Rapid manufacturing method for ship sections

Abstract

The invention relates to the technical field of ship manufacturing, and discloses a rapid manufacturing method for ship segments. The method comprises the following steps: S1, carrying out module division according to ship body design requirements, and dividing a ship body into a plurality of sections through a modular design method; s2, establishing a segmented digital twinborn model, wherein the digital twinborn model comprises segmented geometric information, structural stress information and process information; and S3, based on the digital twin model, performing segmented manufacturing, and performing module processing by using a numerical control cutting and self-adaptive welding technology. By adopting the multi-objective optimization technical scheme of module division, the technical effects of reasonable module division and reduced welding workload are achieved by dynamically optimizing the total length of the welding seam, the manufacturing complexity and the structural strength deviation. Compared with the problems that in the prior art, module division depends on artificial experience, the number of welding seams is large, and stress distribution is uneven, the defects that module division efficiency is low, and assembly errors are large are overcome.

Description

Technical Field

[0001] The invention relates to the technical field of shipbuilding, and in particular to a method for rapid manufacturing of ship sections. Background Art

[0002] As the shipbuilding industry's requirements for efficiency and precision gradually increase, modular manufacturing has become an important trend in the industry's development. The modular production method of hull sections breaks down the overall manufacturing of the ship into multiple functional modules, which helps to improve production efficiency and reduce costs. However, the module division, manufacturing and assembly processes involved in the existing technology still have some technical bottlenecks and shortcomings.

[0003] Most of the existing module division methods are based on manual experience or rough rule judgment, and fail to fully consider the number of welds between modules, the geometric complexity of modules and the uniformity of force distribution. This method often leads to an increase in the total length of welds and increased processing difficulty, especially in large hulls. The number of module interfaces is large and the welding workload is greatly increased, which not only increases the manufacturing cost, but also has an adverse effect on the strength and accuracy of subsequent assembly.

[0004] In the prior art, although digital tools such as 3D modeling have been introduced for the preliminary design of modules, these tools are limited to static geometric modeling and cannot achieve real-time dynamic updates and adjustments in the actual production process. Once errors occur in manufacturing or assembly, the design data cannot dynamically adjust the process parameters, resulting in the gradual accumulation of errors. Especially in modular assembly, the accumulated errors often directly affect the overall dimensional accuracy and structural stability of the hull.

[0005] Existing module assembly technologies mostly use manual positioning and welding methods, relying on the workers' experience and skills to complete module docking. This method is not only labor-intensive, but also the alignment accuracy of the module interface is often difficult to control, and the assembly deviation problem is prominent. In addition, traditional manual welding technology has limited effect in controlling welding thermal deformation, which is easy to cause assembly instability problems, further affecting the overall performance of the hull structure. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a method for rapid manufacturing of ship sections, which solves the problems of unoptimized module division, low manufacturing and assembly precision, and error accumulation that cannot be adjusted in real time in ship section manufacturing.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for rapid manufacturing of ship sections, comprising the following steps; S1. Divide the hull into multiple sections according to the requirements of the hull design. S2. Establishing a digital twin model of the segment, wherein the digital twin model includes geometric information, structural force information and process information of the segment; S3. Based on the digital twin model, segmented manufacturing is carried out, and module processing is carried out using CNC cutting and adaptive welding technology, and manufacturing process parameters are adjusted according to real-time detection data to reduce thermal deformation and processing errors; S4, the assembly process uses laser positioning and industrial robot-assisted assembly technology to dock the manufactured segments with high precision, and uses the error detection system to calibrate the assembly position; S5. Perform quality inspection on the assembled segments, form error feedback based on the laser scanning or ultrasonic inspection results, update the error information to the digital twin model, generate subsequent manufacturing adjustment parameters, and achieve closed-loop optimization.

[0008] Preferably, the module division in step S1 is determined according to the segment volume, functional area division and transportation conditions, and the module boundaries meet the unified docking standards.

[0009] Preferably, the module division in step S1 is achieved by constructing a multi-objective optimization model, and the optimization model takes minimizing the total length of the weld, minimizing the manufacturing complexity and minimizing the structural strength deviation as the goals to generate an optimal module division scheme.

[0010] Preferably, the manufacturing complexity is calculated based on the geometric complexity of the segment and the difficulty of the manufacturing process. The geometric complexity of the segment is determined by the ratio of the surface area to the volume of the module, and the difficulty of the manufacturing process is set according to the corresponding difficulty coefficient based on the processing method.

[0011] Preferably, the digital twin model in step S2 collects data generated during the manufacturing process in real time, the data including the actual size of the module, welding deviation and processing environment parameters, and compares it with the theoretical design data to generate error information.

[0012] Preferably, when the error exceeds a preset threshold, the error is corrected by adjusting parameters of the manufacturing equipment in real time, including the welding current, welding speed and cutting path.

[0013] Preferably, in the step S4, the assembly process guides the segmented docking position through a laser positioning system, and the laser positioning system generates an adjustment matrix according to the theoretical position and actual detection position of the module to correct the position and posture of the module.

[0014] Preferably, in the industrial robot-assisted assembly process in step S4, the module interfaces are precisely docked by a multi-degree-of-freedom robot, and the robot path planning aims at minimizing energy consumption.

[0015] Preferably, the quality inspection in step S5 obtains actual data of the segmented module, including welding strength, dimensional deviation and surface flatness, through laser scanning and ultrasonic testing equipment, and uses the test results to update the digital twin model.

[0016] Preferably, the closed-loop optimization in step S5 is implemented based on a digital twin model, and the digital twin model is dynamically updated through real-time collected data, and generates new manufacturing process parameters for subsequent segmented manufacturing and assembly.

[0017] The present invention provides a method for rapid manufacturing of ship sections, which has the following beneficial effects: 1. The present invention adopts a multi-objective optimization technical solution for module division, and achieves the technical effect of reasonable module division and reduced welding workload by dynamically optimizing the total length of the weld, manufacturing complexity and structural strength deviation. Compared with the problem of large number of welds and uneven force distribution caused by module division relying on manual experience in the prior art, the shortcomings of low module division efficiency and large assembly error are solved.

[0018] 2. The present invention introduces a digital twin model throughout the entire manufacturing and assembly process, realizing real-time dynamic association of module geometry information, force analysis, and process data, and achieving the technical effect of comprehensive control of manufacturing and assembly accuracy. In the prior art, digital models are only used for static design, fail to provide real-time feedback on manufacturing errors, and are difficult to deal with dynamic changes in complex module processing.

[0019] 3. The present invention successfully realizes high-precision adjustment and path optimization of module docking through the combination of laser positioning system and industrial robot-assisted assembly technology, achieving the technical effect of reducing manual intervention and improving assembly efficiency. Compared with the current situation that the traditional manual adjustment method relies on the experience of workers for assembly accuracy, the present invention effectively solves the problems of unstable accuracy and low efficiency in manual assembly.

[0020] 4. The present invention uses laser scanning and ultrasonic detection technology to dynamically adjust subsequent manufacturing and assembly parameters through closed-loop optimization, thereby achieving the technical effect of reducing error accumulation and improving the overall quality of the module. Compared with the process flow in the prior art where detection and manufacturing are separated and error feedback cannot be formed, the problem that the detection results cannot effectively guide subsequent production is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the 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.

[0023] Please see attached Figure 1 , an embodiment of the present invention provides a method for rapid manufacturing of ship sections, comprising the following steps; S1. Divide the hull into multiple sections according to the requirements of the hull design. Specifically, in this embodiment, the hull design is refined into multiple modules through an optimized module division method, so that each module meets the process and assembly requirements in terms of size, shape and interface. A dynamic division method based on multi-objective optimization is adopted to minimize the weld length and optimize the structural strength of the segment while ensuring that the difficulty of module manufacturing is reduced; The hull is divided into multiple modules according to functional areas. The modules can meet the actual needs of production, transportation and assembly, and optimize the weld length and manufacturing complexity as much as possible. At the same time, the strength and stress characteristics of the modules also need to meet the requirements of the overall hull design; In this embodiment, the module division is optimized by constructing a multi-objective optimization model. The optimization model aims to minimize the total length of the weld, minimize the manufacturing complexity, and minimize the structural strength deviation, and generates the optimal module division scheme; The multi-objective optimization model can be expressed as follows;

[0024] in: The manufacturing complexity of the module; The total length of the weld after dividing the modules; is the strength deviation of the module; Specifically: Manufacturing complexity ; The manufacturing complexity of a module is mainly measured by its geometric complexity and processing difficulty. The geometric complexity is the ratio of the module's surface area to its volume. The difficulty of processing is expressed as the type of processing technology involved in the module manufacturing process. ; The formula is as follows;

[0025] in: is the number of modules; For modules The surface area to volume ratio is calculated as ; in; is the surface area, is the volume; For modules The processing difficulty coefficient, the specific value is preset according to the process type; are weight coefficients, which are used to balance the effects of geometric complexity and processing difficulty. Total weld length ; The total length of the weld directly determines the number of interfaces between modules and the assembly workload. The weld length is calculated by the total length of the dividing boundary, and the formula is as follows;

[0026] in: is the number of welds; For the Length of the strip weld; Module strength deviation ; The module division must meet the structural strength requirements, that is, the actual stress distribution of the module must be as close as possible to the design allowable stress ,The strength deviation of the module can be defined as the sum of the absolute differences between the actual stress and the allowable stress;

[0027] For modules The maximum stress value under the design load, usually calculated by finite element analysis (FEA); It is the design allowable stress, which is determined by the module material properties and the use environment; The genetic algorithm (GA) is used to solve the multi-objective optimization model. The genetic algorithm generates the optimal module division scheme by simulating the biological evolution process, which includes the following steps: Initialize the population: generate multiple module partitioning schemes, each of which is represented by the location of the module boundary; Fitness function calculation: According to the objective function The weighted linear combination of is used to calculate the fitness of each solution;

[0028] in; are the weights of each target respectively; Crossover and mutation: Perform crossover and mutation operations on existing solutions to generate new partitioning solutions; Screening and updating: Select the best solution according to the fitness value and iterate until the fitness converges or reaches the maximum number of generations; The initial scheme of module division can be obtained through functional area division. The engine room, cargo hold and living compartment of the hull are divided into independent modules. The initial module boundary is determined according to the position of the bulkhead. Subsequently, the module boundary position is adjusted through the above-mentioned optimization algorithm to reduce the number of welds and optimize the force distribution.

[0029] S2. Establishing a digital twin model of the segment, wherein the digital twin model includes geometric information, structural force information and process information of the segment; Specifically, this embodiment uses a digital twin model to perform virtual modeling on each module. The digital twin model provides comprehensive data support for module manufacturing and assembly by combining geometric information, process parameters, and mechanical data. The digital twin model is not only used for simulation verification before manufacturing, but also receives actual data collected during the manufacturing process in real time to form a feedback mechanism, providing a basis for adjustment and optimization of subsequent links; In this embodiment, the digital twin model It consists of three types of information, specifically represented as;

[0030] in: The geometric information of the module includes the three-dimensional boundary, interface position and weld distribution of the module; Mechanical information of the module, including static load, dynamic load and local stress distribution; Process information of the module, including cutting path, welding parameters and distribution of heat-affected zone; Module geometry information ; Geometric information is the basis of the digital twin model, which mainly includes the three-dimensional boundary of the module, the location of the interface and the distribution of the welds; Specifically, the 3D boundary of the module is generated by 3D modeling software, and the boundary data is stored in the form of point cloud and used to determine the processing path and assembly docking position; The interface position and weld distribution are automatically generated according to the division scheme in step S1. The interface position is represented by standardized geometric data, including the coordinates of the interface center point, including the coordinates of the interface center point and the normal vector , the weld distribution is determined by calculating the path curve of the interface boundary; The weld path in the geometric information can be further refined and represented as;

[0031] in: is the point set of the weld path; The first The three-dimensional coordinates of a point; is the number of weld path points; Module Mechanical Information ; The mechanical information of the module consists of static load and dynamic load. The static load mainly includes the weight of the module and external pressure, while the dynamic load is caused by environmental factors. Through finite element analysis (FEA), the stress distribution of the module under static load and dynamic load can be calculated; Specifically, in one possible implementation, the mechanical information is described by the following formula;

[0032] in: For modules Maximum stress under design load; To act on the module Total load on the vehicle, including static and dynamic loads; For modules The force bearing area; The local stress distribution of the module can also be represented by the set of stress values ​​of the finite element mesh nodes. The local stress field can be described as;

[0033] in: For grid cells The stress value of The number of rows and columns for the grid division; Module process information ; The process information of the module includes cutting path, welding parameters and distribution of heat-affected zone; Specifically, the cutting path is directly extracted from the geometric model by digital modeling tools, which is represented as a sequence of control points of the machining equipment;

[0034] in: is the set of cutting path points; For the first The coordinates of the points; is the total number of cutting path points; Welding parameters include welding current , welding speed and welding voltage ,These parameters can be determined by welding material properties and interface width calculation;

[0035] in: is the welding current; is the welding width; is the thickness of the welding material; is the calculation function of welding parameters; The heat affected zone is determined by simulation tools, and its boundary can be represented by the heating temperature field as;

[0036] in: is the temperature field inside the module; is the critical temperature of the material; In this embodiment, the digital twin model obtains actual data in the manufacturing process through a real-time data acquisition system, including deviations of cutting boundaries, changes in weld shape, and status data of processing equipment. The actual data is compared with theoretical data to calculate errors;

[0037] in: The actual module data collected; is the theoretical model data; is the error between the two; When the error exceeds the preset threshold, the system generates feedback instructions based on the digital twin model, dynamically adjusts the parameters of the processing equipment, and adjusts the coordinates of the cutting path points. Correct processing deviations.

[0038] S3. Based on the digital twin model, segmented manufacturing is carried out, and module processing is carried out using CNC cutting and adaptive welding technology, and manufacturing process parameters are adjusted according to real-time detection data to reduce thermal deformation and processing errors; Specifically, in this embodiment, the module manufacturing must not only meet the design size and geometry requirements, but also effectively control the welding heat effect, processing errors and the forming accuracy of complex surfaces during the processing. The quality of the manufacturing stage directly determines the accuracy and efficiency of the subsequent assembly process; In this embodiment, module cutting: Specifically, the cutting of the module boundary is completed by CNC cutting equipment. The cutting path is provided by the geometric information in the digital twin model, and the path data is represented by a point cloud sequence;

[0039] During the cutting process, the actual deviation of the processing boundary is also collected in real time and correct the cutting trajectory by adjusting the coordinates of the path points, the power of the cutting equipment and speed It can also be adjusted dynamically based on material properties and boundary complexity. The relationship between power and speed can be determined by the following formula;

[0040] in: is the thermal conductivity of the cutting material; is the cutting speed; is the thickness of the material; In this embodiment, the modules are welded; Specifically, welding parameters include welding current , welding speed and voltage , these parameters are determined by the interface geometry and material thickness;

[0041] The welding equipment is equipped with a laser detection system to detect the real-time shape of the weld. The weld shape can be expressed as;

[0042] When the weld width is too large, the welding speed can be increased Or reduce power to correct; In this embodiment, for module areas with complex geometries or difficult to complete with traditional processing, the printing path is provided by the digital twin model, which is represented as;

[0043] in: is the point set of the printing path; is the three-dimensional coordinate of the path point; is the total number of path points; In this embodiment, during the module manufacturing process, the sensor system will collect equipment status and processing result data in real time, including the dimensional deviation of the cutting boundary, the change of the weld shape and the accuracy of the printing path. The actual data is compared with the design data in the digital twin model to calculate the error value; In this embodiment, high-precision module processing can be achieved, providing segmented modules that meet design requirements for subsequent assembly. The module processing accuracy is highly consistent with the digital twin model, ensuring the coordination and reliability of the entire manufacturing process.

[0044] S4, the assembly process uses laser positioning and industrial robot-assisted assembly technology to dock the manufactured segments with high precision, and uses the error detection system to calibrate the assembly position; Specifically, in this embodiment, the manufactured modules are accurately docked according to the design requirements to ensure assembly quality and docking accuracy. At the same time, the deviation problem caused by manufacturing errors or environmental changes during the assembly process is solved through a real-time error detection system and a dynamic adjustment mechanism; In this embodiment, positioning guidance: Specifically, the initial positioning of the module is completed by a laser positioning system. The laser positioning system is based on the theoretical position of the module. and the actual location detected in real time , generate the adjustment matrix , used to correct the module's position and adjust the matrix The calculation formula is:

[0045] in: is the rotation matrix, which is used to correct the angle deviation of the module; is the translation vector, used to adjust the translation deviation of the module; Specifically, the rotation matrix The components of can be expressed as;

[0046] in; is the rotation angle of the module; Translation Vector The calculation of can be determined by the following formula;

[0047] in: and are the coordinate vectors of the theoretical position and actual position of the module respectively; Docking adjustment: The robot adjusts the matrix according to the laser positioning system , dynamically adjust the interface position and angle of the module; The robot path planning aims to minimize energy consumption and maximize welding accuracy. The path planning formula is:

[0048] in: is the total energy consumption of the robot path; is the joint angle vector of the robot; is the joint angular velocity; and are the start time and end time of the assembly operation respectively; Specifically, the robot docking operation mainly includes the pre-alignment and precise calibration of the module interface. The robot monitors the geometric error of the module interface in real time through the visual guidance system, and calibrates the interface position in combination with the error compensation algorithm. When the actual position deviation of the interface is When the robot adjusts the interface position to;

[0049] in: is the corrected interface position; Initial docking position Welding completed: The final docking of the modules is achieved through welding operations. The present invention uses adaptive welding technology to dynamically adjust welding parameters according to the interface shape and material properties; Specifically, welding parameters include welding current , welding speed and welding voltage These parameters depend on the weld shape. and material thickness Calculate and determine;

[0050] The width of the weld is monitored in real time during the welding process and depth When the weld width or depth deviates from the design value, the system will automatically adjust the welding speed. and power To correct the welding effect; In this embodiment: During the assembly process, the sensor system will collect the module interface docking deviation data in real time, including position deviation , angle deviation and changes in weld shape These data are compared with the theoretical values ​​in the digital twin model to calculate the error value;

[0051] in: is the actual assembly result; Theoretical assembly parameters When the error When it exceeds the allowable range, the system will generate adjustment instructions and dynamically adjust the robot's operating path and welding parameters to ensure that the assembly accuracy meets the design requirements; It can effectively solve the position error and angle deviation problems in module interface docking, and realize high-precision welding operations.

[0052] S5. Perform quality inspection on the assembled segments, form error feedback based on the laser scanning or ultrasonic inspection results, update the error information to the digital twin model, generate subsequent manufacturing adjustment parameters, and achieve closed-loop optimization; Specifically, in this embodiment, laser scanning and ultrasonic detection technology are used to perform comprehensive error detection and analysis on the assembled segmented modules, and the detection results are compared with the theoretical data in the digital twin model to generate error information, which is then updated to the digital twin model. Through a closed-loop feedback mechanism, the parameter settings of subsequent manufacturing and assembly processes are optimized, thereby gradually improving the accuracy and efficiency of the overall process. In this embodiment, the geometric accuracy detection: Specifically, the geometric accuracy detection of the segmented module is completed by a laser scanning device. The scanning device captures the actual geometric shape of the module based on the point cloud data and compares it with the theoretical geometric data in the digital twin model. The point cloud data can be expressed as;

[0053] in: is the point set captured by the scan; For point cloud The three-dimensional coordinates of a point; is the total number of point cloud points; The calculation formula of geometric error is:

[0054] in: is the geometric error value; Design point sets for the theory in the digital twin model; When the geometric error When the preset tolerance range is exceeded, the system will record the coordinates of the error point and generate an error distribution map to guide subsequent adjustment operations; Weld quality inspection: The quality of the weld directly affects the structural strength of the segmented module. Ultrasonic testing technology is used to analyze the sound wave reflection signal inside the weld to determine whether there are cracks, holes or unfused defects in the weld. Specifically, ultrasonic testing data includes the depth of the weld ,width and defect location , the test results can be expressed as;

[0055] in: It is an ultrasonic detection dataset; and Respectively Depth and width of strip welds; is the position coordinate of the weld defect; is the number of welds; When the reflected signal strength When it is above a certain threshold, there may be a crack; when the signal strength is lower, it may be a hole; Error feedback and closed-loop optimization: After the inspection is completed, the system will associate the error information with the digital twin model to generate adjustment parameters for optimizing subsequent manufacturing and assembly processes.

[0056] Specifically, the error feedback process includes the following steps; First, the overall error vector of the module is calculated

[0057] in: is the geometric error; is the deviation of weld width; is the deviation of weld depth; Then, the error vector Mapped to the digital twin model, the process parameters of the module are updated. For example, when the weld width is detected to be too small, the welding speed can be adjusted. and current relationship;

[0058] in: and are the initial welding speed and weld width, respectively; is the deviation of weld width; The deviation trend chart is based on the detection results of multiple segmented modules to analyze the distribution of errors. When it is found that the angle deviation of a module interface is large, the cutting path or assembly angle of the next module can be adjusted to reduce the cumulative effect of the overall error. This embodiment can effectively detect errors in the assembly process and gradually improve the accuracy of the overall process through dynamic adjustment of the digital twin model. The detection and optimization process forms a closed-loop control to ensure that the manufacturing and assembly of each segment module always meets the design requirements, providing reliable guarantee for the overall quality of the hull.

[0059] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for rapid manufacturing of ship sections, characterized in that: The steps include: S1. Divide the hull into multiple sections according to the requirements of the hull design. S2. Establishing a digital twin model of the segment, wherein the digital twin model includes geometric information, structural force information and process information of the segment; S3. Based on the digital twin model, segmented manufacturing is carried out, and module processing is carried out using CNC cutting and adaptive welding technology, and manufacturing process parameters are adjusted according to real-time detection data to reduce thermal deformation and processing errors; S4, the assembly process uses laser positioning and industrial robot-assisted assembly technology to dock the manufactured segments with high precision, and uses the error detection system to calibrate the assembly position; S5. Perform quality inspection on the assembled segments, form error feedback based on the laser scanning or ultrasonic inspection results, update the error information to the digital twin model, generate subsequent manufacturing adjustment parameters, and achieve closed-loop optimization.

2. The method for rapid manufacturing of a ship in sections according to claim 1, characterized in that: In step S1, the module division is determined according to the segment volume, functional area division and transportation conditions, and the module boundary meets the unified docking standard.

3. The method for rapid manufacturing of a ship in sections according to claim 1, characterized in that: The module division in step S1 is achieved by constructing a multi-objective optimization model, which takes minimizing the total length of the weld, minimizing the manufacturing complexity and minimizing the structural strength deviation as the goals, and generates an optimal module division scheme.

4. The method for rapid manufacturing of a ship in sections according to claim 3, characterized in that: The manufacturing complexity is calculated based on the geometric complexity of the segment and the difficulty of the manufacturing process. The geometric complexity of the segment is determined by the ratio of the surface area to the volume of the module, and the difficulty of the manufacturing process is set according to the corresponding difficulty coefficient based on the processing method.

5. The method for rapid manufacturing of ship sections according to claim 1, characterized in that: In step S2, the digital twin model collects data generated during the manufacturing process in real time, including the actual size of the module, welding deviation and processing environment parameters, and compares it with the theoretical design data to generate error information.

6. The method for rapid manufacturing of ship sections according to claim 5, characterized in that: When the error exceeds a preset threshold, the error is corrected by adjusting the parameters of the manufacturing equipment in real time, including the welding current, welding speed and cutting path.

7. The method for rapid manufacturing of ship sections according to claim 1, characterized in that: In the step S4, the assembly process guides the segmented docking position through the laser positioning system, and the laser positioning system generates an adjustment matrix according to the theoretical position and the actual detection position of the module to correct the position and posture of the module.

8. The method for rapid manufacturing of ship sections according to claim 1, characterized in that: During the industrial robot-assisted assembly process in step S4, the module interfaces are precisely docked by a multi-degree-of-freedom robot, and the robot path planning aims at minimizing energy consumption.

9. The method for rapid manufacturing of ship sections according to claim 1, characterized in that: In step S5, the quality inspection obtains actual data of the segmented module through laser scanning and ultrasonic testing equipment, including welding strength, dimensional deviation and surface flatness, and uses the inspection results to update the digital twin model.

10. The method for rapid manufacturing of ship sections according to claim 1, characterized in that: The closed-loop optimization in step S5 is implemented based on a digital twin model, which is dynamically updated through real-time collected data and generates new manufacturing process parameters for subsequent segmented manufacturing and assembly.

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