Additive manufacturing storage box assembling method based on digital twinning
By employing a digital twin-based additive manufacturing method, through 3D scanning and digital twin construction, additive manufacturing deviations are precisely quantified, tolerance analysis is integrated, and processing schemes are optimized. This solves the problem of insufficient part assembly accuracy in additive manufacturing, enabling efficient and precise tank assembly.
Patent Information
- Application Number
- CN202610137858.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-30
- Publication Date
- 2026-05-15
AI Technical Summary
Additive manufacturing technology is prone to dimensional deviations and residual stress when manufacturing complex structures, resulting in insufficient geometric accuracy of parts. Traditional assembly methods are difficult to compensate for these deviations, affecting the assembly accuracy and sealing performance of products such as storage tanks. Existing virtual assembly technology cannot effectively integrate actual manufacturing deviations.
By performing 3D scanning on 3D printed tank parts, point cloud data is generated, a digital twin is constructed and compared with the CAD model, the assembly process is simulated for tolerance analysis, an optimized processing plan is generated, a closed-loop feedback of design-manufacturing-assembly is formed, and the tolerance analysis function is integrated to predict the impact of deviations and optimize the assembly sequence and path.
High-precision assembly was achieved, with deviation compensation accuracy improved to 0.01mm, assembly success rate increased by 90%, assembly cycle shortened by 30%, and full-process digital traceability was formed, with systematic optimization of tolerances and printing strategies.
Smart Images
Figure CN122046702A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of tank assembly technology, and in particular to an additive manufacturing tank assembly method based on digital twins. Background Technology
[0002] While additive manufacturing technology can achieve integrated molding of complex structures, it is prone to dimensional deviations, residual stress, and deformation during the manufacturing process, leading to insufficient geometric accuracy of parts. Traditional assembly methods rely on sequential assembly and trial and error with physical objects, which are difficult to compensate for these deviations, often causing problems such as interference and gap mismatch, affecting the assembly accuracy and sealing performance of products such as storage tanks. Existing virtual assembly technologies are mostly based on ideal CAD models, which cannot effectively integrate actual manufacturing deviations, resulting in a disconnect between simulation and physical objects. Although digital twin technology can construct virtual models, it lacks direct integration with additive manufacturing deviations and a closed-loop feedback optimization mechanism, limiting assembly accuracy and efficiency.
[0003] To address the aforementioned problems, this invention proposes an additive manufacturing method for assembling storage tanks based on digital twins, which enables high-precision assembly of storage tanks. Summary of the Invention
[0004] This invention provides an additive manufacturing method for assembling storage tanks based on digital twins, which enables high-precision assembly of storage tanks.
[0005] In a first aspect, embodiments of the present invention provide an additive manufacturing method for assembling a storage tank based on digital twins, comprising: The 3D-printed tank parts are scanned in three dimensions to obtain point cloud data of the actual geometric model; The point cloud data is compared with the original CAD design model to generate a digital twin representing manufacturing deviations; The digital twin and mating part models are imported into the assembly simulation environment to simulate the assembly process, analyze the assembly problems, perform tolerance analysis on the assembly problems, and evaluate the impact of manufacturing tolerances on the performance of the assembly. Based on the assembly issues and evaluation results, an optimized processing plan is generated.
[0006] Optionally, generating an optimized processing plan based on the assembly issues and evaluation results includes: When there are evaluation results that exceed the preset value, the required processing amount and processing path of the part with assembly problems are determined based on the evaluation results. The obtained processing amount and processing path are used to feed back to the processing equipment to process and optimize the tank parts. When no evaluation result exceeds the preset value, optimize the assembly sequence and path.
[0007] Optionally, after determining the required machining amount and machining path for the location where the assembly problem occurs, the method further includes: Collect machining and assembly data, including the parts with assembly problems, machining quantities, and machining paths, and feed the machining and assembly data back to the design and manufacturing stages to form a closed-loop optimization.
[0008] Optionally, the three-dimensional scanning is performed using a laser scanner or a structured light scanning device with a scanning accuracy greater than 0.05 mm.
[0009] Optionally, the analysis yields an assembly problem, and tolerance analysis is performed on the assembly problem, including: Conduct a joint analysis of assembly deviations and structural deformations, analyze interference problems on assembly surfaces, analyze the fit clearances and geometric tolerances of welded surfaces, and analyze the dimensions and geometric tolerances of sealing positions.
[0010] Compared with the prior art, the present invention has at least the following beneficial effects: 1) This invention uses 3D scanning and digital twin construction to accurately quantify additive manufacturing deviations, making virtual assembly closer to reality and improving deviation compensation accuracy to 0.01mm.
[0011] 2) This invention integrates tolerance analysis function to predict the impact of deviations on performance, avoid assembly risks in advance, and improve the first-time assembly success rate by more than 90%.
[0012] 3) This invention reduces the cost of physical trial and error and shortens the assembly cycle by 30% through assembly process optimization and adaptive processing guidance.
[0013] 4) This invention forms a closed-loop feedback between design, manufacturing, and assembly, systematically optimizing tolerances and printing strategies to reduce deviations from the source.
[0014] 5) This invention enables full-process digital traceability, providing a data foundation for intelligent manufacturing. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart illustrating the assembly and feedback process based on digital twins in this invention. Figure 2 A schematic diagram of an additive manufacturing tank structure, showing the component layout; Figure 3This is a virtual assembly simulation diagram demonstrating the deviation model; Figure 4 This is a schematic diagram for adaptive machining guidance, illustrating the planning of local milling paths. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0018] This invention provides an additive manufacturing method for assembling a storage tank based on digital twins, comprising: The 3D-printed tank parts are scanned in three dimensions to obtain point cloud data of the actual geometric model; The point cloud data is compared with the original CAD design model to generate a digital twin representing manufacturing deviations; The digital twin and mating part models are imported into the assembly simulation environment to simulate the assembly process, analyze the assembly problems, perform tolerance analysis on the assembly problems, and evaluate the impact of manufacturing tolerances on the performance of the assembly. Based on the assembly issues and evaluation results, an optimized processing plan is generated.
[0019] In this embodiment, digital twin technology is used to predict, analyze and solve assembly problems caused by dimensional deviations in additive manufacturing in a virtual environment by employing a physical model with biases. This enables virtual assembly and process guidance, and provides feedback to the design and manufacturing processes, forming a closed-loop optimization from manufacturing to design.
[0020] In some embodiments of the present invention, generating an optimized processing scheme based on the assembly problem and evaluation results includes: When there are evaluation results that exceed the preset value, the required processing amount and processing path of the part with assembly problems are determined based on the evaluation results. The obtained processing amount and processing path are used to feed back to the processing equipment to process and optimize the tank parts. When no evaluation result exceeds the preset value, optimize the assembly sequence and path.
[0021] In some embodiments of the present invention, after determining the amount of machining and machining path required for the location where the assembly problem occurs, the method further includes: Collect machining and assembly data, including the parts with assembly problems, machining quantities, and machining paths, and feed the machining and assembly data back to the design and manufacturing stages to form a closed-loop optimization.
[0022] In some embodiments of the present invention, the three-dimensional scanning is performed using a laser scanner or a structured light scanning device, with a scanning accuracy greater than 0.05 mm.
[0023] In some embodiments of the present invention, the analysis reveals assembly problems, and tolerance analysis is performed on the assembly problems, including: Conduct a joint analysis of assembly deviations and structural deformations, analyze interference problems on assembly surfaces, analyze the fit clearances and geometric tolerances of welded surfaces, and analyze the dimensions and geometric tolerances of sealing positions.
[0024] Taking the assembly process of an elliptical additive manufacturing propulsion tank made of a certain titanium alloy as an example, the specific implementation process of the present invention is illustrated below. Figure 1 and Figure 2 As shown, the implementation process of this invention is as follows: 1) Use 3D scanning equipment (such as a laser scanner or structured light scanner) to acquire point cloud data of the additively manufactured part. The scanning accuracy needs to be better than 0.05mm to ensure the reliability of the deviation model.
[0025] 2) Align the scanned data with the original CAD model using point cloud processing software (Geomagic Control X), perform deviation analysis, and generate a color deviation map (see...). Figure 3 The deviation model is output in STEP or IGES format and input as a digital twin into the assembly simulation environment.
[0026] 3) Import the digital twin and mating part models into simulation software (such as ANSYS Additive Suite or Siemens NX). Set assembly constraints (such as fit and alignment) and tolerance parameters.
[0027] 4) Simulate the assembly process using a physics engine (such as Simcenter 3D), dynamically detect interference (e.g., highlighted in red) and gaps (e.g., chromatographic analysis in blue), and generate an assembly performance analysis report.
[0028] 5) Adaptive processing guidance: When unavoidable interference is detected, a local processing plan is generated. Figure 4 The system outputs numerical control code through a CAM module (such as Siemens NX CAM) to drive the five-axis machine tool for post-processing.
[0029] 6) Feedback optimization: The system records the warping and assembly deviation problems of the elliptical tank and feeds them back to the design end. It is recommended to adjust the tank printing support strategy, increase the machining allowance from 3mm to 5mm, and adjust the laser power to reduce warping.
[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for assembling a storage tank using additive manufacturing based on digital twins, characterized in that, include: The 3D-printed tank parts are scanned in three dimensions to obtain point cloud data of the actual geometric model; The point cloud data is compared with the original CAD design model to generate a digital twin representing manufacturing deviations; The digital twin and mating part models are imported into the assembly simulation environment to simulate the assembly process, analyze the assembly problems, perform tolerance analysis on the assembly problems, and evaluate the impact of manufacturing tolerances on the performance of the assembly. Based on the assembly issues and evaluation results, an optimized processing plan is generated.
2. The assembly method according to claim 1, characterized in that, The step of generating an optimized processing plan based on the assembly issues and evaluation results includes: When there are evaluation results that exceed the preset value, the required processing amount and processing path of the part with assembly problems are determined based on the evaluation results. The obtained processing amount and processing path are used to feed back to the processing equipment to process and optimize the tank parts. When no evaluation result exceeds the preset value, optimize the assembly sequence and path.
3. The assembly method according to claim 2, characterized in that, After determining the required machining amount and machining path for the location where the assembly problem occurs, the process also includes: Collect machining and assembly data, including the parts with assembly problems, machining quantities, and machining paths, and feed the machining and assembly data back to the design and manufacturing stages to form a closed-loop optimization.
4. The assembly method according to claim 1, characterized in that, The three-dimensional scanning is performed using a laser scanner or structured light scanning equipment, with a scanning accuracy greater than 0.05 mm.
5. The assembly method according to claim 1, characterized in that, The analysis revealed assembly problems, and tolerance analysis was performed on these assembly problems, including: Conduct a joint analysis of assembly deviations and structural deformations, analyze interference problems on assembly surfaces, analyze the fit clearances and geometric tolerances of welded surfaces, and analyze the dimensions and geometric tolerances of sealing positions.