Manufacturing process for lightweight composite material fuselage of medium-large unmanned aerial vehicle

Abstract

The invention discloses a medium and large unmanned aerial vehicle lightweight composite material fuselage manufacturing process, which relates to the technical field of automation, and comprises the following steps: carrying out stress analysis on a fuselage three-dimensional model, determining a stress area, recording stress distribution data, inputting the stress distribution data of the fuselage into a layering optimization algorithm, generating a layering path, and according to the generated layering path, carrying out layering optimization on the fuselage. The method comprises the following steps: constructing a digital twin model, monitoring process parameters, laying a composite material by using robot laying equipment based on monitored process parameter data, performing, putting a preformed machine body into an autoclave, completing high-temperature and high-pressure curing, demolding and trimming the cured machine body, detecting by using ultrasonic detection equipment, and obtaining internal defect data of the machine body. And according to the internal defect data of the fuselage, the internal defect data is fed back to the layering optimization algorithm and the digital twin platform, and the process parameters are optimized, so that the problems of insufficient stress distribution optimization, process parameter monitoring and defect detection in the manufacturing of the composite fuselage are solved.

Description

Technical Field

[0001] The present invention relates to the field of automation technology, and in particular to a manufacturing process for a lightweight composite fuselage of a medium and large-sized unmanned aerial vehicle (UAV). Background Art

[0002] With the rapid development of UAV technology, medium and large-sized UAVs are increasingly widely used in military, civilian, and commercial fields. In order to improve the endurance and maneuverability of UAVs, lightweight design has become one of the key technologies in fuselage manufacturing. Due to its high strength, low density, and excellent fatigue resistance, composite materials have gradually become the preferred materials for UAV fuselage manufacturing.

[0003] In the prior art, the manufacturing process of composite fuselages often lacks precise analysis and optimization of the stress distribution of the fuselage, resulting in low material utilization rate and inability to further reduce the fuselage weight. At the same time, the existing process has insufficient ability to monitor and adjust process parameters in real time during the laying process, making it difficult to ensure the stability of the laying quality. In addition, the detection means for internal defects of the cured fuselage are limited and cannot provide timely feedback and optimize the manufacturing process. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a manufacturing process for a lightweight composite fuselage of a medium and large-sized UAV to solve the problems of insufficient optimization of stress distribution, process parameter monitoring, and defect detection in composite fuselage manufacturing.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a manufacturing process for a lightweight composite fuselage of a medium and large-sized UAV, characterized in that it includes:

[0008] Performing a force analysis on the three-dimensional model of the fuselage to determine the force-bearing area and record the stress distribution data;

[0009] Inputting the stress distribution data of the fuselage into a layup optimization algorithm to generate a layup path;

[0010] Constructing a digital twin model and monitoring process parameters according to the generated layup path;

[0011] Based on the monitored process parameter data, using a robotic layup device to lay composite materials and preform;

[0012] Placing the preformed fuselage into an autoclave to complete high-temperature and high-pressure curing;

[0013] Demolding and trimming the cured fuselage, and using an ultrasonic testing device to detect and obtain the internal defect data of the fuselage;

[0014] Based on the internal defect data of the fuselage, feedback it to the ply optimization algorithm and the digital twin platform and optimize the process parameters;

[0015] Based on the optimized process parameters, use the digital twin model for virtual assembly and functional simulation testing.

[0016] As a preferred embodiment of the manufacturing process of the lightweight composite fuselage of the medium and large unmanned aerial vehicle described in the present invention, wherein: performing a force analysis on the three-dimensional model of the fuselage, determining the stress-bearing area, and recording the stress distribution data. The specific steps are as follows,

[0017] Import the three-dimensional model of the fuselage and divide tetrahedral meshes through a mesh generation tool;

[0018] Based on the tetrahedral meshes, set the ply angles and thicknesses, fix the degrees of freedom of the connection points, apply aerodynamic and inertial loads, and run finite element analysis through a solver;

[0019] Based on the defined boundary conditions and loads, select the static structural analysis function through ANSYS Mechanical, enable the large deformation option, submit the task and monitor the solution process to generate preliminary stress results;

[0020] Extract the Von Mises stress and principal stress distributions from the finite element analysis results, record the coordinates and stress values of the high stress regions, save them in CSV format, and complete the acquisition of stress distribution data.

[0021] As a preferred embodiment of the manufacturing process of the lightweight composite fuselage of the medium and large unmanned aerial vehicle described in the present invention, wherein: inputting the stress distribution data of the fuselage into the ply optimization algorithm to generate ply paths. The specific steps are as follows,

[0022] Read the stress distribution data from the CSV file, perform normalization processing and eliminate outliers to provide input conditions for the optimization algorithm;

[0023] Based on the preprocessed data, with the goal of minimizing weight, set the ply angle and thickness ranges, and constrain the thickness of the high stress regions;

[0024] According to the set ply angles, thickness ranges and the thickness of the high stress regions, and input them into an iterative calculation through a genetic algorithm to generate the optimal ply distribution;

[0025] Convert the optimal ply distribution result into a ply path file, including angles, thicknesses and sequences.

[0026] As a preferred embodiment of the manufacturing process of the lightweight composite fuselage of the medium and large unmanned aerial vehicle described in the present invention, wherein: constructing a digital twin model and monitoring process parameters according to the generated ply paths. The specific steps are as follows,

[0027] Import the ply path file into the digital twin platform, parse the angle, thickness, and order data, construct a digital twin model based on the parsed data, and embed virtual sensors in the digital twin model to simulate the acquisition of temperature T(t), pressure P(t), and ply laying speed v(t). Introduce the formula:

[0028]

[0029] where Q is the ply quality index, α, β, and γ are the weight coefficients of temperature, pressure, and speed on ply quality respectively, μ is the time center point of the process, and σ is the width of the time distribution;

[0030] If Q < 0.8, analyze the sensitivity of process parameters to Q, determine the adjustment direction and amplitude, and feedback to the genetic algorithm to recalculate the optimal ply distribution to obtain the ply path file.

[0031] As a preferred solution of the manufacturing process of the lightweight composite fuselage of medium and large unmanned aerial vehicles described in the present invention, wherein: for the case of Q < 0.8, analyze the sensitivity of process parameters to Q, determine the adjustment direction and amplitude, and feedback to the genetic algorithm to recalculate the optimal ply distribution. The specific steps are as follows;

[0032] Extract the process parameters temperature T(t), pressure P(t), and ply laying speed v(t) at the current time point t from the digital twin platform and input them into the partial derivative Calculate the influence degree of each parameter on Q;

[0033] If then increase the temperature T(t), if then increase the pressure P(t), if then decrease the ply laying speed v(t), and adjust the amplitude according to the calculation situation

[0034] Update the process parameters according to the adjustment amplitude as T′(t) = T(t) + ΔT(t), P′(t) = P(t) + ΔP(t), v′(t) = v(t) + Δv(t), and feedback the calculation result to the genetic algorithm to recalculate the optimal ply distribution;

[0035] Update the digital twin model, recalculate Q. If Q < 0.8, repeat the above steps 1 - 3 until the requirements are met.

[0036] As a preferred solution of the manufacturing process of the lightweight composite fuselage of medium and large unmanned aerial vehicles described in the present invention, wherein: based on monitoring the process parameter data, use a robotic ply laying device to lay the composite material and preform it. The specific steps are as follows,

[0037] Load the optimized ply path file and parse it to generate robotic arm motion instructions;

[0038] Collect the temperature T(t), pressure P(t), and layup speed v(t) through the digital twin platform, calculate Q, and dynamically adjust the process parameters;

[0039] Based on the real-time process parameters, control the robotic arm to lay the composite material along the layup path, synchronizing the temperature T(t), pressure P(t), and layup speed v(t);

[0040] After each layer of layup is completed, use a preforming tool to compact the material, and detect the interlayer bonding quality through an embedded sensor, and provide real-time feedback to the digital twin platform;

[0041] According to the quality inspection results, adjust the subsequent layup parameters, and finally complete the fuselage preforming.

[0042] As a preferred solution of the manufacturing process of the lightweight composite material fuselage of the medium and large unmanned aerial vehicle described in the present invention, wherein: putting the preformed fuselage into an autoclave to complete high-temperature and high-pressure curing, the specific steps are as follows,

[0043] Receive the fuselage from the preforming process, check the surface quality to ensure no defects;

[0044] Fix the fuselage using a fixture, smoothly place it into the autoclave and seal it;

[0045] Start the autoclave, monitor the temperature and pressure in real time to ensure that the fuselage is evenly heated and pressed;

[0046] After the treatment is completed, naturally cool and take out the fuselage to complete the high-temperature and high-pressure curing process.

[0047] As a preferred solution of the manufacturing process of the lightweight composite material fuselage of the medium and large unmanned aerial vehicle described in the present invention, wherein: demolding and trimming the cured fuselage, and using ultrasonic testing equipment to detect and obtain the internal defect data of the fuselage, the specific steps are as follows,

[0048] Take out the cured fuselage from the autoclave and use a demolding tool to separate the mold;

[0049] Remove the excess material, grind the edges to be smooth, and check the surface flatness;

[0050] Use ultrasonic testing equipment to scan the inside of the fuselage to obtain defect data.

[0051] As a preferred solution of the manufacturing process of the lightweight composite material fuselage of the medium and large unmanned aerial vehicle described in the present invention, wherein: according to the internal defect data of the fuselage, feedback to the layup optimization algorithm and the digital twin platform and optimize the process parameters, the specific steps are as follows,

[0052] According to the ultrasonic testing results, analyze the defect type, location, and size, and generate a defect distribution map;

[0053] Input the defect data into the ply optimization algorithm to recalculate the ply angles, thicknesses, and sequences.

[0054] Based on the optimized ply path, update the fuselage geometry and process parameters in the digital twin model.

[0055] According to the updated model, adjust the temperature, pressure, and ply speed to generate new process parameters and implement them.

[0056] The beneficial effects of the present invention are as follows: By performing force analysis on the precise three-dimensional model of the fuselage and using the ply optimization algorithm to generate the optimal ply path, the material utilization rate is significantly improved, the fuselage weight is reduced, and the structural strength is ensured. Using digital twin technology to real-time monitor key process parameters such as temperature, pressure, and speed during the ply laying process, and dynamically adjust them through intelligent algorithms, effectively ensuring the stability and consistency of the ply laying quality and reducing human errors. Using ultrasonic testing equipment to accurately detect internal defects in the cured fuselage and feedback the detection results to the ply optimization algorithm and the digital twin platform to timely optimize subsequent manufacturing processes, improving the strength and durability of the fuselage structure. In addition, through robotic ply laying equipment and automated process control, the dependence on manual operations is reduced, the production efficiency is improved, and the consistency and reliability of the product are ensured. The present invention solves the problems of insufficient stress distribution optimization, inaccurate process parameter monitoring, and limited defect detection means in the prior art, significantly improving the manufacturing efficiency and quality of lightweight composite material fuselages for medium and large unmanned aerial vehicles. Description of the Drawings

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0058] Figure 1 It is a flowchart of the manufacturing process for a lightweight composite material fuselage of a medium and large unmanned aerial vehicle in Embodiment 1. Detailed Embodiments

[0059] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings in the specification.

[0060] Many specific details are set forth in the following description to fully understand the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0061] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0062] Embodiment 1, referring to Figure 1 , is the first embodiment of the present invention. This embodiment provides a manufacturing process for a lightweight composite material fuselage of a medium and large-sized unmanned aerial vehicle, including the following steps:

[0063] S1. Perform a force analysis on the three-dimensional model of the fuselage, determine the force-bearing area, and record the stress distribution data.

[0064] Furthermore, import the three-dimensional model of the fuselage and divide the tetrahedral mesh through a mesh generation tool.

[0065] It should be noted that first, import the three-dimensional model of the unmanned aerial vehicle into the finite element analysis software, and use the mesh generation tool to divide the model into tetrahedral meshes. This mesh generation method can better adapt to complex geometric shapes and ensure the accuracy of the analysis. Through tetrahedral mesh generation, the complex fuselage structure can be discretized into multiple small elements, facilitating subsequent finite element analysis. This discretization process can more accurately simulate the force-bearing situation of the fuselage, especially at complex curved surfaces and joints. Through precise mesh generation, the accuracy of subsequent finite element analysis is ensured, avoiding problems such as stress concentration or distortion caused by improper mesh generation. This provides a reliable basis for subsequent stress analysis and ply optimization.

[0066] Furthermore, based on the tetrahedral mesh, set the ply angle and thickness, fix the degrees of freedom of the connection points, and apply aerodynamic and inertial loads to run the finite element analysis through a solver.

[0067] It should be noted that after the mesh generation is completed, set the ply angle and thickness of the composite material and fix the degrees of freedom (i.e., constraint conditions) of the connection points. Then, apply aerodynamic loads (such as aerodynamic forces during flight) and inertial loads (such as inertial forces when the unmanned aerial vehicle accelerates or turns), and perform finite element analysis through a solver. By setting the ply angle and thickness, the mechanical properties of the composite material in different directions can be simulated. Fixing the degrees of freedom of the connection points is to simulate the constraint conditions of the fuselage during actual flight and ensure that the boundary conditions of the analysis are consistent with the actual working conditions. Applying aerodynamic and inertial loads is to simulate the real force-bearing situation of the unmanned aerial vehicle during flight. Through precise boundary condition and load settings, the authenticity of the finite element analysis results is ensured. This provides accurate input data for subsequent stress distribution analysis and avoids errors caused by improper boundary condition settings.

[0068] Furthermore, based on the defined boundary conditions and loads, the static structural analysis function is selected through ANSYS Mechanical, the large deformation option is enabled, the task is submitted and the solution process is monitored to generate preliminary stress results.

[0069] It should be noted that in ANSYS Mechanical, the static structural analysis function is selected, the large deformation option is enabled, and the task is submitted for solution. During the solution process, the system monitors the calculation process and generates preliminary stress distribution results. Static structural analysis is used to simulate the stress conditions of the UAV under steady-state flight conditions. The large deformation option is enabled to consider the large deformation that may occur in the material after being stressed, to ensure the accuracy of the analysis results. Through static structural analysis and large deformation options, the stress conditions of the UAV in flight can be simulated more realistically, especially in high stress areas. This provides a reliable basis for the subsequent stress distribution data extraction.

[0070] Furthermore, the VonMises stress and principal stress distribution were extracted from the finite element analysis results, the coordinates and stress values of the high stress area were recorded and saved in CSV format to complete the acquisition of stress distribution data.

[0071] It should be noted that the Von Mises stress and principal stress distribution data are extracted from the finite element analysis results, the coordinates and stress values of the high stress areas are recorded, and these data are saved as CSV format files. Von Mises stress is a comprehensive stress index that can reflect the overall stress condition of the material. The principal stress distribution can help identify the stress condition of the material in different directions. The coordinates and stress values of the high stress areas are recorded to provide key data for subsequent ply optimization. By extracting and recording the stress data of the high stress areas, accurate input can be provided for subsequent ply optimization. This ensures that the ply optimization algorithm can focus on optimizing high stress areas, thereby minimizing the weight of the fuselage while ensuring the strength of the fuselage.

[0072] S2. Input the stress distribution data of the fuselage into the ply optimization algorithm to generate a ply path.

[0073] Furthermore, the stress distribution data are read from the CSV file, normalized and outliers are removed to provide input conditions for the optimization algorithm.

[0074] It should be noted that the stress distribution data obtained through finite element analysis before are read from the CSV file, and these data are normalized to ensure that the data are on the same order of magnitude. At the same time, outliers (such as noise data or obviously unreasonable data) are removed to ensure the quality of the input data. The normalization process is to eliminate the magnitude differences between different stress values and ensure that the optimization algorithm can fairly process the stress data in each region. Removing outliers is to avoid the interference of these abnormal data on the optimization algorithm and ensure the accuracy of the optimization results. Through the normalization process and outlier removal, the quality and consistency of the input data are ensured, providing reliable input conditions for the subsequent ply optimization algorithm. This avoids the deviation of the optimization results caused by data quality problems and improves the stability and accuracy of the optimization algorithm.

[0075] Furthermore, based on the preprocessed data, with the goal of minimizing weight, the ply angle and thickness range are set, and the thickness of the high-stress region is constrained.

[0076] It should be noted that based on the preprocessed data, the optimization goal is set to minimize the fuselage weight. At the same time, the range of ply angles and thicknesses is set, and the thickness of the high-stress region is constrained to ensure that the ply thickness in these regions can meet the strength requirements. Minimizing weight is a key goal for the lightweight design of the UAV, while setting the range of ply angles and thicknesses is to ensure the mechanical properties of the composite material in different directions. Constraining the thickness of the high-stress region is to ensure that these regions can still meet the strength requirements while reducing weight. By setting the optimization goal and constraints, it is ensured that the optimization algorithm can minimize the fuselage weight to the greatest extent while ensuring the fuselage strength. This provides a clear direction for the subsequent ply path generation and ensures the rationality and practicality of the optimization results.

[0077] Furthermore, according to the set ply angle, thickness range, and thickness of the high-stress region, they are input into the iterative calculation through the genetic algorithm to generate the optimal ply distribution.

[0078] It should be noted that the set ply angle, thickness range, and thickness of the high-stress region are used as inputs, and the iterative calculation is carried out through the genetic algorithm to generate the optimal ply distribution. The genetic algorithm simulates the process of natural selection and gradually optimizes the ply distribution until the optimal solution is found. The genetic algorithm is a global optimization algorithm that can effectively handle complex multi-objective optimization problems. Through the iterative calculation of the genetic algorithm, a ply distribution scheme with the lightest weight can be found under the premise of meeting the strength requirements. Through the iterative calculation of the genetic algorithm, the optimal ply distribution scheme can be generated to ensure that the fuselage has sufficient strength in the high-stress region and can reduce weight as much as possible in other regions. This provides a scientific basis for the subsequent ply path generation and ensures the efficiency and reliability of the optimization results.

[0079] Furthermore, the optimal ply distribution result is converted into a ply path file, which includes angles, thicknesses, and sequences.

[0080] It should be noted that the optimal ply distribution result generated by the genetic algorithm is converted into a ply path file, which contains the ply angles, thicknesses, and ply sequences of each layer. These data will be used for the subsequent operations of the robotic layup equipment. The ply path file is a bridge connecting the optimization algorithm and the actual manufacturing process. By converting the optimization result into a specific ply path file, it can guide the robotic layup equipment to lay composite materials according to the optimal plan. By converting the optimization result into a ply path file, the seamless connection between the optimization algorithm and the actual manufacturing process is ensured. This provides precise guidance for the subsequent robotic layup operations, ensures the accuracy and consistency of the layup process, and avoids human operation errors.

[0081] S3. According to the generated ply path, construct a digital twin model and monitor process parameters.

[0082] Furthermore, import the ply path file into the digital twin platform, parse the angle, thickness, and sequence data, construct a digital twin model based on the parsed data, and embed virtual sensors in the digital twin model to simulate the acquisition of temperature T(t), pressure P(t), and layup speed v(t). Introduce the formula:

[0083]

[0084] where Q is the ply quality index, α, β, and γ are the weight coefficients of temperature, pressure, and speed on the ply quality respectively, μ is the time center point of the process, and σ is the width of the time distribution

[0085] It should be noted that the generated ply path file is imported into the digital twin platform, where the ply angle, thickness, and sequence data in the file are parsed, and a digital twin model is constructed based on this data. The digital twin model is a virtual simulation of the actual manufacturing process, which can reflect various parameters in the manufacturing process in real time. By constructing the digital twin model, virtual simulation of the ply laying process can be carried out, potential problems in the manufacturing process can be predicted in advance, and manufacturing process parameters can be optimized to avoid problems in actual manufacturing. Parsing the ply path file is to ensure that the digital twin model can accurately reflect the ply laying scheme. By constructing the digital twin model, virtual simulation of the ply laying process is realized, potential problems can be discovered before actual manufacturing, and process parameters can be optimized. This reduces the trial-and-error cost in actual manufacturing, improves manufacturing efficiency and quality. By simulating and collecting process parameters through virtual sensors, the temperature, pressure, and speed changes during the ply laying process can be monitored in real time to ensure that these parameters fluctuate within a reasonable range and avoid ply laying quality problems caused by out-of-control parameters. By monitoring process parameters in real time through virtual sensors, parameter anomalies can be discovered in advance, and the process can be adjusted in a timely manner to ensure the stability of ply laying quality. This reduces quality fluctuations in actual manufacturing and improves product consistency. The calculation formula of the ply laying quality index Q is introduced. This formula comprehensively considers the effects of temperature T(t), pressure P(t), and ply laying speed v(t) on ply laying quality. By calculating the ply laying quality index Q, the quality changes during the ply laying process can be quantified, and it can be judged whether the current process parameters meet the quality requirements. The weight coefficients and time distribution parameters in the formula can be adjusted according to actual process requirements. By introducing the ply laying quality index, the ply laying quality can be quantitatively evaluated, the influence of process parameters on quality can be discovered in a timely manner, and a scientific basis can be provided for subsequent optimization of process parameters. This improves the controllability of ply laying quality and reduces quality fluctuations.

[0086] Furthermore, if Q < 0.8, analyze the sensitivity of process parameters to Q, determine the adjustment direction and amplitude, and feedback them to the genetic algorithm to recalculate the optimal ply distribution and obtain the ply path file.

[0087] It should be noted that if the ply laying quality index Q is less than 0.8, it indicates that the current process parameters do not meet the quality requirements. At this time, it is necessary to analyze the sensitivity of temperature T(t), pressure P(t), and ply laying speed v(t) to Q, determine the adjustment direction and amplitude, and feedback the adjusted parameters to the genetic algorithm to recalculate the optimal ply distribution. By analyzing the sensitivity of process parameters to Q, it is possible to determine which parameters have the greatest impact on ply laying quality, and thus adjust the process parameters targeted. Feedback to the genetic algorithm is to re-optimize the ply distribution to ensure ply laying quality. Through sensitivity analysis and parameter adjustment, process parameters can be optimized in a timely manner to ensure that the ply laying quality index Q meets the requirements. This improves the adjustment efficiency of process parameters and avoids quality problems caused by improper parameters.

[0088] Further, extract the process parameters of temperature T(t), pressure P(t), and layup speed v(t) at the current time point t from the digital twin platform and input them into the partial derivative Calculate the influence degree of each parameter on Q.

[0089] It should be noted that by calculating the partial derivative, the influence degree of each process parameter on the layup quality can be quantified, and it can be determined which parameters contribute the most to Q, thereby providing a basis for process parameter adjustment. Through the partial derivative calculation, the quantitative analysis of the influence of process parameters is realized, ensuring the scientificity and accuracy of process parameter adjustment. This provides data support for the subsequent process parameter optimization and avoids layup quality problems caused by improper parameter adjustment.

[0090] Further, if then increase the temperature T(t), if then increase the pressure P(t), if then decrease the layup speed v(t), and adjust the amplitude according to the calculation results

[0091] It should be noted that by dynamically adjusting the process parameters, the layup quality index Q is ensured to reach the target value, thereby ensuring the stability of the layup quality. Through the dynamic adjustment of the process parameters, the real-time control of the layup quality is realized, ensuring the stability and consistency of the layup process. This provides real-time feedback for the subsequent process parameter optimization and avoids layup defects caused by parameter fluctuations.

[0092] Further, update the process parameters according to the adjustment amplitude T′(t) = T(t) + ΔT(t), P′(t) = P(t) + ΔP(t), v′(t) = v(t) + Δv(t), and feedback the calculation results to the genetic algorithm to recalculate the optimal layup distribution.

[0093] It should be noted that update the process parameters according to the adjustment amplitude and feedback the updated parameters to the genetic algorithm to recalculate the optimal layup distribution. By updating the process parameters and feedbacking them to the genetic algorithm, the layup path can be re-optimized to ensure the layup quality. Through the feedback mechanism, the dynamic optimization of the layup path is realized, ensuring the stability and consistency of the layup quality. This provides real-time feedback for the subsequent process parameter optimization and avoids layup defects caused by improper parameter adjustment.

[0094] Further, update the digital twin model, recalculate Q, if Q < 0.8, repeat the above steps 1 - 3 until the requirements are met.

[0095] It should be noted that when updating the digital twin model and recalculating the ply quality index Q, if it is still lower than 0.8, repeat the above steps until the target value is reached. Through iterative optimization, ensure that the ply quality index Q reaches the target value, thereby ensuring the stability of the ply quality. Through iterative optimization, continuous improvement of the ply quality is achieved, ensuring the stability and consistency of the ply process. This provides continuous feedback for subsequent process parameter optimization and avoids ply defects caused by improper process parameters.

[0096] S4. Based on the monitored process parameter data, use a robotic layup device to lay and preform composite materials.

[0097] Furthermore, load the optimized ply path file and parse it to generate robotic arm motion instructions.

[0098] It should be noted that by parsing the ply path file, the robotic layup device can lay composite materials according to the optimized layup scheme, ensuring that the ply angle, thickness, and sequence of each layer meet the design requirements. By loading and parsing the optimized ply path file, it is ensured that the robotic layup device can accurately execute the layup task and avoid layup deviations caused by incorrect path files or improper parsing. This provides accurate guidance for subsequent layup operations and ensures the accuracy and consistency of the layup process.

[0099] Furthermore, collect the temperature T(t), pressure P(t), and layup speed v(t) through the digital twin platform, calculate Q, and dynamically adjust the process parameters.

[0100] It should be noted that by real-time monitoring and adjusting the process parameters, ensure that the ply quality index Q is always maintained within a reasonable range, thereby ensuring the stability of the ply quality. By real-time collecting and dynamically adjusting the process parameters, precise control of the layup process is achieved, ensuring the stability of the ply quality. This provides real-time feedback for subsequent layup operations and avoids layup defects caused by process parameter fluctuations.

[0101] Furthermore, based on the real-time process parameters, control the robotic arm to lay composite materials along the ply path and synchronize the temperature T(t), pressure P(t), and layup speed v(t).

[0102] It should be noted that by synchronously adjusting the process parameters, ensure that the ply quality of each layer meets the design requirements and avoid layup defects caused by asynchronous process parameters. By synchronously adjusting the process parameters, precise control of the layup process is achieved, ensuring that the ply quality of each layer meets the design requirements. This provides real-time feedback for subsequent layup operations and avoids layup defects caused by asynchronous process parameters.

[0103] Furthermore, after each layer is laid, a preforming tool is used to compact the material, and an embedded sensor is used to detect the interlayer bonding quality, which is then fed back to the digital twin platform in real time.

[0104] It should be noted that after each layer is laid, a preforming tool is used to compact the material, and an embedded sensor is used to detect the interlayer bonding quality. The detection results are fed back to the digital twin platform in real time. By using the preforming tool to compact the material, it ensures that the composite materials of each layer are tightly bonded, avoiding the occurrence of interlayer voids or delamination. By using the embedded sensor to detect the interlayer bonding quality, it can discover and correct laying defects in real time. Through the use of the preforming tool and the embedded sensor, it ensures that the composite materials of each layer are tightly bonded, avoiding interlayer voids or delamination. This provides real-time feedback for subsequent laying operations, ensuring the stability and consistency of the laying quality.

[0105] Furthermore, according to the quality inspection results, the subsequent laying parameters are adjusted, and finally the fuselage preforming is completed.

[0106] It should be noted that according to the detection results of the embedded sensor, the process parameters of the subsequent laying are adjusted to ensure that the laying quality of each layer meets the design requirements, and finally the fuselage preforming is completed. By adjusting the subsequent laying parameters, it ensures that the laying quality of each layer meets the design requirements, avoiding the cumulative effect caused by the laying quality problem of the previous layer. By adjusting the subsequent laying parameters, it realizes the continuous optimization of the laying process and ensures that the laying quality of each layer meets the design requirements. This provides a high-quality preformed fuselage for subsequent autoclave curing and defect detection, ensuring the efficiency and reliability of the entire manufacturing process.

[0107] S5. Place the preformed fuselage into the autoclave and complete the high-temperature and high-pressure curing.

[0108] Furthermore, receive the fuselage from the preforming process, check the surface quality, and ensure there are no defects.

[0109] It should be noted that after the preforming process is completed, receive the preformed fuselage and check its surface quality to ensure there are no obvious defects (such as cracks, bubbles, delamination, etc.). By checking the surface quality, it ensures that there are no obvious defects in the preformed fuselage before high-temperature and high-pressure curing, avoiding the further expansion of these defects or the failure of curing during the curing process. Through the surface quality inspection, it ensures that the quality of the preformed fuselage meets the curing requirements and avoids the curing failure or insufficient fuselage strength caused by surface defects. This provides a high-quality preformed fuselage for subsequent high-temperature and high-pressure curing, ensuring the smooth progress of the curing process.

[0110] Furthermore, use a fixture to fix the fuselage, place it smoothly into the autoclave and seal it.

[0111] It should be noted that a special fixture is used to fix the preformed fuselage to ensure its stable position and shape in the autoclave. Then, the fuselage is smoothly placed into the autoclave and sealed. By fixing the fuselage with the fixture, it is ensured that it will not deform or displace under high temperature and high pressure environments, thus ensuring that the shape and dimensions of the cured fuselage meet the design requirements. Sealing the autoclave is to ensure the stability of temperature and pressure during the curing process. By fixing the fuselage with the fixture and sealing the autoclave, it is ensured that the fuselage maintains a stable shape and dimensions during the curing process, avoiding curing failures caused by fuselage deformation or pressure leakage. This provides a stable process environment for subsequent high-temperature and high-pressure curing, ensuring the efficiency and consistency of the curing process.

[0112] Furthermore, start the autoclave and monitor the temperature and pressure in real time to ensure that the fuselage is uniformly heated and pressurized.

[0113] It should be noted that start the autoclave and monitor the temperature and pressure inside the autoclave in real time to ensure that the fuselage is uniformly heated and pressurized throughout the curing process. By monitoring the temperature and pressure in real time, it is ensured that the fuselage is uniformly heated and pressurized throughout the curing process, avoiding curing defects (such as local overheating, insufficient pressure, etc.) caused by uneven temperature or pressure. By monitoring the temperature and pressure in real time, it is ensured that the fuselage is uniformly heated and pressurized throughout the curing process, avoiding curing defects caused by uneven temperature or pressure. This provides guarantee for the subsequent curing quality, ensuring that the cured fuselage has uniform mechanical properties and structural strength.

[0114] Furthermore, after the treatment is completed, let it cool naturally and take out the fuselage to complete the high-temperature and high-pressure curing process.

[0115] It should be noted that after the curing process is completed, let the autoclave cool naturally, and then take out the cured fuselage to complete the high-temperature and high-pressure curing process. By natural cooling, it is ensured that the fuselage will not generate internal stress or deformation due to rapid cooling after curing. After taking out the fuselage, the curing process is officially completed, and the fuselage has the required mechanical properties and structural strength. By natural cooling, internal stress or deformation caused by rapid cooling is avoided, ensuring the dimensional stability and mechanical properties of the cured fuselage. This provides a high-quality cured fuselage for subsequent demolding, trimming, and defect detection, ensuring the efficiency and reliability of the entire manufacturing process.

[0116] S6. Demold and trim the cured fuselage, and use ultrasonic testing equipment to detect and obtain the internal defect data of the fuselage.

[0117] Furthermore, take out the cured fuselage from the autoclave and use a demolding tool to separate the mold.

[0118] It should be noted that after the high-temperature and high-pressure curing is completed, the cured fuselage is taken out of the autoclave, and a demolding tool is used to separate the fuselage from the mold. By using the demolding tool to separate the fuselage from the mold, it is ensured that the fuselage can be smoothly taken out of the mold, avoiding damage or deformation of the fuselage caused by improper demolding. By using the demolding tool, it is ensured that the fuselage can be smoothly taken out of the mold, avoiding damage or deformation of the fuselage caused by improper demolding. This provides a complete cured fuselage for subsequent trimming and inspection, ensuring the structural integrity and surface quality of the fuselage.

[0119] Furthermore, remove the excess material, grind the edges until smooth, and check the surface flatness.

[0120] It should be noted that after demolding, remove the excess material (such as flash, burrs, etc.) on the surface of the fuselage, and use a grinding tool to grind the edges until smooth. Finally, check the surface flatness of the fuselage. By removing the excess material and grinding the edges, it is ensured that the surface of the fuselage is smooth and flat, avoiding stress concentration or appearance defects caused by rough or uneven surfaces. Checking the surface flatness is to ensure the appearance quality and structural integrity of the fuselage. By removing the excess material and grinding the edges, it is ensured that the surface of the fuselage is smooth and flat, avoiding stress concentration or appearance defects caused by rough or uneven surfaces. This provides a high-quality fuselage surface for subsequent ultrasonic testing, ensuring the accuracy of the test results.

[0121] Furthermore, use ultrasonic testing equipment to scan the inside of the fuselage to obtain defect data.

[0122] It should be noted that use ultrasonic testing equipment to scan the inside of the fuselage to obtain defect data (such as bubbles, cracks, delaminations, etc.) inside the fuselage. Through the ultrasonic testing equipment, the defects inside the fuselage can be detected non-destructively, ensuring that the internal quality of the fuselage meets the design requirements. The obtained defect data will be used for subsequent process optimization and quality control. By using the ultrasonic testing equipment, non-destructive detection of the defects inside the fuselage is achieved, ensuring that the internal quality of the fuselage meets the design requirements. This provides a scientific basis for subsequent process optimization and quality control, ensuring the structural strength and durability of the fuselage.

[0123] S7. According to the defect data inside the fuselage, feedback to the ply optimization algorithm and the digital twin platform and optimize the process parameters.

[0124] Furthermore, according to the ultrasonic testing results, analyze the defect type, location, and size, and generate a defect distribution map.

[0125] It should be noted that the ultrasonic testing equipment obtains the defect data inside the fuselage, analyzes the types of defects (such as bubbles, cracks, delamination, etc.), locations, and sizes, and generates a defect distribution map. By analyzing the types, locations, and sizes of defects, the weak areas inside the fuselage can be accurately identified. Generating a defect distribution map helps to visually display the distribution of defects, providing data support for subsequent process optimization. By generating a defect distribution map, the defect situation inside the fuselage can be visually displayed, helping to identify weak areas and providing a scientific basis for subsequent ply optimization and process parameter adjustment. This provides a clear direction for process optimization and avoids insufficient fuselage strength or reduced durability caused by defects.

[0126] Furthermore, input the defect data into the ply optimization algorithm to recalculate the ply angles, thicknesses, and sequences.

[0127] It should be noted that inputting the defect data into the ply optimization algorithm to recalculate the ply angles, thicknesses, and sequences, and optimize the ply scheme to reduce or eliminate defects. By recalculating the ply angles, thicknesses, and sequences, the defect areas can be optimized to ensure that the ply schemes in these areas can effectively reduce or eliminate defects, improving the overall strength and durability of the fuselage. By recalculating the ply scheme, the defect areas can be optimized to ensure that the ply schemes in these areas can effectively reduce or eliminate defects. This provides an optimized scheme for subsequent ply operations, ensuring the structural strength and durability of the fuselage.

[0128] Furthermore, based on the optimized ply path, update the fuselage geometry and process parameters in the digital twin model.

[0129] It should be noted that according to the optimized ply path, update the fuselage geometry and process parameters in the digital twin model to ensure that the digital twin model can accurately reflect the optimized ply scheme. By updating the digital twin model, the optimized ply scheme can be reflected in real time, ensuring that the digital twin model is consistent with the actual manufacturing process and providing an accurate virtual simulation environment for subsequent process parameter adjustment. By updating the digital twin model, the consistency between the digital twin model and the actual manufacturing process is ensured, providing an accurate virtual simulation environment for subsequent process parameter adjustment. This provides real-time feedback for process optimization, ensuring the effectiveness and feasibility of the optimization scheme.

[0130] Furthermore, according to the updated model, adjust the temperature, pressure, and ply speed to generate new process parameters and implement them.

[0131] It should be noted that according to the updated digital twin model, process parameters such as temperature, pressure, and laying speed during the laying process are adjusted to generate new process parameters and implement them. By adjusting the process parameters, it is ensured that the optimized laying plan can be effectively implemented in the actual manufacturing process, avoiding laying defects caused by improper process parameters. By adjusting the process parameters, it is ensured that the optimized laying plan can be effectively implemented in the actual manufacturing process, avoiding laying defects caused by improper process parameters. This provides the optimized process parameters for the subsequent laying operation, ensuring the stability and consistency of the laying quality.

[0132] In summary, the present invention: generates the optimal laying path through accurate stress analysis of the fuselage three-dimensional model and laying optimization algorithm, significantly improving the material utilization rate and reducing the fuselage weight while ensuring the structural strength; uses digital twin technology to monitor key process parameters such as temperature, pressure, and speed during the laying process in real time, and dynamically adjusts them through intelligent algorithms, effectively ensuring the stability and consistency of the laying quality and reducing human errors; uses ultrasonic testing equipment to accurately detect internal defects of the cured fuselage and feeds the detection results back to the laying optimization algorithm and digital twin platform to optimize the subsequent manufacturing process in a timely manner, improving the strength and durability of the fuselage structure; in addition, through robotic laying equipment and automated process control, the dependence on manual operations is reduced, the production efficiency is improved, and the consistency and reliability of the product are ensured. The present invention solves the problems of insufficient stress distribution optimization, inaccurate process parameter monitoring, and limited defect detection means in the prior art, and significantly improves the manufacturing efficiency and quality of the lightweight composite material fuselage of medium and large unmanned aerial vehicles.

[0133] Example 2, referring to the data record form, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the manufacturing process of the lightweight composite material fuselage of medium and large unmanned aerial vehicles are given.

[0134] Contents of test preparation and detailed implementation process

[0135] To verify the superiority of the manufacturing process of the lightweight composite material fuselage of medium and large unmanned aerial vehicles described in the present invention, a group of comparative experiments were designed. The experiments were divided into two groups: one group used the traditional composite material fuselage manufacturing process of the prior art, and the other group used the lightweight composite material fuselage manufacturing process described in the present invention. The experimental objects were two medium and large unmanned aerial vehicle fuselages of the same size, named "traditional process fuselage" and "lightweight process fuselage" respectively.

[0136] Test preparation:

[0137] Material preparation: Both groups of fuselages used the same carbon fiber composite material, with the laying thickness ranging from 0.1 mm to 0.5 mm and the laying angle ranging from 0° to 90°.

[0138] Equipment Preparation: The traditional process group uses manual layup equipment, and the lightweight process group uses robotic layup equipment, equipped with a digital twin platform and ultrasonic testing equipment.

[0139] Process Parameters: The temperature, pressure, and layup speed of the traditional process group are manually controlled by operators, while the process parameters of the lightweight process group are monitored and adjusted in real time by the digital twin platform.

[0140] Implementation Process:

[0141] Layup Optimization: The lightweight process group first conducts finite element analysis on the three-dimensional model of the fuselage to determine high-stress areas and generates the optimal layup path through genetic algorithms. The traditional process group uses empirical formulas for layup design.

[0142] Layup Operation: The lightweight process group uses robotic layup equipment to perform layup according to the optimized layup path and monitors the temperature, pressure, and layup speed in real time through the digital twin platform. The traditional process group conducts manual layup by operators, and the process parameters are controlled by experience.

[0143] Curing Process: Both groups of fuselages are placed in the same type of autoclave for high-temperature and high-pressure curing. The curing temperature is 180 °C, the pressure is 0.6 MPa, and the curing time is 2 hours.

[0144] Defect Detection: After curing, ultrasonic testing equipment is used to detect internal defects in both groups of fuselages, and the number, location, and size of the defects are recorded.

[0145] Quality Evaluation: Based on the defect detection results, the quality of both groups of fuselages is evaluated, and the weight, strength, and durability of the fuselages are recorded.

[0146] Data Record Table

[0147]

[0148] Table Data Analysis

[0149] By comparing the experimental data, it can be clearly seen that the lightweight composite fuselage manufacturing process described in the present invention is superior to the traditional process in many aspects.

[0150] Fuselage Weight: The weight of the fuselage manufactured by the lightweight process is 10.8 kg, which is 13.6% lighter than the 12.5 kg of the fuselage manufactured by the traditional process. This result shows that through precise layup optimization algorithms and digital twin technology, the present invention can significantly reduce the fuselage weight and achieve the goal of lightweight design.

[0151] Stress value in the maximum stress area: The stress value in the maximum stress area of the lightweight process fuselage is 280 MPa, which is lower than 350 MPa of the traditional process fuselage. This shows that through finite element analysis and ply optimization, the present invention effectively reduces the stress concentration in the high-stress area and improves the structural strength of the fuselage.

[0152] Number of internal defects: The number of internal defects in the lightweight process fuselage is 5, far lower than 15 of the traditional process fuselage. This indicates that through real-time monitoring and adjustment of process parameters, the present invention effectively reduces the defects in the ply laying process and improves the internal quality of the fuselage.

[0153] Average defect size: The average defect size of the lightweight process fuselage is 1.2 mm, smaller than 2.5 mm of the traditional process fuselage. This shows that through ultrasonic testing and feedback mechanism, the present invention can timely detect and correct ply laying defects, ensuring the structural integrity of the fuselage.

[0154] Ply laying time: The ply laying time of the lightweight process fuselage is 5 hours, which is 37.5% shorter than 8 hours of the traditional process fuselage. This indicates that through robotic ply laying equipment and digital twin technology, the present invention significantly improves the ply laying efficiency and shortens the manufacturing cycle.

[0155] Ply laying quality index Q: The ply laying quality index Q of the lightweight process fuselage is 0.92, higher than 0.65 of the traditional process fuselage. This shows that through real-time monitoring and dynamic adjustment of process parameters, the present invention ensures the stability and consistency of ply laying quality.

[0156] Strength of the fuselage after curing: The strength of the lightweight process fuselage after curing is 520 MPa, higher than 450 MPa of the traditional process fuselage. This indicates that through optimizing the ply laying scheme and process parameters, the present invention improves the mechanical properties of the fuselage.

[0157] Durability: The durability of the lightweight process fuselage is 15,000 cycle loads, higher than 10,000 of the traditional process fuselage. This shows that through optimized design and process control, the present invention significantly improves the durability of the fuselage.

[0158] In summary, the manufacturing process of the lightweight composite material fuselage described in the present invention shows significant advantages in aspects such as reducing the fuselage weight, improving the structural strength, reducing internal defects, shortening the manufacturing cycle, improving the ply laying quality and durability. Through comparing the experimental data, it can be clearly seen that the present invention is superior to the traditional process in multiple key indicators, with obvious innovation and novelty.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A manufacturing process for a lightweight composite material fuselage of a medium and large-sized unmanned aerial vehicle, characterized in that: including, performing a force analysis on the three-dimensional model of the fuselage to determine the force-bearing areas and recording the stress distribution data; inputting the stress distribution data of the fuselage into a ply optimization algorithm to generate a ply path; constructing a digital twin model based on the generated ply path and monitoring the process parameters; laying and preforming composite materials using a robotic layup device based on the monitored process parameter data; placing the preformed fuselage into an autoclave to complete high-temperature and high-pressure curing; demolding and trimming the cured fuselage, and using ultrasonic testing equipment to detect and obtain the internal defect data of the fuselage; feeding back the internal defect data of the fuselage to the ply optimization algorithm and the digital twin platform and optimizing the process parameters; performing virtual assembly and functional simulation tests using the digital twin model based on the optimized process parameters.

2. The manufacturing process of the lightweight composite material fuselage for medium and large-sized unmanned aerial vehicles according to claim 1, characterized in that: The steps for performing a force analysis on the three-dimensional model of the fuselage to determine the force-bearing areas and recording the stress distribution data are specifically as follows: importing the three-dimensional model of the fuselage and dividing tetrahedral meshes using a mesh generation tool; based on the tetrahedral meshes, setting the ply angles and thicknesses, fixing the degrees of freedom of the connection points, applying aerodynamic and inertial loads, and running finite element analysis through a solver; based on the defined boundary conditions and loads, selecting the static structural analysis function through ANSYS Mechanical, enabling the large deformation option, submitting the task, and monitoring the solution process to generate preliminary stress results; extracting the Von Mises stress and principal stress distributions from the finite element analysis results, recording the coordinates and stress values of the high-stress areas, and saving them in CSV format to complete the acquisition of stress distribution data.

3. The manufacturing process of the lightweight composite material fuselage of a medium and large-sized unmanned aerial vehicle according to claim 2, wherein: The steps for inputting the stress distribution data of the fuselage into a ply optimization algorithm to generate a ply path are specifically as follows: reading the stress distribution data from a CSV file, performing normalization processing and removing outliers to provide input conditions for the optimization algorithm; based on the preprocessed data, aiming to minimize the weight, setting the ply angle and thickness ranges, and constraining the thickness of the high-stress areas; according to the set ply angles, thickness ranges, and the thickness of the high-stress areas, and inputting them into an iterative calculation through a genetic algorithm to generate an optimal ply distribution; converting the optimal ply distribution result into a ply path file, including angles, thicknesses, and sequences.

4. The manufacturing process of the lightweight composite material fuselage of medium and large-sized unmanned aerial vehicles according to claim 3, characterized in that: The steps for constructing a digital twin model based on the generated ply path and monitoring the process parameters are specifically as follows: importing the ply path file into the digital twin platform, parsing the angle, thickness, and sequence data, constructing a digital twin model based on the parsed data, and embedding virtual sensors in the digital twin model to simulate the acquisition of temperature T(t), pressure P(t), and ply speed v(t), introducing the formula: where Q is the ply quality index, α, β, and γ are the weight coefficients of temperature, pressure, and speed on the ply quality respectively, μ is the time center point of the process, and σ is the width of the time distribution; if Q < 0.8, analyzing the sensitivity of the process parameters to Q, determining the adjustment direction and amplitude, and feeding back to the genetic algorithm to recalculate the optimal ply distribution to obtain a ply path file.

5. The manufacturing process of the lightweight composite material fuselage for medium and large-sized unmanned aerial vehicles according to claim 4, characterized in that: The steps for, if Q < 0.8, analyzing the sensitivity of the process parameters to Q, determining the adjustment direction and amplitude, and feeding back to the genetic algorithm to recalculate the optimal ply distribution are specifically as follows; Extract the process parameters of temperature T(t), pressure P(t), and layup speed v(t) at the current time point t from the digital twin platform and input them into the partial derivative Calculate the influence degree of each parameter on Q; If then increase the temperature T(t), if then increase the pressure P(t), if then decrease the layup speed v(t), and adjust the amplitude according to the calculation results Update the process parameters according to the adjustment range: T′(t) = T(t) + ΔT(t), P′(t) = P(t) + ΔP(t), v′(t) = v(t) + Δv(t). Feed the calculation results back to the genetic algorithm and recalculate the optimal ply distribution. Update the digital twin model and recalculate Q. If Q < 0.8, repeat the above steps 1 - 3 until the requirements are met.

6. The manufacturing process of the lightweight composite material fuselage for medium and large-sized unmanned aerial vehicles according to claim 5, characterized in that: Based on the monitored process parameter data, use a robotic layup device to lay the composite material and preform it. The specific steps are as follows: Load the optimized layup path file and parse it to generate robotic arm motion instructions. Collect the temperature T(t), pressure P(t), and layup speed v(t) through the digital twin platform, calculate Q, and dynamically adjust the process parameters. Based on the real - time process parameters, control the robotic arm to lay the composite material along the layup path, synchronizing the temperature T(t), pressure P(t), and layup speed v(t). After each layer is laid, use a preforming tool to compact the material and detect the interlayer bonding quality through an embedded sensor, and feedback it to the digital twin platform in real - time. Adjust the subsequent layup parameters according to the quality inspection results and finally complete the fuselage preforming.

7. The manufacturing process of the lightweight composite material fuselage for medium and large-sized unmanned aerial vehicles according to claim 6, characterized in that: Put the preformed fuselage into an autoclave to complete the high - temperature and high - pressure curing. The specific steps are as follows: Receive the fuselage from the preforming process, check the surface quality to ensure no defects. Fix the fuselage with a fixture, gently place it into the autoclave and seal it. Start the autoclave and monitor the temperature and pressure in real - time to ensure that the fuselage is evenly heated and pressed. After the process is completed, let it cool naturally and take out the fuselage to complete the high - temperature and high - pressure curing process.

8. The manufacturing process of the lightweight composite material fuselage for medium and large-sized unmanned aerial vehicles according to claim 7, characterized in that: Demold and trim the cured fuselage, and use an ultrasonic testing device to detect and obtain the internal defect data of the fuselage. The specific steps are as follows: Take out the cured fuselage from the autoclave and use a demolding tool to separate the mold. Remove the excess material, polish the edges until smooth, and check the surface flatness. Use an ultrasonic testing device to scan the inside of the fuselage to obtain defect data.

9. The manufacturing process of the lightweight composite material fuselage of medium and large-sized unmanned aerial vehicles according to claim 8, characterized in that: Feed the internal defect data of the fuselage back to the layup optimization algorithm and the digital twin platform and optimize the process parameters. The specific steps are as follows: Analyze the defect type, location, and size according to the ultrasonic testing results and generate a defect distribution map. Input the defect data into the layup optimization algorithm to recalculate the layup angle, thickness, and sequence. Based on the optimized layup path, update the fuselage geometry and process parameters in the digital twin model. According to the updated model, adjust the temperature, pressure, and layup speed, generate new process parameters and implement them.

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