Method and system for optimizing and reversely solving process parameters of forced positioning and clamping of aviation composite thin-wall structure

Abstract

The invention provides an aviation composite thin-wall structure forced positioning and clamping process parameter optimization reverse solving method and system. The method comprises the steps that S1, key positioning and clamping process parameters are obtained through sensitivity calculation; s2, constructing a positive mapping relation mathematical model of the assembly process parameters and the assembly quality of the composite thin-wall structure; s3, constructing a multi-objective / multi-constraint reverse optimization objective function, and determining optimal process parameters of forced positioning and clamping through a gradual iteration reverse derivation mode; and S4, verifying the optimal forced positioning clamping process parameters and feeding back a reverse optimization effect of the process parameters. According to the method, the geometric and physical assembly performance state influencing the composite thin-wall structure and the range allowed by the constraint of the required technological parameters are comprehensively considered, the clamping technological parameters are optimized through the reverse solving technology, the optimal clamping scheme is reversely deduced, the distribution and magnitude of the clamping force are accurately controlled, and the clamping precision is improved. Deformation of the composite material thin-wall structure in the assembling process can be effectively limited, and damage induced by assembling stress is prevented.

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 shortcomings: ① Due to reliance on manual operation and empirical judgment, it is difficult to achieve high-precision assembly requirements. The process of manual adjustment and repeated trial assembly is time-consuming, which increases the production cycle and leads to low efficiency. ② Multiple adjustments, rework and scrap generation during the assembly process directly increase manufacturing costs. ③ The lack of systematic process parameter optimization means, insufficient identification of key factors in the assembly process, and difficulty in achieving multi-objective optimization. ④ Faced with complex and changeable thin-walled structure designs, traditional methods are difficult to adjust flexibly and have limited adaptability to new materials and complex structures. ⑤ The lack of effective real-time feedback and iterative optimization mechanisms leads to delayed discovery of assembly quality problems and slow implementation of improvement measures, which is not conducive to continuous improvement and process upgrades.

[0004] In view of the shortcomings of the above-mentioned traditional adjustment methods, the key to using forced clamping to improve the assembly quality of aviation composite thin-walled structures is how to combine automation and intelligent technology, intelligent algorithm optimization, real-time monitoring and feedback systems, etc., to obtain a set of forced clamping process parameters that can maximize the reduction of assembly gaps and improve assembly quality, while effectively controlling and reducing internal stress and damage generated during the assembly process.

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

[0006] In view of this, the present invention provides an optimization inverse method and system for the forced positioning and clamping process parameters of aviation composite thin-walled structures, which can obtain the process parameters that have a greater impact on the assembly quality based on sensitivity calculation, and obtain a set of reasonable forced positioning and clamping process parameters by constructing a mathematical model of the forward and reverse mapping relationship between the assembly process parameters and the assembly quality, and using intelligent algorithms and data learning and mining methods to solve it, verifying and feeding back whether the required 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] In one aspect, the present invention provides a method for optimizing the inverse process parameters of forced positioning and clamping of aerospace composite thin-walled structures. The method comprises the following steps:

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

[0009] 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;

[0010] 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;

[0011] 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.

[0012] According to the aspects described above and any possible implementation method, an implementation method is further provided, wherein the parameters affecting assembly quality in S1 include but are not limited to the size and position distribution of clamping force, clamping sequence, temperature control parameters, pressure adjustment parameters, positioning accuracy parameters and stability parameters.

[0013] According to the aspects and any possible implementations described above, an implementation is further provided, wherein the method for determining the parameters affecting assembly quality in S1 includes but is not limited to theoretical analytical calculation, finite element simulation test analysis, and test data evaluation.

[0014] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein obtaining key process parameters by sensitivity calculation in S1 specifically includes:

[0015] 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;

[0016] 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;

[0017] 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;

[0018] 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;

[0019] S15: Determine the key process parameters required for inverse analysis in the subsequent optimization inverse analysis process based on the second set.

[0020] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S2 specifically 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 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.

[0023] According to the above aspects and any possible implementation, an implementation is further provided, wherein S3 is specifically:

[0024] S31: Based on the forward mapping relationship mathematical model, construct a multi-objective / multi-constrained reverse optimization objective function;

[0025] 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;

[0026] 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;

[0027] 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.

[0028] According to the above aspects and any possible implementation, an implementation is further provided, wherein S4 specifically includes:

[0029] 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;

[0030] 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;

[0031] S43: Compare the test measurement data with the preset expected values. For the optimal process parameters that do not meet the preset expected values, the data needs to be analyzed in depth to identify the possible factors that cause 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 and clamping process parameters for the best assembly quality.

[0032] According to the aspects described above and any possible implementation method, an implementation method is further provided. In S43, if the established expected assembly quality target 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.

[0033] According to the aspects and any possible implementations described above, an implementation is further provided, wherein 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.

[0034] According to the above aspects and any possible implementation, a system for optimizing reverse engineering of process parameters of forced positioning and clamping of thin-walled aerospace composite materials is further provided, wherein the system for optimizing reverse engineering of process parameters of forced positioning and clamping of thin-walled aerospace composite materials comprises:

[0035] 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;

[0036] 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;

[0037] 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.

[0038] 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.

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

[0040] 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.

[0041] 2) Taking the key process parameters as constraints and minimizing the differences between assembly quality, assembly clearance and geometric deformation and expected values ​​as the optimization goal, a mathematical model was constructed to obtain the forward mapping relationship between assembly process parameters and composite thin-wall assembly quality.

[0042] 3) Based on intelligent optimization algorithms and data learning mining, the mathematical model between the above-mentioned assembly process parameters and the quality of composite thin-wall assembly is solved, and while satisfying the constraints such as clamping force size, position and sequence, the global optimal solution close to the forced positioning clamping process parameters is obtained.

[0043] 4) The forced positioning and clamping process parameters obtained above are experimentally verified to obtain a set of forced positioning and clamping process parameters required for the assembly of an ideal composite thin-walled structure, thereby guiding the completion of high-performance assembly operations.

[0044] 5) By comparing the test data with the expected goals, a closed-loop feedback and continuous improvement mechanism is provided to adjust and optimize the process parameters, so that the inverse assembly process parameters and assembly quality can achieve a closed-loop control effect.

[0045] Of course, any product implementing the present invention does not necessarily need to achieve all of the above-mentioned technical effects at the same time.

Brief Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0048] Figure 2 This is a flow chart of the forced positioning clamping process parameter optimization inverse search technology provided by an embodiment of the present invention;

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

[0050] Figure 4 It is a diagram illustrating the composition structure and adjustable functions of a typical wing box assembly tooling of the present invention. [Specific implementation method]

[0051] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0052] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0054] The invention provides an inverse optimization method for forced positioning and clamping process parameters of an aviation composite thin-wall structure. The inverse optimization method for forced positioning and clamping process parameters of an aviation composite thin-wall structure mainly includes the following four parts: first, a set of process parameters that have a greater impact on assembly quality is obtained through sensitivity calculation; second, the variation of process parameters that have a greater impact on assembly quality is used as a constraint condition, combined with an expected value analysis of the assembly quality of aviation composite thin-walls, and the minimization of the error of assembly quality, assembly clearance, assembly stress and damage expected value is used as an optimization goal, and a mathematical model is constructed to obtain a forward mapping relationship between assembly process parameters and composite thin-wall assembly quality; then, a multi-objective / multi-constrained reverse optimization objective function is constructed, and the mathematical model of the above process parameters and assembly quality is solved through an intelligent optimization algorithm and technical means of data learning and mining, and the optimal process parameters of forced positioning and clamping are derived through step-by-step iteration; finally, the inversely obtained assembly process parameters are verified and closed-loop feedback is provided.

[0055] Specifically, the steps include:

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

[0057] 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;

[0058] 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;

[0059] 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.

[0060] The parameters affecting assembly quality in S1 include, but are not limited to, the size and position distribution of clamping force, clamping sequence, temperature control parameters, pressure adjustment parameters, positioning accuracy parameters and stability parameters.

[0061] According to the aspects and any possible implementations described above, an implementation is further provided, wherein the method for determining the parameters affecting assembly quality in S1 includes but is not limited to theoretical analytical calculation, finite element simulation test analysis, and test data evaluation.

[0062] The key process parameters obtained by sensitivity calculation in S1 specifically include:

[0063] 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;

[0064] 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;

[0065] 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;

[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: Determine the key process parameters required for inverse analysis in the subsequent optimization inverse analysis process based on the second set.

[0068] The S2 specifically includes:

[0069] S21: Establish specific expected indicators representing 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 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.

[0071] The S3 is specifically:

[0072] S31: Based on the forward mapping relationship mathematical model, construct a multi-objective / multi-constrained reverse optimization objective function;

[0073] 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;

[0074] 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;

[0075] 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.

[0076] The S4 specifically includes:

[0077] 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;

[0078] 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;

[0079] S43: Compare the test measurement data with the preset expected values. For the optimal process parameters that do not meet the preset expected values, the data needs to be analyzed in depth to identify the possible factors that cause 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 and clamping process parameters for the best assembly quality.

[0080] 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.

[0081] 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.

[0082] The present invention also provides an optimization and inverse search system for forced positioning and clamping process parameters of aerospace composite thin-walled structures, and the optimization and inverse search system for forced positioning and clamping process parameters of aerospace composite thin-walled structures comprises:

[0083] 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;

[0084] 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;

[0085] 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.

[0086] 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.

[0087] Figure 2 The specific implementation flow chart of this solution in this embodiment is as follows. The overall idea adopted by the present invention to solve its technical problem is:

[0088] In the process of optimizing the assembly of thin-walled aerospace composites, firstly, the process parameters that have a significant impact on the assembly quality, such as the clamping force, position and sequence, are identified through calculation, and the influence of these parameters is measured by sensitivity analysis method, and then the key parameter set is constructed and arranged in descending order of sensitivity. Secondly, based on the above operations, the various expected indicators of assembly quality, such as geometric clearance, damage degree, position error, etc., are clarified. At the same time, under the constraints of ensuring that the thin-walled aerospace composites are not damaged and that the key process parameters are within the applicable range of their respective engineering requirements, the difference between the actual value A of the assembly quality and the expected value E of the assembly quality is minimized as the optimization goal, and the complex coupling relationship between each process parameter and the ideal assembly quality indicator is deeply analyzed, and a forward mapping mathematical model between the assembly process parameters and the assembly quality of the composite thin-walled composites is constructed. Thirdly, through intelligent optimization algorithms and the use of data learning and mining techniques, the iterative solution of the reverse mathematical optimization model under multi-objective and multi-constraint conditions is completed. Combined with the fitness function evaluation, the reverse deduction and solution are determined to determine the set of process parameters that can maximize the assembly quality of the composite thin-walled structure under the forced positioning and clamping process, including the design variables such as the clamping position, sequence, and force of the tooling locator, and determine the optimal parameter configuration that can maximize the assembly quality. Finally, the test plan is designed based on the optimized process parameters, and actual clamping operations and performance tests are carried out. High-precision instruments are used to monitor the stress, strain, and damage of the composite thin wall, and 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 iterate continuously until the expected assembly quality and stress requirements are met. The entire process systematically ensures the high quality and efficiency of aviation composite thin-wall assembly.

[0089] The present invention provides an inverse optimization technology for forced positioning and clamping process parameters of aviation composite thin-walled materials. The forced positioning and clamping process parameter optimization inverse optimization technology is aimed at the assembly stage of aviation composite thin-walled materials. While using forced positioning and clamping to eliminate the geometric gap between the bonding surfaces of aviation composite thin-walled materials, it also causes excessive assembly stress inside the composite thin-walled materials, which may cause damage and other problems. The technology mainly starts from the actual state of the assembly quality of aviation composite thin-walled materials, reversely searches for key parameters that affect the precision of aviation composite thin-walled materials, and obtains the key parameter values ​​in the actual state, so that the difference between the actual value of the assembly quality and the expected value is minimized, thereby achieving the effect of optimizing the assembly quality of aviation composite thin-walled structures, such as Figure 1 As shown, the inverse technology for optimizing the process parameters of thin-walled forced positioning and clamping of aviation composite materials includes the following steps:

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

[0091] The S1 specifically includes:

[0092] 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;

[0093] S12: Combined with the assembly process of the composite thin-walled structure, all the forced clamping initial process parameters that affect the assembly quality are formed into a first set P = {p1, p2, ..., p n}.

[0094] S13: In the first set of process parameters, a sensitivity analysis method is used to measure the specific impact of different process parameters on the assembly quality results. For each parameter p i , define its sensitivity S(p i ) is A with p i A quantitative indicator of the rate of change, namely:

[0095]

[0096] The assembly quality A can be represented by geometric gap H, assembly internal stress S(x), damage degree D(x), shape and position and other geometric error types δ. Then the set of assembly geometry and physical quality states can be represented by: A = {A1, A2, A3, ..., An}. Based on the sensitivity value, the sensitivity value set corresponding to the key process parameters is constructed: S = {S(p1), S(p2), S(p3), ..., S(p n )}.

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

[0098] Specifically, firstly, the key process parameters that affect the assembly quality are identified, and various process variables are deeply analyzed, such as 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, etc. Through theoretical analytical calculation, finite element analysis and experimental data evaluation, the key parameters that have a decisive influence on the final assembly quality, such as the clamping force size p1, the position p2 and the sequence p3, are selected to form a set P = {p1, p2, ..., p n}.

[0099] Furthermore, sensitivity analysis is used to analyze the influence of key process parameters on assembly quality. By calculating the response degree ΔA1, ΔA2, ΔA3 of each key process parameter to the assembly quality, such as the geometric gap size H, assembly internal stress S(x), damage control degree D(x) of the composite structure, and shape and position matching error δ between thin walls of aviation composite materials, the assembly quality response matrix ΔA={ΔA1, ΔA2, ΔA3, ..., ΔAn} is constructed to convert the influence degree into quantitative data, namely sensitivity calculation:

[0100]

[0101] The calculated sensitivity of each key process parameter to the assembly quality constitutes 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 arranged in descending order, and a descending parameter set K of key process parameters is obtained based on the second set of forced clamping process parameters to form a priority list. Based on this, the key process parameters required for inverse work in subsequent optimization inverse work are determined.

[0103] S2: Description of expected parameters of assembly quality and construction of multi-objective optimization model between key process parameters and assembly quality under constraints.

[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 assembly quality, then the expected value set: E = {E1, E2, E3, ..., En}.

[0106] S22: Based on the range allowed by key process parameters The thin wall of aviation composites is affected by key process parameters without damage D(p1, p2, ..., p n )≤0 is taken as the constraint condition, and the minimum difference between the actual value A of assembly quality and the expected value E of assembly quality is taken as the optimization goal. The complex coupling relationship between each forced positioning and clamping process parameter and the ideal assembly quality index is deeply analyzed, and a mathematical model between the assembly process parameters and the assembly quality of composite thin-walled materials is constructed.

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

[0108] Furthermore, the allowable ranges of key process parameters such as clamping force p1, position p2, and the sequence p3 between multiple clamping processes are as follows: And the thin wall of aviation composites is not damaged by the key process parameters: S(p1,p2,...,p n )≥0 as the constraint condition, and taking the minimum difference between the actual value A of the aviation composite thin-wall assembly quality and the expected value E of the assembly quality as the optimization goal, the complex coupling relationship between each process parameter and the ideal assembly quality index is deeply analyzed, and the forward mathematical mapping model between the assembly process parameters and the composite thin-wall assembly quality is constructed:

[0109]

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

[0111] S3: Use intelligent optimization algorithms, data learning and mining to develop a quality assurance strategy for composite thin-walled structure assembly and obtain a set of key assembly parameters.

[0112] The S3 specifically includes:

[0113] S31: Based on the forward mapping relationship mathematical model in S22, a multi-objective / multi-constrained reverse optimization objective function is constructed.

[0114] S32: Using intelligent optimization algorithm, the key process parameters that affect the quality of thin-wall assembly of aviation composite materials, such as clamping force, position and sequence, are encoded as individual genotypes in the genetic algorithm. Each set of parameter configuration is regarded as an independent individual to form the initial population.

[0115] S33: Under the premise of ensuring the allowable range of key process parameters and that the thin-walled aviation composite material is affected by the key process parameters without damage constraints, the initial process parameter population is iteratively optimized using an intelligent optimization algorithm.

[0116] S34: By continuously performing the above operations and using data learning and mining methods (such as machine learning, support vector regression), combined with fitness function evaluation, the optimal process number configuration that can maximize the quality of thin-wall assembly of aviation composites under the forced positioning and clamping process is determined, and according to the assembly hierarchy relationship, the optimal process parameters are reversely deduced and solved step by step to obtain the third set of forced positioning and clamping process parameters.

[0117] Specifically, the key factors that affect the assembly quality, such as the clamping force, position and assembly sequence, are encoded as individual "genotypes" 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, under the premise of ensuring that all process parameter operations are carried out within a safe and effective range and avoiding any damage to the thin-walled aerospace composites, 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 that affect the quality of the thin-walled composite assembly, selects individuals with high fitness for the next generation of reproduction, and gradually eliminates individuals with low fitness.

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

[0120] S4: Based on the desired process parameters, the adjustment control parameters are selected and the test plan is designed, executed and feedback is provided to verify the on-site effect of the reverse optimization of the forced clamping process parameters.

[0121] The S4 specifically includes:

[0122] S41: According to the optimal process parameters in the third set, set up flexible assembly and adjustment equipment and aviation composite thin-wall structure samples that meet the requirements of the required process parameters, and establish accurate stress measurement methods and standards to design the test plan.

[0123] S42: Perform actual clamping operations on the thin-walled composite materials for aviation according to the optimal process parameters. Subsequently, high-precision instruments are used to comprehensively measure the stress-strain distribution and damage of the thin-walled composite materials in the clamped state, and detailed data records are collected.

[0124] S43: Compare the test measurement data with the preset expected values. For the optimal process parameters that do not meet the preset expected values, the data needs to be analyzed in depth to identify the possible factors that cause 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 and clamping process parameters for the best assembly quality.

[0125] Specifically, select special flexible assembly and adjustment equipment that can accurately achieve the required clamping force, precise position control and specific clamping sequence, formulate a technical route for measuring internal stress in the assembly, calibrate highly sensitive sensors and analytical instruments, and set the position, frequency and standard of the measuring points.

[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 constraints and minimizing the differences between assembly quality, assembly clearance and geometric deformation and expected values ​​as the optimization goal, a mathematical model was constructed to obtain the forward mapping relationship between assembly process parameters and composite thin-wall assembly quality.

[0132] 3) Based on intelligent optimization algorithms and data learning mining, the mathematical model between the above-mentioned assembly process parameters and the quality of composite thin-wall assembly is solved, and while satisfying the constraints such as clamping force size, position and sequence, the global optimal solution close to the forced positioning clamping process parameters is obtained.

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

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

[0135] Embodiment 1:

[0136] To elaborate on this scheme, the optimization and inverse search of process parameters in the forced clamping of a certain type of composite wing box component is taken as an example. In this embodiment, an exploded diagram of the wing box of this scheme is drawn, as shown in FIG. Figure 3 The flexible assembly and adjustment tooling system used in the wing box of this scheme is as follows Figure 4 shown.

[0137] First, the specific assembly process of the wing box is clarified, and the key parameter set affecting the assembly quality is identified by combining theoretical analytical calculation, ABAQUS finite element simulation analysis software and historical data, including component size, material properties, assembly sequence, positioning and clamping conditions and environmental factors. Secondly, the indicators reflecting the assembly quality, such as assembly internal stress distribution, assembly clearance, geometric fit, assembly damage distribution, etc., are analyzed and obtained, and the key parameters are sorted through sensitivity analysis to guide subsequent optimization. Thirdly, a mathematical model between assembly quality and key process parameters is established, and a weighted method is used to balance the importance of different precision indicators. Genetic algorithms are introduced for parameter optimization, and the ideal assembly strategy is gradually approached by simulating the natural selection mechanism. Through the iteration of intelligent optimization algorithms and data learning and mining methods, the optimal combination of process parameters such as the clamping position, sequence, and clamping force of the tooling locator is selected, and the actual assembly test and production of the components of the wing box components are guided accordingly. After that, the assembly operation is performed strictly in accordance with the optimized forced clamping process parameters, and advanced detection technology is used to comprehensively monitor various indicators of assembly quality, compare the collected measured data with the preset expected values, and evaluate the effectiveness of the assembly plan. If there is any deviation, it will be fed back to the data analysis link, and the process parameters will be adjusted according to the source of the deviation, and a new optimization cycle will be started until the wing box assembly quality fully meets the predetermined geometric accuracy, internal stress and damage standards, realizing a closed-loop optimization process from theoretical analysis to experimental verification, ensuring high-quality completion of the composite wing box component assembly.

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

[0139] Specifically, the assembly process of this type of wing box components is clarified: first, fix the left and right walls and the first and fourth frames on the tooling; then use this as the positioning reference to install the second and fourth frames; then install the composite lower wall panel; and finally install the composite upper wall panel. The above wing box assembly process is imported into ABAQUS for simulation, and combined with historical test data, the main parameters affecting the assembly quality of the composite wing box are obtained, such as: the dimensional parameters of each component such as the left wall and the right wall, as well as the material properties of each component, assembly sequence, positioning clamping force, position, clamping sequence, and assembly environment, which are recorded as p1, p2, p3, etc., and constitute a set P = {p1, p2, ..., p n}.

[0140] Furthermore, the index parameters that can represent the wing box assembly quality are analyzed, such as the internal assembly stress of the wing box structure, the assembly gap between the fitting surfaces of the wing box components, and the parallelism, verticality and step difference between the components. By changing the main parameters that affect the assembly quality of the composite wing box, the changes in the assembly quality index are obtained, the sensitivity of each main parameter is calculated, and then the sensitivity of each key process parameter to the assembly quality is formed into a sensitivity matrix: S = {S(p1), S(p2), S(p3), ..., S(p n )}.

[0141] Furthermore, the sensitivity of the above parameters to the wing box assembly quality is arranged in descending order to form a descending key parameter set K, forming a priority list.

[0142] Furthermore, the expected value that can represent the wing box assembly quality is expressed by a series of specific parameters, such as the expected assembly geometric clearance E1, the expected assembly damage degree E2, the expected assembly shape and position error E3, the expected assembly internal stress distribution E4, etc., forming an expected value set: E = {E1, E2, E3, ..., E n}.

[0143] Furthermore, with the engineering allowable range of key process parameters affecting the wing box assembly quality and the wing box not being damaged as constraints, and 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, a mathematical mapping model between assembly process parameters and composite wing box assembly quality is constructed:

[0144]

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

[0146] Furthermore, the key process parameters that affect the wing box assembly quality are encoded as individual "genotypes" in the genetic algorithm. Each specific parameter configuration is regarded as an independent individual in the algorithm, which carries different assembly strategy information.

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

[0148] Furthermore, by continuously repeating the above iterative optimization and evaluation process, combined 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, the wing box assembly test was carried out strictly in accordance with the optimal key process parameters of wing box assembly obtained by the above genetic algorithm. First, the left and right walls and the first and fourth frames were fixed by the positioner on the tooling; then, the second and fourth frames were installed based on this as the positioning reference to complete the skeleton; after that, the composite upper and lower wall panels were installed based on the skeleton.

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

[0151] Furthermore, the obtained parameter test data representing the wing box assembly quality is compared with the previously set assembly quality expectation value 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 source of deviation. Based on the analysis results, necessary adjustments and optimizations are made to the process parameters, key process parameters are recalculated and identified, and a new round of optimization iterations is started until the test results fully meet the established requirements for assembly geometry accuracy, internal stress and damage distribution.

[0152] The present invention aims at the problem that due to its material properties and structural characteristics, the thin-walled structure of aviation composites is very easy to deform due to uneven force during the forced clamping process, and problems such as local buckling or delamination occur. Considering that these problems will seriously affect the dimensional accuracy and structural performance of the final product, in order to reasonably assemble the thin-walled composites and optimize the forced clamping, it is necessary to perform prediction and inverse search according to the assembly requirements before assembly to obtain the forced positioning clamping process parameters to guide the assembly of the thin-walled composites. The present invention comprehensively considers the physical and geometric properties that affect the thin-walled composites and the required range of assembly quality, optimizes the clamping parameters of the forced positioning process through inverse search technology, reversely calculates the optimal clamping scheme according to the specific geometric characteristics and material properties of the structure, accurately controls the distribution and size of the clamping force, effectively limits the deformation of the thin-walled structure during the assembly process, and prevents the occurrence of damage induced by assembly stress.

[0153] The above is a detailed introduction to the inverse method and system for optimizing the process parameters of forced positioning and clamping of thin-walled composite aviation structures provided in the embodiments of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application; at the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0154] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different nouns to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of components as the criteria for distinction. As mentioned throughout the specification and claims, "including" and "comprising" are open-ended terms, so they should be interpreted as "including / including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect. The subsequent description of the specification is a preferred embodiment of the present application, but the description is for the purpose of illustrating the general principles of the present application, and is not used to limit the scope of the present application. The scope of protection of the present application shall be determined by the definition of the attached claims.

[0155] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such a product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0156] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0157] The above description shows and describes several preferred embodiments of the present application, but as mentioned above, it should be understood that the present application is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the application concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present application, and should be within the scope of protection of the claims attached to 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.

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 parameters affecting assembly quality in S1 include, but are not limited to, the size and position distribution of clamping force, clamping sequence, temperature control parameters, pressure adjustment parameters, positioning accuracy parameters and 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 parameters affecting 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: The key process parameters obtained by sensitivity calculation in S1 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 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.

7. 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 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, the data needs to be analyzed in depth to identify the possible factors that cause 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 and clamping process parameters for the best assembly quality.

8. The inverse optimization method for forced positioning and clamping process parameters of aviation composite thin-walled structures according to claim 7 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.

9. The inverse method for optimizing process parameters of forced positioning and clamping of thin-walled aviation composite 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.

10. 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. 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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