A creep aging process method for reinforced components and a forming surface prediction method

Abstract

The present invention discloses a creep aging process method for reinforced components and a method for predicting formed surfaces. The process method includes: obtaining multiple sets of strain-time data under different creep aging process conditions at the specimen level; establishing a material constitutive model for the entire creep aging process; revising the constitutive model based on actual experiments and specimen finite element simulation results; obtaining key features of the reinforced component; using a neural network to construct a mapping relationship between process conditions, discrete feature points of the component, and formed surfaces; revising the neural network structure based on creep aging forming results; and optimizing process parameters and component structure. Most of the experimental data of the present invention comes from finite element simulation, and the simulation has been verified in practice, which can effectively ensure the reliability of finite element simulation and neural network prediction; the present invention realizes process parameter optimization and prediction of key positions of forming surface point clouds, can quickly obtain a creep aging process suitable for component forming, and adopts this process to quickly predict the surface of the component after forming.

Description

Technical Field

[0001] The present invention relates to the technical field of creep aging forming, and in particular to a creep aging process method for a reinforced component and a forming surface prediction method. Background Art

[0002] The actual load-bearing conditions of a rocket body structure are complex, and its design must ensure sufficient strength and stiffness while minimizing weight to increase payload and overall performance. The use of ribbed components can meet these requirements by providing additional strength and stiffness without significantly increasing weight. Creep aging forming technology is an advanced forming technology that meets the high-performance requirements and low-cost manufacturing of ribbed thin-walled components. Using creep aging forming technology to achieve coordinated manufacturing of component formability, the key lies in shortening the process development cycle to quickly obtain a suitable creep aging process and predict the component profile after forming using this process, thereby guiding the actual creep aging production of ribbed components. In existing technologies, the specific creep aging process for a component is determined mainly by using finite element simulation to obtain the forming profile under different process parameters. The target profile is then compared with the formed profile error, and the three parameters of aging temperature, aging time, and loading pressure are continuously iterated to ensure that the component forming accuracy meets the standard, thereby obtaining the final creep aging process. This method not only has low overall R&D efficiency and a long exploration cycle, but also has high computational costs using finite element simulation.

[0003] In response to the above-mentioned problems, scholars in the industry have conducted some relevant research. For example, the invention patent with authorization announcement number CN115828423B discloses a method for predicting the rebound of the outer plate of marine equipment based on a dual-branch deep learning algorithm. This method obtains an outer plate rebound prediction model based on a convolutional neural network to predict the rebound of the outer plate forming. However, the three-dimensional morphology of the outer plate is input into the convolutional neural network, which leads to a slow training process, a long debugging cycle, and high requirements for equipment. For example, the invention patent with authorization announcement number CN109508488B discloses a method for predicting the process parameters of shot peening based on a genetic algorithm to optimize the BP neural network. This method uses a genetic algorithm to optimize the weights and thresholds of the BP neural network to predict the process parameters of shot peening. However, it does not input part features into the neural network and the data source is all actual experiments. This is costly, the small amount of data is prone to overfitting, and the process development cycle is long.

[0004] Therefore, there is an urgent need to explore a method that can quickly obtain the optimal creep aging process parameters suitable for the forming of reinforced components and obtain the target rebound surface of the component under the process parameters. Summary of the Invention

[0005] The object of the present invention is to provide a creep aging forming method for ribbed components to solve the problems of low R&D efficiency, long exploration cycle and high cost of existing creep aging process methods mentioned in the background art.

[0006] To achieve the above object, the present invention provides a creep aging process for reinforced components, comprising the following steps:

[0007] Step 1: Conduct a specimen-level creep aging forming test to obtain the strain-time data of the specimen under multiple sets of different creep aging process parameters;

[0008] Step 2: Based on the strain-time data obtained in step 1, establish the material constitutive model for the entire creep aging process;

[0009] Step 3: Embed the constitutive model established in step 2 into the finite element simulation software, and simulate the entire creep aging forming process of the specimens under multiple sets of different process parameters; and conduct basic creep aging experiments under the same process parameters on the specimens; modify the parameters of the constitutive model based on the results of the basic experiments and simulations;

[0010] Step 4: Establish a spatial coordinate system and measure the overall dimensions of multiple different reinforced components in the spatial coordinate system;

[0011] Step 5: Identify the key structural features of the component and discretize the identified key structural features to achieve dimensionality reduction from the three-dimensional morphology to discrete points;

[0012] Step 6: Based on step 5, the discrete feature points of the component are obtained by the optimal Latin hypercube sampling method;

[0013] Step 7: Based on the correction results of step 3 and the discrete feature points obtained in step 6, perform component-level orthogonal finite element simulation to obtain the corresponding surface point cloud data after creep aging under different process conditions and different discrete feature points of components;

[0014] Step 8: Use a neural network to construct a mapping relationship between process conditions, component discrete feature points, and component forming surface; use the process conditions and the initial positions of the component discrete feature points as the input layer, and the positions of the component discrete feature points after forming as the output layer to construct a neural network model, and then judge the forming accuracy of the component and whether there are defects on the forming surface based on the positions of the component discrete feature points after forming;

[0015] Step 9: Modify the neural network model constructed in step 8 according to the actual creep aging forming results of the component;

[0016] Step 10: Use optimization algorithms to optimize the creep aging process parameters and component structure, and ultimately obtain the optimal creep aging process parameters;

[0017] In the above steps, steps 1 to 3 are performed in parallel with steps 4 to 6, or steps 1 to 3 are performed followed by steps 4 to 6, or steps 4 to 6 are performed first and then steps 1 to 3; the execution of step 7 is premised on the completion of steps 1 to 3 and steps 4 to 6.

[0018] Furthermore, in step 1, the number of groups of creep aging process parameters is 6 to 18, the aging temperature is 140-210° C., the aging stress is 240-420 MPa, and the aging time is 6-12 h.

[0019] Furthermore, the constitutive model in step 2 includes the strain changes in the loading stage, the heating stage, the cooling stage, and the heat preservation and loading stage, which is used to realize the prediction of the strain-time change of the component in the whole process.

[0020] Furthermore, the key structural features of the component in step 5 include rib position, rib height and rib thickness.

[0021] Furthermore, the optimization algorithm in step 10 is an optimization algorithm based on Levy flight strategy, chaos mapping, random walk strategy or sine-cosine optimization strategy.

[0022] The present invention also provides a method for predicting the creep aging forming surface of a ribbed component. The prediction method uses the forming process method as described above to obtain the optimal creep aging process parameters, and then feeds the optimal creep aging process parameters and structural characteristics into the neural network model established in step 8 to obtain the forming surface of the target component.

[0023] The present invention also provides another creep aging process for reinforced components, comprising the following steps:

[0024] Step 1: Conduct a specimen-level creep aging forming test to obtain the strain-time data of the specimen under multiple sets of different creep aging process parameters;

[0025] Step 2: Based on the strain-time data obtained in step 1, establish the constitutive model of the material during the entire creep aging process;

[0026] Step 3: Embed the constitutive model established in step 2 into the finite element simulation software, and simulate the entire creep aging forming process of the specimens under multiple sets of different process parameters; and conduct basic creep aging experiments under the same process parameters on the specimens; modify the parameters of the constitutive model based on the results of the basic experiments and simulations;

[0027] Step 4: Establish a spatial coordinate system and measure the overall dimensions of multiple different reinforced components in the spatial coordinate system;

[0028] Step 5: Identify the key structural features of the component and discretize the identified key structural features to achieve dimensionality reduction from the three-dimensional morphology to discrete points;

[0029] Step 6: Based on step 5, the discrete feature points of the component are obtained by the optimal Latin hypercube sampling method;

[0030] Step 7: Based on the correction results of step 3 and the discrete feature points obtained in step 6, perform component-level orthogonal finite element simulation to obtain the corresponding surface point cloud data after creep aging under different process conditions and different discrete feature points of components;

[0031] Step 8: Use a neural network to construct a mapping relationship between process conditions, component discrete feature points, and component forming surface. Use the process conditions and the initial positions of the component discrete feature points as the output layer, and the positions of the component discrete feature points after forming as the input layer to construct a neural network. Directly input the desired surface point cloud position, and then output the corresponding creep aging process parameters.

[0032] In the above steps, steps 1 to 3 are performed in parallel with steps 4 to 6, or steps 1 to 3 are performed followed by steps 4 to 6, or steps 4 to 6 are performed first and then steps 1 to 3; the execution of step 7 is premised on the completion of steps 1 to 3 and steps 4 to 6.

[0033] The invention also provides another method for predicting the creep aging forming surface of a ribbed component. The prediction method uses the creep aging process parameters of the component obtained by the forming process method as described above, and then corrects the neural network structure constructed in step 8 according to the actual creep aging forming results of a representative component. The corrected result is then fed back into the neural network model established in step 8 to obtain the forming surface of the target component.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The creep aging process method for reinforced components of the present invention modifies the parameters of the constitutive model based on the results of basic experiments and simulations. The experimental data is mostly derived from finite element simulations, and the simulations are actually verified, which can effectively ensure the reliability of finite element simulations and neural network predictions. The structural features of the component are discretized to achieve dimensionality reduction from three-dimensional morphology to discrete data points, greatly reducing the amount of calculation. The present invention calls the neural network prediction model twice to achieve process parameter optimization and prediction of key positions of the forming surface point cloud. It can quickly obtain a creep aging process suitable for component forming, and the use of this process can quickly predict the surface of the formed component.

[0036] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0038] Figure 1 This is a schematic flow chart of a creep aging forming process for a reinforced component according to the present invention;

[0039] Figure 2 Schematic diagram of the neural network mapping relationship constructed in an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the distribution of discrete characteristic points obtained after forming a component and its springback profile after forming in an embodiment of the present invention;

[0041] Figure 4 Schematic diagram of the predicted surface accuracy of the creep aging forming surface prediction method for reinforced components in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in this field based on these embodiments are all within the scope of protection of the present invention.

[0043] See Figure 1 This embodiment provides a creep aging process method for a reinforced component, comprising the following steps:

[0044] Step 1: Conduct a specimen-level creep aging forming test to obtain strain-time data for multiple different specimens under multiple sets of creep aging process parameters; wherein the number of creep aging process parameter sets is 6 to 12, the aging temperature is 140-210°C, the aging stress is 240-420 MPa, and the aging time is 6-12 hours. Preferably, the number of creep aging process parameter sets is 9, the aging temperatures are 150°C, 180°C, and 210°C, and the aging stresses are 240 MPa, 280 MPa, and 320 MPa, respectively.

[0045] Step 2: Based on the strain-time data obtained in step 1, a constitutive model of the material during the entire creep aging process is established. The constitutive model includes the strain changes during the loading stage, heating stage, cooling stage, and heat preservation and loading stage, and is used to predict the strain-time changes of the component during the entire process.

[0046] Step 3: Embed the constitutive model established in step 2 into the finite element simulation software, and simulate the entire creep aging forming process with multiple sets of different process parameters for multiple different specimens; and carry out basic creep aging experiments under the same process parameters for multiple different specimens; modify the parameters of the constitutive model based on the results of the basic experiments and simulations.

[0047] Step 4: Establish a spatial coordinate system and measure the overall dimensions of multiple different reinforced components in the spatial coordinate system.

[0048] Step 5: Identify the key structural features of the component, including rib position, rib height, and rib thickness; and discretize the identified key structural features to achieve dimensionality reduction from the component's three-dimensional morphology to discrete points.

[0049] Step 6: Based on step 5, the discrete feature points of the component are obtained through the optimal Latin hypercube sampling method.

[0050] Step 7: Based on the correction results from Step 3 and the discrete feature points obtained in Step 6, perform component-level orthogonal finite element simulations to obtain surface point cloud data corresponding to multiple sets of different process conditions and discrete feature points after creep aging. In this step, perform component-level creep aging forming simulations under multiple sets of orthogonal process conditions to obtain surface point cloud data corresponding to the discrete feature points of the corresponding components after creep aging. Finite element simulations with different process parameters can be submitted via batch processing, and post-processing results can also be automatically extracted.

[0051] Step 8: Use a neural network to construct a mapping relationship between creep aging process conditions, discrete feature points of components, and component forming surfaces; use the creep aging process conditions of components and the initial positions of discrete feature points of components as the input layer, and the positions of discrete feature points of components after forming as the output layer to construct a neural network model. The positions of discrete feature points of components after forming can be used to determine the forming accuracy of the component and whether defects such as wrinkles appear on the forming surface.

[0052] Step 9: Modify the neural network model constructed in step 8 based on the actual creep aging forming results of representative components to improve prediction accuracy. In this step, the surface of the representative components after actual creep aging forming is used to optimize the database and neural network.

[0053] Step 10: Optimize the creep aging process parameters and component structure using an optimization algorithm. Lévy flights are a medium- and short-range hybrid random search method that conforms to the Lévy distribution and possesses excellent global search capabilities. Using the neural network prediction error as the fitness function, an optimization algorithm based on the Lévy flight strategy is used to optimize the process parameters. This step can improve global search capabilities by not only using the Lévy flight strategy but also introducing chaotic mapping, random walk strategies, and sine-cosine optimization strategies, all of which can achieve the objectives of the present invention.

[0054] In the above steps, steps 1 to 3 are performed in parallel with steps 4 to 6, or steps 1 to 3 are performed followed by steps 4 to 6, or steps 4 to 6 are performed first and then steps 1 to 3; the execution of step 7 is premised on the completion of steps 1 to 3 and steps 4 to 6.

[0055] The present invention also provides a method for predicting the creep aging forming surface of a ribbed component. The prediction method adopts the forming method as described above. After obtaining the optimal creep aging process parameters, the optimal creep aging process parameters and structural characteristics are fed back into the neural network model established in step 8 to obtain the forming surface of the target component.

[0056] An embodiment of the present invention also provides another creep aging forming process method for a ribbed component, comprising the following steps:

[0057] Step 1: Conduct a specimen-level creep aging forming test to obtain specimen-level strain-time data under multiple sets of different creep aging process parameters;

[0058] Step 2: Based on the strain-time data obtained in step 1, establish the constitutive model of the material during the entire creep aging process;

[0059] Step 3: Embed the constitutive model established in step 2 into the finite element simulation software, and simulate the entire creep aging forming process of the specimens under multiple sets of different process parameters; and conduct basic creep aging experiments under the same process parameters on the specimens; modify the parameters of the constitutive model based on the results of the basic experiments and simulations;

[0060] Step 4: Establish a spatial coordinate system and measure the overall dimensions of multiple different reinforced components in the spatial coordinate system;

[0061] Step 5: Identify the key structural features of the component and discretize the identified key structural features to achieve dimensionality reduction from the three-dimensional morphology to discrete points;

[0062] Step 6: Based on step 5, the discrete feature points of the component are obtained by the optimal Latin hypercube sampling method;

[0063] Step 7: Based on the correction results from Step 3 and the discrete feature points obtained in Step 6, perform component-level orthogonal finite element simulations to obtain surface point cloud data corresponding to different process conditions and discrete feature points after creep aging. Perform multiple sets of component-level creep aging forming simulations with different orthogonal process conditions to obtain surface point cloud data corresponding to discrete feature points after creep aging. Finite element simulations with different process parameters can be submitted via batch processing, and post-processing results can also be automatically extracted.

[0064] Step 8: Use a neural network to construct the mapping relationship between the creep aging process parameter conditions of the component, the discrete feature points of the component, and the component forming surface; use the creep aging process parameter conditions of the component and the initial position of the discrete feature points of the component as the output layer, and use the position of the discrete feature points of the component after forming as the input layer to construct a neural network, and directly input the desired surface point cloud position to output the corresponding process parameters.

[0065] In the above steps, steps 1 to 3 are performed in parallel with steps 4 to 6, or steps 1 to 3 are performed followed by steps 4 to 6, or steps 4 to 6 are performed first and then steps 1 to 3; the execution of step 7 is premised on the completion of steps 1 to 3 and steps 4 to 6.

[0066] The present invention also provides another method for predicting the creep aging forming surface of a reinforced component. The prediction method uses the creep aging process parameters of the component obtained by the other creep aging process method for reinforced components as described above, and then corrects the neural network structure constructed in step 8 based on the actual creep aging forming results of representative components. The corrected results are then fed back into the neural network model established in step 8 to obtain the forming surface of the target component.

[0067] Example

[0068] The embodiment of the present invention takes the high-rib wall panel as an example, and the modeling parameters are as follows:

[0069]

[0070]

[0071] The above modeling parameters are used to construct Figure 2 Neural network mapping relationship.

[0072] A batch of sample points of the flat state of the high-rib siding are predicted. By integrating the data of the three modeling parameters of the discrete point x coordinate after rebound, the discrete point rebound amount, and the discrete point z coordinate, the x coordinate, y coordinate, and z coordinate of the discrete sample points are corresponded, and the point cloud data after rebound of the discrete sample points is formed. Finally, the rebound profile of the siding is generated by fitting the point cloud data; Figure 3As shown in the figure, (a) is the distribution position of discrete feature points obtained after the component is formed, which can be equivalent to the point cloud position after springback; (b) is the springback surface obtained by fitting the point cloud data.

[0073] The creep aging forming surface prediction method of the reinforced component of the present invention is used to predict the surface accuracy of the wall panel with an aging temperature of 180℃, an aging time of 1.3h, and a mold radius of R2150. Figure 4 .Depend on Figure 4 It can be seen that this method has high prediction accuracy. The deviation between the springback prediction result and the actual forming result in most areas is ±0.8mm. The RMSE is used to evaluate the prediction effect. The calculation formula is as follows. The calculated RMSE error is 0.724mm, and the prediction effect is good.

[0074]

[0075] Where y is the calculated value of the surface springback of the neural network, i is the surface springback value of the actual component, is the profile deviation value, and p represents the number of profile points taken.

[0076] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A creep aging process for reinforced components, characterized in that: The steps include: Step 1: Conduct a specimen-level creep aging forming test to obtain the strain-time data of the specimen under multiple sets of different creep aging process parameters; Step 2: Based on the strain-time data obtained in step 1, establish the material constitutive model for the entire creep aging process; Step 3: Embed the constitutive model established in step 2 into the finite element simulation software, and simulate the entire creep aging forming process of the specimens under multiple sets of different process parameters; and conduct basic creep aging experiments under the same process parameters on the specimens; modify the parameters of the constitutive model based on the results of the basic experiments and simulations; Step 4: Establish a spatial coordinate system and measure the overall dimensions of multiple different reinforced components in the spatial coordinate system; Step 5: Identify the key structural features of the component and discretize the identified key structural features to achieve dimensionality reduction from the three-dimensional morphology to discrete points; Step 6: Based on step 5, the discrete feature points of the component are obtained by the optimal Latin hypercube sampling method; Step 7: Based on the correction results of step 3 and the discrete feature points obtained in step 6, perform component-level orthogonal finite element simulation to obtain the corresponding surface point cloud data after creep aging under different process conditions and different discrete feature points of components; Step 8: Use the neural network to construct the mapping relationship between process conditions, discrete feature points of the component and the component forming surface; A neural network model is constructed with the process conditions and the initial positions of the discrete feature points of the component as the input layer, and the positions of the discrete feature points of the component after forming as the output layer. The forming accuracy of the component and the presence of defects on the formed surface are then determined based on the positions of the discrete feature points of the component after forming. Step 9: Modify the neural network model constructed in step 8 according to the actual creep aging forming results of the component; Step 10: Use optimization algorithms to optimize the creep aging process parameters and component structure, and ultimately obtain the optimal creep aging process parameters; In the above steps, steps 1 to 3 are performed in parallel with steps 4 to 6, or steps 1 to 3 are performed followed by steps 4 to 6, or steps 4 to 6 are performed first and then steps 1 to 3; the execution of step 7 is premised on the completion of steps 1 to 3 and steps 4 to 6.

2. A creep aging process for reinforced components according to claim 1, characterized in that: The number of groups of creep aging process parameters in step 1 is 6 to 18, the aging temperature is 140-210° C., the aging stress is 240-420 MPa, and the aging time is 6-12 h.

3. The creep aging process of reinforced components according to claim 1, characterized in that: The constitutive model in step 2 includes the strain changes in the loading stage, the heating stage, the cooling stage, and the heat preservation and loading stage, and is used to realize the prediction of the strain-time change of the component throughout the whole process.

4. The creep aging process for reinforced components according to claim 1, characterized in that: The key structural features of the component in step 5 include rib position, rib height and rib thickness.

5. The creep aging process of reinforced components according to claim 1, characterized in that: The optimization algorithm in step 10 is an optimization algorithm based on Levy flight strategy, chaos mapping, random walk strategy or sine-cosine optimization strategy.

6. A method for predicting the creep aging forming surface of a reinforced component, characterized in that: After obtaining the optimal creep aging process parameters using the process method described in any one of claims 1 to 5, the prediction method feeds the optimal creep aging process parameters and structural features back into the neural network model established in step 8, thereby obtaining the forming surface of the target component.

7. A creep aging process for reinforced components, characterized in that: The following steps are involved: Step 1: Conduct a specimen-level creep aging forming test to obtain the strain-time data of the specimen under multiple sets of different creep aging process parameters; Step 2: Based on the strain-time data obtained in step 1, establish the constitutive model of the material during the entire creep aging process; Step 3: Embed the constitutive model established in step 2 into the finite element simulation software, and simulate the entire creep aging forming process of the specimens under multiple sets of different process parameters; and conduct basic creep aging experiments under the same process parameters on the specimens; modify the parameters of the constitutive model based on the results of the basic experiments and simulations; Step 4: Establish a spatial coordinate system and measure the overall dimensions of multiple different reinforced components in the spatial coordinate system; Step 5: Identify the key structural features of the component and discretize the identified key structural features to achieve dimensionality reduction from the three-dimensional morphology to discrete points; Step 6: Based on step 5, the discrete feature points of the component are obtained by the optimal Latin hypercube sampling method; Step 7: Based on the correction results of step 3 and the discrete feature points obtained in step 6, perform component-level orthogonal finite element simulation to obtain the corresponding surface point cloud data after creep aging under different process conditions and different discrete feature points of components; Step 8: Use the neural network to construct the mapping relationship between process conditions, discrete feature points of the component and the component forming surface; A neural network model is constructed with the process conditions and the initial positions of the discrete feature points of the component as the output layer, and the positions of the discrete feature points of the component after forming as the input layer. The desired surface point cloud position is directly input, and the corresponding creep aging process parameters are output. In the above steps, steps 1 to 3 are performed in parallel with steps 4 to 6, or steps 1 to 3 are performed followed by steps 4 to 6, or steps 4 to 6 are performed first and then steps 1 to 3; the execution of step 7 is premised on the completion of steps 1 to 3 and steps 4 to 6.

8. A method for predicting the creep aging forming surface of a reinforced component, characterized in that: The prediction method adopts the creep aging process parameters of the component obtained by the process method described in claim 7 above, and then corrects the neural network structure constructed in step 8 according to the actual creep aging forming results of the representative component, and then feeds the obtained correction results back into the neural network model established in step 8 to obtain the forming surface of the target component.

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