible factors causing the deviation. Based on the analysis results, adjust and optimize the process parameters, and repeat S1 - S3 until the results meet the established assembly quality and stress requirements, obtaining the fourth set of forced positioning clamping process parameters for the best assembly quality.

[0032] In the aspect and any possible implementation described above, a further implementation is provided. In S43, if it does not meet the established assembly quality expectation target, analyze the assembly quality data to identify the possible factors causing the deviation. Based on the analysis results, adjust and optimize the forced clamping process parameters, and repeat S1 - S3 until the design requirements of the assembly quality state are met.

[0033] In the aspect and any possible implementation described above, a further implementation is provided. In S13, the representation methods of assembly quality include but are not limited to assembly geometric clearance, position error, internal stress distribution, and damage degree index.

[0034] In the aspect and any possible implementation described above, a further implementation provides an inverse solution system for optimizing the forced positioning clamping process parameters of an aviation composite thin - wall structure. The inverse solution system for optimizing the forced positioning clamping process parameters of an aviation composite thin - wall structure includes:

[0035] A key process parameter identification and calculation module, used to obtain the key clamping process parameters by calculating the sensitivity values of the forced clamping process parameters that affect the assembly quality.

[0036] A forward mapping model establishment module, used to take the allowable range of the key clamping process parameters as the constraint condition, combined with the analysis of the expected values of the assembly quality of the aviation composite thin - wall structure, and take the minimization of the errors between the assembly quality, assembly clearance, assembly stress, and damage expected values as the optimization goal, to construct a mathematical model of the forward mapping relationship between the assembly process parameters and the composite thin - wall assembly quality.

[0037] An optimal process parameter inverse solution calculation module, used to construct a multi - objective / multi - constraint reverse optimization objective function, through intelligent optimization algorithms, and by using technical means of data learning and mining, to formulate a reverse solution strategy for the mathematical model of the forward mapping relationship, and to gradually iteratively reverse - deduce the optimal forced positioning clamping process parameters.

[0038] A result verification and feedback module, used to perform assembly operations according to the set of the optimal forced positioning clamping process parameters, and verify and feedback the inverse solution optimization effect of the optimal forced positioning clamping process parameters.

[0039] Compared with the prior art, the present invention can achieve the following technical effects:

[0040] 1) Identify and quantify the forced clamping process parameters that have a great impact on the assembly quality of thin-walled aviation composite materials through theoretical modeling calculations, finite element simulation analysis and actual test data, combined with sensitivity analysis.

[0041] 2) Taking the key process parameters as the constraint conditions and minimizing the differences between the assembly quality, assembly gap and geometric deformation and the expected values as the optimization objectives, construct a mathematical model to obtain the positive mapping relationship between the assembly process parameters and the assembly quality of thin-walled composite materials.

[0042] 3) Solve the mathematical model between the above-mentioned assembly process parameters and the assembly quality of thin-walled composite materials based on intelligent optimization algorithms and data learning and mining. While satisfying the constraint conditions such as the magnitude, position and sequence of the clamping force, obtain the global optimal solution approaching the forced positioning clamping process parameters.

[0043] 4) Conduct experimental verification on the obtained forced positioning clamping process parameters to obtain the set of forced positioning clamping process parameters required to complete the assembly of the ideal thin-walled composite structure, and guide the completion of high-performance assembly operations.

[0044] 5) By comparing the experimental data with the expected target, provide a closed-loop feedback and continuous improvement mechanism to adjust and optimize the process parameters, so that the reverse-engineered assembly process parameters and assembly quality achieve the effect of closed-loop control.

[0045] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned technical effects simultaneously.

BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0047] Figure 1 is a technical step diagram for optimizing and reverse-solving the forced positioning clamping process parameters of a thin-walled aviation composite structure of the present invention;

[0048] Figure 2 is a technical flow chart for optimizing and reverse-solving the forced positioning clamping process parameters provided by an embodiment of the present invention;

[0049] Figure 3 is an exploded view of the overall assembly structure of a certain type of composite wing box provided by an embodiment of the present invention;

[0050] Figure 4 It is an explanatory diagram of the composition structure and adjustable functions of the typical wing box assembly tooling of the present invention.

Specific Embodiment

[0051] For a better understanding of the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present invention.

[0053] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms of "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0054] The present invention provides an optimization and inverse solution method for the forced positioning and clamping process parameters of an aerospace composite thin-walled structure. The optimization and inverse solution method for the forced positioning and clamping process parameters of the aerospace composite thin-walled structure mainly includes the following four parts: First, a set of process parameters that have a greater impact on the assembly quality is obtained through sensitivity calculation; Second, with the variation of the process parameters that have a greater impact on the assembly quality as the constraint condition, combined with the analysis of the expected value of the assembly quality of the aerospace composite thin-walled structure, and taking the minimization of the errors of the assembly quality, assembly clearance, assembly stress and damage expected value as the optimization goal, a mathematical model is constructed to obtain the forward mapping relationship between the assembly process parameters and the composite thin-walled assembly quality; Then, a multi-objective / multi-constraint inverse optimization objective function is constructed, and through intelligent optimization algorithms and the technical means of data learning and mining, the mathematical model of the above process parameters and assembly quality is solved, and the optimal process parameters of the forced positioning and clamping are deduced through step-by-step iteration; Finally, the inverse-solved assembly process parameters are verified and closed-loop feedback is performed.

[0055] Specifically, it includes the following steps:

[0056] S1: Obtain the key clamping process parameters by calculating the sensitivity values of the forced clamping process parameters that affect the assembly quality.

[0057] S2: With the allowable range of the key clamping process parameters as the constraint condition, combined with the analysis of the expected value of the assembly quality of the aerospace composite thin-walled structure, and taking the minimization of the errors of the assembly quality, assembly clearance, assembly stress and damage expected value as the optimization goal, construct a mathematical model of the forward mapping relationship between the assembly process parameters and the composite thin-walled assembly quality.

[0058] S3: Construct a multi-objective / multi-constraint reverse optimization objective function, and formulate a reverse solution strategy for the forward mapping relationship mathematical model through intelligent optimization algorithms and technical means of data learning and mining, and gradually iterate to reverse deduce the optimal forced positioning and clamping process parameters;

[0059] S4: Perform assembly operations based on the set of optimal forced positioning and clamping process parameters, and verify and feedback the reverse optimization effect of the optimal forced positioning and clamping process parameters.

[0060] The assembly quality influence parameters in the above-mentioned S1 include but are not limited to the magnitude and position distribution of the clamping force, the clamping sequence, the temperature control parameters, the pressure regulation parameters, the positioning accuracy parameters, and the stability parameters.

[0061] In the above-mentioned aspects and any possible implementation manners, a further implementation manner is provided. The method for determining the assembly quality influence parameters in the above-mentioned S1 includes but is not limited to theoretical analytical calculation, finite element simulation test analysis, and experimental data evaluation.

[0062] The specific process of obtaining the key process parameters through sensitivity calculation in the above-mentioned S1 includes:

[0063] S11: Collect theoretical modeling data, finite element simulation data, and experimental detection data related to the forced positioning and clamping process and quality effect, and form a data analysis set;

[0064] S12: Combine the assembly process of the composite thin-walled combined structure, and form a first set of all the initial forced clamping process parameters that affect the assembly quality from the data analysis set;

[0065] S13: Construct an assembly quality response matrix through the response degree of the assembly quality corresponding to the change of each assembly quality influence parameter in the first set, and convert the influence degree into quantitative data;

[0066] S14: Arrange the quantitative data results in descending order to obtain a set of process parameters that have a greater impact on the assembly quality state, that is, obtain a second set including a priority list;

[0067] S15: Based on the second set, determine the key process parameters to be inversely solved in the subsequent optimization and inverse solution process.

[0068] The specific content in the above-mentioned S2 includes:

[0069] S21: Establish specific expected indicators representing the assembly quality based on the second set, and obtain an expected value set:

[0070] S22: Taking the allowable range of key process parameters and the condition that the thin-walled aerospace composite materials are not damaged under the influence of key forced clamping process parameters as constraints, and taking the minimum difference between the actual value and the expected value of the assembly quality as the optimization objective, analyze the complex coupling relationship between each process parameter and the ideal assembly quality index, and construct a mathematical model of the positive mapping relationship between the assembly process parameters and the assembly quality of the composite thin wall.

[0071] The specific content of S3 is as follows:

[0072] S31: According to the mathematical model of the positive mapping relationship, construct a multi-objective / multi-constraint reverse optimization objective function;

[0073] S32: Adopt an intelligent optimization algorithm to encode the key process parameters affecting the assembly quality of the thin-walled aerospace composite materials as the individual genotypes in the genetic algorithm. Each set of parameter configurations is regarded as an independent individual to form an initial population;

[0074] S33: On the premise of ensuring the allowable range of key process parameters and the constraint that the thin-walled aerospace composite materials are not damaged under the influence of key process parameters, perform iterative optimization on the initial process parameter population;

[0075] S34: Repeat S31 - S32 multiple times. Through fitness function evaluation, determine the optimal process number configuration that can maximize the assembly quality of the thin-walled aerospace composite materials under the forced positioning clamping process. According to the assembly hierarchy relationship, inversely and gradually iterate and deduce to solve and obtain the optimal process parameters, and obtain the third set of forced positioning clamping process parameters.

[0076] The specific content of S4 includes:

[0077] S41: According to the optimal process parameters in the third set, set up a flexible assembly and adjustment device, a thin-walled aerospace composite structure sample that meets the requirements of the desired process parameters, and establish an accurate stress measurement method and standard to design an experimental plan;

[0078] S42: Perform actual clamping operations on the thin-walled aerospace composite structure parts according to the optimal process parameters, measure the stress and strain distribution and damage conditions of the composite thin wall in the clamped state, and collect detailed on-site data records;

[0079] S43: Compare the data obtained from the experimental measurement with the preset expected value. For the optimal process parameters that do not meet the preset expected value, it is necessary to deeply analyze the data to identify the possible factors causing the deviation. Based on the analysis results, adjust and optimize the process parameters, and repeat S1 - S3 until the results meet the established assembly quality and stress requirements, and obtain the fourth set of forced positioning clamping process parameters for the best assembly quality.

[0080] In S43, if the established assembly quality expectation target is not met, the assembly quality data is analyzed to identify possible factors causing deviations. Based on the analysis results, the forced clamping process parameters are adjusted and optimized, and S1 - S3 are repeated until the design requirements of the assembly quality state are met.

[0081] In S13, the representation methods of assembly quality include but are not limited to assembly geometric clearance, position error, internal stress distribution, and damage degree index.

[0082] The present invention also provides an inverse solution system for optimizing the forced positioning and clamping process parameters of an aerospace composite thin - wall structure. The inverse solution system for optimizing the forced positioning and clamping process parameters of an aerospace composite thin - wall structure includes:

[0083] A key process parameter identification and calculation module, which is used to obtain key clamping process parameters by calculating the sensitivity values of the forced clamping process parameters that affect the assembly quality.

[0084] A forward mapping model establishment module, which is used to construct a mathematical model of the forward mapping relationship between the assembly process parameters and the composite thin - wall assembly quality with the allowable range of the key clamping process parameters as the constraint condition, combined with the analysis of the expected value of the assembly quality of the aerospace composite thin - wall structure, and with the minimization of the errors between the assembly quality, assembly clearance, assembly stress, and damage expected values as the optimization goal.

[0085] An optimal process parameter inverse solution calculation module, which is used to construct a multi - objective / multi - constraint reverse optimization objective function, formulate a reverse solution strategy for the mathematical model of the forward mapping relationship through intelligent optimization algorithms and technical means of data learning and mining, and gradually iterate to reverse - derive the optimal forced positioning and clamping process parameters.

[0086] A result verification and feedback module, which is used to perform assembly operations according to the set of the optimal forced positioning and clamping process parameters, verify and feedback the inverse solution optimization effect of the optimal forced positioning and clamping process parameters.

[0087] Figure 2 In this embodiment, it is the specific implementation flowchart of the present solution. The overall idea for the present invention to solve its technical problems is:

[0088] In the process of optimizing the assembly of thin-walled composite materials in aviation, first, process parameters that have a significant impact on the assembly quality are identified through calculation, such as the magnitude, position, and sequence of clamping forces, etc. The sensitivity analysis method is used to measure the influence of these parameters, and then a key parameter set is constructed and arranged in descending order of sensitivity. Secondly, based on the above operations, various expected indicators of the assembly quality are clarified, such as geometric clearance, damage degree, position error, etc. At the same time, under the constraint conditions that ensure no damage to the thin-walled composite materials in aviation and each key process parameter is within the applicable range of its respective engineering requirements, with the goal of minimizing the difference between the actual value A of the assembly quality and the expected value E of the assembly quality, the complex coupling relationship between each process parameter and the ideal assembly quality index is deeply analyzed, and a forward mapping mathematical model between the assembly process parameters and the assembly quality of the thin-walled composite materials is constructed. Thirdly, through intelligent optimization algorithms and the technical means of data learning and mining, the iterative solution of the reverse mathematical optimization model under multiple objectives and multiple constraint conditions is completed. Combined with the evaluation of the fitness function, the process parameter set that can maximize the optimization of the assembly quality of the thin-walled composite material structure under the forced positioning clamping process is deduced and solved in reverse, including design variables such as the clamping position, sequence, and force magnitude of the tooling locator, and the optimal parameter configuration that can maximize the assembly quality is determined. Finally, an experimental plan is designed based on the optimized process parameters, actual clamping operations and performance tests are carried out, and high-precision instruments are used to monitor the stress-strain and damage conditions of the thin-walled composite materials in aviation. The results are compared and analyzed with the preset targets. If deviations are found, they are fed back to the parameter identification and optimization stage, and continuous iteration is carried out until the expected assembly quality and stress requirements are met. The entire process systematically ensures the high quality and high efficiency of the assembly of thin-walled composite materials in aviation.

[0089] The present invention provides a reverse calculation technology for optimizing the process parameters of forced positioning clamping of thin-walled composite materials in aviation. The reverse calculation technology for optimizing the process parameters of forced positioning clamping is aimed at the assembly stage of thin-walled composite materials in aviation. When using forced positioning clamping to eliminate the geometric clearance of the mating surface of the thin-walled composite materials in aviation, it may cause excessive internal assembly stress in the thin-walled composite materials, which may lead to problems such as damage. It mainly starts from the actual state of the assembly quality of the thin-walled composite materials in aviation, reversely calculates the key parameters that affect the accuracy of the thin-walled composite materials in aviation, obtains the key parameter values under the actual state, and minimizes the difference between the actual value and the expected value of the assembly quality, so as to achieve the effect of optimizing the assembly quality of the thin-walled composite material structure, such as Figure 1 shown, the reverse calculation technology for optimizing the process parameters of forced positioning clamping of thin-walled composite materials in aviation includes the following steps:

[0090] S1: Obtain the key clamping process parameters by calculating the sensitivity values of the forced clamping process parameters that affect the assembly quality.

[0091] The specific content of S1 includes:

[0092] S11: Collect theoretical modeling data, finite element simulation data, and experimental detection data related to the forced positioning clamping process and quality effect to form a data analysis set;

[0093] S12: Combine with the assembly process of the composite thin-walled combined structure, and form the first set P = {p1, p2,..., p n} of all the initial process parameters of forced clamping that affect the assembly quality.

[0094] S13: In the first set of process parameters, use the sensitivity analysis method to measure the specific impact on the assembly quality result when different process parameters change. For each parameter p i , define its sensitivity S(p i ) to the assembly quality A as a quantitative index of the change rate of A with respect to p i , that is:

[0095]

[0096] The assembly quality A can be represented by geometric clearances H, assembly internal stresses S(x), damage degrees D(x), geometric error types δ such as shape and position, etc. Then the set of the geometric and physical quality states of the assembly can be represented by: A = {A1, A2, A3,..., An}. Based on the sensitivity values, construct a sensitivity numerical set corresponding to the key process parameters: S = {S(p1), S(p2), S(p3),..., S(p n )}.

[0097] S14: For the key process parameters in the key parameter set S, sort them in descending order according to their sensitivity values to obtain a key parameter set K in descending order, that is, obtain the second set containing the priority list.

[0098] Specifically, first, identify the key process parameters that affect the assembly quality, deeply analyze various process variables, such as the magnitude and position distribution of the clamping force, the clamping sequence, the temperature control parameters, the pressure regulation parameters, the positioning accuracy parameters, and the stability parameters, etc. Through theoretical analysis and calculation, finite element analysis, and experimental data evaluation, screen out the key parameters such as the magnitude p1, position p2, and sequence p3 of the clamping force that have a decisive impact on the final assembly quality, and form a set P = {p1, p2,..., p n}.

[0099] Furthermore, sensitivity analysis is used to analyze the influence degree of key process parameters on the assembly quality. By calculating the response degrees ΔA1, ΔA2, ΔA3, etc. of each key process parameter's small change on the assembly quality, such as the geometric clearance size H, the assembly internal stress S(x), the damage control degree D(x) of the composite structure, and the shape and position matching error δ between the thin-walled parts of the aviation composite material, etc., an assembly quality response matrix ΔA = {ΔA1, ΔA2, ΔA3,..., ΔAn} is formed, and the influence degree is converted into quantitative data, that is, sensitivity calculation:

[0100]

[0101] The sensitivities of each key process parameter to the assembly quality obtained by calculation form a sensitivity matrix: S = {S(p1), S(p2), S(p3),..., S(p n )}.

[0102] S15: Based on the above sensitivity calculation, the sensitivity values of each key process parameter to the assembly quality are sorted in descending order. Based on the second set of forced clamping process parameters, a descending parameter set K of key process parameters is obtained to form a priority list. Based on this, the key process parameters to be inversely solved in the subsequent optimization inverse solution work are determined.

[0103] S2: Construction of a multi-objective optimization model between the expected parameters of the assembly quality and the key process parameters under the constraint conditions.

[0104] The S2 specifically includes:

[0105] S21: Based on the second set of key forced clamping process parameters, use E to clearly represent the specific expected indicators of the assembly quality, then the expected value set: E = {E1, E2, E3,..., En}.

[0106] S22: Taking the range allowed by the key process parameters The thin-walled parts of the aviation composite material are not damaged under the influence of key process parameters D(p1, p2,..., p n ) ≤ 0 as the constraint condition, and taking the minimum difference between the actual value A of the assembly quality and the expected value E of the assembly quality as the optimization goal, deeply analyze the complex coupling relationship between each forced positioning and clamping process parameter and the ideal assembly quality index, and construct a mathematical model between the assembly process parameters and the assembly quality of the composite thin-walled parts.

[0107] Specifically, the expected values of the assembly quality are expressed through specific parameters, such as the expected geometric clearance E1, the expected damage degree E2, the expected shape and position error E3, etc. These specific values form the assembly quality expected value set: E = {E1, E2, E3,..., E n}.

[0108] Furthermore, the allowable ranges of key process parameters such as the magnitude p1 of the clamping force, the position p2, and the sequence p3 among multiple clamping processes, etc.: and the aerospace composite thin-walled structure not being damaged under the influence of key process parameters: S(p1, p2,..., p n )≥0 as the constraint condition, with the minimum difference between the actual value A and the expected value E of the assembly quality of the aerospace composite thin-walled structure as the optimization objective, deeply analyze the complex coupling relationship between each process parameter and the ideal assembly quality index, and construct a positive mathematical mapping model between the assembly process parameters and the assembly quality of the composite thin-walled structure:

[0109]

[0110] In the formula, w i represents the weights of each precision index, reflecting the different contributions of different indexes to the overall assembly quality.

[0111] S3: Use intelligent optimization algorithms, data learning and mining, etc. to formulate a reverse solution guarantee strategy for the assembly quality of the composite thin-walled structure and obtain a set of key assembly parameters.

[0112] The specific content of S3 includes:

[0113] S31: Based on the positive mapping relationship mathematical model in S22, construct a multi-objective / multi-constraint reverse optimization objective function.

[0114] S32: Adopt an intelligent optimization solution algorithm to encode the key process parameters affecting the assembly quality of the aerospace composite thin-walled structure, such as the magnitude, position, and sequence of the clamping force, etc., as the individual genotypes in the genetic algorithm. Each set of parameter configurations is regarded as an independent individual to form an initial population.

[0115] S33: On the premise of ensuring the allowable range of key process parameters and the constraint that the aerospace composite thin-walled structure is not damaged under the influence of key process parameters, use an intelligent optimization algorithm to iteratively optimize the initial process parameter population.

[0116] S34: By continuously performing the above operations and using data learning and mining means (such as machine learning, support vector regression), combined with fitness function evaluation, determine the optimal process number configuration that can maximize the assembly quality of the aerospace composite thin-walled structure under the forced positioning clamping process, and reversely and gradually iteratively deduce and solve according to the assembly hierarchical relationship to obtain the optimal process parameters, and obtain the third set of forced positioning clamping process parameters.

[0117] Specifically, key factors such as the magnitude, position, and assembly sequence of the clamping force, which have a greater impact on the assembly quality, are encoded as the "genotype" of individuals in the intelligent optimization algorithm. Each specific parameter configuration is regarded as an independent individual in the algorithm, and these individuals carry different assembly strategy information.

[0118] Furthermore, on the premise of ensuring that all process parameter operations are carried out within a safe and effective range to avoid any damage to the thin-walled aerospace composite material, the population is continuously iterated through operations such as selection, crossover, and mutation. In each iteration, the algorithm evaluates the fitness of the parameter configuration of the key factors affecting the assembly quality of the thin-walled composite material. Individuals with high fitness are selected for reproduction in the next generation, while individuals with low fitness are gradually eliminated.

[0119] Furthermore, by continuously repeating the above iterative optimization and evaluation process and combining with the fitness function, the system can gradually obtain the optimal process parameter configuration that can make the assembly quality of the thin-walled aerospace composite material closest to the expected value under the forced positioning clamping process conditions, that is, the third set of forced positioning clamping process parameters.

[0120] S4: Based on the selection of adjustment and control parameters for the obtained process parameters, the design, execution, and feedback of the test plan are carried out to verify the on-site effect of the inverse optimization of the forced clamping process parameters.

[0121] The specific content of S4 includes:

[0122] S41: According to the optimal process parameters in the third set, a flexible adjustment equipment that meets the requirements of the obtained process parameters, a thin-walled aerospace composite material structure sample, and an accurate stress measurement method and standard are set to design the test plan.

[0123] S42: According to the optimal process parameters, actual clamping operations are carried out on the thin-walled aerospace composite material. Subsequently, high-precision instruments are used to comprehensively measure the stress-strain distribution and damage conditions of the thin-walled composite material in the clamped state, and detailed data records are collected.

[0124] S43: The data obtained from the test measurement is compared with the preset expected value. For the optimal process parameters that do not meet the preset expected value, the data needs to be deeply analyzed to identify the possible factors causing the deviation. Based on the analysis results, the process parameters are adjusted and optimized, and S1 - S3 are repeated until the results meet the established assembly quality and stress requirements, and the fourth set of forced positioning clamping process parameters for the best assembly quality is obtained.

[0125] Specifically, a special flexible adjustment equipment that can accurately achieve the required clamping force, precise position control, and specific clamping sequence is selected, a technical route for measuring the internal stress during assembly is formulated, high-sensitivity sensors and analysis instruments are calibrated, and the positions, frequencies, and standards of the measurement points are set.

[0126] Furthermore, the aviation composite thin-wall is clamped strictly according to the optimized process parameters. After clamping, digital image correlation technology and ultrasonic testing equipment are used to systematically monitor the stress and strain distribution of the composite thin-wall and potential signs of damage, such as delamination and cracks, and the measurement data is recorded in detail to provide an original basis for subsequent analysis.

[0127] Furthermore, after data collection is completed, the test data is compared with the previously set expected values ​​of various parameters representing assembly quality to evaluate the actual effect of the assembly solution. If the measurement results fail to meet the expected goals, in-depth data analysis is required to identify the source of deviation. Based on the analysis results, necessary adjustments and optimizations are made to the process parameters. After the optimization is completed, return to S1, recalculate and identify key process parameters, and start a new round of optimization iterations until the test results fully meet the established assembly geometry quality and internal stress / damage requirements.

[0128] The present invention provides an inverse technology for optimizing process parameters of forced positioning and clamping of thin-walled aviation composites, which provides an effective solution to the problems of identifying and quantifying the influence of key process parameters on assembly quality in thin-walled aviation composites assembly, establishing a mathematical correlation model between assembly quality and process parameters, optimizing process parameters to maximize assembly quality, and verifying the effectiveness and rationality of optimized process parameters.

[0129] The present invention mainly includes four parts, namely: by systematically identifying and quantifying the key process parameters of forced clamping that affect the assembly quality, such as clamping force, position, clamping sequence, etc., and using sensitivity analysis to construct a mathematical model of parameter influence; taking the process parameters that have a greater impact on the assembly quality of composite thin-walled structures as constraints, combined with the expected value analysis of the assembly quality of aviation composite thin-walled structures, taking the minimization of the error between the assembly quality, assembly gap, geometric deformation, internal stress and damage distribution and the expected value as the optimization goal, constructing a mathematical model to obtain the forward mapping relationship between the assembly process parameters and the assembly quality of composite thin-walled structures; through intelligent optimization algorithms, and using data learning and mining techniques, completing the iterative solution of the reverse mathematical optimization model under multi-objective and multi-constraint conditions, seeking to maximize the optimization of the process parameter set that can maximize the optimization of the assembly quality of composite thin-walled structures under the constraints of ensuring material safety and avoiding damage; through detailed experimental verification, combined with digital image correlation technology and precision instruments such as ultrasonic detection, the actual effect evaluation of the optimized process parameters is carried out to form a feedback loop, and the optimization is continuously adjusted until the assembly result meets the predetermined accuracy, stress and damage distribution coordination requirements. The present invention can achieve:

[0130] 1) Through theoretical modeling calculation, finite element simulation analysis and actual test data, combined with sensitivity analysis, the forced clamping process parameters that have a great impact on the quality of thin-wall assembly of aviation composites are identified and quantified.

[0131] 2) Taking the key process parameters as the constraint conditions and minimizing the differences between the assembly quality, assembly clearance, and geometric deformation and the expected values as the optimization objectives, a mathematical model is constructed to obtain the positive mapping relationship between the assembly process parameters and the composite thin-walled assembly quality.

[0132] 3) Based on means such as intelligent optimization algorithms and data learning and mining, the mathematical model between the above-mentioned assembly process parameters and the composite thin-walled assembly quality is solved. While satisfying the constraint conditions such as the magnitude, position, and sequence of the clamping force, the global optimal solution approaching the forced positioning clamping process parameters is obtained.

[0133] 4) The obtained forced positioning clamping process parameters are experimentally verified to obtain the set of forced positioning clamping process parameters required to complete the assembly of the ideal composite thin-walled structure, guiding the completion of high-performance assembly operations.

[0134] 5) By comparing the experimental data with the expected target, a closed-loop feedback and continuous improvement mechanism is provided to adjust and optimize the process parameters, so that the reverse-engineered assembly process parameters and the assembly quality achieve the effect of closed-loop control.

[0135] Example 1:

[0136] To elaborate on this solution in detail, taking the optimization and reverse engineering of the process parameters in the forced clamping of a certain type of composite wing box component as an example, in this embodiment, an exploded view of the wing box of this solution is drawn, as Figure 3 shown. The flexible assembly and adjustment tooling system used for the wing box of this solution is as Figure 4 shown.

[0137] First, clarify the specific assembly process of the wing box, and identify the key parameter set affecting the assembly quality through theoretical analysis calculation, ABAQUS finite element simulation analysis software, and historical data, including component dimensions, material properties, assembly sequence, positioning and clamping conditions, and environmental factors. Secondly, analyze and obtain indicators reflecting the assembly quality, such as the distribution of assembly internal stress, assembly gap, geometric fit, and assembly damage distribution, and form the ranking of key parameters through sensitivity analysis to guide subsequent optimization. Thirdly, establish a mathematical model between the assembly quality and key process parameters, use a weighted method to balance the importance of different accuracy indicators, introduce a genetic algorithm for parameter optimization, and gradually approach the ideal assembly strategy by simulating the natural selection mechanism. Through the iteration of intelligent optimization algorithms and data learning and mining methods, screen out the optimal combination of process parameters such as the clamping position, sequence, and clamping force of the tooling locator, and use this to guide the actual assembly test and production of each component of the wing box. After that, strictly execute the assembly operation according to the optimized forced clamping process parameters, and use advanced detection technologies to comprehensively monitor various indicators of the assembly quality, compare the collected measured data with the preset expected values, and evaluate the effectiveness of the assembly plan. If there are deviations, feedback to the data analysis link, adjust the process parameters according to the deviation source, and start a new optimization cycle until the assembly quality of the wing box fully meets the predetermined geometric accuracy, internal stress, and damage standards, realizing a closed-loop optimization process from theoretical analysis to experimental verification to ensure the high-quality completion of the composite wing box component assembly.

[0138] The inverse technology of the forced positioning and clamping process parameters for the composite wing box assembly of a certain type of aircraft includes the following steps:

[0139] Specifically, clarify the assembly process of this type of wing box component: First, fix the left and right walls, the first frame, and the fourth frame on the tooling; then, taking this as the positioning reference, install the second frame and the fourth frame; then install the composite lower wall panel; finally, install the composite upper wall panel. Import the above wing box assembly process into ABAQUS for simulation, and combine historical test data to obtain the main parameters affecting the assembly quality of the composite wing box, such as the dimension parameters of each component such as the left wall and the right wall, as well as the material properties, assembly sequence, positioning clamping force magnitude, position, clamping sequence, and assembly environment of each component, which are denoted as p1, p2, p3, etc., and form a set P = {p1, p2,..., p n}.

[0140] Further, analyze the index parameters that can represent the assembly quality of the wing box, such as the internal assembly stress of the wing box structure, the assembly gap between the mating surfaces of each component of the wing box, and the parallelism, perpendicularity, and step difference between each component, etc. By changing the main parameters that affect the assembly quality of the composite wing box, obtain the change of the assembly quality index, calculate the sensitivity of each main parameter, and then form a sensitivity matrix from the sensitivities of each key process parameter to the assembly quality: S = {S(p1), S(p2), S(p3),..., S(p n )}.

[0141] Further, sort the sensitivities of the above parameters to the wing box assembly quality in descending order, form a key parameter set K in descending order, and form a priority list.

[0142] Further, express the expected value that can represent the wing box assembly quality through a series of specific parameters, such as the expected assembly geometric gap E1, the expected assembly damage degree E2, the expected assembly shape and position error E3, the expected assembly internal stress distribution E4, etc., to form an expected value set: E = {E1, E2, E3,..., E n}.

[0143] Further, taking the allowable range in engineering of the key process parameters that affect the wing box assembly quality and the wing box not being damaged as constraints, and taking the minimum absolute value of the difference between each parameter representing the wing box assembly quality and its corresponding expected value as the optimization goal, construct a mathematical mapping model between the assembly process parameters and the assembly quality of the composite wing box:

[0144]

[0145] In the formula, w i represents the weight of each precision index, reflecting the different contributions of different indexes to the overall assembly quality.

[0146] Further, encode the key process parameters that affect the wing box assembly quality as the "genotype" of individuals in the genetic algorithm. Each specific parameter configuration is regarded as an independent individual in the algorithm, and these individuals carry different assembly strategy information.

[0147] Further, continuously evolve the population through operations such as selection, crossover, and mutation. In each iteration, the algorithm evaluates the fitness of the key factor parameter configurations that affect the wing box assembly quality, selects the individuals with high fitness for reproduction in the next generation, and gradually eliminates the individuals with low fitness.

[0148] Further, by continuously repeating the above iterative optimization and evaluation process and combining with the fitness function, the system can gradually obtain the optimal process parameter configuration that can make the wing box assembly quality closest to the expected value.

[0149] Furthermore, strictly in accordance with the optimal key process parameters of wing box assembly obtained by the above genetic algorithm, a wing box assembly test is carried out. First, the left and right walls, the first frame, and the fourth frame are fixed by the locators on the tooling; then, taking this as the positioning reference, the second frame and the fourth frame are installed to complete the skeleton; after that, the composite upper and lower wall panels are installed based on the skeleton.

[0150] Furthermore, after the wing box assembly is completed, the digital image correlation technology and ultrasonic testing equipment are used to systematically monitor the parallelism, perpendicularity, step difference, fitting surface gap, assembly internal stress and other parameters representing the assembly quality between the left and right walls and the first and fourth frames of the wing box, between the left and right walls and the two rib plates, and between the skeleton and the upper and lower composite wall panels, and record them in detail.

[0151] Furthermore, the experimental data of the parameters representing the wing box assembly quality obtained above are compared with the previously set expected values of the assembly quality to evaluate the actual effect of the assembly scheme. If the measurement results fail to meet the expected goals, in-depth data analysis is required to identify the sources of deviation. Based on the analysis results, the process parameters are adjusted and optimized as necessary, the key process parameters are recalculated and identified, and a new round of optimization iteration is started until the test results fully meet the requirements of the established assembly geometric accuracy, internal stress, and damage distribution.

[0152] In view of the problems that due to its material properties and structural characteristics, the aviation composite thin-walled structure is extremely prone to deformation due to uneven stress during the forced clamping process, resulting in local buckling or delamination, etc. Considering that these problems will seriously affect the dimensional accuracy and structural performance of the final product, therefore, in order to reasonably assemble the composite thin-walled structure and optimize the forced clamping, it is necessary to perform predictive inverse solution according to the assembly requirements before assembly to obtain the forced positioning clamping process parameters to guide the assembly of the composite thin-walled structure. The present invention comprehensively considers the physical and geometric characteristics affecting the composite thin-walled structure and the range of assembly quality changes required, optimizes the forced positioning process clamping parameters through inverse solution technology, and reversely calculates the optimal clamping scheme according to the specific geometric characteristics and material properties of the structure, accurately controls the distribution and magnitude of the clamping force, effectively restricts the deformation of the thin-walled structure during the assembly process, and prevents the occurrence of damage induced by assembly stress.

[0153] The above has introduced in detail a method and system for optimizing the inverse solution of the forced positioning clamping process parameters of an aviation composite thin-walled structure provided by an embodiment of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

[0154] As used in the specification and claims, certain terms are used to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. The specification and claims do not distinguish components by the difference in names, but by the difference in functions. As used throughout the specification and claims, the terms "comprising" and "including" are open-ended terms and should be interpreted as "comprising / including but not limited to". "Substantially" means within an acceptable error range. Those skilled in the art can solve the technical problems within a certain error range and basically achieve the technical effects. The following description in the specification is the preferred embodiment for implementing the present application, but the description is for the purpose of explaining the general principles of the present application and not for limiting the scope of the present application. The protection scope of the present application shall be subject to what is defined by the appended claims.

[0155] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a good or system including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such good or system. Without further limitation, an element defined by the phrase "including a..." does not exclude the presence of additional identical elements in the good or system including said element.

[0156] It should be understood that the term "and / or" used herein is merely a description of the relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0157] The above description illustrates and describes several preferred embodiments of the present application. However, as mentioned above, it should be understood that the present application is not limited to the form disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the application concept described herein through the above teachings or the technology or knowledge in the relevant field. And any changes and modifications made by those skilled in the art without departing from the spirit and scope of the present application shall fall within the protection scope of the appended claims of the present application.

Claims

1. A method for optimizing the inverse process parameters of forced positioning and clamping of thin-walled aviation composite structures, characterized in that: The inverse method for optimizing the process parameters of forced positioning and clamping of aerospace composite thin-walled structures comprises the following steps: S1: Obtain key clamping process parameters by calculating the sensitivity values ​​of forced clamping process parameters that affect assembly quality; S2: Taking the allowed range of key clamping process parameters as constraints, combined with the expected value analysis of the assembly quality of aviation composite thin-walled structures, and minimizing the errors of assembly quality, assembly clearance, assembly stress and damage expectation values ​​as the optimization goal, a mathematical model of the forward mapping relationship between assembly process parameters and composite thin-walled assembly quality is constructed; S3: Construct a multi-objective / multi-constrained reverse optimization objective function, formulate a reverse solution strategy for the forward mapping relationship mathematical model through intelligent optimization algorithms and data learning and mining techniques, and reversely derive the optimal forced positioning and clamping process parameters through step-by-step iteration; S4: Perform assembly operations based on the optimal set of forced positioning and clamping process parameters, verify and provide feedback on the inverse optimization effect of the optimal forced positioning and clamping process parameters; The S3 is specifically: S31: Based on the forward mapping relationship mathematical model, construct a multi-objective / multi-constrained reverse optimization objective function; S32: Using intelligent optimization solution algorithm, the key process parameters affecting the quality of thin-wall assembly of aviation composite materials are encoded into individual genotypes in the genetic algorithm. Each set of parameter configuration is regarded as an independent individual to form an initial population; S33: Iteratively optimize the initial process parameter population under the premise of ensuring the allowable range of key process parameters and that the thin wall of the aviation composite material is affected by the key process parameters without damage constraints; S34: Repeat S31-S32 multiple times, and determine the optimal process number configuration that can maximize the assembly quality of thin-walled aviation composites under the forced positioning and clamping process through fitness function evaluation. According to the assembly hierarchy relationship, reversely deduce and solve the optimal process parameters step by step to obtain the third set of forced positioning and clamping process parameters.

2. The inverse method for optimizing process parameters of forced positioning and clamping of aviation composite thin-walled structures according to claim 1 is characterized in that: The forced clamping process parameters in S1 that affect the assembly quality include, but are not limited to, the size and position distribution of the clamping force, the clamping sequence, the temperature control parameters, the pressure adjustment parameters, the positioning accuracy parameters and the stability parameters.

3. The inverse method for optimizing process parameters of forced positioning and clamping of aviation composite thin-walled structures according to claim 1 is characterized in that: The method for determining the forced clamping process parameters that affect the assembly quality in S1 includes but is not limited to theoretical analytical calculation, finite element simulation test analysis and test data evaluation.

4. The inverse optimization method for forced positioning and clamping process parameters of aviation composite thin-walled structures according to claim 1 is characterized in that: In S1, by calculating the sensitivity value of the forced clamping process parameter that affects the assembly quality, the key clamping process parameters are obtained, which specifically include: S11: Collect theoretical modeling data, finite element simulation data, and test data related to the forced positioning clamping process and quality effects to form a data analysis set; S12: combining the assembly process of the composite thin-walled structure, all forced clamping initial process parameters that affect the assembly quality are formed into a first set from the data analysis set; S13: constructing an assembly quality response matrix through the response degree of the assembly quality corresponding to the change of each assembly quality influencing parameter in the first set, and converting the influence degree into quantitative data; S14: Arrange the quantitative data results in descending order to obtain a set of process parameters that have a greater impact on the assembly quality state, that is, obtain a second set including a priority list; S15: Determine the key process parameters required for inverse analysis in the subsequent optimization inverse analysis process based on the second set.

5. The inverse method for optimizing process parameters of forced positioning and clamping of aviation composite thin-walled structures according to claim 4 is characterized in that: The S2 specifically includes: S21: Establish specific expected indicators representing assembly quality based on the second set, and obtain an expected value set: S22: Taking the allowable range of key process parameters and the fact that the thin-walled aerospace composites are affected by key forced clamping process parameters without being damaged as constraints, and minimizing the difference between the actual assembly quality value and the expected assembly quality value as the optimization goal, the complex coupling relationship between each process parameter and the ideal assembly quality index is analyzed, and a mathematical model of the forward mapping relationship between assembly process parameters and the assembly quality of thin-walled composites is constructed.

6. The inverse optimization method for forced positioning and clamping process parameters of aviation composite thin-walled structures according to claim 1 is characterized in that: The S4 specifically includes: S41: According to the optimal process parameters in the third set, flexible assembly and adjustment equipment and aviation composite thin-walled structure samples that meet the required process parameter requirements are set, and accurate stress measurement methods and standards are established to design test plans; S42: Perform actual clamping operations on aviation composite thin-walled structural parts according to the optimal process parameters, measure the stress-strain distribution and damage of the composite thin-walled parts under clamping conditions, and collect detailed on-site data records; S43: Compare the test measurement data with the preset expected values. For the optimal process parameters that do not meet the preset expected values, it is necessary to conduct in-depth analysis of the data to identify possible factors causing the deviation. Based on the analysis results, adjust and optimize the process parameters, and repeat S1-S3 until the results meet the established assembly quality and stress requirements, thereby obtaining the fourth set of forced positioning and clamping process parameters for optimal assembly quality.

7. The inverse optimization method for forced positioning and clamping process parameters of aviation composite thin-walled structures according to claim 6 is characterized in that: In S43, if the expected target of the assembly quality is not met, the assembly quality data is analyzed to identify possible factors causing the deviation. Based on the analysis results, the forced clamping process parameters are adjusted and optimized, and S1-S3 are repeated until the design requirements of the assembly quality status are met.

8. The inverse optimization method for forced positioning and clamping process parameters of aviation composite thin-walled structures according to claim 4 is characterized in that: In S13, the expression of assembly quality includes but is not limited to assembly geometric clearance, position error, internal stress distribution and damage degree index.

9. An optimization and inverse search system for process parameters of forced positioning and clamping of thin-walled aviation composite structures, characterized in that: The aerospace composite thin-walled structure forced positioning clamping process parameter optimization inverse search system comprises: The key process parameter identification and calculation module is used to obtain the key clamping process parameters by calculating the sensitivity values ​​of the forced clamping process parameters that affect the assembly quality; The forward mapping model building module is used to construct a mathematical model of the forward mapping relationship between assembly process parameters and composite thin-wall assembly quality, taking the allowable range of key clamping process parameters as constraints, combining the expected value analysis of the assembly quality of aerospace composite thin-wall structures, and minimizing the errors of assembly quality, assembly clearance, assembly stress and damage expected values ​​as optimization goals; The optimal process parameter reverse calculation module is used to construct a multi-objective / multi-constrained reverse optimization objective function. Through intelligent optimization algorithms and data learning and mining techniques, a reverse solution strategy for the forward mapping relationship mathematical model is formulated, and the optimal forced positioning and clamping process parameters are reversely derived through step-by-step iteration. Specifically: Based on the forward mapping relationship mathematical model, a multi-objective / multi-constrained reverse optimization objective function is constructed; By using intelligent optimization algorithm, the key process parameters that affect the quality of thin-wall assembly of aviation composite materials are encoded as individual genotypes in the genetic algorithm. Each set of parameter configuration is regarded as an independent individual to form an initial population. Under the premise of ensuring the allowable range of key process parameters and that the thin wall of aviation composites is affected by key process parameters without damage constraints, the initial process parameter population is iteratively optimized; S31-S32 is repeated many times, and the optimal process number configuration that can maximize the assembly quality of thin-walled aviation composite materials under the forced positioning and clamping process is determined through fitness function evaluation. According to the assembly hierarchy relationship, the optimal process parameters are derived and solved in reverse step by step iteration, and the third set of forced positioning and clamping process parameters is obtained; The result verification and feedback module is used to perform assembly operations based on the optimal set of forced positioning and clamping process parameters, verify and feedback the optimal forced positioning and clamping process parameters inverse optimization effect.

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