Plastic Mechanical Property Prediction Method and System Combined with Finite Element Simulation

Abstract

The present invention discloses a method and system for predicting the mechanical properties of plastics in combination with finite element simulation, which relates to the technical field of data processing. The method includes: obtaining a sample aging data set of the target plastic, including sample environmental data and corresponding sample mechanical property data; based on the sample environmental data, performing segmentation clustering on the sample aging data set and establishing an intrinsic aging characteristic equation of the target plastic; interacting with the target application scenario of the target plastic fitting, obtaining the scenario environmental characteristics, performing scenario configuration on the intrinsic aging characteristic equation, and obtaining a scenario-based aging characteristic equation; according to the scenario-based aging characteristic equation, configuring a mechanical property data sequence of the target plastic, performing finite element simulation analysis on the target plastic fitting, obtaining a mechanical property change curve of the target plastic fitting, and outputting it as the predicted mechanical property. Furthermore, the technical effects of improving the prediction accuracy and reliability and reducing the prediction cost are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for predicting the mechanical properties of plastics combined with finite element simulation. Background Art

[0002] Plastic materials are widely used in various fields. In practical applications, the mechanical properties of plastic materials and their products are affected by environmental factors (such as temperature, humidity, ultraviolet rays, etc.) and age, resulting in a decline in mechanical properties, which in turn affects the safety and reliability of products. Existing methods for testing the mechanical properties of plastics mostly rely on experimental testing or single finite element simulation analysis, and there are technical problems such as low prediction accuracy and reliability and high prediction costs. Summary of the Invention

[0003] The present invention provides a method and system for predicting the mechanical properties of plastics combined with finite element simulation to solve the technical problems of low prediction accuracy and reliability and high prediction costs in the prior art, and achieve the technical effects of improving prediction accuracy and reliability and reducing prediction costs.

[0004] In a first aspect, the present invention provides a method for predicting the mechanical properties of plastics combined with finite element simulation, wherein the method includes:

[0005] Obtain a sample aging data set of the target plastic, wherein the sample aging data set includes sample environmental data and corresponding sample mechanical property data.

[0006] Based on the sample environmental data, perform segmentation clustering of the sample aging data set to obtain a grouped aging sample data set, and establish an intrinsic aging characteristic equation of the target plastic according to the grouped aging sample data set.

[0007] Interact with the target application scenario of the target plastic fitting to obtain scenario environmental characteristics, and perform scenario configuration on the intrinsic aging characteristic equation according to the scenario environmental characteristics to obtain a scenario-based aging characteristic equation.

[0008] According to the scenario-based aging characteristic equation, configure a mechanical property data sequence of the target plastic, perform finite element simulation analysis on the target plastic fitting, obtain a mechanical property change curve of the target plastic fitting, and output it as the predicted mechanical property.

[0009] In a second aspect, the present invention further provides a system for predicting the mechanical properties of plastics combined with finite element simulation, wherein the system includes:

[0010] A sample data acquisition module, which is used to obtain a sample aging data set of the target plastic, wherein the sample aging data set includes sample environmental data and corresponding sample mechanical property data.

[0011] Characteristic equation construction module, which is used to perform segmentation clustering on the sample aging data set based on the sample environment data, obtain a grouped aging sample data set, and establish an intrinsic aging characteristic equation of the target plastic according to the grouped aging sample data set.

[0012] Scene configuration module, which is used to interact with the target application scenario of the target plastic fitting, obtain scene environment characteristics, and perform scene configuration on the intrinsic aging characteristic equation according to the scene environment characteristics to obtain a scene-based aging characteristic equation.

[0013] Mechanical property prediction module, which is used to configure the mechanical property data sequence of the target plastic according to the scene-based aging characteristic equation, perform finite element simulation analysis on the target plastic fitting, obtain the mechanical property change curve of the target plastic fitting, and output it as the predicted mechanical property.

[0014] The present invention discloses a method and system for predicting the mechanical properties of plastics combined with finite element simulation, including: obtaining a sample aging data set of the target plastic containing sample environment data and corresponding mechanical property data; based on the sample environment data, performing segmentation clustering on the sample aging data set to obtain a grouped aging sample data set, and using this data set to establish an intrinsic aging characteristic equation of the target plastic; in the application scenario of the target plastic fitting, obtaining scene environment characteristics, and performing scene configuration on the intrinsic aging characteristic equation according to these characteristics to obtain a scene-based aging characteristic equation; according to the scene-based aging characteristic equation, configuring the mechanical property data sequence of the target plastic, performing finite element simulation analysis, obtaining the mechanical property change curve of the target plastic fitting, and finally outputting the predicted mechanical property. The method and system for predicting the mechanical properties of plastics combined with finite element simulation disclosed by the present invention solve the technical problems of low prediction accuracy and reliability and high prediction cost, and achieve the technical effects of improving prediction accuracy and reliability and reducing prediction cost. Description of the Drawings

[0015] Figure 1 It is a schematic flow chart of the method for predicting the mechanical properties of plastics combined with finite element simulation according to the present invention;

[0016] Figure 2 It is a schematic structural diagram of the system for predicting the mechanical properties of plastics combined with finite element simulation according to the present invention.

[0017] Description of the reference numerals: Sample data acquisition module 11, Characteristic equation construction module 12, Scene configuration module 13, Mechanical property prediction module 14. Detailed Embodiments

[0018] In the embodiments of the present invention, the overall idea adopted to solve the technical problems of low prediction accuracy and reliability and high prediction cost existing in the prior art is as follows:

[0019] First, a task receiving module is used to receive the heating task of the heating wire module. The heating wire module includes a plurality of heating wires, and the heating task includes a heating target load and the corresponding load heating demand. Then, a load heating expectation parsing module parses the heating target load according to the load heating demand to determine a multi-level load temperature expectation curve. Next, a temperature control decision module makes a temperature control decision on the heating wire module according to the multi-level load temperature expectation curve and in combination with the constraint conditions of heating loss, and generates a multi-level temperature control strategy. Then, a temperature control monitoring module controls the heating wire module according to the multi-level temperature control strategy to heat the heating target load, and obtains the load temperature data and the monitoring data of the heating wire in real time. Furthermore, a monitoring data analysis module analyzes and performs a temperature control compensation operation on the heating wire module according to the real-time temperature data of the load and the monitoring data of the heating wire to ensure the accuracy of temperature control.

[0020] The above technical solution will be described in detail below in combination with the specification drawings and specific embodiments to better understand the above technical solution. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments for explaining the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention. In addition, it should be noted that for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0021] Embodiment 1

[0022] Figure 1 It is a schematic flow chart of a method for predicting the mechanical properties of plastics in combination with finite element simulation according to the present invention. The method includes:

[0023] Obtain a sample aging data set of the target plastic, where the sample aging data set includes sample environment data and corresponding sample mechanical property data.

[0024] Specifically, first, obtain a sample aging data set of the target plastic, which includes sample environment data and corresponding sample mechanical property data, and is used to study the response relationship between the aging behavior of plastic materials and environmental factors. Among them, the sample aging data set of the target plastic is experimental data obtained based on mechanical experiments.

[0025] Optionally, the sample mechanical property data includes: tensile properties (such as tensile strength, tensile modulus, elongation at break, etc.), flexural properties (such as flexural strength, flexural modulus, etc.), impact properties (such as impact strength), hardness, fatigue properties (such as fatigue life, fatigue strength, etc.)

[0026] Optionally, the sample environmental data includes environmental temperature, environmental humidity, environmental light (such as ultraviolet intensity, light time), chemical conditions (such as redox potential, acid-base corrosion conditions, etc.).

[0027] Based on the sample environmental data, perform segmentation clustering on the sample aging data set to obtain a grouped aging sample data set, and establish an intrinsic aging characteristic equation of the target plastic according to the grouped aging sample data set.

[0028] In some embodiments, based on the sample environmental data, performing segmentation clustering on the sample aging data set to obtain a grouped aging sample data set includes:

[0029] Taking environmental temperature, environmental light, and environmental oxidation intensity as coordinate dimensions respectively, constructing a sample environmental space, mapping the sample environmental data to the sample environmental space to obtain a sample environmental distribution; mapping standard environmental data to the sample environmental space as a segmentation origin, calculating the distances from multiple sample points in the sample environmental distribution to the segmentation origin to obtain a segmentation distance set; serializing the segmentation distance set, extracting sample points from small to large according to the serialization result, when the number of extracted sample points meets the segmentation quantity limit, storing the corresponding sample environmental data and sample mechanical property data as an aging sample data group, and iteratively extracting sample points to obtain multiple such aging sample data groups, and outputting as the grouped aging sample data set.

[0030] Specifically, first, select environmental temperature, environmental light, and environmental oxidation intensity as three coordinate dimensions, construct an orthogonal three-dimensional space as the sample environmental space. Then, map the collected sample environmental data into the above three-dimensional space to form a sample environmental distribution. Each sample has a corresponding spatial point in the three-dimensional space, and the coordinates of the spatial point represent its environmental characteristics. Among them, environmental temperature, environmental light (such as ultraviolet intensity), and environmental oxidation intensity (such as redox potential) are the main influencing factors on the mechanical properties of plastics. Constructing the sample environmental space with the above three-dimensional environmental characteristics can better understand and analyze the influence of different environmental factors on the sample performance and form an intuitive sample environmental distribution map.

[0031] Specifically, next, map the standard environmental data into the sample environmental space as the segmentation origin. Then, calculate the distance from each sample point in the sample environmental distribution to this segmentation origin to obtain a set of segmentation distances. The distances in the set of segmentation distances are used to quantify the difference between the sample and the standard environment, thereby characterizing the intuitive severity of environmental factors. Among them, the set of segmentation distances can be calculated based on Mahalanobis distance or Euclidean distance.

[0032] Specifically, perform serialization processing on the set of segmentation distances, and extract sample points in the sample environmental distribution from near to far according to the set segmentation quantity limit to form an aged sample data group. Among them, the aged sample data group includes associated sample environmental data and sample mechanical property data, which is convenient for subsequent analysis and modeling.

[0033] Further, through an iterative extraction method, generate multiple aged sample data groups and summarize them into a grouped aged sample data set. Through the above-mentioned segmentation distances, according to the distribution of the sample environmental data, multiple groups of aged sample data groups are obtained, and each group of aged sample data groups corresponds to a relatively similar environmental situation, which helps to improve the accuracy of the subsequent established intrinsic aging characteristic equation.

[0034] In some embodiments, establishing an intrinsic aging characteristic equation for the target plastic according to the grouped aged sample data set includes:

[0035] Extract the first aged sample data group from the grouped aged sample data set, and perform regression analysis on the first aged sample data group to construct a first piecewise aging characteristic equation; traverse the grouped aged sample data set, establish multiple regression equations based on multiple of the aged sample data groups, and output them as multiple piecewise aging characteristic equations; perform multi-segment fitting on all the generated piecewise aging characteristic equations to construct an intrinsic aging characteristic equation, where the intrinsic aging characteristic equation is a piecewise equation.

[0036] Specifically, first, extract the first aged sample data group from the grouped aged sample data set. This data group contains sample environmental data and sample mechanical property data in a specific environmental interval, such as sample data with lower temperature and light, and medium oxidation intensity. Then, perform regression analysis on the first aged sample data group to construct a first piecewise aging characteristic equation. Among them, regression analysis can adopt linear regression, polynomial regression or other suitable data fitting methods to accurately describe the aging characteristics in the current environmental interval. Next, traverse the entire grouped aged sample data set, perform regression analysis on each aged sample data group, and establish multiple regression equations. Among them, each regression equation is a piecewise aging characteristic equation, characterizing the aging characteristics in one environmental interval.

[0037] Specifically, the aging characteristics in different environmental intervals may vary significantly. That is, the continuous aging characteristic equation generated by single fitting is difficult to accurately reflect the aging characteristics of the target plastic. Therefore, based on the sample data in different environmental intervals, multiple aging characteristic equations are fitted. Then, multiple-segment fitting is performed on all the generated segmented aging characteristic equations to connect multiple segmented aging characteristic equations and construct the intrinsic aging characteristic equation. Among them, multiple-segment fitting can adopt piecewise linear fitting, spline interpolation or other suitable methods. The obtained intrinsic aging characteristic equation is a segmented equation, and different environmental intervals apply to different equation segments.

[0038] Interact with the target application scenario of the target plastic fitting to obtain the scenario environmental characteristics, and configure the intrinsic aging characteristic equation according to the scenario environmental characteristics to obtain the scenario-based aging characteristic equation.

[0039] Specifically, scenario-based configuration is carried out based on the environmental characteristics of the target application scenario to more accurately predict the aging behavior of the target plastic fitting in a specific application scenario. The scenario environmental characteristics of different application scenarios (such as outdoor, indoor, industrial environment, etc.) can be regarded as permutations of different combinations of environmental characteristics, such as a working day or an operation cycle.

[0040] In some embodiments, interacting with the target application scenario of the target plastic fitting to obtain the scenario environmental characteristics, and configuring the intrinsic aging characteristic equation according to the scenario environmental characteristics to obtain the scenario-based aging characteristic equation includes:

[0041] Based on the scenario environmental characteristics, define multiple types of standard scenario environmental characteristics; according to the multiple types of standard scenario environmental characteristics, generate the standard scenario units of the target application scenario, where the standard scenario units are determined based on the minimum cycle period of the target application scenario; according to the standard scenario units, call the intrinsic aging characteristic equation and establish a mapping relationship with multiple types of the standard scenario environmental characteristics, and the output is the scenario-based aging characteristic equation.

[0042] Specifically, first, based on the environmental characteristics of the target application scenario, define multiple types of standard scenario environmental characteristics. Among them, the multiple types of standard scenario environmental characteristics include all the environmental conditions of the target application scenario. For example, the multiple types of standard scenario environmental characteristics can correspond to various working conditions in an industrial scenario, and corresponding combinations of standard scenario environmental characteristics can be defined according to different working conditions.

[0043] Further, according to the environmental characteristics of multiple standard scenarios, standard scenario units of the target application scenario are generated. Among them, the standard scenario units are determined based on the minimum cycle period of the target application scenario and are used to reflect the typical environmental changes in the application scenario. For example, in a daily outdoor application scenario, the minimum cycle period can be one day, including the temperature, light, and oxidation intensity changes during the day and night. The temperature is high and the ultraviolet intensity is high during the day, while the temperature is low and the ultraviolet intensity is low at night.

[0044] Further, according to the defined standard scenario units, the environmental intervals to which the multiple environmental characteristics of the standard scenario units belong are determined, and then the corresponding equation segments in the intrinsic aging characteristic equation are called. At the same time, the mapping relationships between multiple corresponding equation segments and multiple environmental characteristics of the standard scenario are stored, and they are jointly output as a scenario-based aging characteristic equation, which can reflect the aging behavior of the target plastic fitting in a specific application scenario.

[0045] Through the above steps, the scenario-based aging characteristic equation of the target plastic fitting in a specific application scenario can be systematically obtained, so as to more accurately predict its aging behavior.

[0046] According to the scenario-based aging characteristic equation, configure the mechanical property data sequence of the target plastic, perform finite element simulation analysis on the target plastic fitting, obtain the mechanical property change curve of the target plastic fitting, and output it as the predicted mechanical property.

[0047] In some embodiments, according to the scenario-based aging characteristic equation, configuring the mechanical property data sequence of the target plastic and performing finite element simulation analysis on the target plastic fitting includes:

[0048] Analyze the standard scenario unit to obtain the standard scenario environmental characteristic sequence; call the intrinsic aging characteristic equation according to the mapping relationship, calculate the mechanical property data string of the standard scenario environmental characteristic sequence; combine the target application scenario, arrange and combine the mechanical property data string, output it as the mechanical property data sequence, and dynamically adjust the material properties and boundary conditions with the mechanical property data sequence to perform finite element simulation analysis.

[0049] Specifically, the standard scenario unit includes a corresponding standard scenario environmental characteristic sequence, which is used to reflect the environmental characteristic changes at each time point within the standard scenario unit. In other words, the standard scenario environmental characteristic sequence is used to equivalent the continuously changing environmental characteristics with uncertain states into the repetition and combination of multiple determined environmental states. For example, the standard scenario unit is a one-day cycle, and the environmental characteristic sequence includes the temperature, light intensity, and oxidation intensity at different time points within a day (such as early morning, morning, noon, afternoon, evening, night, early morning, etc.).

[0050] Specifically, according to the mapping relationship between the standard scenario environmental characteristic sequence and the scenario-based aging characteristic equation, the intrinsic aging characteristic equation is called to calculate the mechanical property data at each time point. The mechanical property data string represents the change in mechanical properties at each time point within the standard scenario unit. Among them, the standard scenario environmental characteristics at different time points are different, and the corresponding levels of mechanical property changes are different, that is, the change rates of mechanical property data under different standard scenario environmental characteristics are not the same.

[0051] Furthermore, in combination with the target application scenario, the mechanical property data string is arranged and combined to form a mechanical property data sequence, which covers the mechanical property changes throughout the simulation period and is used to dynamically adjust the material properties and boundary conditions in the finite element model. For example, if the standard scenario unit is a one-day cycle and the entire simulation period is one year, then the mechanical property data sequence is the arrangement and combination of the mechanical property data string based on the annual working day distribution.

[0052] In some implementation manners, performing finite element simulation analysis further includes:

[0053] Calculating the performance volatility of the multi-dimensional mechanical property indexes in the mechanical property data sequence to obtain a multi-dimensional performance fluctuation curve; based on a preset multi-dimensional weight set, performing weighted fusion on the multi-dimensional performance fluctuation curve to obtain a material performance fluctuation curve, where the multi-dimensional weight set is defined based on the target application scenario of the target plastic fitting; calculating the distribution density of simulation points based on the material performance fluctuation curve, and determining a plurality of simulation analysis points according to the distribution density of the simulation points. Specifically, first, for each time point in the mechanical property data sequence, calculate the performance volatility of the multi-dimensional mechanical property indexes, and then, fit the performance volatilities of multiple time points to generate a multi-dimensional performance fluctuation curve, which reflects the change rate of the multi-dimensional mechanical property indexes throughout the simulation period.

[0054] Specifically, based on a preset multi-dimensional weight set, perform weighted fusion on the multi-dimensional performance fluctuation curve to obtain a material performance fluctuation curve, which comprehensively reflects the overall fluctuation of the multi-dimensional mechanical property indexes. Among them, the multi-dimensional weight set should be defined according to the target application scenario of the target plastic fitting. For example, if the material strength is more important than the elastic modulus in the application scenario, a higher weight is assigned to the mechanical property indexes related to the material strength.

[0055] Furthermore, based on the material performance fluctuation curve, calculate the distribution density of the simulation points. Time points with larger fluctuations in material performance require a higher simulation frequency to ensure the time resolution and accuracy during simulation analysis, while time points with smaller fluctuations can reduce the simulation frequency, thereby reducing the computing power consumption.

[0056] Through the above steps, calculate the performance volatility, obtain the multi-dimensional performance fluctuation curve, and evaluate the comprehensive fluctuation level to determine the simulation analysis points, so as to more accurately simulate the mechanical property changes of the target plastic fitting under specific application scenarios. At the same time, it helps to reduce the waste of computing power in simulation analysis and optimize the computing resource allocation of finite element simulation.

[0057] Further, obtain the mechanical property change curve of the target plastic fitting and output it as the predicted mechanical property. After that, it further includes:

[0058] Obtain the operation performance constraints of the target plastic fitting, where the operation performance constraints include mechanical property constraints and durability performance constraints; taking the mechanical property constraints as the termination condition, traverse the mechanical property change curve in the forward direction. When any point on the mechanical property change curve does not meet the mechanical property constraints, extract the corresponding time mark and output it as the predicted durability performance; compare the predicted durability performance with the durability performance constraints to conduct the compliance determination of the target plastic fitting.

[0059] Specifically, obtain the operation performance constraints. Among them, the mechanical property constraints are used to define the mechanical property indexes that the plastic fitting must meet during use, such as minimum strength, maximum deformation, fracture toughness, etc. The durability performance constraints define the expected service life of the plastic fitting in a specific application scenario, such as at least how many years or how many working cycles it should be used.

[0060] Optionally, call the mechanical property change curve of the target plastic fitting output by the aforementioned finite element simulation analysis. This mechanical property change curve reflects the performance changes of the plastic fitting during the entire service life. Then, taking the mechanical property constraints as the termination condition, traverse the mechanical property change curve in the forward direction. When any point on the mechanical property change curve does not meet the mechanical property constraints, that is, when any dimensional mechanical property index on the mechanical property change curve does not meet the operation performance constraints, it is considered that the target plastic fitting cannot meet the operation requirements at this time, extract the corresponding time mark, and output it as the predicted durability performance.

[0061] Further, compare the predicted durability performance with the preset durability performance constraints. If the predicted durability performance meets or exceeds the durability performance constraints, it is determined that the target plastic fitting meets the standard; otherwise, it is determined that it does not meet the standard. Through the above steps, it is possible to evaluate the service life and performance of the target plastic fitting made of the target plastic under specific application scenarios and ensure that it meets the expected operation requirements.

[0062] In summary, the plastic mechanical property prediction method combining finite element simulation provided by the present invention has the following technical effects:

[0063] By obtaining a sample aging dataset of the target plastic containing sample environmental data and corresponding mechanical property data; based on the sample environmental data, segmenting and clustering the sample aging dataset to obtain a grouped aging sample dataset, and using this dataset to establish the intrinsic aging characteristic equation of the target plastic; in the application scenario of the target plastic fitting, obtaining the scenario environmental characteristics, and performing scenario configuration on the intrinsic aging characteristic equation according to these characteristics, so as to obtain the scenario aging characteristic equation; according to the scenario aging characteristic equation, configuring the mechanical property data sequence of the target plastic, performing finite element simulation analysis, obtaining the mechanical property change curve of the target plastic fitting, and finally outputting the predicted mechanical properties. Thus, the technical effects of improving the prediction accuracy and reliability and reducing the prediction cost are achieved.

[0064] Embodiment 2

[0065] Figure 2 It is a schematic structural diagram of the plastic mechanical property prediction system combined with finite element simulation of the present invention. For example, Figure 1 In the present invention, the flowchart of the plastic mechanical property prediction method combined with finite element simulation can be implemented by a structure as shown in Figure 2 shown.

[0066] Based on the same concept as the plastic mechanical property prediction method combined with finite element simulation in the above embodiment, the plastic mechanical property prediction system combined with finite element simulation provided by the present invention further includes:

[0067] The sample data acquisition module 11 is used to obtain the sample aging dataset of the target plastic, wherein the sample aging dataset includes sample environmental data and corresponding sample mechanical property data.

[0068] The characteristic equation construction module 12 is used to segment and cluster the sample aging dataset based on the sample environmental data, obtain the grouped aging sample dataset, and establish the intrinsic aging characteristic equation of the target plastic according to the grouped aging sample dataset.

[0069] The scenario configuration module 13 is used to interact with the target application scenario of the target plastic fitting, obtain the scenario environmental characteristics, and perform scenario configuration on the intrinsic aging characteristic equation according to the scenario environmental characteristics to obtain the scenario aging characteristic equation.

[0070] The mechanical property prediction module 14 is used to configure the mechanical property data sequence of the target plastic according to the scenario aging characteristic equation, perform finite element simulation analysis on the target plastic fitting, obtain the mechanical property change curve of the target plastic fitting, and output the predicted mechanical properties.

[0071] Among them, the characteristic equation construction module 12 includes:

[0072] A sample environment space construction and data mapping unit is used to construct a sample environment space with environmental temperature, environmental light, and environmental oxidation intensity as coordinate dimensions respectively, map the sample environmental data to the sample environment space, and obtain a sample environment distribution.

[0073] A segmentation origin mapping and segmentation distance calculation unit is used to map standard environmental data to the sample environment space as a segmentation origin, calculate the distances from multiple sample points in the sample environment distribution to the segmentation origin, and obtain a segmentation distance set.

[0074] A segmentation distance set serialization and aged sample data group extraction unit is used to serialize the segmentation distance set, extract sample points from smallest to largest according to the serialization result. When the number of extracted sample points meets the segmentation quantity limit, store the corresponding sample environmental data and sample mechanical property data as an aged sample data group, and iteratively extract sample points to obtain multiple aged sample data groups, which are output as the grouped aged sample data set.

[0075] In some embodiments, the characteristic equation construction module 12 further includes:

[0076] A regression analysis unit is used to extract the first aged sample data group from the grouped aged sample data set and perform regression analysis on the first aged sample data group to construct a first piecewise aging characteristic equation.

[0077] An analysis iteration unit is used to traverse the grouped aged sample data set, establish multiple regression equations based on multiple aged sample data groups, and output multiple piecewise aging characteristic equations.

[0078] A multi-segment fitting construction unit is used to perform multi-segment fitting on all generated piecewise aging characteristic equations to construct an intrinsic aging characteristic equation, where the intrinsic aging characteristic equation is a piecewise equation.

[0079] In some embodiments, the scenario configuration module 13 includes:

[0080] A standard scenario feature definition unit is used to define multiple types of standard scenario environmental features based on the scenario environmental features.

[0081] A standard scenario unit generation unit is used to generate a standard scenario unit for the target application scenario according to the multiple types of standard scenario environmental features, where the standard scenario unit is determined based on the minimum cycle period of the target application scenario.

[0082] A scenario-based aging characteristic equation establishment unit is used to call the intrinsic aging characteristic equation according to the standard scenario unit and establish a mapping relationship with multiple types of standard scenario environmental features, and output the scenario-based aging characteristic equation.

[0083] In some embodiments, the mechanical property prediction module 14 includes:

[0084] A standard scenario unit parsing unit, configured to parse the standard scenario unit to obtain a standard scenario environment feature sequence.

[0085] A mechanical property data calculation unit, configured to call the intrinsic aging characteristic equation according to the mapping relationship to calculate a mechanical property data string of the standard scenario environment feature sequence.

[0086] A mechanical property data sequence generation unit, configured to combine with a target application scenario, permute and combine the mechanical property data string, output it as the mechanical property data sequence, and dynamically adjust material properties and boundary conditions with the mechanical property data sequence to perform finite element simulation analysis.

[0087] In some embodiments, the mechanical property data sequence generation unit in the mechanical property prediction module 14 includes:

[0088] A multi-dimensional fluctuation analysis unit, configured to calculate the performance fluctuation rate of multi-dimensional mechanical property indexes in the mechanical property data sequence to obtain a multi-dimensional performance fluctuation curve.

[0089] A multi-dimensional fluctuation evaluation unit, configured to perform weighted fusion of the multi-dimensional performance fluctuation curve based on a preset multi-dimensional weight set to obtain a material performance fluctuation curve, where the multi-dimensional weight set is defined based on the target application scenario of the target plastic fitting.

[0090] An analysis point distribution unit, configured to calculate the distribution density of simulation points based on the material performance fluctuation curve and determine a plurality of simulation analysis points according to the distribution density of the simulation points.

[0091] In some embodiments, the system further includes:

[0092] An operation performance constraint acquisition unit, configured to acquire the operation performance constraints of the target plastic fitting, where the operation performance constraints include mechanical property constraints and durability performance constraints.

[0093] A mechanical property traversal unit, configured to travers the mechanical property change curve forward with the mechanical property constraint as the termination condition. When any point on the mechanical property change curve does not meet the mechanical property constraint, extract the corresponding time mark and output it as the predicted durability performance.

[0094] A durability performance compliance determination unit, configured to compare the predicted durability performance with the durability performance constraint to perform compliance determination of the target plastic fitting.

[0095] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the foregoing Embodiment 1 are equally applicable to the plastic mechanical property prediction system combined with finite element simulation described in Embodiment 2. For the sake of brevity of the specification, no further elaboration will be made here.

[0096] It should be understood that the disclosed embodiments of the present invention and the above descriptions enable those skilled in the art to implement the present invention using the present invention. At the same time, the present invention is not limited to the part of the embodiments mentioned above. It should be understood that those of ordinary skill in the art can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for predicting plastic mechanical properties combined with finite element simulation, characterized in that: The method comprises: Acquire a sample aging data set of a target plastic, wherein the sample aging data set includes sample environmental data and corresponding sample mechanical property data; Based on the sample environment data, segment and cluster the sample aging data set to obtain a grouped aging sample data set, and establish an intrinsic aging characteristic equation of the target plastic according to the grouped aging sample data set; Interact with the target application scenario of the target plastic accessory, obtain the scenario environment characteristics, and perform scenario configuration on the intrinsic aging characteristic equation according to the scenario environment characteristics to obtain the scenario aging characteristic equation; According to the scenario-based aging characteristic equation, a mechanical property data sequence of the target plastic is configured, a finite element simulation analysis of the target plastic accessory is performed, and a mechanical property change curve of the target plastic accessory is obtained, and the output is a predicted mechanical property; The target application scenario of the interactive target plastic accessory is used to obtain the scenario environment characteristics, and the intrinsic aging characteristic equation is configured in a scenario-based manner according to the scenario environment characteristics to obtain the scenario-based aging characteristic equation, including: Based on the scene environment characteristics, define multiple categories of standard scene environment characteristics; Generate a standard scene unit of a target application scene according to the multiple types of standard scene environment features, wherein the standard scene unit is determined based on a minimum cycle period of the target application scene; According to the standard scene unit, calling the intrinsic aging characteristic equation and establishing a mapping relationship with multiple types of standard scene environment features, and outputting the scenario-based aging characteristic equation; According to the scenario-based aging characteristic equation, a mechanical property data sequence of the target plastic is configured, and a finite element simulation analysis of the target plastic accessory is performed, including: Parsing the standard scene unit to obtain a standard scene environment feature sequence; Calling the intrinsic aging characteristic equation according to the mapping relationship to calculate the mechanical property data string of the standard scene environment characteristic sequence; In combination with the target application scenario, the mechanical property data strings are arranged and combined, and output as the mechanical property data sequence, and the material properties and boundary conditions are dynamically adjusted with the mechanical property data sequence to perform finite element simulation analysis.

2. The method for predicting plastic mechanical properties combined with finite element simulation according to claim 1, characterized in that: Based on the sample environment data, segmentation and clustering of the sample aging data set is performed to obtain a grouped aging sample data set, including: Constructing a sample environment space using the ambient temperature, ambient light, and ambient oxidation intensity as coordinate dimensions, and mapping the sample environment data to the sample environment space to obtain the sample environment distribution; Mapping the standard environment data to the sample environment space as a segmentation origin, calculating the distances from a plurality of sample points in the sample environment distribution to the segmentation origin, and obtaining a segmentation distance set; The segmentation distance set is serialized, and sample points are extracted from small to large according to the serialization results. When the number of extracted sample points meets the segmentation number limit, the corresponding sample environment data and sample mechanical property data are stored as an aging sample data group, and sample points are extracted iteratively to obtain multiple aging sample data groups, which are output as the grouped aging sample data set.

3. The method for predicting plastic mechanical properties combined with finite element simulation according to claim 2, characterized in that: The intrinsic aging characteristic equation of the target plastic is established according to the grouped aging sample data set, including: Extracting a first aging sample data group from the grouped aging sample data set, and performing regression analysis on the first aging sample data group to construct a first segmented aging characteristic equation; Traversing the grouped aging sample data sets, establishing multiple regression equations based on multiple aging sample data sets, and outputting them as multiple segmented aging characteristic equations; Multi-segment fitting is performed on all the generated segmented aging characteristic equations to construct an intrinsic aging characteristic equation, wherein the intrinsic aging characteristic equation is a segmented equation.

4. The method for predicting plastic mechanical properties combined with finite element simulation according to claim 1, characterized in that: Conduct finite element simulation analysis, including: Calculating the performance fluctuation rate of the multidimensional mechanical performance index in the mechanical performance data sequence to obtain a multidimensional performance fluctuation curve; Based on a preset multidimensional weight set, weighted fusion of the multidimensional performance fluctuation curves is performed to obtain a material performance fluctuation curve, wherein the multidimensional weight set is defined based on a target application scenario of a target plastic accessory; The simulation point distribution density is calculated based on the material property fluctuation curve, and a plurality of simulation analysis points are determined according to the simulation point distribution density.

5. The method for predicting plastic mechanical properties combined with finite element simulation according to claim 1, characterized in that: Obtain the mechanical properties change curve of the target plastic parts, and output the predicted mechanical properties. After that, it also includes: Obtaining the operating performance constraints of the target plastic parts, wherein the operating performance constraints include mechanical constraints and durability constraints; Taking the mechanical constraint as the termination condition, traversing the mechanical property change curve in a forward direction, when any point on the mechanical property change curve does not satisfy the mechanical constraint, extracting the corresponding time mark, and outputting it as the predicted durability performance; The predicted durability performance is compared with the durability performance constraint to determine whether the target plastic part meets the standard.

6. Plastic mechanical properties prediction system combined with finite element simulation, characterized in that: The system is used to execute the plastic mechanical property prediction method combined with finite element simulation according to any one of claims 1 to 5, and the system comprises: A sample data acquisition module, wherein the sample data acquisition module is used to obtain a sample aging data set of a target plastic, wherein the sample aging data set includes sample environmental data and corresponding sample mechanical property data; A characteristic equation building module, the characteristic equation building module is used to perform segmentation and clustering of the sample aging data set based on the sample environment data, obtain a grouped aging sample data set, and establish an intrinsic aging characteristic equation of the target plastic according to the grouped aging sample data set; A scenario configuration module, the scenario configuration module is used to interact with the target application scenario of the target plastic accessory, obtain the scenario environment characteristics, and perform scenario configuration on the intrinsic aging characteristic equation according to the scenario environment characteristics to obtain the scenario aging characteristic equation; A mechanical property prediction module is used to configure a mechanical property data sequence of a target plastic according to the scenario-based aging characteristic equation, perform finite element simulation analysis of a target plastic accessory, obtain a mechanical property change curve of the target plastic accessory, and output the predicted mechanical property.

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