Fatigue life prediction method and device of non-standard structural component, equipment and storage medium

Abstract

The invention discloses a fatigue life prediction method and device for a non-standard structural member, equipment and a storage medium. The method comprises the following steps: establishing a geometric model according to entity measurement data of a target structural member; performing grid division on the geometric model to obtain a grid model; based on the grid model, performing static analysis according to a preset material attribute, a preset constraint condition and a preset applied load to obtain an analysis result; determining a stress variation coefficient according to the analysis result, and determining the cycle characteristics of the target structural member according to the stress variation coefficient; and determining the fatigue life of the target structural member according to the cycle characteristic and a preset pressed working frequency. According to the technical scheme provided by the invention, the cycle characteristics are optimized based on the variation theory, so that the calculated cycle characteristics better conform to the actual working condition with unbalance loading, and the prediction result of the fatigue life is more accurate.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of structural design, and in particular to a fatigue life prediction method, device, equipment and storage medium for non-standard structural parts. Background Art

[0002] For non-standard structural parts, low-cycle fatigue damage may occur under long-term reciprocating cycles, thus affecting the accuracy of the whole machine and posing safety hazards. Therefore, it is usually necessary to calculate the fatigue life of designated non-standard structural parts in a targeted manner in order to make reasonable designs and ensure the safety of design and use.

[0003] At present, the fatigue life calculation process usually uses the cycle characteristics obtained by comparing the SN curve of the low-cycle fatigue cycle characteristics to calculate the fatigue life. However, in the actual working process, due to the possible problem of local eccentric loading, the obtained cycle characteristics may be lower than the actual cycle characteristics, thus affecting the accuracy of the fatigue life estimation results. Summary of the invention

[0004] The embodiments of the present invention provide a fatigue life prediction method, device, equipment and storage medium for non-standard structural parts to solve the problem of low accuracy of prediction results due to calculation deviation of cyclic characteristics in the current fatigue life prediction process of non-standard structural parts.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting fatigue life of a non-standard structural component, the method comprising:

[0006] Establishing geometric model based on the entity measurement data of target structural parts;

[0007] Meshing the geometric model to obtain a mesh model;

[0008] Based on the grid model, a static analysis is performed according to preset material properties, preset constraint conditions and preset applied loads to obtain an analysis result;

[0009] Determining a stress variation coefficient according to the analysis result, and determining a cyclic characteristic of the target structural component according to the stress variation coefficient;

[0010] The fatigue life of the target structural component is determined according to the cycle characteristics and the preset number of compression operations.

[0011] Optionally, determining a stress variation coefficient according to the analysis result, and determining a cyclic characteristic of the target structural component according to the stress variation coefficient, includes:

[0012] Extracting the first principal stress, the second principal stress and the third principal stress from the analysis results;

[0013] Calculating the average value and standard deviation of the first principal stress, the second principal stress and the third principal stress;

[0014] The stress variation coefficient is calculated as the cycle characteristic according to the average value and the standard deviation, and the calculation formula is: stress variation coefficient=standard deviation / average value×100%.

[0015] Optionally, determining the fatigue life of the target structural component according to the cycle characteristics and a preset number of times of pressure-bearing work includes:

[0016] Calculate the safety factor according to the cycle characteristics and the preset number of times of pressure-bearing operation;

[0017] The fatigue life is calculated based on the safety factor and the cycle characteristics.

[0018] Optionally, establishing a geometric model according to the entity measurement data of the target structural part includes:

[0019] The geometric model is established in a 1:1 ratio based on the entity measurement data through CAD software and exported to a preset format file.

[0020] Optionally, before meshing the geometric model to obtain a mesh model, the method further includes:

[0021] Importing the preset format file into CAE software;

[0022] The step of meshing the geometric model to obtain a mesh model comprises:

[0023] Meshing the geometric model using the CAE software;

[0024] Based on the grid model, static analysis is performed according to preset material properties, preset constraint conditions and preset applied loads to obtain analysis results, including:

[0025] Static analysis was performed using the CAE software.

[0026] Optionally, the preset format file is an xt format file or an iges format file.

[0027] In a second aspect, an embodiment of the present invention further provides a fatigue life prediction device for non-standard structural parts, the device comprising:

[0028] A model building module is used to build a geometric model based on the entity measurement data of the target structural part;

[0029] A meshing module, used for meshing the geometric model to obtain a mesh model;

[0030] A static analysis module, used to perform static analysis based on the grid model according to preset material properties, preset constraint conditions and preset applied loads to obtain analysis results;

[0031] a cyclic characteristic determination module, used to determine a stress variation coefficient according to the analysis result, and determine the cyclic characteristic of the target structural component according to the stress variation coefficient;

[0032] The fatigue life determination module is used to determine the fatigue life of the target structural component according to the cycle characteristics and the preset number of compression working times.

[0033] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:

[0034] one or more processors;

[0035] A memory for storing one or more programs;

[0036] When the one or more programs are executed by the one or more processors, the one or more processors implement the fatigue life prediction method for non-standard structural parts provided by any embodiment of the present invention.

[0037] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the fatigue life prediction method for non-standard structural parts provided by any embodiment of the present invention.

[0038] In a fifth aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program, and when the program is executed by a processor, it implements the fatigue life prediction method of non-standard structural parts provided by any embodiment of the present invention.

[0039] An embodiment of the present invention provides a fatigue life prediction method for non-standard structural parts. First, a geometric model is established based on the physical measurement data of the target structural part. Then, the geometric model is meshed to obtain a mesh model. Then, based on the mesh model, a static analysis is performed according to preset material properties, preset constraints, and preset applied loads to obtain analysis results. Then, the stress variation coefficient is determined based on the analysis results, and the cyclic characteristics of the target structural part are determined based on the stress variation coefficient. Then, the fatigue life of the target structural part is determined based on the cyclic characteristics and the preset number of times of compression work. The fatigue life prediction method for non-standard structural parts provided by the embodiment of the present invention optimizes the cyclic characteristics based on the variation theory, so that the calculated cyclic characteristics are more in line with the actual working conditions with eccentric loads, thereby making the prediction results of fatigue life more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart of a fatigue life prediction method for non-standard structural parts provided in Embodiment 1 of the present invention;

[0041] Figure 2 A grid model of an exemplary main frame provided in the first embodiment of the present invention;

[0042] Figure 3 A Von Mises equivalent stress cloud diagram of an exemplary main frame provided in the first embodiment of the present invention;

[0043] Figure 4 A deformation cloud diagram of an exemplary main frame provided in the first embodiment of the present invention;

[0044] Figure 5 A Von Mises equivalent stress cloud diagram of an exemplary main frame fixed beam provided in the first embodiment of the present invention;

[0045] Figure 6 An exemplary main frame body safety factor cloud diagram provided in the first embodiment of the present invention;

[0046] Figure 7 A schematic diagram of the structure of a fatigue life prediction device for non-standard structural parts provided in the second embodiment of the present invention;

[0047] Figure 8 This is a schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0049] It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0050] Embodiment 1

[0051] Figure 1This is a flowchart of a fatigue life prediction method for non-standard structural parts provided in Example 1 of the present invention. This embodiment is applicable to the case of fatigue life prediction for various non-standard structural parts. The method can be executed by a fatigue life prediction device for non-standard structural parts provided in an embodiment of the present invention, which can be implemented by hardware and / or software and can generally be integrated into a computer device. Figure 1 As shown, the specific steps include:

[0052] S11. Establishing a geometric model according to the entity measurement data of the target structural part.

[0053] Specifically, for the target structural part whose fatigue life is to be predicted, the entity can first be manually measured to obtain entity measurement data. Then, the entity measurement data input by the user can be obtained, so as to establish a geometric model based on the entity measurement data. Among them, for example, the target structural part can be the main frame of a certain type of kitchen waste press, and the frame can be a beam-column type, a frame type, or a wire winding type, etc. Taking the beam-column type as an example, the measured entity measurement data can include: the thickness of the fixed beams at both ends is 510mm, the length is 2750mm, and the width is 2250mm. The fixed beam is a cast steel part made of ZG20SiMn, which has good casting and welding properties. There are 4 anchor supports, each with a clear height of 1400mm, which is convenient for the bottom to install a matching material receiving device. The bottom contact area is 700×700mm, and the minimum shaft diameter of the frame pull rod is 210mm, and the corresponding basic dimensions such as nuts and washers are also 210mm.

[0054] Extrusion technology is a new type of waste resource pretreatment method. It squeezes out the water and soluble substances in the mixed materials, separates the dry and wet components, and facilitates subsequent resource utilization. The kitchen waste press developed for this purpose is an environmentally friendly non-standard heavy equipment, and the press is subjected to long-term reciprocating cycles. The whole machine frame will suffer low-cycle fatigue damage, and the main beam, pressing chamber, etc. are easily damaged due to fatigue, which affects the accuracy of the whole machine and poses a safety hazard. At the same time, from the perspective of practical application, due to the complexity of the feed waste material, local overload will occur during the pressing process. Therefore, the prediction method provided in this embodiment can be applied to predict the fatigue life of structural parts in this type of product.

[0055] Optionally, establishing a geometric model according to the entity measurement data of the target structural part includes: establishing the geometric model according to a 1:1 ratio based on the entity measurement data by using CAD software, and exporting it as a preset format file. The CAD software may include but is not limited to SolidWorks, Proe, and UG, etc. Further optionally, the preset format file is an xt format file or an iges format file.

[0056] S12, meshing the geometric model to obtain a mesh model.

[0057] Optionally, before meshing the geometric model to obtain the mesh model, the method further includes: importing the preset format file into CAE software. Further, optionally, meshing the geometric model to obtain the mesh model includes: meshing the geometric model using the CAE software.

[0058] Specifically, CAE software may include but is not limited to ANSYS finite element analysis software. The preset format file exported by the CAD software can be imported into ANSYS, and the Workbench module is entered. Static Structural is selected, the target structural model is imported, and Solid 186 is selected as the unit type. Then, the material properties of each part of the target structural part are defined in the Engineering Data module. For example, taking the above-mentioned main frame as an example, the main beam material is ZG20SiMn low-carbon alloy steel, which is an isotropic material (of course, the prediction method provided in this embodiment is also applicable to orthotropic materials, and the parametric setting can directly define the material constitutive relationship), and the proposed elastic modulus is 2.06e11Pa, the Poisson's ratio is 0.3, and the density is 7860Kg / m 3 The pull rod is made of 45 steel, medium frequency quenching ≥610MPa, σs ≥355MPa.

[0059] Furthermore, the parametric geometric model of the target structural part can be meshed in the Workbench module. When meshing, you can use a combination of quadrilateral units and hexagonal units, and manually mesh to make the mesh as regular and uniform as possible. At the same time, you need to pay attention to maintaining a certain mesh density, and you need to increase the mesh density appropriately at the chamfer to ensure that the calculation results are true and reliable. Taking the above main frame as an example, the resulting mesh model has a total of 401,753 nodes and 197,995 mesh units. Figure 2 As shown. Of course, meshing can also be done using other meshing tools, such as Hypermesh, ANSA, etc.

[0060] S13. Based on the grid model, perform static analysis according to preset material properties, preset constraint conditions and preset applied loads to obtain analysis results.

[0061] Optionally, the performing static analysis based on the grid model according to preset material properties, preset constraints and preset applied loads to obtain analysis results includes: performing static analysis through the CAE software.

[0062] Specifically, after the mesh model is generated based on the CAE software, the user can set the constraints and applied loads according to the actual working conditions of the target structural parts, thereby obtaining the preset constraints and preset applied loads, and obtaining the preset material properties based on the material properties defined above. Then, the solution can be used to automatically solve the calculation and obtain the static analysis results. Taking the main frame as an example, the constraints may include: the support feet are fixed and the other parts of the frame are assembled according to the actual assembly relationship. The applied load may include: during the calculation process, the cylinder model is bolted, so the overall strength is considered, the middle loading bin and the pressing chamber are omitted, and the middle connection part is replaced by a long straight spacer sleeve. The thrust of the pressing cylinder is 9954945N, which is 1.2 times the preload force of the pull rod. The hydraulic cylinder force is loaded in the tangential direction of the inner wall of the connecting bolt hole (axial direction of the main frame), and the single foot base is loaded with a 50t equipment dynamic load.

[0063] After the calculation is completed, post-processing parameters can be extracted to obtain the single-cycle static analysis results. Taking the above main frame as an example, Figure 3 The Von Mises equivalent stress cloud diagram of the main frame is shown as follows: Figure 4 The deformation cloud diagram of the main frame is shown as follows: Figure 5 This is the Von Mises equivalent stress cloud diagram of the fixed beam. It can be seen that the maximum force on the fixed beam is the same as that on the entire frame, so the fixed beam is considered to be the largest force-bearing part of the frame, and the maximum value is within the elastic range, which is lower than the yield strength of 345MPa. There are very few singular grid units on the edge of the middle hole, and most of the stress values ​​are below 80MPa. The maximum deformation occurs in the middle of the fixed beam, with a maximum deformation of 1.1mm, which is within the elastic force range and far lower than 1.5‰. Both the strength and stiffness meet the design requirements. The Von Mises equivalent stress is 61.8MPa, which is lower than the yield strength of the material ZG20SiMn, which means that the single static structure of the structure design meets the strength requirements. In this case, fatigue analysis becomes even more important.

[0064] S14. Determine a stress variation coefficient according to the analysis result, and determine a cyclic characteristic of the target structural component according to the stress variation coefficient.

[0065] Optionally, determining the stress variation coefficient according to the analysis results, and determining the cyclic characteristics of the target structural component according to the stress variation coefficient, includes: extracting the first principal stress, the second principal stress and the third principal stress from the analysis results; calculating the average value and the standard deviation of the first principal stress, the second principal stress and the third principal stress; calculating the stress variation coefficient as the cyclic characteristics according to the average value and the standard deviation, and the calculation formula is: stress variation coefficient = standard deviation / average value × 100%.

[0066] Among them, the coefficient of variation is a relative indicator to measure the degree of data dispersion, which eliminates the influence of dimension and average value, and the standard deviation is an important indicator to reflect the degree of data dispersion. Taking the above main frame as an example, the first principal stress is extracted from the analysis results to be 189.6MPa, the second principal stress is 155.3MPa, and the third principal stress is 47.4MPa. The average value of the three is 130.77, and the standard deviation is 74.2. The stress variation coefficient is calculated to be (74.2 / 130.77)×100%=57%, and the cycle characteristic based on the variation theory is 0.57. Under the corresponding working conditions, the conventional deviation cycle characteristic calculated according to the low-cycle fatigue cycle characteristic calculation formula is 0.25, that is, the ratio of the minimum stress to the maximum stress in the stress cycle. The cycle characteristics based on the variation theory are larger than the conventional ideal state cycle characteristics, covering the influence of uneven feeding and large eccentric load, and are more in line with the actual situation.

[0067] S15. Determine the fatigue life of the target structural component according to the cycle characteristics and the preset number of compression operations.

[0068] Specifically, the fatigue life of the target structural component can be calculated by the fatigue analysis tool Fatigue Tool of the CAE software ANSYS, wherein the preset number of times of compression work is the number of times the target structural component works in one cycle under the preset applied load. Optionally, the fatigue life of the target structural component is determined according to the cycle characteristics and the preset number of times of compression work, including: calculating the safety factor according to the cycle characteristics and the preset number of times of compression work; calculating the fatigue life according to the safety factor and the cycle characteristics. That is, after entering the Fatigue Tool, select "Fatigue Life", enter the preset number of times of compression work and the cycle characteristics based on the variation theory calculated above, and the safety factor can be automatically calculated. Then enter the safety factor and the cycle characteristics in the Fatigue Tool to automatically calculate the fatigue life. Taking the above-mentioned main frame as an example, the fatigue life calculated and predicted under actual parameters is greater than 1 million times, and the minimum safety factor is 1.65, which is consistent with the actual designed life. The cloud diagram of the safety factor of the main frame body is as follows Figure 6 shown.

[0069] On the basis of improving the accuracy of cyclic characteristics, the overall calculation efficiency is effectively improved by adopting the joint calculation method of CAD / CAE software, solving the problems of large amount of calculation, long time consumption and low calculation accuracy in conventional mechanical calculation of strength and stiffness of large non-standard structural parts. Then, through the analysis of the prediction results, theoretical reference and guidance can be provided for the design of corresponding non-standard structural parts.

[0070] The technical solution provided by the embodiment of the present invention first establishes a geometric model based on the physical measurement data of the target structural part, then meshes the geometric model to obtain a mesh model, and then performs static analysis based on the mesh model according to preset material properties, preset constraints and preset applied loads to obtain analysis results, and then determines the stress variation coefficient based on the analysis results, and determines the cyclic characteristics of the target structural part based on the stress variation coefficient, and then determines the fatigue life of the target structural part based on the cyclic characteristics and the preset number of compressive working times. By optimizing the cyclic characteristics based on the variation theory, the calculated cyclic characteristics are more consistent with the actual working conditions with eccentric loads, so that the prediction results of fatigue life are more accurate.

[0071] Embodiment 2

[0072] Figure 7 This is a schematic diagram of the structure of the fatigue life prediction device for non-standard structural parts provided in the second embodiment of the present invention. The device can be implemented by hardware and / or software, and can generally be integrated into a computer device to execute the fatigue life prediction method for non-standard structural parts provided in any embodiment of the present invention. Figure 7 As shown, the device comprises:

[0073] A model building module 71 is used to build a geometric model according to the entity measurement data of the target structural part;

[0074] A meshing module 72, used for meshing the geometric model to obtain a mesh model;

[0075] A static analysis module 73, configured to perform static analysis based on the mesh model according to preset material properties, preset constraint conditions and preset applied loads to obtain analysis results;

[0076] a cyclic characteristic determination module 74, configured to determine a stress variation coefficient according to the analysis result, and determine a cyclic characteristic of the target structural component according to the stress variation coefficient;

[0077] The fatigue life determination module 75 is used to determine the fatigue life of the target structural component according to the cycle characteristics and the preset number of compression working times.

[0078] The technical solution provided by the embodiment of the present invention first establishes a geometric model based on the physical measurement data of the target structural part, then meshes the geometric model to obtain a mesh model, and then performs static analysis based on the mesh model according to preset material properties, preset constraints and preset applied loads to obtain analysis results, and then determines the stress variation coefficient based on the analysis results, and determines the cyclic characteristics of the target structural part based on the stress variation coefficient, and then determines the fatigue life of the target structural part based on the cyclic characteristics and the preset number of compressive working times. By optimizing the cyclic characteristics based on the variation theory, the calculated cyclic characteristics are more consistent with the actual working conditions with eccentric loads, so that the prediction results of fatigue life are more accurate.

[0079] On the basis of the above technical solution, optionally, the cycle characteristic determination module 74 is specifically used for:

[0080] Extracting the first principal stress, the second principal stress and the third principal stress from the analysis results;

[0081] Calculating the average value and standard deviation of the first principal stress, the second principal stress and the third principal stress;

[0082] The stress variation coefficient is calculated as the cycle characteristic according to the average value and the standard deviation, and the calculation formula is: stress variation coefficient=standard deviation / average value×100%.

[0083] Based on the above technical solution, optionally, the fatigue life determination module 75 is specifically used for:

[0084] Calculate the safety factor according to the cycle characteristics and the preset number of times of pressure-bearing operation;

[0085] The fatigue life is calculated based on the safety factor and the cycle characteristics.

[0086] On the basis of the above technical solution, optionally, the model building module 71 is specifically used for:

[0087] The geometric model is established in a 1:1 ratio based on the entity measurement data through CAD software and exported to a preset format file.

[0088] On the basis of the above technical solution, optionally, the fatigue life prediction device of the non-standard structural part further includes:

[0089] A file import module, used for importing the preset format file into CAE software before meshing the geometric model to obtain the mesh model;

[0090] The grid division module 72 is specifically used for:

[0091] Meshing the geometric model using the CAE software;

[0092] The static analysis module 73 is specifically used for:

[0093] Static analysis was performed using the CAE software.

[0094] Based on the above technical solution, optionally, the preset format file is an xt format file or an iges format file.

[0095] The fatigue life prediction device for non-standard structural parts provided in the embodiment of the present invention can execute the fatigue life prediction method for non-standard structural parts provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0096] It is worth noting that in the above-mentioned embodiment of the fatigue life prediction device for non-standard structural parts, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0097] Embodiment 3

[0098] Figure 8 The schematic diagram of the structure of the computer device provided for the third embodiment of the present invention shows a block diagram of an exemplary computer device suitable for implementing the implementation mode of the present invention. Figure 8 The computer device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 8 As shown, the computer device includes a processor 81, a memory 82, an input device 83 and an output device 84; the number of processors 81 in the computer device can be one or more. Figure 8 Taking a processor 81 as an example, the processor 81, memory 82, input device 83 and output device 84 in the computer device can be connected through a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0099] The memory 82, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the fatigue life prediction method for non-standard structural parts in the embodiment of the present invention (for example, the model building module 71, the meshing module 72, the static analysis module 73, the cycle characteristics determination module 74 and the fatigue life determination module 75 in the fatigue life prediction device for non-standard structural parts). The processor 81 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 82, that is, realizes the above-mentioned fatigue life prediction method for non-standard structural parts.

[0100] The memory 82 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 82 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 82 may further include a memory remotely arranged relative to the processor 81, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0101] The input device 83 can be used to obtain physical measurement data of the target structural member, and generate key signal input related to user settings and function control of the computer device, etc. The output device 84 can include a display screen, which can be used to show the predicted fatigue life, etc. to the user.

[0102] Embodiment 4

[0103] Embodiment 4 of the present invention further provides a storage medium including computer executable instructions, wherein the computer executable instructions are used to execute a fatigue life prediction method for non-standard structural parts when executed by a computer processor, the method comprising:

[0104] Establishing geometric model based on the entity measurement data of target structural parts;

[0105] Meshing the geometric model to obtain a mesh model;

[0106] Based on the grid model, a static analysis is performed according to preset material properties, preset constraint conditions and preset applied loads to obtain an analysis result;

[0107] Determining a stress variation coefficient according to the analysis result, and determining a cyclic characteristic of the target structural component according to the stress variation coefficient;

[0108] The fatigue life of the target structural component is determined according to the cycle characteristics and the preset number of compression operations.

[0109] The storage medium may be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system, which is connected to the computer system via a network (such as the Internet). The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). The storage medium may store program instructions (e.g., embodied as a computer program) that may be executed by one or more processors.

[0110] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the fatigue life prediction method for non-standard structural parts provided in any embodiment of the present invention.

[0111] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0112] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0113] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0114] Embodiment 5

[0115] Embodiment 5 of the present invention also provides a computer program product, which includes a computer program (also referred to as code, instruction), which can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it is used to execute the fatigue life prediction method for non-standard structural parts provided in any of the above embodiments, and has the corresponding beneficial effects of the execution method.

[0116] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A fatigue life prediction method for non-standard structural parts, characterized in that: include: Establishing geometric model based on the entity measurement data of target structural parts; Meshing the geometric model to obtain a mesh model; Based on the grid model, a static analysis is performed according to preset material properties, preset constraint conditions and preset applied loads to obtain an analysis result; Determining a stress variation coefficient according to the analysis result, and determining a cyclic characteristic of the target structural component according to the stress variation coefficient; The fatigue life of the target structural component is determined according to the cycle characteristics and the preset number of compression operations.

2. The fatigue life prediction method of non-standard structural parts according to claim 1 is characterized in that: Determining the stress variation coefficient according to the analysis result, and determining the cyclic characteristics of the target structural component according to the stress variation coefficient, includes: Extracting the first principal stress, the second principal stress and the third principal stress from the analysis results; Calculating the average value and standard deviation of the first principal stress, the second principal stress and the third principal stress; The stress variation coefficient is calculated as the cycle characteristic according to the average value and the standard deviation, and the calculation formula is: stress variation coefficient=standard deviation / average value×100%.

3. The fatigue life prediction method of non-standard structural parts according to claim 1 is characterized in that: The step of determining the fatigue life of the target structural component according to the cycle characteristics and the preset number of times of pressure-bearing work includes: Calculate the safety factor according to the cycle characteristics and the preset number of times of pressure-bearing operation; The fatigue life is calculated based on the safety factor and the cycle characteristics.

4. The fatigue life prediction method of non-standard structural parts according to claim 1 is characterized in that: The step of establishing a geometric model according to the entity measurement data of the target structural part comprises: The geometric model is established in a 1:1 ratio based on the entity measurement data through CAD software and exported to a preset format file.

5. The fatigue life prediction method of non-standard structural parts according to claim 4, characterized in that: Before meshing the geometric model to obtain a mesh model, the method further includes: Importing the preset format file into CAE software; The step of meshing the geometric model to obtain a mesh model comprises: Meshing the geometric model using the CAE software; Based on the grid model, static analysis is performed according to preset material properties, preset constraint conditions and preset applied loads to obtain analysis results, including: Static analysis was performed using the CAE software.

6. The fatigue life prediction method of non-standard structural parts according to claim 4 is characterized in that: The preset format file is an xt format file or an iges format file.

7. A fatigue life prediction device for non-standard structural parts, characterized in that: include: A model building module is used to build a geometric model based on the entity measurement data of the target structural part; A meshing module, used for meshing the geometric model to obtain a mesh model; A static analysis module, used to perform static analysis based on the grid model according to preset material properties, preset constraint conditions and preset applied loads to obtain analysis results; a cyclic characteristic determination module, used to determine a stress variation coefficient according to the analysis result, and determine the cyclic characteristic of the target structural component according to the stress variation coefficient; The fatigue life determination module is used to determine the fatigue life of the target structural component according to the cycle characteristics and the preset number of compression working times.

8. A computer device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the fatigue life prediction method for non-standard structural parts as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the fatigue life prediction method for non-standard structural parts as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the fatigue life prediction method for non-standard structural parts as described in any one of claims 1 to 6.

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