Composite porous sandwich structure mixed additive manufacturing method

Abstract

The invention discloses a mixed additive manufacturing method for a composite porous sandwich structure, which is used for manufacturing the composite porous sandwich structure consisting of a panel and a core layer, and comprises the following steps: (1) carrying out mechanical property driven topological optimization on the composite porous sandwich structure; (2) optimizing a mixed additive manufacturing process on the basis of the porous sandwich structure of the composite material subjected to topological optimization; and (3) based on the optimized mixed additive manufacturing process, mixed additive manufacturing intelligent planning is conducted, and mixed additive manufacturing is conducted after planning is completed. By means of the method, efficient and high-quality manufacturing of the complex porous sandwich structure can be achieved, and the structural design and the forming process are organically unified.

Description

Technical Field

[0001] The present invention relates to the field of composite material additive manufacturing, and in particular to a hybrid additive manufacturing method for a composite material porous sandwich structure. Background Art

[0002] In recent years, composite porous sandwich structures have gained wide attention in aerospace, vehicle transportation, and biomedicine due to their light weight, high specific strength, and excellent vibration isolation and energy absorption performance. By carefully designing the pore layout, such structures can not only optimize the amount of material used, but also significantly improve the overall stability and impact resistance. At the same time, the rapid development of additive manufacturing technology has made it possible to manufacture complex porous structures, thereby greatly broadening the design space and manufacturing accuracy, and providing solid technical support for the efficient realization of porous sandwich structures.

[0003] For example, a Chinese patent document with publication number CN110641083A discloses a foam-filled three-periodic minimal surface porous structure sandwich panel and a preparation method, in which a core composed of three-periodic minimal surface porous structure cells is placed between an upper panel and a lower panel; an outer panel is connected to an inner panel, and the inner panel is integrated with the core by printing and fusion.

[0004] A Chinese patent document with publication number CN115716364A discloses a lightweight impact-resistant composite material plate with a hedgehog-like microstructure, including: a porous structure core layer composed of transverse reinforcement ribs and longitudinal reinforcement ribs and an upper and lower panel structure, the porous structure core layer adopts a short fiber epoxy resin material, the upper and lower panels adopt a mixed fiber epoxy resin material with variable modulus and variable density, and the upper and lower panels adopt a fiber layup design, and the overall structure is prepared by 3D printing one-piece molding.

[0005] However, a single additive manufacturing process often cannot meet the requirements of both high efficiency and high-quality molding.

[0006] Multi-process hybrid additive manufacturing can give full play to the advantages of different processes and provide a new technical idea for the efficient molding of high-performance composite sandwich structures. However, in the hybrid additive manufacturing process, it is necessary not only to fully consider the coupling effect between the various processes, but also to meet the strict requirements of the mechanical properties of the sandwich structure.

[0007] Traditional topology optimization methods mainly start from the perspective of density distribution, ignoring the influence of the actual stress state of the structure and the need for local strengthening, and their optimization means are relatively rough. To achieve lightweight, meet functional requirements, and at the same time ensure the load-bearing capacity and reliability during the manufacturing process, it is necessary to achieve collaborative optimization among various levels of performance. More critically, the design of the porous sandwich structure not only needs to meet the requirements of structural conformability and stiffness at the macroscopic level, but also requires fine control of the material arrangement and pore geometry of the porous units at the microscale, comprehensively considering the mechanical coupling effects at multiple scales and the process constraints during the additive manufacturing process. Summary of the Invention

[0008] The present invention provides a hybrid additive manufacturing method for a composite material porous sandwich structure, which can achieve the efficient and high-quality manufacturing of complex porous sandwich structures, and organically unify the structural design and the forming process.

[0009] A hybrid additive manufacturing method for a composite material porous sandwich structure, used to manufacture a composite material porous sandwich structure composed of a faceplate and a core layer, includes:

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

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

[0012] (3) Based on the optimized hybrid additive manufacturing process, conduct intelligent planning for hybrid additive manufacturing. After the planning is completed, perform hybrid additive manufacturing.

[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 triply periodic minimal surface structure, and a bionic structure.

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

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

[0016] (1-1) Input the sample design domain and optimization objectives of the porous sandwich structure function, where the optimization objectives include target mechanical properties and target porosity;

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

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

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

[0020] (1-5) Define a multi-objective optimization function;

[0021] (1-6) Define an acquisition function;

[0022] (1-7) Optimize the acquisition function to obtain the next sample point;

[0023] (1-8) Perform computer-aided design and finite element analysis on the next sample point, update the dataset D, and update the optimal multi-objective function value;

[0024] (1-9) Iteratively execute steps (1-4) to (1-8), and stop when the maximum number of iterations is reached or the improvement is less than the threshold;

[0025] (1-10) Output the optimized sample point set and the corresponding mechanical property and porosity indexes.

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

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

[0028] (2-1) Input the equipment manufacturing capacity parameters, physical properties of the processing material, and geometric dimensions and specifications of the topologically optimized porous sandwich structure to establish a basic dataset for hybrid additive manufacturing;

[0029] (2-2) Based on the basic dataset, use the orthogonal experiment method 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 during the additive manufacturing process, and process the data;

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

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

[0033] Design the structure of the process parameter database for the design process and implement parametric storage to ensure that the process parameters and machining quality data related to the topologically optimized porous sandwich structure can be retrieved quickly;

[0034] Develop an intelligent query and parameter recommendation function based on requirements, and provide optimized process parameter suggestions for the topologically optimized porous sandwich structure according to different requirements;

[0035] Output the hybrid additive manufacturing process parameter database for 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 the process parameter data in real time during the hybrid manufacturing process, 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 property indicators for quantitative measurement;

[0039] Eliminate outliers and filter noise from the process parameter data and quality data, and standardize the parameters with different dimensions.

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

[0041] Input the information of the workpiece to be processed and the optimized process parameter database;

[0042] Initialize the intelligent planning, analyze the workpiece features, and pre-plan the additive manufacturing layer and path strategy;

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

[0044] (3-3-1) Construct the additive manufacturing core layer; (3-3-2) Calculate the intelligent alternating timing, calculate the optimal process switching point and subsequent machining strategy based on surface curvature and potential interference analysis; (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, determine the best process window and start timing for the next stage; (3-3-5) Continue to construct the core layer with optimized parameters; (3-3-6) Conduct intelligent deviation analysis and correction on the plan;

[0045] (3-4) Determine whether the workpiece machining is completed. If not, continue with step (3-3). After completion, enter step (3-5);

[0046] (3-5) Generate post-processing solutions and codes, encapsulate key process parameters and serialize execution instructions;

[0047] (3-6) Store manufacturing data in the knowledge base and record the parameter optimization process;

[0048] (3-7) Output executable codes for hybrid additive manufacturing.

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

[0050] 1. Through topology optimization design driven by mechanical properties, the porous sandwich structure can effectively reduce weight while meeting the structural strength requirements, and finely regulate the material arrangement and pore shape of the porous unit at the microscale. This method overcomes the deficiency of traditional optimization relying only on density distribution and can perform precise design for local strengthening requirements.

[0051] 2. Based on multi-process hybrid additive manufacturing, fully considering the coupling effect of different additive processes, through steps such as dataset construction, orthogonal experimental design, parameter-quality mapping model and intelligent recommendation, precise optimization of key process parameters is achieved. This method takes into account both high efficiency and high-quality forming, expands the degrees of freedom of structure and material, shortens the manufacturing cycle and ensures mechanical properties at the same time.

[0052] 3. During 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 codes and recording in the knowledge base, efficient connection and precise control are realized, greatly improving the manufacturing efficiency and forming quality. Description of the Drawings

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

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

[0055] Figure 3 It is a flow chart of the topology optimization method driven by mechanical properties in the embodiment of the present invention.

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

[0057] Figure 5 It is a flow chart of the hybrid additive manufacturing intelligent planning method in the embodiment of the present invention. Detailed Embodiments

[0058] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention and do not impose any limitations on it.

[0059] As Figure 1 shown, a composite material porous sandwich structure according to an embodiment of the present invention is composed of an upper panel 1, a core layer 2, and a lower panel 3, and can be prepared by an additive manufacturing process. Among them, the panel usually selects a composite material to take into account high strength and good fatigue resistance; and the porous structure of the core layer can be flexibly selected between a composite material and a pure thermoplastic resin according to application requirements. In addition, in order to further improve the lightweight and functionalization degree of the sandwich structure, the core layer is not limited to the traditional honeycomb structure in design, but can also adopt various new porous topologies such as a corrugated structure and a triply periodic minimal surface d. Through the additive manufacturing process, the pore shape and distribution of the porous structure can be flexibly designed to achieve performance optimization for mechanics, heat transfer, vibration reduction, etc. under different working conditions.

[0060] As Figure 2 shown, a hybrid additive manufacturing device adopted in an embodiment of the present invention is composed of a first additive manufacturing device 4, a second additive manufacturing device 5, and a base 6. Among them, the first additive manufacturing device 4 can form the core layer 2, and the second additive manufacturing device 5 can form the upper panel 1 and the lower panel 3. The first additive manufacturing device 4 is an automatic fiber placement device, and its heating methods include but are not limited to hot gas torch heating, infrared heating, and laser heating, etc. And the second additive manufacturing device 5 includes but is not limited to a pure resin fused deposition device, a short fiber reinforced resin fused deposition device, and a continuous fiber reinforced resin fused deposition device. In the hybrid additive manufacturing process, the manufacturing sequences of the first additive manufacturing device 4 and the second additive manufacturing device 5 are alternating, that is, the second additive manufacturing device 5 can first manufacture a part of the upper panel 1, then the first additive manufacturing device 4 manufactures a part of the core layer 2, and then the second additive manufacturing device 5 manufactures a part of the upper panel 1, etc., and the two additive manufacturing processes are alternated in turn.

[0061] The hybrid additive manufacturing method for a composite material porous sandwich structure is composed of a topology optimization method driven by mechanical properties, a hybrid additive manufacturing process optimization method, and a hybrid additive manufacturing intelligent planning method. Next, they will be introduced in turn.

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

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

[0064] Step 102: Generate an initial sample point set using Latin hypercube sampling

[0065] Use the Latin Hypercube Sampling (LHS) method to uniformly generate an initial sample point set within the defined design space The LHS method can ensure coverage within the range of each design variable, thus ensuring that the initial samples can represent 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 data set D.

[0067] Perform finite element analysis (FEA) and computer-aided design (CAD) calculations on the initial sample points to obtain the mechanical properties F (flexural strength, compressive / shear strength, etc.) and porosity p, and construct the data set D.

[0068] Step 104: Construct a Kriging surrogate model.

[0069] Based on the existing data set (D), use the Kriging interpolation method to construct a surrogate model for estimating the mechanical properties F and porosity p in the design space. The Kriging model can fit the response relationship between the mechanical properties and porosity through known data points. Train the Kriging model with the existing data set D to ensure that the model can accurately predict the performance outside the sample points.

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

[0071] f p (x 1 ,x 2 ,x 3 ) represents the porosity, and f b (x 1 ,x 2 ,x 3 ) represents the flexural strength, and f s (x 1 ,x 2 ,x 3 ) represents the shear strength.

[0072] Step 106: Define the acquisition function.

[0073] The acquisition function selects the Expected Improvement (EI) method, which 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 the 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 calculations on the next sample point to update the dataset D and update the optimal multi-objective function value.

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

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

[0079] As Figure 4 shown, a method for optimizing the hybrid additive manufacturing process of a composite material porous sandwich structure includes the following steps:

[0080] Step 201: Collect and input the equipment manufacturing capacity parameters, physical properties of the processing materials, and geometric dimensions and specifications of the topologically optimized porous sandwich structure to establish a basic dataset for hybrid manufacturing.

[0081] Step 202: Design a pre-experiment plan under different materials and working conditions based on the orthogonal experiment method to ensure that the experimental coverage is comprehensive and statistically significant.

[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 the process parameters in the hybrid manufacturing process in real time, including key variable data such as temperature, pressure, and speed.

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

[0085] Step 203-3: Remove outliers and filter noise from the collected raw data, and standardize the parameters with different dimensions to make the data suitable for subsequent modeling and analysis.

[0086] Step 204: Use a multi-objective optimization algorithm to construct a quantitative mapping relationship model between the process parameters and the processing quality of the topology-optimized porous sandwich structure, and reveal the influence mechanism of key parameters on the processing quality.

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

[0088] Step 206: Design a reasonable database structure and implement parametric storage to ensure that the process parameters and processing quality data related to the topology-optimized porous sandwich structure can be quickly retrieved.

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

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

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

[0092] Step 301: Input workpiece specifications and requirements, load the process parameter library, analyze and match material characteristics, and set process constraint conditions and quality goals.

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

[0094] Step 303: Hybrid Additive Manufacturing Cycle:

[0095] Step 303-1: Additive Manufacturing Core Layer Construction;

[0096] Step 303-2: Intelligent Alternation Timing Calculation, calculate the optimal process switching point and subsequent processing strategy based on surface curvature and potential interference analysis;

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

[0098] Step 303-4: Re-evaluate the workpiece status and parameter feedback, determine the optimal process window and start timing for the next stage;

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

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

[0101] Step 304: Determine whether the workpiece machining is completed. If not, continue with Step 303; if completed, proceed to Step 305.

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

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

[0104] Step 307: Integrate the optimization results and generate executable codes and process documents for hybrid additive manufacturing in standard format.

[0105] The above-described embodiments have elaborated on the technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, supplements, and equivalent replacements made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A hybrid additive manufacturing method for a composite porous sandwich structure, used to manufacture a composite porous sandwich structure consisting of a panel and a core layer, characterized in that: include: (1) Topological optimization of porous sandwich structures driven by mechanical properties; (2) Based on the topologically optimized composite porous sandwich structure, the hybrid additive manufacturing process is optimized; (3) Based on the optimized hybrid additive manufacturing process, intelligent planning of hybrid additive manufacturing is carried out, and after the planning is completed, hybrid additive manufacturing is carried out.

2. The hybrid additive manufacturing method for a composite porous sandwich structure according to claim 1, characterized in that: 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.

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

4. The hybrid additive manufacturing method for a composite porous sandwich structure according to claim 1, characterized in that: In step (1), a topology optimization driven by mechanical properties is performed, including the following steps: (1-1) Input the sample design domain and optimization target of the porous sandwich structure function, where the optimization target includes target mechanical properties and target porosity; (1-2) Sampling to generate an initial sample point set; (1-3) Perform computer-aided design and finite element analysis on the initial sample point set to obtain mechanical properties F and porosity p and construct a data set D; (1-4) Based on the constructed data set D, a proxy model is constructed and trained so that the proxy model can predict the mechanical properties and porosity of sample points outside the data set D; (1-5) Define a multi-objective optimization function; (1-6) Define the acquisition function; (1-7) Optimize the acquisition function to obtain the next sample point; (1-8) Perform computer-aided design and finite element analysis on the next sample point, update the data set D, and update the optimal multi-objective function value; (1-9) Iterate steps (1-4) to (1-8) and stop when the maximum number of iterations is met or the improvement is less than a threshold; (1-10) Output the optimized sample point set and the corresponding mechanical properties and porosity indicators.

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

6. The hybrid additive manufacturing method for a composite porous sandwich structure according to claim 1, characterized in that: In step (2), the hybrid additive manufacturing process is optimized, including the following steps: (2-1) Input equipment manufacturing capability parameters, physical properties of processed materials, and geometric dimensions and specifications of topologically optimized porous sandwich structures to establish a basic data set for hybrid additive manufacturing; (2-2) Based on the basic data set, the orthogonal experimental method is used to design preliminary experimental schemes under different processing materials and working conditions; (2-3) Collecting process parameters and processing quality data of additive manufacturing process and processing the data; (2-4) Establish a quantitative mapping relationship model between process parameters and the processing quality of topology optimized porous sandwich structures to reveal the influence mechanism of key process parameters on processing quality; (2-5) Using a test set to verify the accuracy of the established quantitative mapping relationship model, and optimizing the process parameter combination based on the prediction results of the model to ensure that the performance requirements of the topologically optimized porous sandwich structure are met; (2-6) Design the structure of the process parameter database and implement parameterized storage to ensure rapid retrieval of process parameters and processing quality data related to the topology optimized porous sandwich structure; (2-7) Develop demand-based intelligent query and parameter recommendation functions to provide optimized process parameter suggestions for topologically optimized porous sandwich structures according to different requirements; (2-8) Output the hybrid additive manufacturing process parameter database based on topologically optimized porous sandwich structure for subsequent process application and improvement.

7. The hybrid additive manufacturing method for a composite porous sandwich structure according to claim 6, characterized in that: The specific process of step (2-3) is: Use sensors and data acquisition systems to record real-time process parameter data during the hybrid manufacturing process, including temperature, pressure, and speed; Use precision measuring equipment to measure the quality data of the topologically optimized porous sandwich structure after processing, including quantitative measurement of dimensional accuracy, surface roughness and mechanical performance indicators; Outliers and noise are eliminated from process parameter data and quality data, and parameters of different dimensions are standardized.

8. The hybrid additive manufacturing method for a composite porous sandwich structure according to claim 1, characterized in that: 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, analyzing workpiece features and pre-planning additive manufacturing layers and path strategies; (3-3) Hybrid additive manufacturing cycle, specifically including: (3-3-1) Construct the additive manufacturing core layer; (3-3-2) Intelligent alternating timing calculation, based on surface curvature and potential interference analysis, calculate the optimal process switching point and subsequent processing strategy; (3-3-3) Switch to skin mode, accurately control surface quality parameters, and ensure the perfect combination of skin and core layer; (3-3-4) Evaluate the workpiece status and parameter feedback to determine the optimal process window and start time for the next stage; (3-3-5) Continue to construct the core layer with optimized parameters; (3-3-6) Perform intelligent deviation analysis and correction on the plan; (3-4) Determine whether the workpiece processing is completed. If not, proceed to step (3-3). If completed, proceed to step (3-5); (3-5) Generate post-processing solutions and codes, encapsulate key process parameters and serialize execution instructions; (3-6) Store the manufacturing data into the knowledge base and record the parameter optimization process; (3-7) Output hybrid additive manufacturing executable code.

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