Design method for bolt connection combined type piston skirt top contact special-shaped profile

Abstract

The invention discloses a bolt connection combined piston skirt top contact special-shaped profile design method. The method comprises the steps that firstly, a finite element simulation model of a piston is established, and radial and circumferential surface deformation of skirt top contact is obtained; fitting is carried out on the surface deformation, and radial and circumferential initial molded lines are obtained respectively; then selecting an optimized target parameter and sampling to obtain a molded line sample; simulating a finite element simulation model corresponding to the molded line sample to train a machine learning agent model; after verification, obtaining a trained machine learning agent model of which the precision meets the requirement; iterative solution is carried out on the basis of the model to obtain an optimal target parameter; and finally, obtaining an optimal molded line according to the optimal target parameters, and performing linear superposition to form a three-dimensional curved surface as an optimal molded surface. By means of a data driving method of machine learning, the artificial intelligence algorithm is innovatively applied to the design of the skirt top contact special-shaped profile of the bolt connection combined type piston.

Description

Technical Field

[0001] The invention relates to the technical field of pistons, in particular to a method for designing a bolt-connected combined piston skirt top contact profile surface. Background Art

[0002] As the engine strengthening index continues to improve, the power density and explosion pressure of the engine are also getting higher and higher, and the piston faces higher thermal and mechanical loads. The current piston structure skirt contact methods on the market include welding, bolt connection and skirt casting. Due to process limitations and considering the defects of welding and casting, there are still many styles of pistons that still use skirt bolt connection. However, with the high-load performance requirements of the engine, after the bolts are tightened, large deformation and uneven contact will generally occur near the tightening parts. On this basis, under the action of explosion pressure in the actual working process, the inconsistent radial deformation inside and outside will further cause the skirt contact surface to be subjected to more uneven force.

[0003] At present, the main design scheme for the skirt top contact surface is an empirically based linear slope contact surface structure. Although the contact characteristics of the skirt top can be improved to a certain extent, since different contact pressure distributions of pistons with different structures are not taken into account, the empirical design method will inevitably lead to failure of the skirt top contact structure. For example, the document with application number 201911321834.1 discloses a method and device for determining the pin hole profile. According to the radial deformation of each preset sampling point of the pin hole profile, the pin hole profile is optimized to obtain the target pin hole profile. According to the target pin hole profile, the pin hole structure of the piston structure model is adjusted to obtain the target piston structure model, and the target piston structure model is subjected to thermomechanical coupling calculation to obtain the distribution results of physical quantities such as contact pressure and stress of the pin hole structure. When the physical quantity distribution results meet the preset distribution conditions, the final target pin hole profile is determined. The document with application number 202311383310.1 discloses a piston pin hole profile design optimization method, device, electronic device and storage medium, wherein the design optimization method includes: obtaining the cross-sectional radial deformation of the piston pin under preset working conditions; based on the cross-sectional radial deformation, fitting the piston pin surface deformation curve, and using the piston pin surface deformation curve as the piston pin hole axial profile; constructing a piston pin hole simulation analysis model, and the piston pin hole profile in the piston pin hole simulation analysis model is obtained based on the piston pin hole axial profile and the preset ellipticity mapping of the piston pin hole section; performing simulation calculations to obtain the physical quantity distribution results of the piston pin hole; and judging whether it meets the preset distribution requirements.

[0004] Considering the design cost and design effect, the above method inevitably has the following technical defects: (1) Long design cycle: The design methods in the above literature use finite element modeling simulation or extraction of corresponding deformation through experiments in the early stage. Considering factors such as modeling and processing, a single round of modeling optimization takes several days, and the correlation between the preliminary line of the model and the actual load of the engine is not strong. Therefore, the first round of analysis often cannot complete the optimization, and multiple rounds of optimization are required. Therefore, considering the trial and error time cost of empirical design, the time will be longer. At present, the design of automotive engines is fast-paced, so it is often difficult to implement this method. In addition, the preliminary line of this scheme has a large range and is not accurately related to the engine load. The preliminary design line is quite different from the actual one, resulting in a large number of optimization times and further lengthening the design cycle.

[0005] (2) High test cost: The design method in the above literature collects the deformation of the contact part through experiments, which involves component-level testing of the piston, and extracts the deformation value based on the component-level test, and pre-processes the data value. This process involves not only the design of test tooling, the construction of the hydraulic test system, the formulation of the test plan, and the purchase of corresponding supporting facilities, but even in the OEM test with hydraulic test conditions, this process for one of the pistons will cost hundreds of thousands. This is costly for the fast-paced design requirements of the engine, so there is an urgent need for a low-cost and accurate design method.

[0006] (3) It is difficult to guarantee the design effect: In the design methods of the above literature, the contact deformation between the piston pin hole and the piston pin is extracted by experimental means, which often leads to errors in the radial deformation data of the pin hole at the sampling point. In addition, the thermomechanical coupling model does not propose effective methods and measures for the accuracy of the contact modeling between the pin hole and the piston pin. The pin hole profile first proposed by this method lacks smoothness. Due to the problem of the accuracy of the contact modeling between the pin hole and the piston pin, the physical quantity results of the pin hole structure calculated in the subsequent calculation are also prone to abnormalities, which brings difficulties to the optimization evaluation. In addition, the radial deformation of the pin hole calculated by the thermomechanical coupling is limited, and the design cannot be completed in one go, resulting in more subsequent optimization rounds and a longer cycle. It is difficult to adapt to the current fast-paced design and is difficult to apply in practice. Summary of the invention

[0007] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a method for designing a bolt-connected combined piston skirt top contact profile surface.

[0008] The technical solution of the present invention to solve the technical problem is to provide a method for designing a bolt-connected combined piston skirt top contact profile surface, characterized in that the method comprises the following steps: Step 1, establishing a finite element simulation model of the piston; Step 2: Under the preset simulation conditions, the finite element simulation model of step 1 is subjected to thermal-mechanical coupling simulation to obtain the radial and circumferential surface deformations of the piston skirt top contact; Step 3, fitting the radial and circumferential surface deformations of the piston skirt top contact in step 2 to obtain the radial and circumferential initial profiles of the skirt top contact respectively; wherein the fitting function that is most similar to the circumferential surface deformation state of the skirt top contact is used to characterize the circumferential initial profile of the skirt top contact; Step 4: In the radial direction of the skirt top contact, the position and the indentation of the key deformation point are selected from the initial radial profile of the skirt top contact obtained in step 3 as the target parameters for optimization; in the circumferential direction of the skirt top contact, the function eigenvalue of the fitting function of the initial circumferential profile obtained in step 3 is selected as the target parameter for optimization; Step 5: Sample the radial and circumferential target parameters of the skirt top contact according to the sampling sample interval to obtain n 1 Group line samples; Step 6: According to n in step 5 1 The finite element simulation model of step 1 is adjusted to obtain the same scale n 1 The finite element simulation models of different profiles are grouped together; the finite element simulation is then performed on each finite element simulation model to obtain the simulation results as the output value of the training machine learning proxy model, and the corresponding profile samples are used as the input value of the training machine learning proxy model to obtain the trained machine learning proxy model; the simulation results include contact pressure and stress; Step 7: Repeat steps 5 to 6 to extract n 2 The mold line samples are assembled, and then the finite element simulation is performed according to step 6. The obtained simulation results and the corresponding mold line samples are used as the verification set of the trained machine learning proxy model obtained in step 6, and the corresponding accuracy verification indicators are selected to verify the accuracy of the trained machine learning proxy model; if the accuracy does not meet the requirements, the sampling method of step 5 is adjusted, and the machine learning proxy model is retrained until the accuracy meets the requirements, and the trained machine learning proxy model with the accuracy meeting the requirements is saved to replace the finite element simulation process; Step 8: Based on the machine learning agent model obtained in step 7, the contact pressure and stress of the skirt top contact are used as optimization indicators, the position and indentation of the key deformation points selected in the radial profile of the skirt top contact, and the function eigenvalue of the circumferential fitting function of the skirt top contact are used as optimization target parameters, and the optimization indicators are continuously iterated and solved through a multi-objective optimization algorithm to obtain the best target parameters; Step 9: refit according to the optimal target parameters obtained in step 8 to obtain the optimal radial and circumferential profiles of the skirt top contact, and then linearly superimpose the radial and circumferential optimal profiles to form a three-dimensional surface as the optimal profile at the contact surface of the piston top or the piston skirt.

[0009] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention innovatively applies artificial intelligence algorithms to the design of the contact profile of the bolted combined piston skirt top by using a data-driven method of machine learning. In view of the blindness and inefficiency of the previous design of the piston skirt top contact profile, the present invention uses a small sample simulation to train a high-precision proxy model in the early stage of design, and optimizes the skirt top profile scheme through a multi-objective optimization algorithm. This design method can realize the skirt top contact profile design of any bolted combined piston under the premise of known contact materials, and obtain the optimal profile scheme more efficiently.

[0010] (2) In the radial direction of the skirt top contact surface, the present invention preferably proposes a three-segment skirt top contact radial profile scheme, that is, the skirt top surface of the piston is composed of a curved segment-a straight segment-a curved segment. This can significantly improve the stress concentration phenomenon at both ends of the contact surface of the piston skirt top.

[0011] (3) In the circumferential direction of the skirt top contact surface, the present invention preferably proposes to obtain the profile scheme of the skirt top circumferential scheme by using a function fitting method. The present invention preferably recommends using a trigonometric function to fit the circumferential profile line. This design concept can reduce the excessive mutual extrusion of the skirt top contact surface caused by the bolt preload, and can improve the service life and reliability of the piston.

[0012] (4) The profile design method proposed in the present invention does not involve any experiments, thus avoiding the time cost and expense of experimental design, procurement, experimental testing, and data processing related processes. Instead, the method greatly improves the design efficiency and design effect while ensuring the design accuracy through a machine learning algorithm. It can improve the design efficiency, shorten the design cycle while ensuring the design effect, reduce the design cost, and quickly obtain the skirt top contact profile design scheme according to different piston structures.

[0013] (5) The initial profile of the present invention is obtained by fitting the deformation of the piston skirt top contact surface based on the finite element simulation of the piston. The radial and circumferential surface deformation of the skirt top contact is obtained by loading the piston assembly with a preset simulation condition. The radial and circumferential surface deformation obtained by this method is closer to the indentation of the actual skirt top contact profile. Therefore, the problem of excessive reliance on design experience and database for the current piston skirt top contact profile design is solved. The profile deformation is corrected by function fitting, which not only achieves smoothness of the profile, but also improves the design accuracy and is more convenient for profile processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is the overall flow chart of the present invention; Figure 2 It is a structural schematic diagram of a bolt-connected combined piston of the present invention; Figure 3It is a schematic diagram of a radial three-segment profile of the piston skirt top contact profile of the present invention; Figure 4 It is a schematic diagram of the circumferential fitting function profile of the piston skirt top contact profile of the present invention.

[0015] In the figure, there are piston top 1, piston skirt 2, positioning pin hole 3, and bolt hole 4. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention are given below. The specific embodiments are only used to further illustrate the present invention in detail and do not limit the protection scope of the present invention.

[0017] The present invention provides a method for designing a bolt-connected assembled piston skirt top contact profile surface (hereinafter referred to as the method), characterized in that the method comprises the following steps: Step 1, establishing a finite element simulation model of the combined piston; Preferably, in step 1, the finite element simulation model includes a piston body, a piston pin, a cylinder liner and a connecting rod, which more accurately simulates the actual working condition of the piston.

[0018] Preferably, in step 1, during the modeling process, the skirt top contact (i.e., the contact between the piston top 1 and the piston skirt 2) is established in a point-to-point manner, and the subsequent contact surface is obtained by mapping, and the contact surface nodes are copied one by one, which can reduce the contact pressure and stress concentration and stress singular points caused by the mismatch of nodes during the simulation process, thereby ensuring the accuracy of finite element simulation.

[0019] Step 2: Under the preset simulation conditions, the finite element simulation model of step 1 is subjected to a thermal-mechanical coupling simulation (i.e., a thermal stress and mechanical stress coupling simulation) to obtain the radial and circumferential surface deformations of the piston skirt top contact; Preferably, in step 2, on the load boundary, the preset simulation working condition takes into account the thermal load and mechanical load borne by the piston during actual operation; the thermal load is the thermal load generated under the steady-state temperature field of the piston, and the mechanical load is the multi-source mechanical load borne by the piston under the maximum explosion pressure, which is the most dangerous working condition of the piston; On the constraint boundary, the preset simulation condition fully constrains the piston-cylinder sleeve part, and constrains the connecting rod body direction and the vertical connecting rod swing direction of the piston connecting rod big end part.

[0020] Among them, for the steady-state temperature field of the piston, if there is no actually measured temperature in the piston test, the steady-state temperature field of the piston is set according to the maximum temperature of the piston temperature design limit; if there is an actually measured temperature in the piston test, the final steady-state temperature field is benchmarked with the actually measured temperature in the test, so that the error value between the steady-state temperature field obtained by simulation and the actually measured temperature in the test is controlled within 10%.

[0021] Among them, the multi-source mechanical loads include bolt preload, gas pressure generated under maximum explosion pressure, inertial force and lateral force given by the cylinder liner.

[0022] Step 3, fitting the radial and circumferential surface deformations of the piston skirt top contact in step 2 to achieve the purpose of smoothing the profile, improve the design accuracy, and obtain the radial and circumferential initial profiles of the skirt top contact respectively, solving the problem that the current initial profile design of the piston skirt top contact is overly dependent on design experience and database; wherein, a fitting function that is most similar to the circumferential surface deformation state of the skirt top contact is used to characterize the circumferential initial profile of the skirt top contact; Preferably, in step 3, the initial radial profile of the skirt top contact is a three-segment profile. Figure 3 As shown, a structural form of a straight line segment in the middle and curved segments on both sides (i.e., a profile formed by connecting the first curve, the straight line segment, and the second curve end to end) is adopted, which does not affect the load-bearing effect while reducing the stress concentration phenomenon at the radial contact parts at both ends.

[0023] Preferably, in step 3, in this embodiment, Figure 4 As shown, point G is the bolt hole 4, point H is the locating pin hole 3, point I is the bolt hole 4, and point J is the locating pin hole 3. The bolt hole 4 and the locating pin hole 3 are spaced apart and 90° apart in the circumferential direction. The circumferential surface deformation of the piston skirt top contact is fitted to obtain a fitting function, in which the contact parts around the locating pin hole 3 and the bolt hole 4 adopt trigonometric functions or high-order power functions.

[0024] Step 4: In the radial direction of the skirt top contact, the position and the indentation of the key deformation point are selected from the initial radial profile of the skirt top contact obtained in step 3 as the target parameters for optimization; in the circumferential direction of the skirt top contact, the function eigenvalue of the fitting function of the initial circumferential profile obtained in step 3 is selected as the target parameter for optimization; Preferably, in step 4, the key deformation points of the radial initial profile contacted by the skirt top are the end points on both sides of the profile and the middle inflection point.

[0025] Preferably, in step 4, in this embodiment, in the radial direction, taking a three-segment profile as an example, Figure 3 As shown in the figure, select points B, C, D and E as key deformation points, and indent point B by H. B 、E point indentation H E 、Radial position R of point C C The radial position R of point D D as the target parameter for optimization.

[0026] Preferably, in step 4, in this embodiment, in the circumferential direction, as Figure 4As shown, considering the magnitude of the deformation around the positioning pin hole 3 and the bolt hole 4, the peaks and wavelengths of the trigonometric functions at the four locations G, H, I, and J are respectively used as target parameters for optimization.

[0027] Step 5: Sample the radial and circumferential target parameters of the skirt top contact according to the sampling sample interval to obtain n 1 Group line samples; Preferably, in step 5, the sampling sample interval is displacement amount × 50% to displacement amount × 150%.

[0028] Preferably, in step 5, Latin hypercube sampling is used for sampling; the Latin hypercube sampling method can make every small interval in the interval be sampled, and is the preferred sampling method for training machine learning proxy models. Due to the uniformity of sampling, the problem of insufficient fitting of the trained model can be reduced.

[0029] Preferably, in step 5, in order to balance the computation time and model accuracy, n 1 =30.

[0030] Step 6: According to n in step 5 1 The finite element simulation model of step 1 is adjusted to obtain the same scale n 1 The finite element simulation models of different profiles are grouped together; the finite element simulation is then performed on each finite element simulation model to obtain the simulation results as the output value of the training machine learning proxy model, and the corresponding profile samples are used as the input value of the training machine learning proxy model to obtain the trained machine learning proxy model; the simulation results include contact pressure and stress; Preferably, in step 6, the adjustment method includes a grid mapping method, a method for adjusting the contact gap, and a method for adjusting the coordinates of the grid nodes on the surface of the contact part in the finite element simulation model.

[0031] Among them, the mesh mapping method is to establish a corresponding reference fitting surface during the mesh pre-processing process, and project the nodes on the contact surface into the established plane to achieve the adjustment of the finite element simulation model.

[0032] The method for adjusting the contact gap is to set different initial contact gaps for different nodes, thereby achieving the adjustment of the finite element simulation model.

[0033] Among them, the method for adjusting the surface grid node coordinates of the contact part in the finite element simulation model is to recalculate the position coordinates of the nodes on the surface where the contact surface is located, and redefine the node positions in the finite element simulation model, thereby realizing the adjustment of the finite element simulation model.

[0034] Preferably, in step 6, the contact pressure is the maximum contact pressure of the piston skirt top contact surface, and the stress includes the maximum Mises stress of the piston skirt top contact surface, the maximum Mises stress at the locating pin hole 3 and the maximum Mises stress at the bolt hole 4.

[0035] Preferably, in step 6, the machine learning agent model is a response surface model, a Kriging model or a neural network model. In view of the possible nonlinearity between the profile parameters and the corresponding simulation results, the Kriging model and the neural network model are preferred, which have a better fitting effect on nonlinear sample data and can obtain more accurate calculation results.

[0036] Step 7: Repeat steps 5 to 6 to extract n 2 The sample of the type line is assembled, and then the finite element simulation is performed according to step 6. The obtained simulation results and the corresponding type line samples are used as the verification set of the trained machine learning proxy model obtained in step 6, and the corresponding accuracy verification index is selected to verify the accuracy of the trained machine learning proxy model; if the accuracy does not meet the requirements (that is, the error exceeds the preset tolerance range), the sampling method of step 5 is adjusted, and the machine learning proxy model is retrained until the accuracy meets the requirements, and the trained machine learning proxy model with the accuracy meeting the requirements is saved to replace the finite element simulation process; Preferably, in step 7, in order to ensure that the validation set is representative while minimizing the computational cost, n 2 = 10. The main function of the validation set is to test the performance of the machine learning agent model on unseen data to evaluate its generalization ability and accuracy. The validation set should be independent of the training set to avoid overfitting the model to specific data.

[0037] Preferably, in step 7, the accuracy verification index adopts one of the determination coefficient, mean square error, root mean square error and mean absolute error index, preferably the determination coefficient R 2 As an indicator of the accuracy of the predicted value, R 2 It reflects the proportion of all changes in the dependent variable explained by the independent variable through the regression relationship, that is, using the average value of the data as a benchmark, observing the degree of deviation of the prediction error from the average reference error.

[0038] Preferably, in step 7, adjusting the sampling method of step 5 includes increasing the number of profile samples and / or reducing the sampling sample interval. Preferably, the number of profile samples is increased to 1.3 to 2 times the number of profile samples in step 5, and in this embodiment, it is increased from 30 groups to 50 groups, so as to improve the diversity and representativeness of the training data; the sampling sample interval is reduced to 0.5 to 0.8 times the sampling sample interval in step 5, and in this embodiment, it is reduced from displacement × 50% to displacement × 150% to displacement × 70% to displacement × 130%, so as to capture more possible variations.

[0039] Step 8: Based on the machine learning agent model obtained in step 7, the contact pressure and stress of the skirt top contact are used as optimization indicators, the position and indentation of the key deformation points selected in the radial profile of the skirt top contact, and the function eigenvalues ​​of the fitting function of the skirt top contact circumference are used as optimization target parameters, and the optimization indicators are continuously iterated and solved through a multi-objective optimization algorithm until the set constraint value or the maximum number of iterations is reached, and the optimal target parameters are obtained; Preferably, in step 8, the multi-objective optimization algorithm adopts an improved particle swarm algorithm or a genetic algorithm.

[0040] The improved particle swarm algorithm is an optimization algorithm based on swarm intelligence. It simulates the behavior of bird flocks foraging and finds the optimal solution by sharing information between individuals in the group. In the improved particle swarm algorithm, a combination of local optimality and global optimality is adopted, and a hybrid update strategy is designed to enhance the local search capability while maintaining the global search capability, which can reduce the situation of falling into the local optimal solution. The improved particle swarm algorithm is simple to implement, has fewer parameters, is suitable for continuous problems, and has a fast convergence speed. However, it is easy to fall into the local optimality, and the search capability is weak for complex multi-peak problems.

[0041] Genetic algorithm is an optimization algorithm based on natural selection and genetic mechanism. It searches for the optimal solution by simulating the biological evolution process. The elite retention strategy is to directly retain the best individuals in each generation to the next generation to avoid losing the optimal solution during the crossover and mutation process. This ensures the continuous existence of the optimal solution and improves the stability and convergence speed of the algorithm. In contrast, genetic algorithms are suitable for discrete and continuous problems, have strong global search capabilities, and maintain population diversity through crossover and mutation, but have more parameters, are more complex to adjust, and converge slower than particle swarm algorithms.

[0042] Preferably, in step 8, the contact pressure and stress of the skirt top contact are specifically: the optimization indicators are the maximum contact pressure of the piston skirt top contact surface, the maximum Mises stress of the piston skirt top contact surface, the maximum Mises stress at the locating pin hole 3 and the maximum Mises stress at the bolt hole 4.

[0043] Step 9: refit according to the optimal target parameters obtained in step 8 to obtain the optimal radial and circumferential profiles of the skirt top contact, respectively, and then linearly superimpose the radial and circumferential optimal profiles to form a three-dimensional curved surface as the optimal profile at the contact surface of the piston top 1 or the piston skirt 2 (preferably the piston skirt 2).

[0044] Preferably, the method also includes: step 10, result verification: the optimal profile obtained in step 9 is adjusted by the adjustment method of step 6, and loaded into the finite element simulation model of step 1 to perform simulation calculations under preset simulation conditions (i.e., perform simulation verification) to obtain simulation results of contact pressure and stress; then the simulation results are compared with the simulation results of step 6 to evaluate the optimization effect.

[0045] Preferably, in step 10, the comparison includes contact pressure and stress. In this embodiment, in the simulation results, the contact pressure distribution is: the middle of the skirt top contact surface is the main contact pressure bearing area, and the distribution is uniform, and the contact pressure peak value is significantly improved compared with the structure of the non-optimized profile solution, confirming that the skirt top contact profile has high reliability; the stress distribution is: the Mises stress value of the structure at the piston skirt top contact surface does not reach or is far below the yield limit of the piston material.

[0046] Any matters not described in the present invention are applicable to the prior art.

Claims

1. A method for designing a bolt-connected combined piston skirt top contact profile surface, characterized in that: The method comprises the following steps: Step 1, establishing a finite element simulation model of the piston; Step 2: Under the preset simulation conditions, the finite element simulation model of step 1 is subjected to thermal-mechanical coupling simulation to obtain the radial and circumferential surface deformations of the piston skirt top contact; Step 3, fitting the radial and circumferential surface deformations of the piston skirt top contact in step 2 to obtain the radial and circumferential initial profiles of the skirt top contact respectively; wherein the fitting function that is most similar to the circumferential surface deformation state of the skirt top contact is used to characterize the circumferential initial profile of the skirt top contact; Step 4: In the radial direction of the skirt top contact, the position and the indentation of the key deformation point are selected from the initial radial profile of the skirt top contact obtained in step 3 as the target parameters for optimization; in the circumferential direction of the skirt top contact, the function eigenvalue of the fitting function of the initial circumferential profile obtained in step 3 is selected as the target parameter for optimization; Step 5, sampling the radial and circumferential target parameters of the skirt top contact according to the sampling sample interval to obtain n1 groups of profile samples; Step 6: According to the n1 group of profile samples in step 5, the finite element simulation model in step 1 is adjusted to obtain finite element simulation models of n1 groups of different profiles of the same scale; then, finite element simulation is performed on each finite element simulation model to obtain simulation results as output values ​​for training the machine learning proxy model, and the corresponding profile samples are used as input values ​​for training the machine learning proxy model to obtain the trained machine learning proxy model; the simulation results include contact pressure and stress; Step 7, repeat steps 5 and 6, extract n2 groups of profile samples, and then perform finite element simulation according to step 6, use the obtained simulation results and their corresponding profile samples as the verification set of the trained machine learning proxy model obtained in step 6, and select the corresponding accuracy verification index to verify the accuracy of the trained machine learning proxy model; if the accuracy does not meet the requirements, adjust the sampling method of step 5, retrain the machine learning proxy model until the accuracy meets the requirements, and save the trained machine learning proxy model with the accuracy meeting the requirements to replace the finite element simulation process; Step 8: Based on the machine learning agent model obtained in step 7, the contact pressure and stress of the skirt top contact are used as optimization indicators, the position and indentation of the key deformation points selected in the radial profile of the skirt top contact, and the function eigenvalue of the circumferential fitting function of the skirt top contact are used as optimization target parameters, and the optimization indicators are continuously iterated and solved through a multi-objective optimization algorithm to obtain the best target parameters; Step 9: refit according to the optimal target parameters obtained in step 8 to obtain the optimal radial and circumferential profiles of the skirt top contact, and then linearly superimpose the radial and circumferential optimal profiles to form a three-dimensional surface as the optimal profile at the contact surface of the piston top or the piston skirt.

2. The method for designing the contact profile of the bolted combined piston skirt top according to claim 1, characterized in that: In step 1, the finite element simulation model includes a piston body, a piston pin, a cylinder liner and a connecting rod; During the modeling process, the contact relationship of the skirt top is established in a point-to-point manner, and the subsequent contact surface is obtained through mapping, and the contact surface nodes are copied one by one.

3. The method for designing the contact profile of the bolted combined piston skirt top according to claim 1, characterized in that: In step 2, on the load boundary, the preset simulation working condition takes into account the thermal load and mechanical load that the piston bears during the actual operation; the thermal load is the thermal load generated under the steady-state temperature field of the piston, and the mechanical load is the multi-source mechanical load that the piston bears under the maximum explosion pressure, which is the most dangerous working condition of the piston; On the constraint boundary, the preset simulation condition fully constrains the piston-cylinder sleeve part, and constrains the connecting rod body direction and the vertical connecting rod swing direction of the piston connecting rod big end part.

4. The method for designing the contact profile of the bolted combined piston skirt top according to claim 3 is characterized in that: In step 2, for the steady-state temperature field of the piston, if there is no actual measured temperature of the piston test, the steady-state temperature field of the piston is set according to the maximum temperature of the piston temperature design limit; if there is an actual measured temperature of the piston test, the final steady-state temperature field is compared with the actual measured temperature of the test, so that the error value between the steady-state temperature field obtained by simulation and the actual measured temperature of the test is controlled within 10%; The multi-source mechanical loads include bolt preload, gas pressure generated at maximum explosion pressure, inertia force and lateral force given by the cylinder liner.

5. The method for designing the contact profile of the bolted combined piston skirt top according to claim 1, characterized in that: In step 4, the initial radial profile of the skirt top contact is a three-segment profile, which is a structural form of a straight line segment in the middle and curved segments on both sides, and its key deformation points are the endpoints on both sides of the profile and the inflection point in the middle.

6. The method for designing the contact profile of the bolted combined piston skirt top according to claim 1, characterized in that: In step 5, the sampling interval is displacement × 50% to displacement × 150%; The sampling method used is Latin hypercube sampling.

7. The method for designing the contact profile of the bolted combined piston skirt top according to claim 1, characterized in that: In step 6, the adjustment method includes a grid mapping method, a method for adjusting the contact gap, and a method for adjusting the coordinates of the grid nodes on the surface of the contact part in the finite element simulation model; The machine learning agent model is a response surface model, a Kriging model, or a neural network model; The contact pressure is the maximum contact pressure of the piston skirt top contact surface, and the stress includes the maximum Mises stress of the piston skirt top contact surface, the maximum Mises stress at the locating pin hole, and the maximum Mises stress at the bolt hole.

8. The method for designing the contact profile of the bolted combined piston skirt top according to claim 1, characterized in that: In step 7, the accuracy verification index adopts one of the determination coefficient, mean square error, root mean square error and mean absolute error index; Adjusting the sampling method of step 5 includes increasing the number of mold line samples and / or reducing the sampling sample interval; wherein, increasing the number of mold line samples is 1.3 to 2 times the number of mold line samples in step 5; reducing the sampling sample interval is 0.5 to 0.8 times the sampling sample interval in step 5.

9. The method for designing a bolt-connected assembled piston skirt top contact profile according to claim 1, characterized in that: In step 8, the multi-objective optimization algorithm adopts an improved particle swarm algorithm or a genetic algorithm.

10. The method for designing the contact profile of the bolted assembled piston skirt top according to claim 1, characterized in that: The method also includes: step 10, result verification: the optimal profile obtained in step 9 is adjusted by the adjustment method of step 6, and then loaded into the finite element simulation model of step 1 to perform simulation calculations under preset simulation conditions to obtain simulation results of contact pressure and stress; then the simulation results are compared with the simulation results of step 6 to evaluate the optimization effect.

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