Metal part service life prediction method and device, controller and storage medium

Abstract

The invention discloses a metal piece service life prediction method and device, a controller and a storage medium, relates to the technical field of metal material performance prediction, and at least solves the problem of low prediction result precision of a prediction model constructed on the basis of uniaxial smooth sample test data. The method comprises the following steps: determining a first pure fatigue life of a target metal piece in a notch structure in a non-notch smooth structure state, target holding time of creep and a target notch stress concentration coefficient; obtaining a corresponding target preset model associated with the target metal material of the target metal piece; the target preset model represents the stress concentration coefficient, the load holding time, and the mapping relation between the pure fatigue service life and the creep-fatigue service life in the unnotched smooth structure state; and based on the target preset model, obtaining the target service life of the target metal piece under the first pure fatigue life, the target holding time and the target notch stress concentration coefficient.

Description

Technical Field

[0001] The present application relates to the technical field of metal material performance prediction, and in particular to a metal part life prediction method, device, controller and storage medium. Background Art

[0002] High-temperature creep-fatigue fracture has always been one of the important causes of structural failure for high-temperature structures. Therefore, in order to better judge the damage of metal materials, designing relevant metal life prediction models to predict the service life of components containing notches is the focus of current scientific attention.

[0003] The current service life prediction model for notched components is a basic model constructed based on the uniaxial smooth specimen test data of the stress output of the notched component in one direction. The creep effect caused by the holding time is then analyzed to correct the service life predicted by the basic model, thereby obtaining the service life of the notched component.

[0004] However, since the stress and strain at the local notch are very complex and the stress triaxiality is high, the above-mentioned prediction model does not take into account the superposition effect brought about by creep-fatigue interaction damage when it is corrected based on the uniaxial smooth specimen test data. In addition, the correlation between the service life data and other data on which the correction process depends is poor, that is, the correction model is relatively scattered, resulting in low accuracy of the prediction results when the prediction model uses the same model to predict the service life of notched components with different structural shapes. Summary of the invention

[0005] The present invention provides a metal part life prediction method, device, controller and storage medium, so as to at least solve the problem that the prediction model constructed based on uniaxial smooth specimen test data in the related art predicts the service life of the notched component with low accuracy. The technical solution of the present invention is as follows:

[0006] According to a first aspect of an embodiment of the present invention, a method for predicting the life of a metal part is provided, the method comprising: determining a first pure fatigue life of a target metal part in a notched structure in a notched smooth structure state, a target holding time for creep, and a target notch stress concentration factor; and obtaining a target preset model associated with a target metal material of the target metal part; the target preset model characterizes a mapping relationship between stress concentration factor, holding time, pure fatigue service life in a notched smooth structure state, and creep-fatigue service life; the target preset model is constructed based on data of metal part samples of the target metal material in different structural states under different actual holding times, different stress magnitudes, and different actual stress concentration factors; based on the target preset model, the target service life of the target metal part under the first pure fatigue life, target holding time, and target notch stress concentration factor is obtained.

[0007] The above target preset model is constructed based on the double logarithmic linear relationship between the creep-fatigue service life and the stress concentration factor and the load holding time, respectively.

[0008] Optionally, a target preset model is constructed through triaxial tests or complex stress state tests.

[0009] In one implementation, a triaxial test is used to apply stresses in three orthogonal directions to a metal sample to simulate service life data generated under the three actual stress states.

[0010] In another implementation, a complex stress state test method is used to apply different stresses to the metal sample in multiple directions to simulate the service life data of the metal material under a complex stress state.

[0011] The metal material of the metal part corresponds to the preset model one by one. The stress concentration coefficients of metal parts with different shapes and structures are different, that is, the shape structure of the metal part is associated with the stress concentration coefficient.

[0012] In one possible implementation, before obtaining the target preset model associated with the target metal material of the target metal part, the method also includes: obtaining first actual life data, second actual life data and third actual life data of the metal part sample constructed of the target metal material; the first actual life data includes pure fatigue service life data of the metal part sample in a notched smooth structure state, the second actual life data includes pure fatigue service life data of the metal part sample in a notched structure state under multiple different actual stress concentration factors, and the third actual life data includes creep-fatigue service life data of the metal part sample in a notched structure state under multiple different actual holding times and different actual stress concentration factors corresponding to multiple different actual holding times; the first actual life data, the second actual life data and the third actual life data are used to fit the model coefficients of the preset model that characterizes the mapping relationship to obtain the target preset model.

[0013] It is understandable that the actual life data includes state data under the use state and the corresponding service life under the state data. The use state includes creep state and / or fatigue state. Different actual stresses include different stresses applied in different directions. The actual life data includes service life data under different use states.

[0014] Specifically, the first actual life data includes the pure fatigue service life under different actual stresses and the actual stress corresponding to the pure fatigue service life when the metal sample is in a smooth structure state without a notch. The second actual life data includes the pure fatigue service life under multiple different actual stress concentration factors corresponding to different actual stresses when the metal sample is in different notch structural states and the notch does not creep, and the actual stress concentration factor and actual stress corresponding to each pure fatigue service life. The third actual life data includes the creep-fatigue service life under different actual stress concentration factors corresponding to multiple different actual holding times when the metal sample is in different notch structural states and the notch creeps, and the actual stress, stress concentration factor and actual holding time corresponding to each creep-fatigue service life.

[0015] In this implementation method, based on the three state data constructed under different structural states and different stresses, the change in the usage state of the metal part sample from the notch-free smooth structural state to the notch-free structural state under different actual stresses, as well as the change in the service life under different usage states are determined. Therefore, the pure fatigue effect of the notch state and the superposition effect brought about by the interaction damage of creep and fatigue are reflected through the change law of the service life data under three different states, thereby more accurately characterizing the correlation mapping relationship between the stress concentration factor, holding time, pure fatigue service life of the notch-free smooth structural state and creep-fatigue service life.

[0016] In another possible implementation, the model coefficients include a first model coefficient and a second model coefficient, and the preset model represents the mapping relationship between the first model coefficient power of the stress concentration coefficient, the second model coefficient power of the load holding time, and the pure fatigue service life of the notch-free smooth structure state and the creep-fatigue service life.

[0017] In this implementation, based on the property that the creep-fatigue service life is in a double logarithmic linear relationship with the stress concentration factor and the holding time, a first model coefficient exponential relationship between the creep-fatigue service life and the stress concentration factor, and a second model coefficient exponential relationship between the creep-fatigue service life and the holding time are determined.

[0018] In another possible implementation, the first actual life data, the second actual life data and the third actual life data are used to fit the model coefficients of the preset model that characterizes the mapping relationship to obtain the target preset model, including: obtaining the first coefficient model and the second coefficient model; the first coefficient model characterizes the first double logarithmic linear relationship between the pure fatigue service life of a notched smooth structure state and the pure fatigue service life under the first model coefficient power of the stress concentration coefficient; the second coefficient model characterizes the second double logarithmic linear relationship between the pure fatigue service life under the stress concentration coefficient of the notched structure state and the creep-fatigue service life under the second model coefficient power of the holding time; the first actual life data and the second actual life data are used to fit the first double logarithmic linear relationship represented by the first coefficient model to obtain the first model coefficient; the second actual life data and the third actual life data are used to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient.

[0019] The first coefficient model is constructed based on the double logarithmic linear relationship between the pure fatigue service life under the notched smooth structure state and the pure fatigue service life under the stress concentration factor. The second coefficient model is constructed based on the double logarithmic linear relationship between the pure fatigue service life under the stress concentration factor under the notched structure state and the creep-fatigue service life under the holding time.

[0020] In this implementation, the first model coefficient in the first coefficient model is fitted by the first actual life data and the second actual life data to determine the influence factors of stress and notch structure on fatigue service life, that is, first determine the influence of different stress concentration factors on high temperature low cycle fatigue life without considering the holding time. Then, the second model coefficient of the second coefficient model is fitted by the second actual life data and the third actual life data to determine the influence factors of stress and notch structure on creep-fatigue service life.

[0021] In another possible implementation, the second actual life data and the third actual life data are used to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficients, including:

[0022] Select the third actual life data whose actual holding time is one time unit from the third actual life data; based on the distribution trend of the selected third actual life data whose actual holding time is one time unit, select the second actual life data whose similarity with the distribution trend is greater than the preset similarity threshold from the second actual life data; use the selected second actual life data whose similarity with the distribution trend is greater than the preset similarity threshold and the selected third actual life data whose actual holding time is one time unit to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient.

[0023] Correspondingly, in one implementation, before the first actual life data and the second actual life data are used to fit the first double logarithmic linear relationship represented by the first coefficient model, the third actual life data with an actual holding time of one time unit is first selected from the third actual life data; then, based on the distribution trend of the selected third actual life data with an actual holding time of one time unit, the second actual life data with a similarity to the distribution trend greater than a preset similarity threshold is selected from the second actual life data. The first double logarithmic linear relationship represented by the first coefficient model is fitted using the selected second actual life data with a similarity to the distribution trend greater than the preset similarity threshold and the corresponding first actual life data to obtain the first model coefficient.

[0024] In this implementation, by setting the holding time to 1, the second actual life data and the third actual life data are closely linked, thereby associating the data with the holding time and the data without the holding time. Therefore, the target preset model constructed based on the second actual life data and the third actual life data can more accurately reflect the correlation between the pure fatigue service life under the stress concentration factor of the notch structure state and the creep-fatigue service life under the holding time.

[0025] In another possible implementation, before obtaining the first actual life data, the second actual life data and the third actual life data of the metal part sample constructed of the target metal material, the method also includes: applying a preload force within a preset force range to the metal part sample; heating the metal part sample to a preset temperature, and continuously keeping it warm at the preset temperature and for a preset time.

[0026] The preload force can be 0.4KN.

[0027] In this implementation, a preload force within a preset force range is applied to the metal sample to ensure that the metal sample will not be overloaded due to thermal expansion, and the metal sample is heated to a preset temperature and kept warm at the preset temperature and for a preset time to ensure that the inner and outer surfaces of the metal sample are heated evenly.

[0028] In another possible implementation, the second actual life data is obtained under the condition that the first stress difference between the average stress magnitude of the smallest cross-section at the notch position of the metal part and the stress of the parallel section of the smooth structure without the notch is less than the first stress difference threshold, and the third actual life data is obtained under the condition that the second stress difference between the applied stress magnitude applied to the metal part and the stress of the parallel section of the smooth structure without the notch is less than the second stress difference threshold.

[0029] In this implementation, the second actual life data and the third actual life data are data obtained by performing load holding tests at different maximum stresses, based on the fact that the applied stress level must be consistent with the stress magnitude of the notched smooth specimen.

[0030] According to a second aspect of an embodiment of the present invention, a metal part life prediction device is provided, the device comprising: a determination unit, used to determine a first pure fatigue life of a target metal part in a notched structure in a notched smooth structure state, a target holding time for creep, and a target notch stress concentration factor; an acquisition unit, used to acquire a target preset model associated with a target metal material of the target metal part; the target preset model characterizes a mapping relationship between stress concentration factor, holding time, pure fatigue service life in a notched smooth structure state, and creep-fatigue service life; the target preset model is constructed based on data of metal part samples with the same metal material as the target metal material under different actual holding times, different stress magnitudes, and different actual stress concentration factors; a prediction unit, used to obtain, based on the target preset model, a target service life of the target metal part under the conditions of the first pure fatigue life, target holding time, and target notch stress concentration factor.

[0031] According to a third aspect of an embodiment of the present invention, a controller is provided. The controller is configured to execute the metal part life prediction method as described in the first aspect and any possible implementation thereof.

[0032] According to a fourth aspect of an embodiment of the present invention, a computer device is provided, comprising: a processor and a memory for storing processor executable instructions; wherein the processor is configured to execute executable instructions to implement a metal part life prediction method such as the first aspect and any possible implementation thereof.

[0033] According to a fifth aspect of an embodiment of the present invention, a controller is provided to implement the metal part life prediction method as described in the first aspect and any possible implementation thereof.

[0034] According to the sixth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which instructions are stored. When the instructions in the computer-readable storage medium are executed by a processor of a computer device, the computer device is enabled to execute a metal part life prediction method such as the first aspect and any possible implementation thereof.

[0035] According to the seventh aspect of the embodiment of the present application, a computer program product is provided, which includes computer instructions. When the computer instructions are executed on a computer device, the computer device executes the metal part life prediction method of the above-mentioned first aspect and any possible implementation method thereof.

[0036] The technical solution provided by the embodiments of the present invention brings at least the following beneficial effects: in view of the fact that the creep-fatigue service life of different metal materials is affected by the metal material, stress concentration factor and holding time, the present application considers the influence of metal materials on the service life and sets corresponding preset models for different metal materials. Therefore, based on the correlation and correspondence between the metal material and the preset model, the target preset model corresponding to the target metal material to which the target metal part belongs is used to predict the creep-fatigue service life of the target metal part; and the preset model considers the influence of the two parameters of stress concentration factor and holding time on the pure fatigue service life of the metal part in the notched smooth structure state at the same time, and constructs the preset models corresponding to different metal materials in view of the correlation mapping relationship between stress concentration factor, holding time, pure fatigue service life of notched smooth structure state and creep-fatigue service life, so as to predict the influence of the corresponding metal material on the pure fatigue service life of notched smooth structure state under different stress concentration factors and different holding times, thereby obtaining the creep-fatigue service life of the metal part.

[0037] The above preset model for service life prediction takes into account the superposition effect of creep-fatigue interactive damage. Based on the multi-axial state data obtained by uniaxial stretching of notched structural parts, the preset model of the correlation mapping relationship between stress concentration factor, holding time, pure fatigue service life of smooth structure without notch and creep-fatigue service life is constructed, which makes the service life prediction process more accurate. At the same time, the creep-fatigue service life is in a double logarithmic linear relationship with the stress concentration factor and the holding time respectively. The correlation between the relevant inference data and the creep-fatigue service life in the multi-axial data of different stresses in different directions relied on by the prediction model is high. The construction and acquisition process of the preset model corresponding to different metal materials is the same, so the prediction model is universal, so the same preset model can be used to more accurately predict the life of metal parts under different stress concentration factors and different holding times of the metal materials corresponding to the prediction model, and the complex process of selecting a model matching the stress concentration factor from different scattered models is omitted, simplifying the prediction steps and optimization correction process of the service life, and accelerating the prediction speed of the service life prediction.

[0038] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0040] Figure 1 is a flow chart of a method for predicting the life of a metal part according to an exemplary embodiment;

[0041] Figure 2 is a schematic diagram of structural geometry from a smooth specimen to a notched specimen in a test according to an exemplary embodiment;

[0042] Figure 3 is a schematic diagram of a creep-fatigue cycle loading process with different holding times according to an exemplary embodiment;

[0043] Figure 4 is a schematic diagram showing the relationship between the fatigue life of a smooth specimen and the fatigue life of a notched specimen according to an exemplary embodiment;

[0044] Figure 5 is a schematic diagram showing the relationship between the fatigue life of a notched specimen and the creep-fatigue life of a notched specimen according to an exemplary embodiment;

[0045] Figure 6is a schematic diagram showing a comparison result between a predicted life span and an actually measured life span according to an exemplary embodiment;

[0046] Figure 7 is a block diagram of a metal part life prediction device according to an exemplary embodiment;

[0047] Figure 8 is a schematic diagram of a control device according to an exemplary embodiment. DETAILED DESCRIPTION

[0048] In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0049] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0050] Before introducing in detail the metal part life prediction method provided in the embodiment of the present application, a brief introduction to the application scenarios involved in the embodiment of the present application is first given.

[0051] High-temperature creep-fatigue fracture has always been one of the important causes of structural failure for high-temperature structures. Therefore, in order to better judge the damage of metal materials, designing relevant metal life prediction models to predict the service life of components containing notches is the focus of current scientific attention.

[0052] The current service life prediction model for notched components is a basic model constructed based on the uniaxial tensile specimen test data of notched components in one direction. The creep effect caused by the holding time is then analyzed to correct the service life predicted by the basic model, thereby obtaining the service life of the notched component.

[0053] However, since the stress and strain at the local notch are very complex and the stress triaxiality is high, the above-mentioned prediction model does not take into account the superposition effect brought about by creep-fatigue interaction damage when making corrections based on the uniaxial tensile specimen test data. In addition, the correlation between the service life data and other data on which the correction process relies is poor, that is, the correction model is relatively scattered, resulting in low accuracy of the prediction results when the prediction model uses the same model to predict the service life of notched components with different structural shapes.

[0054] According to research, the establishment of creep-fatigue life prediction models is usually based on the development of damage mechanics and fracture mechanics. Due to the differences in material properties and structural designs between different materials, it becomes more difficult to predict the creep-fatigue life of different materials. In the first stage, relevant creep-fatigue life prediction models were proposed, but at that time, creep-fatigue life prediction was mainly based on time-independent strain parameters. Most of these models can only predict a single creep life or fatigue life. In the second stage, time-dependent strain parameter prediction models were proposed. These models can better predict creep-fatigue life, but most of these models are based on linear creep and fatigue damage superposition for life prediction, without considering the superposition effect brought by creep-fatigue interactive damage, so the prediction accuracy is not high. With the development of science and technology and the improvement of microscopic characterization methods, people have begun to characterize the creep-fatigue damage of materials from the perspective of material microscopicity, starting from the energy dissipation of materials since the 21st century. The benefit of this is that the damage of materials can be judged more accurately, so the prediction accuracy is also higher.

[0055] Most of the above creep-fatigue prediction models are developed based on uniaxial test data, and most of them are only for a single material. When applied to the creep-fatigue life prediction of notched components, some corrections may be required. One of the reasons is that the stress-strain of the notch is very complex and the stress triaxiality is high. Most of the existing prediction models for creep-fatigue life are developed based on uniaxial smooth specimen test data. When applied to notched specimens, the model selection is more difficult due to the multiaxial stress state caused by the notch. The prediction accuracy of the same model for notched specimens of different materials has a large dispersion.

[0056] In response to the above problems, the present application provides a method for predicting the life of metal parts. In view of the fact that the creep-fatigue service life of different metal materials is affected by the metal material, stress concentration factor and holding time, the present application considers the influence of metal materials on the service life and sets corresponding preset models for different metal materials. Therefore, based on the associated correspondence between the metal material and the preset model, the target preset model associated with the target metal material to which the target metal part belongs is used to predict the creep-fatigue service life of the target metal part; and the preset model considers the influence of the two parameters of stress concentration factor and holding time on the pure fatigue service life of the metal part in the notched smooth structure state at the same time, and constructs the preset models corresponding to different metal materials in view of the associated mapping relationship between stress concentration factor, holding time, pure fatigue service life of the notched smooth structure state and creep-fatigue service life, so as to predict the influence of the corresponding metal material on the pure fatigue service life of the notched smooth structure state under different stress concentration coefficients and different holding times, thereby obtaining the creep-fatigue service life of the metal part.

[0057] Through the above prediction method, the preset model for predicting the service life takes into account the superposition effect brought by creep-fatigue interactive damage. Based on the uniaxial stretching of the notched structural parts to obtain multi-axial state data, the preset model of the correlation mapping relationship between the stress concentration factor, the holding time, the pure fatigue service life of the smooth structure state without a notch and the creep-fatigue service life is constructed, which makes the prediction process of the service life more accurate. At the same time, the creep-fatigue service life is respectively in a double logarithmic linear relationship with the stress concentration factor and the holding time. The correlation between the relevant inference data and the creep-fatigue service life in the multi-axial data of different stresses in different directions that the prediction model relies on is high. The construction and acquisition process of the preset model corresponding to different metal materials is the same, so the prediction model is universal, so the same preset model can be used to more accurately predict the life of metal parts under different stress concentration factors and different holding times of the metal materials corresponding to the prediction model, and the complex process of selecting a model matching the stress concentration factor from different scattered models is omitted, simplifying the prediction steps of the service life and accelerating the prediction speed of the service life prediction.

[0058] The metal part life prediction method provided in the embodiment of the present application can be applied to a controller or control device (such as a server) with a metal part life prediction function.

[0059] For ease of understanding, the metal part life prediction method provided in the present application is specifically introduced below with reference to the accompanying drawings.

[0060] Figure 1 is a flow chart of a method for predicting the life of a metal part according to an exemplary embodiment. Figure 1As shown, the metal part life prediction method includes the following steps.

[0061] S11, determining the first pure fatigue life of the target metal part in the notched structure in the smooth structure state without a notch, the target load holding time for creep, and the target notch stress concentration factor.

[0062] S12, obtaining a target preset model corresponding to the target metal material of the target metal part.

[0063] The target preset model characterizes the mapping relationship between stress concentration factor, holding time, pure fatigue service life and creep-fatigue service life of smooth structure without notch.

[0064] The target preset model is constructed based on data of metal sample products made of target metal materials in different structural states under different actual holding times, different stress magnitudes and different actual stress concentration factors.

[0065] S13, based on the target preset model, obtain the target service life of the target metal part under the first pure fatigue life, target load holding time and target notch stress concentration factor.

[0066] The above target preset model is constructed based on the double logarithmic linear relationship between the creep-fatigue service life and the stress concentration factor and the load holding time, respectively.

[0067] Optionally, a target preset model is constructed through triaxial tests or complex stress state tests.

[0068] In one embodiment, a triaxial test is used to apply stresses in three orthogonal directions to the metal sample to simulate the service life data generated under the three actual stress states.

[0069] In another embodiment, a complex stress state test method is used to apply different stresses to the metal sample in multiple directions to simulate the service life data of the metal material under a complex stress state.

[0070] The metal material of the metal part corresponds to the preset model one by one. The stress concentration coefficients of metal parts with different shapes and structures are different, that is, the shape structure of the metal part is associated with the stress concentration coefficient.

[0071] Exemplarily, the mapping relationship is expressed by the following formula (1).

[0072] N c-f =N f0 (K t ) m (t H ) l Formula (1).

[0073] Among them, N c-f Represents creep-fatigue service life, N f0 Indicates the pure fatigue service life of the smooth structure without notch, K t represents the stress concentration factor, t H represents the holding time, m is the material parameter of the stress concentration factor, also known as the first model coefficient, and l is the material parameter of the holding time, also known as the second model coefficient.

[0074] The above-mentioned metal work sample and target metal work are presented in the form of round bars.

[0075] The stress concentration factor is expressed by the following formula (2).

[0076]

[0077] Among them, r n Indicates the radius of the arc at the root of the notch; d n Indicates the net cross-sectional diameter of the notch root; D n Indicates the diameter of the round bar without the notch.

[0078] Furthermore, the average stress of the smallest cross section at the notch position of the notch is expressed by formula (3).

[0079]

[0080] Among them, σ a is the average stress applied to the smallest cross section at the notch position; σ0 is the maximum stress that can be applied to the smallest cross section; d is the diameter of the smallest cross section of the notch; and D is the smallest diameter of the non-notch cross section.

[0081] Preset models are also called predictive models.

[0082] In the above-mentioned embodiment, in view of the fact that the creep-fatigue service life of different metal materials is affected by the metal material, stress concentration factor and holding time, the present application considers the influence of metal materials on the service life and sets corresponding preset models for different metal materials. Therefore, based on the correlation and correspondence between the metal material and the preset model, the target preset model corresponding to the target metal material to which the target metal part belongs is used to predict the creep-fatigue service life of the target metal part; and the preset model considers the influence of the two parameters of stress concentration factor and holding time on the pure fatigue service life of the metal part in the notch-free smooth structure state at the same time, and constructs the preset models corresponding to different metal materials in view of the correlation mapping relationship between stress concentration factor, holding time, pure fatigue service life of notch-free smooth structure state and creep-fatigue service life, so as to predict the influence of the corresponding metal material on the fatigue service life of the notch-free smooth structure state under different stress concentration coefficients and different holding times, thereby obtaining the creep-fatigue service life of the metal part.

[0083] Through the above prediction method, the preset model for predicting the service life takes into account the superposition effect brought by creep-fatigue interactive damage. Based on the multi-axial state data obtained by uniaxial stretching of notched structural parts, the preset model of the correlation mapping relationship between the stress concentration factor, the holding time, the pure fatigue service life of the smooth structure state without a notch and the creep-fatigue service life is constructed, which makes the prediction process of the service life more accurate. At the same time, the creep-fatigue service life is in a double logarithmic linear relationship with the stress concentration factor and the holding time respectively. The correlation between the relevant inference data and the creep-fatigue service life in the multi-axial data of different stresses in different directions that the prediction model relies on is high. The construction and acquisition process of the preset model corresponding to different metal materials is the same, so the prediction model is universal, so the same preset model can be used to more accurately predict the life of metal parts under different stress concentration factors and different holding times of the metal materials corresponding to the prediction model, and the complex process of selecting a model that matches the stress concentration factor from different scattered models is eliminated, simplifying the prediction steps of the service life and accelerating the prediction speed of the service life prediction.

[0084] In one embodiment, before obtaining the target preset model associated with the target metal material of the target metal part, the target preset model is obtained in the following manner.

[0085] First, first actual life data, second actual life data, and third actual life data of a metal part sample made of a target metal material are obtained.

[0086] The first actual life data includes pure fatigue service life data of the metal sample in a notched smooth structure state, the second actual life data includes pure fatigue service life data of the metal sample in a notched structure state under multiple different actual stress concentration factors, and the third actual life data includes creep-fatigue service life data of the metal sample in a notched structure state under multiple different actual holding times and different actual stress concentration factors corresponding to the multiple different actual holding times.

[0087] Secondly, the first actual life data, the second actual life data and the third actual life data are used to fit the model coefficients of the preset model that characterizes the mapping relationship to obtain a target preset model.

[0088] It is understandable that the actual life data includes state data under the use state and the corresponding service life under the state data. The use state includes creep state and / or fatigue state. Different actual stresses include different stresses applied in different directions. The actual life data includes service life data under different use states.

[0089] Specifically, the first actual life data includes the pure fatigue service life under different actual stresses and the actual stress corresponding to the pure fatigue service life when the metal sample is in a smooth structure state without a notch. The second actual life data includes the pure fatigue service life under multiple different actual stress concentration factors corresponding to different actual stresses when the metal sample is in different notch structural states and the notch does not creep, and the actual stress concentration factor and actual stress corresponding to each pure fatigue service life. The third actual life data includes the creep-fatigue service life under different actual stress concentration factors corresponding to multiple different actual holding times when the metal sample is in different notch structural states and the notch creeps, and the actual stress, stress concentration factor and actual holding time corresponding to each creep-fatigue service life.

[0090] In this embodiment, based on three state data formed under different structural states and different stresses, the change in the usage state of the metal part sample from the notch-free smooth structural state to the metal part sample under the notch structural state under different actual stresses, as well as the change in service life under different usage states are determined. Therefore, the pure fatigue effect of the notch state and the superposition effect brought about by the interaction damage of creep and fatigue are reflected through the change law of the service life data under three different states, thereby more accurately characterizing the correlation mapping relationship between the stress concentration factor, the holding time, the pure fatigue service life of the notch-free smooth structural state and the creep-fatigue service life.

[0091] Furthermore, in one embodiment, the model coefficient includes a first model coefficient and a second model coefficient, and the preset model characterizes the mapping relationship between the first model coefficient power of the stress concentration coefficient, the second model coefficient power of the load holding time, and the pure fatigue service life of the notch-free smooth structure state and the creep-fatigue service life.

[0092] In this embodiment, based on the property that the creep-fatigue service life is in a double logarithmic linear relationship with the stress concentration factor and the holding time, a first model coefficient exponential relationship between the creep-fatigue service life and the stress concentration factor, and a second model coefficient exponential relationship between the creep-fatigue service life and the holding time are determined.

[0093] Based on this implementation, the specific fitting process of fitting the model coefficients of the preset model representing the mapping relationship using the first actual life data, the second actual life data and the third actual life data is as follows.

[0094] First, obtain the initially constructed first coefficient model and second coefficient model.

[0095] The first coefficient model characterizes the first double logarithmic linear relationship between the pure fatigue service life of the smooth structure without notch and the pure fatigue service life under the first model coefficient power of the stress concentration factor.

[0096] The second coefficient model characterizes the second double logarithmic linear relationship between the pure fatigue service life under the stress concentration factor of the notch structure state and the creep-fatigue service life under the power of the second model coefficient of the holding time.

[0097] Secondly, the first double logarithmic linear relationship represented by the first coefficient model is fitted using the first actual life data and the second actual life data to obtain the first model coefficient. And, the second double logarithmic linear relationship represented by the second coefficient model is fitted using the second actual life data and the third actual life data to obtain the second model coefficient.

[0098] Exemplarily, the first coefficient model and the second coefficient model are represented by formula (4) and formula (5) respectively.

[0099] N f =N f0 (K t ) m Formula (4).

[0100] N c-f =N f0 (K t ) m (t H ) l Formula (5).

[0101] Among them, N f It is the pure fatigue service life of the smooth structure without notches.

[0102] The first coefficient model is constructed based on the double logarithmic linear relationship between the pure fatigue service life under the notched smooth structure state and the pure fatigue service life under the stress concentration factor. The second coefficient model is constructed based on the double logarithmic linear relationship between the pure fatigue service life under the stress concentration factor under the notched structure state and the creep-fatigue service life under the holding time.

[0103] In this embodiment, the first model coefficient in the first coefficient model is fitted by the first actual life data and the second actual life data to determine the influence factor of stress and notch structure on fatigue service life, that is, first determine the influence of different stress concentration factors on high temperature low cycle fatigue life without considering the holding time factor. Then, the second model coefficient of the second coefficient model is fitted by the second actual life data and the third actual life data to determine the influence factor of stress and notch structure on creep-fatigue service life.

[0104] As a model coefficient fitting method, the second actual life data and the third actual life data are used to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient. The specific process is as follows.

[0105] First, the third actual life data whose actual holding time is one time unit is selected from the third actual life data.

[0106] Secondly, based on the distribution trend of the third actual life data whose actual holding time is selected as a time unit, the second actual life data whose similarity with the distribution trend is greater than a preset similarity threshold is selected from the second actual life data.

[0107] Thirdly, the second actual life data selected whose similarity with the distribution trend is greater than the preset similarity threshold and the third actual life data selected whose actual holding time is one time unit are used to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient.

[0108] Correspondingly, in another model coefficient fitting method, before the first actual life data and the second actual life data are used to fit the first double logarithmic linear relationship represented by the first coefficient model, the third actual life data with an actual holding time of one time unit is first selected from the third actual life data; then, based on the distribution trend of the selected third actual life data with an actual holding time of one time unit, the second actual life data with a similarity to the distribution trend greater than a preset similarity threshold is selected from the second actual life data. The first double logarithmic linear relationship represented by the first coefficient model is fitted using the selected second actual life data with a similarity to the distribution trend greater than the preset similarity threshold and the corresponding first actual life data to obtain the first model coefficient.

[0109] In this embodiment, by setting the holding time to 1, the second actual life data and the third actual life data are closely linked, thereby associating the data with the holding time and the data without the holding time. Therefore, the target preset model constructed based on the second actual life data and the third actual life data can more accurately reflect the correlation between the pure fatigue service life under the stress concentration factor of the notch structure state and the creep-fatigue service life under the holding time.

[0110] As an implementation method, in order to ensure the accuracy of the actual life data used to construct the target prediction model, the following processing process is specifically implemented before obtaining the first actual life data, the second actual life data and the third actual life data of the metal part sample constructed of the target metal material.

[0111] First, a preload within a preset force range is applied to the metal sample.

[0112] Second, the metal sample is heated to a preset temperature and kept warm at the preset temperature and for a preset time.

[0113] The preload force can be 0.4KN.

[0114] In this embodiment, a preload force within a preset force range is applied to the metal sample to ensure that the metal sample will not be overloaded due to thermal expansion, and the metal sample is heated to a preset temperature and kept warm at the preset temperature and for a preset time to ensure that the inner and outer surfaces of the metal sample are heated evenly.

[0115] As another embodiment, the second actual life data is obtained under the condition that the first stress difference between the average stress magnitude of the smallest cross-section at the notch position of the metal part and the stress of the parallel section of the smooth structure without the notch is less than the first stress difference threshold, and the third actual life data is obtained under the condition that the second stress difference between the applied stress magnitude on the metal part and the stress of the parallel section of the smooth structure without the notch is less than the second stress difference threshold.

[0116] In this embodiment, the second actual life data and the third actual life data are data obtained by performing load holding tests at different maximum stresses based on the fact that the applied stress level should be consistent with the stress magnitude of the notched smooth test specimen.

[0117] As a method of obtaining actual life data, the acquisition process is described in Table 1 below, taking the metal sample of 316H stainless steel as an example. The test temperature is selected as 600℃, and the notch stress concentration factor K t are 1.0, 3.2 and 4.2 respectively, and the holding time t H They are 60s, 300s and 1800s respectively. t =1.0 indicates the test result of a smooth specimen (i.e. a metal specimen in a smooth structure without a notch), a fatigue test without a holding time, and a creep-fatigue test with a holding time. The fatigue and creep-fatigue test results of 316H stainless steel under different notch stress concentration factors and different holding times are shown in Table 1. t =1.0 is a smooth specimen, and the nominal maximum stress refers to the average stress of the net section at the root of the notch when the load is loaded to the maximum.

[0118] In some embodiments, the metal sample in a smooth structure without notches is also referred to as a smooth sample. The metal sample in a notched structure is also referred to as a notched sample.

[0119] Specifically, the smooth specimens and notched specimens used in the test have the following structural geometries: Figure 2 Considering the creep-fatigue cyclic loading process with different holding times, Figure 3 In the process of testing the fatigue life test data of smooth specimens, it is necessary to carry out fatigue tests of smooth specimens with no less than 3 different stress magnitudes, and the temperature must be higher than the creep initiation temperature of the metal.

[0120] In some embodiments, according to the test data in Table 1, the relationship between the fatigue life of the smooth specimen and the fatigue life of the notched specimen in the logarithmic coordinate system is as follows: Figure 4 As shown in the figure, it can be seen that the fitting curve m has little to do with the nominal maximum stress level and the stress concentration factor, but is only related to the material properties, and the first model coefficient m = -0.51.

[0121] Table 1 Test data of smooth and notched 316H stainless steel specimens under different conditions

[0122]

[0123] The relationship between the notched specimen fatigue life and the notched specimen creep-fatigue life in the logarithmic coordinate system is as follows: Figure 5As shown in the figure, it can be seen that there is a certain linear relationship between the single cycle time and the creep-fatigue life of the notched specimen, and l is only related to the material properties, so the second model coefficient l = -0.157.

[0124] Therefore, the multiaxial creep-fatigue life prediction model of 316H stainless steel based on notch stress concentration factor, that is, the target prediction model, is obtained, as shown in formula (6).

[0125]

[0126] The prediction model and system provided in the embodiment of the present application are used to predict the notch creep-fatigue life of 316H stainless steel. The comparison results between the predicted life and the measured life are as follows: Figure 6 As shown in the figure, it can be seen that when the temperature and holding time are constant, when this method is used to predict the creep-fatigue life of 316H stainless steel notched specimens, the prediction accuracy is within the range of ±2.5 times, and the prediction accuracy is good, thus verifying the correctness and effectiveness of the life prediction method of this application.

[0127] The prediction model and system provided in the embodiment of the present application are used to predict the notch creep-fatigue life of 316H stainless steel. The comparison results between the predicted life and the measured life are as follows: Figure 6 As shown in the figure, it can be seen that when the temperature and holding time are constant, when this method is used to predict the creep-fatigue life of 316H stainless steel notched specimens, the prediction accuracy is within the range of ±2.5 times, and the prediction accuracy is good, thus verifying the correctness and effectiveness of the life prediction method of this application.

[0128] In order to achieve the above functions, the metal parts life prediction device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0129] The present application also provides a method Figure 7 The device for predicting the life of a metal part shown in the figure comprises: a determination unit 701 , an acquisition unit 702 and a prediction unit 703 .

[0130] The determination unit 701 is used to determine the first pure fatigue life of the target metal part in the notched structure in the smooth structure state without a notch, the target holding time for creep, and the target notch stress concentration factor.

[0131] The acquisition unit 702 is used to acquire a target preset model associated with a target metal material of a target metal part; the target preset model represents a mapping relationship between stress concentration factor, holding time, pure fatigue service life of a notched smooth structure state and creep-fatigue service life; the target preset model is constructed based on data of metal part samples with the same metal material as the target metal part under different actual holding times, different stress magnitudes and different actual stress concentration factors.

[0132] The prediction unit 703 is used to obtain the target service life of the target metal part under the first pure fatigue life, the target load holding time and the target notch stress concentration factor based on the target preset model.

[0133] As an implementation manner, before obtaining the target preset model associated with the target metal material of the target metal part, the obtaining unit 701 is further used to:

[0134] The first actual life data, the second actual life data and the third actual life data of the metal part sample are obtained; the first actual life data include the pure fatigue service life data of the metal part sample in a notched smooth structure state, the second actual life data include the pure fatigue service life data of the metal part sample in a notched structure state under multiple different actual stress concentration factors, and the third actual life data include the creep-fatigue service life data of the metal part sample in a notched structure state under multiple different actual holding times and different actual stress concentration factors corresponding to the multiple different actual holding times; the first actual life data, the second actual life data and the third actual life data are used to fit the model coefficients of the preset model that characterizes the mapping relationship to obtain the target preset model.

[0135] As an implementation method, the model coefficient includes a first model coefficient and a second model coefficient, and the preset model represents the mapping relationship between the first model coefficient power of the stress concentration coefficient, the second model coefficient power of the load holding time, and the pure fatigue service life of the notch-free smooth structure state and the creep-fatigue service life.

[0136] As an implementation mode, the acquisition unit 701 is specifically used to: acquire a first coefficient model and a second coefficient model; the first coefficient model represents a first double logarithmic linear relationship between the pure fatigue service life of a notched smooth structure state and the pure fatigue service life at the first model coefficient power of the stress concentration coefficient; the second coefficient model represents a second double logarithmic linear relationship between the pure fatigue service life under the stress concentration coefficient of the notched structure state and the creep-fatigue service life at the second model coefficient power of the holding time; the first actual life data and the second actual life data are used to fit the first double logarithmic linear relationship represented by the first coefficient model to obtain the first model coefficient; the second actual life data and the third actual life data are used to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient.

[0137] As an implementation mode, the acquisition unit 701 is specifically used to: select third actual life data with an actual holding time of one time unit from the third actual life data; based on the distribution trend of the selected third actual life data with an actual holding time of one time unit, select second actual life data with a similarity to the distribution trend greater than a preset similarity threshold from the second actual life data; use the selected second actual life data with a similarity to the distribution trend greater than the preset similarity threshold and the selected third actual life data with an actual holding time of one time unit to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient.

[0138] As an implementation manner, before obtaining the first actual life data, the second actual life data and the third actual life data of the metal part sample, the obtaining unit 701 is further used to: apply a preload force within a preset force range to the metal part sample;

[0139] Heat the metal sample to a preset temperature and keep it at the preset temperature for a preset time.

[0140] As an implementation mode, the second actual life data is obtained under the condition that the first stress difference between the average stress magnitude of the smallest cross section at the notch position of the metal part and the stress of the parallel section of the smooth structure without the notch is less than the first stress difference threshold, and the third actual life data is obtained under the condition that the second stress difference between the applied stress magnitude on the metal part and the stress of the parallel section of the smooth structure without the notch is less than the second stress difference threshold.

[0141] Regarding the device in the above embodiment, the specific manner in which each unit module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0142] Figure 8 is a schematic diagram of a control device provided by this application. Figure 8The control device 80 may include at least one processor 801 and a memory 803 for storing processor executable instructions. The processor 801 is configured to execute instructions in the memory 803 to implement the metal part life prediction method in the following embodiment.

[0143] In addition, the control device 80 may further include a communication bus 802 , at least one communication interface 804 , an input device 806 , and an output device 805 .

[0144] The processor 801 may be a central processing unit (CPU), a microprocessing unit, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0145] The communication bus 802 may include a pathway for transmitting information between the above-mentioned components.

[0146] The communication interface 804 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0147] The input device 806 is used to receive input signals and the output device 805 is used to output signals.

[0148] The memory 803 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processing unit through a bus. The memory may also be integrated with the processing unit.

[0149] The memory 803 is used to store instructions for executing the solution of the present application, and the execution is controlled by the processor 801. The processor 801 is used to execute the instructions stored in the memory 803, thereby realizing the functions of the method of the present application.

[0150] In a specific implementation, as an embodiment, the processor 801 may include one or more CPUs, such as Figure 8 CPU0 and CPU1 in.

[0151] In a specific implementation, as an embodiment, the control device 80 may include multiple processors, such as Figure 8 801 and processor 807 in the embodiment of the present invention. Each of these processors may be a single-CPU processor or a multi-CPU processor. The processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0152] The control device is Figure 8 The system shown includes: a processor 801 and a memory 803 for storing executable instructions of the processor 801; wherein the processor 801 is configured to execute the executable instructions to implement the metal component life prediction method of any possible implementation method described above. And the same technical effect can be achieved, so it will not be repeated here to avoid repetition.

[0153] The embodiment of the present application further provides a controller configured to execute any of the above-mentioned possible implementation methods for predicting the life of metal parts, and can achieve the same technical effect, which will not be described here in detail to avoid repetition.

[0154] The embodiment of the present application also provides a computer-readable storage medium, when the instructions in the computer-readable storage medium are executed by the processor of the control device or control equipment, the control device or control equipment can execute the metal part life prediction method as any possible implementation method described above. And the same technical effect can be achieved, so it will not be repeated here to avoid repetition.

[0155] The embodiment of the present application also provides a computer program product, including a computer program or instruction, which is executed by a processor to perform a metal component life prediction method in any of the above possible implementations. The same technical effect can be achieved, and it will not be repeated here to avoid repetition.

[0156] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0157] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for predicting the life of a metal part, characterized in that: The method comprises: Determine the first pure fatigue life of the target metal part in the notched structure in the smooth structure state without a notch, the target holding time for creep, and the target notch stress concentration factor; And, obtaining a target preset model associated with the target metal material of the target metal part; the target preset model characterizes the mapping relationship between the stress concentration factor, the holding time, the pure fatigue service life of the smooth structure state without notches and the creep-fatigue service life; the target preset model is constructed based on the data of metal part samples with the same metal material as the target metal part under different actual holding times, different stress magnitudes and different actual stress concentration factors; Based on the target preset model, the first pure fatigue life, the target load holding time and the target service life of the target metal part under the target notch stress concentration factor are obtained.

2. The method according to claim 1, characterized in that Before acquiring the target preset model associated with the target metal material of the target metal part, the method further includes: Obtaining first actual life data, second actual life data, and third actual life data of the metal component sample; the first actual life data includes pure fatigue service life data of the metal component sample in a smooth structure state without a notch, the second actual life data includes pure fatigue service life data of the metal component sample in a notched structure state under multiple different actual stress concentration factors, and the third actual life data includes creep-fatigue service life data of the metal component sample in a notched structure state under multiple different actual holding times and different actual stress concentration factors corresponding to the multiple different actual holding times; The first actual life data, the second actual life data and the third actual life data are used to fit the model coefficients of the preset model that characterizes the mapping relationship to obtain the target preset model.

3. The method according to claim 2, characterized in that The model coefficients include a first model coefficient and a second model coefficient, and the preset model characterizes the mapping relationship between the first model coefficient power of the stress concentration factor, the second model coefficient power of the holding time, and the pure fatigue service life of the notched smooth structure state and the creep-fatigue service life.

4. The method according to claim 3, characterized in that The adopting of the first actual life data, the second actual life data and the third actual life data to fit the model coefficients of the preset model representing the mapping relationship to obtain the target preset model includes: Obtain a first coefficient model and a second coefficient model; the first coefficient model represents a first double logarithmic linear relationship between the pure fatigue service life of a notched smooth structure state and the pure fatigue service life at the first model coefficient power of the stress concentration factor; the second coefficient model represents a second double logarithmic linear relationship between the pure fatigue service life at the stress concentration factor of the notched structure state and the creep-fatigue service life at the second model coefficient power of the holding time; Using the first actual life data and the second actual life data to fit the first double logarithmic linear relationship represented by the first coefficient model to obtain the first model coefficient; The second actual life data and the third actual life data are used to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient.

5. The method according to claim 4, characterized in that The step of fitting the second double logarithmic linear relationship represented by the second coefficient model using the second actual life data and the third actual life data to obtain the second model coefficients includes: Selecting third actual life data whose actual holding time is one time unit from the third actual life data; Based on the selected distribution trend of the third actual life data with the actual holding time being one time unit, selecting the second actual life data having a similarity with the distribution trend greater than a preset similarity threshold from the second actual life data; The second actual life data selected whose similarity with the distribution trend is greater than a preset similarity threshold and the third actual life data selected whose actual holding time is one time unit are used to fit the second double logarithmic linear relationship represented by the second coefficient model to obtain the second model coefficient.

6. The method according to any one of claims 2 to 5, characterized in that Before obtaining the first actual life data, the second actual life data and the third actual life data of the metal part sample, the method further includes: Applying a preload force within a preset force range to the metal sample; The metal sample is heated to a preset temperature and kept warm at the preset temperature for a preset time.

7. The method according to any one of claims 2 to 5, characterized in that The second actual life data is obtained under the condition that the first stress difference between the average stress magnitude of the smallest cross-section at the notch position of the metal part and the stress of the parallel section of the smooth structure without a notch is less than a first stress difference threshold, and the third actual life data is obtained under the condition that the second stress difference between the applied stress magnitude applied to the metal part and the stress of the parallel section of the smooth structure without a notch is less than a second stress difference threshold.

8. A metal parts life prediction device, characterized in that: The device comprises: A determination unit is used to determine the first pure fatigue life of a target metal part in a notched structure in a smooth structure state without a notch, a target holding time for creep, and a target notch stress concentration factor; An acquisition unit is used to acquire a target preset model associated with a target metal material of the target metal part; the target preset model represents a mapping relationship between stress concentration factor, holding time, pure fatigue service life of a smooth structure without a notch and creep-fatigue service life; the target preset model is constructed based on data of metal part samples with the same metal material as the target metal part under different actual holding times, different stress magnitudes and different actual stress concentration factors; A prediction unit is used to obtain the target service life of the target metal part under the first pure fatigue life, the target load holding time and the target notch stress concentration factor based on the target preset model.

9. A controller, characterized in that: The controller is configured to execute the metal part life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor, the processor is enabled to execute the metal part life prediction method according to any one of claims 1 to 7.

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