A hybrid additive manufacturing method for a composite porous sandwich structure

Abstract

The application discloses a kind of hybrid additive manufacturing methods of composite porous sandwich structure, for manufacturing the composite porous sandwich structure consisting of panel and core layer, comprising: (1) the topological optimization of mechanical performance driven to composite porous sandwich structure;(2) based on the topological optimization of composite porous sandwich structure, hybrid additive manufacturing process is optimized;(3) based on the optimized hybrid additive manufacturing process, hybrid additive manufacturing intelligent planning is carried out, and after planning is completed, hybrid additive manufacturing is carried out.The application can realize the efficient, high-quality manufacturing of complex porous sandwich structure, and make the structure design and forming process organic unity.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of composite material additive manufacturing, in particular to a hybrid additive manufacturing method of a composite material porous sandwich structure. BACKGROUND

[0002] In recent years, composite material porous sandwich structures have attracted widespread attention in the fields of aerospace, vehicle transportation and biomedical treatment due to their light weight, high specific strength and excellent vibration isolation and energy absorption performance. Through careful design of the pore layout, such structures not only achieve the optimization of material usage, but also significantly improve the overall stability and impact resistance. At the same time, the rapid development of additive manufacturing technology makes it possible to manufacture complex porous structures, thereby greatly broadening the design space and manufacturing precision, and providing solid technical support for the efficient implementation of porous sandwich structures.

[0003] A foam-filled three-period minimal surface porous structure sandwich plate and a preparation method are disclosed in Chinese patent document CN110641083A, wherein a core composed of three-period minimal surface porous structure cells is placed between an upper panel and a lower panel; an outer panel is connected with an inner panel, and the inner panel is integrally printed and fused with the core.

[0004] Chinese patent document CN115716364A discloses a lightweight impact-resistant composite material plate with a hedgehog-like spine microstructure, which comprises a porous structure core layer composed of transverse and longitudinal reinforcing ribs and upper and lower panel structures. The porous structure core layer is made of short fiber epoxy resin material, and the upper and lower panels are made of hybrid fiber epoxy resin material with variable modulus and density. The upper and lower panels are designed by fiber layering, and the overall structure is integrally formed by 3D printing.

[0005] However, a single additive manufacturing process often cannot meet the requirements of high efficiency and high quality forming at the same time.

[0006] Multi-process hybrid additive manufacturing can fully utilize the advantages of different processes and provide a new technical approach for the efficient forming of high-performance composite material sandwich structures. However, in the hybrid additive manufacturing process, not only the coupling effect between processes needs to be fully considered, but also the strict requirements of sandwich structures in terms of mechanical properties need to be met.

[0007] Traditional topology optimization method mainly starts from the density distribution angle, ignores the influence of the actual stress state of the structure and the local strengthening demand, and the optimization means is relatively rough. To realize lightweight, meet the functional requirements, and at the same time ensure the bearing capacity and reliability in the manufacturing process, it is necessary to realize the collaborative optimization between various levels of performance. More importantly, the design of the porous sandwich structure not only needs to meet the structural adaptability and stiffness requirements on the macro level, but also needs to finely control the material arrangement and pore geometry of the porous unit on the micro scale, and comprehensively consider the mechanical coupling effect under multiple scales and the process constraints in the additive manufacturing process. SUMMARY

[0008] The application provides a hybrid additive manufacturing method for a composite porous sandwich structure, which can realize efficient and high-quality manufacturing of complex porous sandwich structures, and organically unifies structure design and forming process.

[0009] A hybrid additive manufacturing method for a composite porous sandwich structure, for manufacturing a composite porous sandwich structure composed of a panel and a core layer, comprising:

[0010] (1) topology optimization of the composite porous sandwich structure driven by mechanical properties;

[0011] (2) optimization of the hybrid additive manufacturing process based on the composite porous sandwich structure after topology optimization;

[0012] (3) hybrid additive manufacturing intelligent planning based on the optimized hybrid additive manufacturing process, and after the planning is completed, hybrid additive manufacturing is carried out.

[0013] The material of the core layer is a composite material or a pure thermoplastic resin, and the structure of the core layer includes a honeycomb structure, a corrugated structure, a three-period minimal surface structure, and a bionic structure.

[0014] The material of the panel is a composite material, including fiber-reinforced thermoplastic composite material and fiber-reinforced thermosetting composite material.

[0015] In step (1), the topology optimization driven by mechanical properties includes the following steps:

[0016] (1-1) input the sample design domain and optimization target of the porous sandwich structure function, wherein the optimization target includes target mechanical properties and target porosity;

[0017] (1-2) sample to generate an initial sample point set;

[0018] (1-3) computer-aided design and finite element analysis of the initial sample point set to obtain mechanical properties F and porosity p, and construct a data set D;

[0019] (1-4) Based on the constructed data set D, a surrogate model is constructed and trained, so that the surrogate model can predict the mechanical properties and porosity of sample points outside the data set D;

[0020] (1-5) A multi-objective optimization function is defined;

[0021] (1-6) A collection function is defined;

[0022] (1-7) The collection function is optimized to obtain the next sample point;

[0023] (1-8) Computer-aided design and finite element analysis are performed on the next sample point, the data set D is updated, and the optimal multi-objective function value is updated;

[0024] (1-9) Steps (1-4) to (1-8) are iteratively executed until the maximum number of iterations is reached or the improvement is less than a threshold value;

[0025] (1-10) The optimized sample point set and corresponding mechanical properties and porosity indicators are output.

[0026] In step (1-2), the Latin hypercube sampling (LHS) method is used to uniformly generate an initial sample point set within the defined design space.

[0027] In step (2), the hybrid additive manufacturing process is optimized, including the following steps:

[0028] (2-1) Input the equipment manufacturing capability parameters, the physical properties of the processed materials, and the geometric dimensions and specifications of the topologically optimized porous sandwich structure, and establish a basic data set for hybrid additive manufacturing;

[0029] (2-2) Based on the basic data set, an orthogonal experimental method is used to design pre-experiment schemes under different processing materials and working conditions, focusing on the characteristics of the topologically optimized porous sandwich structure

[0030] (2-3) Collect the process parameters and processing quality data of the additive manufacturing process, and process the data;

[0031] (2-4) A quantitative mapping relationship model between process parameters and the processing quality of the topologically optimized porous sandwich structure is established to reveal the influence mechanism of key parameters in the process parameters on the processing quality;

[0032] (2-5) The established quantitative mapping relationship model is verified for accuracy using a test set, and the process parameter combination is optimized based on the prediction results of the model to ensure that the performance requirements of the topologically optimized porous sandwich structure are met;

[0033] (2-6) Design the structure of the process parameter database and implement parameterized storage to ensure that the process parameters related to the topologically optimized porous sandwich structure and the processing quality data can be quickly retrieved;

[0034] (2-7) Develop demand-based intelligent query and parameter recommendation functions to provide optimized process parameter suggestions for the topologically optimized porous sandwich structure according to different needs;

[0035] (2-8) Output the hybrid additive manufacturing process parameter database based on the topologically optimized porous sandwich structure for subsequent process application and improvement.

[0036] The specific process of step (2-3) is as follows:

[0037] Use sensors and data acquisition systems to record process parameter data in real time during hybrid manufacturing, including temperature, pressure, and speed;

[0038] Use precision measurement equipment to measure the quality data of the topologically optimized porous sandwich structure after processing, including dimensional accuracy, surface roughness, and mechanical performance indicators for quantitative measurement;

[0039] Perform outlier rejection and noise filtering on the process parameter data and quality data, and standardize different dimensional parameters.

[0040] In step (3), hybrid additive manufacturing intelligent planning is performed, including the following steps:

[0041] (3-1) Input the workpiece information and the optimized process parameter database;

[0042] (3-2) Intelligent planning initialization, analyze the workpiece features and pre-plan the additive manufacturing level and path strategy;

[0043] (3-3) Hybrid additive manufacturing cycle, specifically including:

[0044] (3-3-1) Build the additive manufacturing core layer; (3-3-2) Intelligent alternating timing calculation, based on surface curvature and potential interference analysis, calculate the best process switching point and subsequent processing strategy; (3-3-3) Switch to the skin mode, precisely control the surface quality parameters to ensure perfect combination of the skin and the core layer; (3-3-4) Evaluate the workpiece state and parameter feedback to determine the best process window and starting time for the next stage; (3-3-5) Continue to build the core layer with optimized parameters; (3-3-6) Intelligent deviation analysis and correction of the plan;

[0045] (3-4) Determine whether the workpiece processing is complete, if not, continue with step (3-3), if completed, proceed to step (3-5);

[0046] (3-5) Generating post-processing scheme and code, encapsulating key process parameters and serializing execution instructions;

[0047] (3-6) Storing manufacturing data into knowledge base, recording parameter optimization history;

[0048] (3-7) Outputting hybrid additive manufacturing executable code.

[0049] Compared with the prior art, the present application has the following beneficial effects:

[0050] 1. The topological optimization design driven by mechanical properties realizes the effective weight reduction of the porous sandwich structure while meeting the structural strength requirements, and finely adjusts the material arrangement and pore shape of the porous unit at the microscale. This method overcomes the shortcomings of traditional optimization relying only on density distribution and can accurately design for local strengthening requirements.

[0051] 2. Based on hybrid additive manufacturing of multiple processes, the coupling effects of different additive processes are fully considered, and through data set construction, orthogonal experimental design, parameter-quality mapping model and intelligent recommendation, etc. Steps, the precise optimization of key process parameters is realized. This method takes into account high efficiency and high quality forming, expands the freedom of structure and material, while shortening the manufacturing cycle and ensuring the mechanical properties.

[0052] 3. In the hybrid additive manufacturing process, through multi-process switching, real-time evaluation and parameter correction in the complete process chain, and post-processing after generating executable code and knowledge base recording, efficient connection and accurate control are realized, which greatly improves the manufacturing efficiency and forming quality. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a schematic diagram of a composite porous sandwich structure in an embodiment of the present application.

[0054] Figure 2 is a schematic diagram of a hybrid additive manufacturing equipment used in an embodiment of the present application.

[0055] Figure 3 is a flow chart of the topological optimization method driven by mechanical properties in an embodiment of the present application.

[0056] Figure 4 is a flow chart of the hybrid additive manufacturing process optimization method in an embodiment of the present application.

[0057] Figure 5 is a flow chart of the hybrid additive manufacturing intelligent planning method in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate the understanding of the present invention and do not constitute any limitation thereof.

[0059] like Figure 1 As shown, a porous sandwich structure of composite material according to an embodiment of the present invention comprises an upper panel 1, a core layer 2, and a lower panel 3, which can be prepared by additive manufacturing. The panel is typically made of composite material to balance high strength and good fatigue resistance; while the porous structure of the core layer can be flexibly selected between composite materials and pure thermoplastic resin depending on the application requirements. Furthermore, to further enhance the lightweight and functional aspects of the sandwich structure, the core layer is not limited to traditional honeycomb structures in its design; various novel porous topologies such as corrugated structures and three-period minimal curved surfaces d can also be adopted. Through additive manufacturing, the pore shape and distribution of the porous structure can be flexibly designed to optimize mechanical, heat transfer, or vibration reduction performance under different working conditions.

[0060] like Figure 2 As shown, this is a hybrid additive manufacturing equipment used in an embodiment of the present invention, consisting of a primary additive manufacturing device 4, a secondary additive manufacturing device 5, and a base 6. The primary additive manufacturing device 4 can form the core layer 2, while the secondary additive manufacturing device 5 can form the upper panel 1 and the lower panel 3. The primary additive manufacturing device 4 is an automatic fiber placement device, and its heating method includes, but is not limited to, hot gas torch heating, infrared heating, and laser heating. The secondary additive manufacturing device 5 includes, but is not limited to, a pure resin melt deposition equipment, a short fiber reinforced resin melt deposition equipment, and a continuous fiber reinforced resin melt deposition equipment. In the hybrid additive manufacturing process, the manufacturing sequence of the primary additive manufacturing device 4 and the secondary additive manufacturing device 5 is alternating. That is, the secondary additive manufacturing device 5 can first manufacture part of the upper panel 1, then the primary additive manufacturing device 4 can manufacture part of the core layer 2, and then the secondary additive manufacturing device 5 can manufacture part of the upper panel 1, and so on, alternating between the two additive manufacturing processes.

[0061] The hybrid additive manufacturing method for porous sandwich structures of composite materials consists of a mechanical property-driven topology optimization method, a hybrid additive manufacturing process optimization method, and a hybrid additive manufacturing intelligent planning method. These will be introduced in turn.

[0062] like Figure 3 As shown, a topology optimization method driven by the mechanical properties of a porous sandwich structure of composite materials includes the following steps:

[0063] Step 101: Input the sample design domain range of the porous sandwich structure function: x 1,min ≤x1≤x 1,max ,x 2,min ≤x2≤x 2,max ,x 3,min ≤x3≤x3,max The optimization objectives are: flexural strength, compressive strength, and porosity.

[0064] Step 102: Latin hypercube sampling generates the initial sample point set.

[0065] The Latin Hypercube Sampling (LHS) method is used to uniformly generate an initial set of sample points within a defined design space. The LHS method ensures coverage across the range of each design variable, thereby ensuring that the initial sample represents the entire design space, avoiding local biases, and improving the efficiency and reliability of subsequent optimization.

[0066] Step 103: Perform finite element analysis and computer-aided design calculations on the initial sample points to obtain the mechanical properties F and porosity p, and construct the dataset D.

[0067] The mechanical properties F (flexural strength, compressive / shear strength, etc.) and porosity p are obtained by performing finite element analysis (FEA) and computer-aided design (CAD) on the initial sample points, and a dataset D is constructed.

[0068] Step 104: Construct the Kriging agent model.

[0069] Based on an existing dataset (D), a surrogate model is constructed using the Kriging interpolation method to estimate the mechanical properties F and porosity p in the design space. The Kriging model can fit the response relationship between mechanical properties and porosity using known data points. The Kriging model is trained using the existing dataset D to ensure that the model can accurately predict the properties outside the sample points.

[0070] Step 105: Define the multi-objective optimization function max F(x) = (f p (x1,x2,x3),f b (x1,x2,x3)f s (x1, x2, x3)); Constraints: x 1,min ≤x1≤x 1,max ,x 2,min ≤x2≤x 2,max ,x 3,min ≤x3≤x 3,max ;

[0071] f p (x1,x2,x3) represent porosity, f b (x1,x2,x3) represents the bending strength, f s (x1,x2,x3) represents the shear strength.

[0072] Step 106: Define the acquisition function.

[0073] The acquisition function selects the next sample point by maximizing the potential improvement.

[0074] Step 107: Optimize the acquisition function to obtain the next sample point.

[0075] By optimizing the acquisition function, the next sample point is selected. The optimization goal is to further explore the design space by increasing uncertainty, thereby improving sampling efficiency. Based on the optimization results of the acquisition function, a new sample point is selected, and subsequent analysis is performed.

[0076] Step 108: Perform FEA and CAD calculation on the next sample point to update the dataset D, and update the best multi-objective function value.

[0077] Step 109: Iteratively execute steps 104, 105, 106, 107, and 108, and stop the iteration loop when the maximum number of iterations is met or the improvement is less than the threshold.

[0078] Step 110: Output the optimized sample point set and corresponding performance indicators (mechanical performance F 1,max ,F 2,max and porosity p min .

[0079] As shown in Figure 4 , a composite porous sandwich structure hybrid additive manufacturing process optimization method includes the following steps:

[0080] Step 201: Collect and input equipment manufacturing capacity parameters, processing material physical properties, and topological optimization of porous sandwich structure geometric dimensions and specifications, and establish a basic dataset for hybrid manufacturing.

[0081] Step 202: Based on the orthogonal experimental method, design pre-experiment schemes under different materials and working conditions to ensure comprehensive coverage and statistical significance.

[0082] Step 203: Collect, measure, and process the process parameter data of the additive manufacturing process:

[0083] Step 203-1: Use sensors and data acquisition systems to record process parameters in real time during hybrid manufacturing, including temperature, pressure, speed, and other key variable data.

[0084] Step 203-2: Use precision measuring equipment to quantitatively measure the size accuracy, surface roughness, and mechanical properties of the processed porous sandwich structure.

[0085] Step 203-3: Outlier rejection and noise filtering are performed on the collected raw data, and different dimension parameters are standardized to make the data suitable for subsequent modeling analysis.

[0086] Step 204: A quantitative mapping relationship model between process parameters and the processing quality of topologically optimized porous sandwich structures is constructed using a multi-objective optimization algorithm, revealing the influence mechanism of key parameters on processing quality.

[0087] Step 205: The accuracy of the established model is verified through an independent test set, and the process parameter combination is optimized based on the model prediction results to improve the processing quality.

[0088] Step 206: Design a reasonable database structure and realize parameterized storage to ensure that the process parameters and processing quality data related to topologically optimized porous sandwich structures can be quickly retrieved.

[0089] Step 207: Develop an intelligent query interface and parameter recommendation algorithm based on demand to provide optimized process parameter combination suggestions for different processing needs.

[0090] Step 208: Integrate all the work results mentioned above to form a complete hybrid manufacturing process parameter optimization database, and provide a friendly user interface to realize intelligent management of process parameters.

[0091] As shown in Figure 5 , a hybrid additive manufacturing method for composite porous sandwich structures includes the following steps:

[0092] Step 301: Workpiece specifications and requirements are input, process parameter library is loaded, material properties are analyzed and matched, and process constraints and quality targets are set.

[0093] Step 302: Start the intelligent planning engine and multi-objective optimization algorithm, analyze the workpiece characteristics, and pre-plan the additive manufacturing levels and path strategy.

[0094] Step 303: Hybrid additive manufacturing cycle:

[0095] Step 303-1: Additive manufacturing core layer construction;

[0096] Step 303-2: Intelligent alternating timing calculation, based on surface curvature and potential interference analysis, to calculate the best process switching point and subsequent processing strategy;

[0097] Step 303-3: Switch to skin mode, precisely control surface quality parameters, and ensure perfect combination of skin and core layer;

[0098] Step 303-4: Re-evaluate the workpiece state and parameter feedback to determine the best process window and starting time for the next stage;

[0099] Step 303-5: Continue to build the core layer with optimized parameters;

[0100] Step 303-6: Perform intelligent bias analysis and correction on the plan.

[0101] Step 304: Determine if the workpiece processing is complete. If not, continue with step 303. If complete, proceed to step 305.

[0102] Step 305: Generate post-processing scheme and code, encapsulate key process parameters and serialize execution instructions.

[0103] Step 306: Store manufacturing data into the knowledge base and record the parameter optimization history.

[0104] Step 307: Integrate the optimization results to generate a standard format hybrid additive manufacturing executable code and process document.

[0105] The above embodiments have described the technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modifications, supplements and equivalent replacements made within the principle range of the present application shall be included within the protection scope of the present application.

Claims

1. A hybrid additive manufacturing method of a composite porous sandwich structure for manufacturing a composite porous sandwich structure composed of a face sheet and a core layer, characterized in that, Comprise: (1) Topology optimization of porous sandwich structure of composite material driven by mechanical properties; comprising the following steps: (1-1) input sample design domain and optimization target of porous sandwich structure function, wherein the optimization target contains target mechanical properties and target porosity; (1-2) sample generation initial sample point set; (1-3) computer aided design and finite element analysis of the initial sample point set, obtain mechanical properties F and porosity p, and construct dataset D; (1-4) based on the constructed dataset D, construct and train the surrogate model, so that the surrogate model can predict the mechanical properties and porosity of the sample points outside the dataset D; (1-5) define multi-objective optimization function; (1-6) define acquisition function; (1-7) optimize the acquisition function to obtain the next sample point; (1-8) computer aided design and finite element analysis of the next sample point, update the dataset D, and update the best multi-objective function value; (1-9) iteratively execute steps (1-4) to (1-8), stop when the maximum number of iterations is reached or the improvement is less than the threshold; (1-10) output the optimized sample point set and the corresponding mechanical properties and porosity indicators; (2) optimization of hybrid additive manufacturing process based on the topology optimized porous sandwich structure of composite material; (3) intelligent planning of hybrid additive manufacturing based on the optimized hybrid additive manufacturing process, and after the planning is completed, hybrid additive manufacturing is carried out.

2. The hybrid additive manufacturing method of a composite porous sandwich structure according to claim 1, wherein, The material of the core layer is composite material or pure thermoplastic resin, and the structure of the core layer includes honeycomb structure, corrugated structure and three-period minimal surface structure.

3. The hybrid additive manufacturing method of a composite porous sandwich structure according to claim 1, wherein, The material of the panel is composite material, including fiber reinforced thermoplastic composite material and fiber reinforced thermosetting composite material.

4. The hybrid additive manufacturing method of a composite porous sandwich structure according to claim 1, wherein, In step (1-2), Latin hypercube sampling LHS method is used to generate initial sample point set uniformly in the defined design space.

5. The hybrid additive manufacturing method of a composite porous sandwich structure according to claim 1, wherein, In step (2), the hybrid additive manufacturing process is optimized, comprising the following steps: (2-1) input equipment manufacturing capability parameters, processing material physical properties, and topology optimized porous sandwich structure geometric dimensions and specifications, and establish the basic dataset of hybrid additive manufacturing; (2-2) based on the basic dataset, design pre-experiment scheme under different processing materials and working conditions by orthogonal experiment method; (2-3) collect process parameters and processing quality data in additive manufacturing process, and process the data; (2-4) establish a quantitative mapping relationship model between process parameters and processing quality of the topology optimized porous sandwich structure, to reveal the influence mechanism of key parameters in process parameters on processing quality; (2-5) test set is used to verify the accuracy of the established quantitative mapping relationship model, and the process parameter combination is optimized based on the prediction results of the model to ensure that the performance requirements of the topology optimized porous sandwich structure are met; (2-6) design the structure of process parameter database and realize parameterized storage, to ensure that the process parameters and processing quality data related to the topology optimized porous sandwich structure can be quickly retrieved; (2-7) develop intelligent query and parameter recommendation functions based on requirements, and provide optimized process parameter suggestions for the topology optimized porous sandwich structure according to different requirements; (2-8) Output the hybrid additive manufacturing process parameter database based on the topology-optimized porous sandwich structure for subsequent process application and improvement.

6. The hybrid additive manufacturing method of a composite porous sandwich structure according to claim 5, wherein, The specific process of step (2-3) is: Use sensors and data acquisition systems to record process parameter data in real time during hybrid manufacturing, including temperature, pressure, and speed; Use precision measurement equipment to measure the mass data of the processed topology-optimized porous sandwich structure, including size accuracy, surface roughness, and mechanical property indicators for quantitative measurement; Perform outlier rejection, noise filtering on process parameter data and quality data, and standardize parameters of different dimensions.

7. The hybrid additive manufacturing method of a composite porous sandwich structure according to claim 1, wherein, In step (3), hybrid additive manufacturing intelligent planning is performed, including the following steps: (3-1) Input the workpiece information and optimized process parameter database; (3-2) Intelligent planning initialization, analyze the workpiece characteristics and pre-plan the additive manufacturing level and path strategy; (3-3) Hybrid additive manufacturing cycle, specifically including: (3-3-1) Build the additive manufacturing core layer; (3-3-2) Intelligent alternating timing calculation, based on surface curvature and potential interference analysis, calculate the best process switching point and subsequent processing strategy; (3-3-3) Switch to the skin mode, precisely control the surface quality parameters, and ensure the perfect combination of the skin and the core layer; (3-3-4) Evaluate the workpiece state and parameter feedback, determine the best process window and starting time for the next stage; (3-3-5) Continue to build the core layer with optimized parameters; (3-3-6) Intelligent deviation analysis and correction of the planning; (3-4) Determine whether the workpiece processing is complete, if not, continue step (3-3), if complete, go to step (3-5); (3-5) Generate post-processing scheme and code, encapsulate key process parameters and serialize execution instructions; (3-6) Store the manufacturing data into the knowledge base and record the parameter optimization history; (3-7) Output the hybrid additive manufacturing executable code.

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