Method for predicting the performance of an elastomeric sealing material and related apparatus

Abstract

The application embodiment provides a performance prediction method and related equipment of rubber and plastic sealing material, and belongs to the technical field of data processing. The method comprises the following steps: acquiring rubber and plastic stress curves at multiple key temperature points respectively; the rubber and plastic stress curve is a relationship between the rubber and plastic sealing material and a corresponding strain value under different stress values; multiple temperature stress models are established according to the rubber and plastic stress curves at the multiple key temperature points; the temperature stress model is used for describing the relationship between the rubber and plastic sealing material and the corresponding stress value under different temperatures; a stress and strain model under a target temperature is predicted according to the multiple temperature stress models; the stress and strain model is a relationship between the stress value and the strain value of the rubber and plastic sealing material. The application embodiment can predict the stress and strain model under the target temperature, can effectively make up for the limitation of experimental measurement, and thus improves the accuracy of quantitative analysis on the performance of the rubber and plastic sealing material in a wide temperature range.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and related equipment for predicting the performance of rubber and plastic sealing materials. Background Technology

[0002] The mechanical properties of rubber and plastic sealing materials are extremely sensitive to changes in ambient temperature. Typical examples include nitrile rubber, silicone rubber, fluororubber, and polytetrafluoroethylene (PTFE). In real-world applications such as aerospace and automotive engines, seals often need to withstand temperature ranges from -60°C to 200°C or even wider. Therefore, accurately obtaining the stress-strain curve of the material across the entire operating temperature range is crucial for sealing structure design, finite element analysis, and lifespan prediction. Summary of the Invention

[0003] The main objective of this application is to propose a method and related equipment for predicting the performance of rubber and plastic sealing materials, thereby improving the accuracy of quantitative analysis of the performance of rubber and plastic sealing materials over a wide temperature range.

[0004] To achieve the above objectives, one aspect of this application proposes a method for predicting the performance of rubber and plastic sealing materials, the method comprising: The rubber and plastic stress curves were obtained at multiple key temperature points; the rubber and plastic stress curves represent the relationship between the rubber and plastic sealing material and the corresponding strain value under different stress values. Based on the rubber and plastic stress curves at multiple key temperature points, multiple temperature stress models are established; the temperature stress models are used to describe the relationship between the rubber and plastic sealing material and the corresponding stress values ​​at different temperatures. The stress-strain model at the target temperature is predicted based on the multiple temperature stress models; the stress-strain model is the relationship between the stress value and the strain value of the rubber-plastic sealing material.

[0005] In some embodiments, prior to obtaining the rubber-plastic stress curves at multiple key temperature points, the method further includes: Obtain the strain-stress dataset at a first critical temperature point; the first critical temperature point is any critical temperature point, and the strain-stress dataset contains multiple strain values ​​obtained from tensile tests on the rubber-plastic sealing material under multiple stress values. Based on the strain-stress dataset at the first critical temperature point, the rubber-plastic stress curve at the first critical temperature point is obtained by fitting.

[0006] In some embodiments, after obtaining the strain-stress dataset at the first critical temperature point, the method further includes: Clustering calculations are performed on the strain-stress dataset at the first critical temperature point to obtain the corresponding strain-stress clusters. The strain values ​​that are not in the strain stress accumulation are deleted to obtain the processed strain stress dataset; The step of fitting the rubber-plastic stress curve at the first critical temperature point based on the strain-stress dataset at the first critical temperature point includes: Based on the processed strain-stress dataset, the rubber-plastic stress curve at the first key temperature point is fitted.

[0007] In some embodiments, establishing multiple temperature stress models based on the rubber-plastic stress curves at multiple key temperature points includes: Set multiple interval strain values; From the stress curves of rubber and plastic at the multiple key temperature points, the stress values ​​corresponding to each interval strain value and the key temperature points are obtained. The stress values ​​corresponding to each interval strain value and the key temperature points are fitted with second-order polynomials to obtain the temperature stress model corresponding to each interval strain value.

[0008] In some embodiments, predicting the stress-strain model at the target temperature based on the plurality of temperature stress models includes: Based on the temperature stress model corresponding to each interval strain value and the target temperature, the stress value corresponding to each interval strain value is calculated. The initial stress-strain model at the target temperature is obtained by fitting the strain values ​​at each interval with the stress values ​​corresponding to the strain values ​​at each interval. The stress-strain model is smoothed to obtain the stress-strain model at the target temperature.

[0009] In some embodiments, smoothing the stress-strain model to obtain the stress-strain model at the target temperature includes: The initial stress-strain model is smoothed by spline interpolation to obtain the stress-strain model at the target temperature.

[0010] In some embodiments, obtaining target user data includes: Responding to performance display commands; According to the performance display instructions, the stress-strain model at the target temperature is rendered in the target interface, and the strain value corresponding to each stress value is displayed according to the time the cursor hovers over the stress-strain model.

[0011] To achieve the above objectives, another aspect of this application provides a performance prediction device for rubber and plastic sealing materials, the device comprising: The curve acquisition module is used to acquire rubber and plastic stress curves at multiple key temperature points; the rubber and plastic stress curves represent the relationship between the rubber and plastic sealing material and the corresponding strain values ​​under different stress values. The model building module is used to establish multiple temperature stress models based on the rubber and plastic stress curves at multiple key temperature points; the temperature stress models are used to describe the relationship between the rubber and plastic sealing material and the corresponding stress values ​​at different temperatures. The model prediction module is used to predict the stress-strain model at the target temperature based on the multiple temperature stress models; the stress-strain model is the relationship between the stress value and the strain value of the rubber-plastic sealing material.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the methods described above. The embodiments of this application include at least the following beneficial effects: This application provides a method, apparatus, electronic device, storage medium, and program product for predicting the performance of rubber and plastic sealing materials. This solution obtains rubber and plastic stress curves at multiple key temperature points. The rubber and plastic stress curves represent the relationship between the rubber and plastic sealing material and the corresponding strain value under different stress values. Multiple temperature stress models are established based on the rubber and plastic stress curves at multiple key temperature points. The temperature stress models are used to describe the relationship between the rubber and plastic sealing material and the corresponding stress value at different temperatures. The stress-strain model at the target temperature is predicted based on the multiple temperature stress models. The stress-strain model represents the relationship between the stress value and strain value of the rubber and plastic sealing material. By implementing the embodiments of this application, multiple temperature stress models can be established using the obtained rubber and plastic stress curves at multiple key temperature points. These models can be used to systematically describe the quantitative influence of temperature changes on the stress behavior of rubber and plastic sealing materials. Furthermore, these temperature stress models can be used to accurately predict the stress-strain model of rubber and plastic sealing materials under target temperature conditions. By obtaining rubber and plastic stress curves from a small number of key temperature points, the stress-strain model at the target temperature can be predicted, effectively compensating for the limitations of experimental measurements and thus improving the accuracy of quantitative analysis of the performance of rubber and plastic sealing materials over a wide temperature range. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application; Figure 2 This is a flowchart of the performance prediction method for rubber and plastic sealing materials provided in the embodiments of this application; Figure 3 This is a stress-strain curve obtained experimentally at different key temperatures in one embodiment; Figure 4 This is a scatter plot of the stress values ​​extracted from one embodiment as a function of temperature, and a schematic diagram of the fitted polynomial. Figure 5 This is a schematic diagram of a stress-strain model at a target temperature in one embodiment; Figure 6 A flowchart illustrating the performance prediction of rubber and plastic sealing materials in one embodiment. Figure 7 This is a schematic diagram of the performance prediction device for rubber and plastic sealing materials provided in the embodiments of this application; Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0017] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0018] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] In related technologies, obtaining the mechanical properties of rubber and plastic sealing materials across the entire temperature range mainly relies on traditional experimental methods. These methods require separate tensile or compression tests at each target temperature, resulting in long testing cycles, high costs, and difficulty in adapting to the rapid pace of product development. Furthermore, because experimental data is often sparsely distributed across the temperature dimension, engineers frequently need to perform rough interpolation or conservative estimates for material properties at unmeasured temperatures. This presents a dilemma for sealing structure design: either overly conservative estimates increase manufacturing costs, or insufficient predictions lead to seal failure risks. Therefore, the industry urgently needs to develop an engineering method specifically for rubber and plastic sealing materials that can achieve rapid and accurate prediction of stress-strain relationships across the entire temperature range with minimal experimental cost.

[0021] In view of this, this application provides a method for predicting the performance of rubber and plastic sealing materials, which improves the accuracy of quantitative analysis of the performance of rubber and plastic sealing materials over a wide temperature range.

[0022] The performance prediction method for rubber and plastic sealing materials provided in this application relates to the field of data processing technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application that implements the performance prediction method for rubber and plastic sealing materials, but is not limited to the above forms.

[0023] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0024] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0025] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided in an embodiment of this application. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0026] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0027] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0028] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the application does not impose any limitations.

[0029] For example, based on Figure 1 The implementation environment shown in this application embodiment provides a performance prediction method for rubber and plastic sealing materials. The following description uses the application of this performance prediction method for rubber and plastic sealing materials in server 101 as an example. It can be understood that this performance prediction method for rubber and plastic sealing materials can also be applied to terminal 102.

[0030] Figure 2This is an optional flowchart of the performance prediction method for rubber and plastic sealing materials provided in the embodiments of this application. The execution subject of the performance prediction method for rubber and plastic sealing materials can be any of the aforementioned electronic devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S201 to S203.

[0031] Step S201: Obtain the rubber and plastic stress curves at multiple key temperature points; the rubber and plastic stress curves represent the relationship between the rubber and plastic sealing material and the corresponding strain value under different stress values.

[0032] In some embodiments, the rubber-plastic stress curve can be a quantitative representation of the mechanical behavior of rubber-plastic sealing materials under different temperature conditions. The horizontal axis independent variable records the strain value, and the vertical axis dependent variable corresponds to the stress value. It can depict the mechanical characteristics of the rubber-plastic sealing material from elastic deformation to plastic flow under the action of external force.

[0033] As an optional implementation, a strain-stress dataset is obtained at a first critical temperature point. The first critical temperature point can be any single critical temperature point, and the strain-stress dataset contains strain values ​​obtained from tensile tests on the rubber-plastic sealing material under multiple stress values. Based on the strain-stress dataset at the first critical temperature point, a rubber-plastic stress curve at the first critical temperature point is fitted. Before obtaining the rubber-plastic stress curves at multiple critical temperature points, a complete set of stress-strain data at the first critical temperature point must first be obtained through standard tensile tests. This critical temperature point can be any representative temperature in a preset temperature sequence, and multiple critical temperature points can be used for tensile tests. In particular, at least four critical temperature points can be used for tensile tests to meet the conditions for subsequent stress-strain model prediction. These four critical temperature points can be -55℃, 25℃ (room temperature), 70℃, and 135℃, and are not specifically limited. The obtained strain-stress dataset systematically records the strain response values ​​of the rubber-plastic sealing material under different stress levels. Based on this experimental dataset, a curve fitting algorithm was used to establish the constitutive relationship of the rubber-plastic sealing material at various key temperature points, thus obtaining the rubber-plastic stress curves corresponding to multiple key temperature points. By transforming discrete experimental data into a continuous mathematical model, the mechanical behavior of the rubber-plastic sealing material at specific temperatures was accurately revealed, providing a key foundation for the subsequent construction of temperature-related material performance prediction models, thereby significantly improving the systematicness and reliability of the performance characterization of rubber-plastic sealing materials.

[0034] Figure 3 This is an example of stress-strain curves experimentally measured at different key temperatures, such as... Figure 4As shown, the stress curves of rubber and plastic at the key temperature points of -55℃, 25℃, 70℃, and 135℃ can be obtained respectively.

[0035] Furthermore, clustering calculations are performed on the strain-stress dataset at the first critical temperature point to obtain the corresponding strain-stress clusters. Strain values ​​not in these clusters are then removed, resulting in a processed strain-stress dataset. Based on this processed dataset, the rubber-plastic stress curve at the first critical temperature point is fitted. Specifically, cluster analysis is performed on the original strain-stress dataset obtained experimentally at the first critical temperature point. This cluster analysis is a pattern recognition method based on data distribution characteristics, which automatically groups data points with similar mechanical response characteristics into the same cluster, thus forming statistically representative strain-stress clusters. Subsequently, discrete data points deviating from the main clusters are considered outliers and removed, ultimately obtaining the processed strain-stress dataset. Based on this processed strain-stress dataset, fitting algorithms such as nonlinear regression can be used to establish the constitutive relationship of the material at this temperature, thus obtaining the rubber-plastic stress curve corresponding to the first critical temperature point.

[0036] By using clustering algorithms to calculate the strain-stress dataset at the first critical temperature point, the quality and consistency of experimental data were effectively improved, the influence of abnormal measurements on model accuracy was eliminated, and the final stress curves more accurately reflected the mechanical behavior of the material at that temperature, laying a reliable data foundation for the subsequent construction of a full-temperature-range material model.

[0037] Step S202: Establish multiple temperature stress models based on the rubber and plastic stress curves at multiple key temperature points; the temperature stress models are used to describe the relationship between the rubber and plastic sealing material and the corresponding stress values ​​at different temperatures.

[0038] In some embodiments, the process of establishing multiple temperature stress models essentially involves systematically integrating the stress values ​​at discrete temperature points to construct an analytical expression that can continuously describe the quantitative relationship between temperature and stress parameters. Temperature stress models can establish a mapping relationship from temperature variables to stress responses, thereby achieving a unified characterization of the mechanical behavior of rubber and plastic sealing materials under different thermal environments.

[0039] As an optional implementation, multiple interval strain values ​​are set; the stress values ​​corresponding to each interval strain value and the key temperature points are read from the rubber-plastic stress curves at multiple key temperature points; second-order polynomial fitting is performed on the stress values ​​corresponding to each interval strain value and the key temperature points respectively to obtain the temperature stress model corresponding to each interval strain value. The stress data corresponding to these strain values ​​and their corresponding temperature values ​​can be extracted from the rubber-plastic stress curves at each key temperature point, and then second-order polynomial fitting is performed on the stress-temperature dataset at each fixed strain value to establish a temperature stress model system. In particular, second-order or third-order polynomials are usually used for fitting to avoid overfitting caused by using higher-order polynomials. Figure 4 This is a scatter plot of the stress values ​​extracted from one embodiment as a function of temperature, and a schematic diagram of the fitted polynomial, as shown below. Figure 4 As shown, the temperature stress models corresponding to the interval strain values ​​of 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.40, 0.50, 0.60 and 0.70 can be obtained respectively.

[0040] By employing polynomial functions to parametrically model the temperature-stress relationship, the nonlinear characteristics of material mechanical properties changing with temperature can be accurately captured. The series of temperature-stress models obtained through this modeling process not only realize the transformation from discrete experimental data to continuous constitutive relations, but more importantly, establish a strain-parameterized temperature-stress mapping relationship. This provides a complete mathematical model foundation for subsequent predictions of material mechanical behavior under arbitrary temperature conditions, significantly improving the accuracy and engineering applicability of predicting the performance of rubber and plastic sealing materials under varying temperature environments.

[0041] Step S203: Predict the stress-strain model at the target temperature based on multiple temperature stress models; the stress-strain model is the relationship between the stress value and strain value of the rubber-plastic sealing material.

[0042] In some embodiments, the established mapping relationship between temperature and stress values ​​can be used to obtain the stress values ​​at each strain point at the target temperature through mathematical interpolation. Then, a continuous constitutive relationship curve can be constructed through curve fitting technology to obtain the stress-strain model at the target temperature.

[0043] As an optional implementation, the stress value corresponding to each interval strain value is calculated based on the temperature-stress model corresponding to each interval strain value and the target temperature. The initial stress-strain model at the target temperature is obtained by fitting each interval strain value to the corresponding stress value. The stress-strain model is then smoothed to obtain the final stress-strain model at the target temperature. Based on the established temperature-stress model corresponding to each interval strain value, the stress value corresponding to each discrete strain point at the target temperature can be calculated by inputting the target temperature. Subsequently, these calculated interval strain values ​​and their corresponding stress values ​​are used as the basic dataset, and an initial stress-strain model at the target temperature is constructed using appropriate curve fitting. To further improve the engineering applicability and physical rationality of the initial stress-strain model, it is necessary to smooth it. This process eliminates local fitting fluctuations and data jumps, enabling the final stress-strain model to more continuously and stably characterize the mechanical behavior of the material at that temperature.

[0044] Figure 5 This is a schematic diagram of a stress-strain model at a target temperature in one embodiment, as shown below. Figure 5 As shown, the curve corresponding to the predicted value is the predicted stress-strain model at the target temperature, and the curve corresponding to the experimental value is the stress-strain model obtained through experimentation at the target temperature. The values ​​of the two are very close, which reflects the accuracy of the predicted stress-strain model.

[0045] Based on the temperature stress model corresponding to each interval strain value and the target temperature, the stress value corresponding to each interval strain value is calculated. The initial stress-strain model at the target temperature is obtained by fitting each interval strain value with the corresponding stress value. The stress-strain model is then smoothed to obtain the stress-strain model at the target temperature. This effectively overcomes the overfitting or underfitting problems that may occur with traditional single direct fitting. Moreover, the stress-strain model at the target temperature can be predicted using only the rubber and plastic stress curves obtained from tensile tests at a few key temperature points, which improves the efficiency of predicting the stress-strain model at the target temperature. At the same time, it also significantly improves the extrapolation ability and prediction accuracy of the stress-strain model under unknown temperature conditions, providing a more reliable constitutive relationship basis for the performance analysis and life prediction of sealed structures in complex thermal environments.

[0046] Furthermore, spline interpolation can be used to smooth the initial stress-strain model, resulting in a stress-strain model at the target temperature. Spline interpolation, by constructing a piecewise polynomial function, effectively eliminates local fluctuations and irregular abrupt changes caused by fitting discrete data in the initial model while ensuring curve continuity. This results in a smoother stress-strain model that accurately reflects the mechanical response characteristics of rubber and plastic sealing materials. Smoothing the initial stress-strain model using spline interpolation to obtain the stress-strain model at the target temperature not only improves the smoothness of the model curves but also ensures the computational stability and rationality of the stress-strain model in numerical simulations, providing a reliable constitutive basis for subsequent accurate sealing performance analysis and life prediction.

[0047] Figure 6 This is a flowchart illustrating the performance prediction of rubber and plastic sealing materials in one embodiment, such as... Figure 3 As shown, firstly, uniaxial tensile or compression experiments are conducted at multiple different temperatures to obtain a series of raw stress-strain curve data. Then, the experimental data are uniformly processed, a standard strain sequence is set, and the stress values ​​at the same strain point are extracted from each curve. Next, for each fixed strain value, polynomial fitting is performed between different temperatures and the corresponding stress data points to establish a mathematical model of stress variation with temperature. In the prediction stage, the target temperature is input, and the predicted stress value at that temperature is calculated by substituting it into the polynomial function corresponding to each strain. Finally, all strain points and predicted stress values ​​are combined, and an interpolation method is used to generate a complete stress-strain curve at the target temperature as the final output.

[0048] In some embodiments, in response to a performance display command, a stress-strain model at a target temperature is rendered on the target interface according to the performance display command, and the strain value corresponding to each stress value is displayed based on the time the cursor hovers over the stress-strain model. Upon receiving a performance display command input by the user, the graphics rendering engine is activated to dynamically draw the stress-strain model curve at the target temperature on the target interface. The user can generate the performance display command by clicking a preset button, or by inputting information via voice or text; no limitation is made here. The dynamic drawing of the stress-strain model curve at the target temperature on the target interface utilizes curve rendering technology from computer graphics, ensuring accurate visualization of the stress-strain model curve through coordinate transformation and anti-aliasing. When the user hovers the cursor over the curve area, the hover duration can be detected in real time, triggering a data prompt mechanism to dynamically display the precise strain value at the stress value corresponding to the current cursor position.

[0049] Responding to performance display commands, the system renders a stress-strain model at the target temperature on the target interface and displays the strain value corresponding to each stress value based on the time the cursor hovers over the stress-strain model. This enables a visual exploration of the stress-strain relationship, allowing users to intuitively analyze the strain response characteristics of rubber and plastic sealing materials under different stress levels. This greatly improves the efficiency of interpreting material performance data and the convenience of engineering applications, providing intuitive and reliable data support for the optimized design of sealing structures.

[0050] Steps S201 to S203 of the embodiments of this application involve obtaining rubber-plastic stress curves at multiple key temperature points. These stress curves represent the relationship between the rubber-plastic sealing material and its corresponding strain value under different stress values. Multiple temperature stress models are established based on these stress curves. These models describe the relationship between the rubber-plastic sealing material and its corresponding stress value at different temperatures. The stress-strain model at the target temperature is then predicted based on these models. The stress-strain model represents the relationship between the stress and strain values ​​of the rubber-plastic sealing material. Multiple temperature stress models can be established using the obtained stress curves at multiple key temperature points to systematically describe the quantitative influence of temperature changes on the stress behavior of the rubber-plastic sealing material. These models accurately predict the stress-strain model of the rubber-plastic sealing material under the target temperature condition. By obtaining stress curves at a small number of key temperature points, the stress-strain model at the target temperature can be predicted, effectively compensating for the limitations of experimental measurements and improving the accuracy of quantitative analysis of the performance of rubber-plastic sealing materials over a wide temperature range.

[0051] Please see Figure 7 This application also provides a performance prediction device for rubber and plastic sealing materials, which can implement the above-mentioned method. The device includes: Curve acquisition module 701 is used to acquire rubber and plastic stress curves at multiple key temperature points; the rubber and plastic stress curves show the relationship between the rubber and plastic sealing material and the corresponding strain value under different stress values. The model building module 702 is used to build multiple temperature stress models based on the rubber and plastic stress curves at multiple key temperature points; the temperature stress models are used to describe the relationship between the rubber and plastic sealing materials and the corresponding stress values ​​at different temperatures. The model prediction module 703 is used to predict the stress-strain model at the target temperature based on multiple temperature stress models; the stress-strain model is the relationship between the stress value and the strain value of the rubber and plastic sealing material.

[0052] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0053] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0054] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0055] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the methods described in the embodiments of this application. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0056] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0057] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0058] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0059] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0060] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0061] The performance prediction method, apparatus, electronic device, storage medium, and program product for rubber and plastic sealing materials provided in this application embodiment acquire rubber and plastic stress curves at multiple key temperature points. These stress curves represent the relationship between the rubber and plastic sealing material and its corresponding strain value under different stress values. Multiple temperature stress models are established based on these stress curves. These models describe the relationship between the rubber and plastic sealing material and its corresponding stress value at different temperatures. A stress-strain model is predicted at a target temperature based on these multiple temperature stress models. This stress-strain model represents the relationship between the stress value and strain value of the rubber and plastic sealing material, improving the accuracy of quantitative analysis of the performance of rubber and plastic sealing materials over a wide temperature range.

[0062] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0063] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0064] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0066] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0067] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0069] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for predicting the performance of rubber and plastic sealing materials, characterized in that, The method includes the following steps: The rubber and plastic stress curves were obtained at multiple key temperature points; the rubber and plastic stress curves represent the relationship between the rubber and plastic sealing material and the corresponding strain value under different stress values. Based on the rubber and plastic stress curves at multiple key temperature points, multiple temperature stress models are established; the temperature stress models are used to describe the relationship between the rubber and plastic sealing material and the corresponding stress values ​​at different temperatures. The stress-strain model at the target temperature is predicted based on the multiple temperature stress models; the stress-strain model is the relationship between the stress value and the strain value of the rubber-plastic sealing material. The establishment of multiple temperature stress models based on the rubber-plastic stress curves at multiple key temperature points includes: Set multiple interval strain values; From the stress curves of rubber and plastic at the multiple key temperature points, the stress values ​​corresponding to each interval strain value and the key temperature points are obtained. The stress values ​​corresponding to each interval strain value and the key temperature points are fitted with second-order polynomials to obtain the temperature stress model corresponding to each interval strain value. The step of predicting the stress-strain model at the target temperature based on the multiple temperature stress models includes: Based on the temperature stress model corresponding to each interval strain value and the target temperature, the stress value corresponding to each interval strain value is calculated. The initial stress-strain model at the target temperature is obtained by fitting the strain values ​​at each interval with the stress values ​​corresponding to the strain values ​​at each interval. The initial stress-strain model is smoothed to obtain the stress-strain model at the target temperature; The process of smoothing the initial stress-strain model to obtain the stress-strain model at the target temperature includes: The initial stress-strain model is smoothed by spline interpolation to obtain the stress-strain model at the target temperature.

2. The method according to claim 1, characterized in that, Before obtaining the rubber-plastic stress curves at multiple key temperature points, the method further includes: Obtain the strain-stress dataset at a first critical temperature point; the first critical temperature point is any critical temperature point, and the strain-stress dataset contains multiple strain values ​​obtained from tensile tests on the rubber-plastic sealing material under multiple stress values. Based on the strain-stress dataset at the first critical temperature point, the rubber-plastic stress curve at the first critical temperature point is obtained by fitting.

3. The method according to claim 2, characterized in that, After obtaining the strain-stress dataset at the first critical temperature point, the method further includes: Clustering calculations are performed on the strain-stress dataset at the first critical temperature point to obtain the corresponding strain-stress clusters. The strain values ​​that are not in the strain stress accumulation are deleted to obtain the processed strain stress dataset; The step of fitting the rubber-plastic stress curve at the first critical temperature point based on the strain-stress dataset at the first critical temperature point includes: Based on the processed strain-stress dataset, the rubber-plastic stress curve at the first key temperature point is fitted.

4. The method according to any one of claims 1 to 3, characterized in that, The performance prediction method for rubber and plastic sealing materials also includes: Responding to performance display commands; According to the performance display instructions, the stress-strain model at the target temperature is rendered in the target interface, and the strain value corresponding to each stress value is displayed according to the time the cursor hovers over the stress-strain model.

5. A performance prediction device for rubber and plastic sealing materials, characterized in that, The device includes: The curve acquisition module is used to acquire rubber and plastic stress curves at multiple key temperature points; the rubber and plastic stress curves represent the relationship between the rubber and plastic sealing material and the corresponding strain values ​​under different stress values. The model building module is used to establish multiple temperature stress models based on the rubber and plastic stress curves at multiple key temperature points; the temperature stress models are used to describe the relationship between the rubber and plastic sealing material and the corresponding stress values ​​at different temperatures. The model prediction module is used to predict the stress-strain model at the target temperature based on the multiple temperature stress models; the stress-strain model is the relationship between the stress value and the strain value of the rubber-plastic sealing material. The establishment of multiple temperature stress models based on the rubber-plastic stress curves at multiple key temperature points includes: Set multiple interval strain values; From the stress curves of rubber and plastic at the multiple key temperature points, the stress values ​​corresponding to each interval strain value and the key temperature points are obtained. The stress values ​​corresponding to each interval strain value and the key temperature points are fitted with second-order polynomials to obtain the temperature stress model corresponding to each interval strain value. The step of predicting the stress-strain model at the target temperature based on the multiple temperature stress models includes: Based on the temperature stress model corresponding to each interval strain value and the target temperature, the stress value corresponding to each interval strain value is calculated. The initial stress-strain model at the target temperature is obtained by fitting the strain values ​​at each interval with the stress values ​​corresponding to the strain values ​​at each interval. The initial stress-strain model is smoothed to obtain the stress-strain model at the target temperature; The process of smoothing the initial stress-strain model to obtain the stress-strain model at the target temperature includes: The initial stress-strain model is smoothed by spline interpolation to obtain the stress-strain model at the target temperature.

6. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 4.

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