Weak combination defect simulation prediction method and device, equipment, storage medium and product
The friction welding operation process is simulated through the finite element thermal simulation model, the temperature and stress data of the welding surface are determined, and the weak bond defects are predicted, which solves the problems of high cost and inaccurate prediction in the traditional method, and achieves efficient and accurate prediction of weak bond defects.
Patent Information
- Application Number
- CN202411626820.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-14
- Publication Date
- 2025-03-14
AI Technical Summary
In welding processes such as friction welding, materials may lead to weak bonding defects on the interface during complex and severe thermal processes, resulting in economic losses and safety risks. Traditional methods require a large amount of manual experiments to optimize process parameters, which is costly.
By establishing a finite element thermal simulation model, the friction welding operation process is simulated, the temperature data and stress data of the welding surface are determined, and the presence or absence of weak bond defects are predicted.
This method can replace a large number of manual process tests through simulation experiments, saving costs and improving the accuracy of weak bond defect prediction.
Smart Images
Figure CN119623155A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of defect simulation prediction, and in particular to a method, device, equipment, storage medium and product for weak-binding defect simulation prediction. Background Art
[0002] In the fields of aerospace, petrochemicals, etc., friction welding and other welding processes are usually used in the production and manufacturing of parts such as aircraft engine rotor components, automobile transmission shafts, and oil drill pipes. During the welding process, the material will undergo a complex and intense thermal process, which may lead to incomplete bonding on the interface, that is, weak bonding defects, resulting in huge economic losses and even a significant impact on the safety of people's lives and property.
[0003] At present, it is often necessary to manually perform process experiments to detect weak bonding defects. In traditional welding process optimization, process parameters without weak bonding defects are usually determined based on a large number of process experiments, so there is a problem of high cost. Summary of the invention
[0004] Based on this, it is necessary to provide a weak binding defect simulation prediction method, device, equipment, storage medium and product that can reduce the cost of weak binding defect prediction in response to the above technical problems.
[0005] In a first aspect, the present application provides a method for simulation prediction of weak binding defects. The method comprises:
[0006] Establishing a finite element thermal simulation model according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters;
[0007] Based on the finite element thermal simulation model, determining temperature data of a welding surface between two welding workpieces during the friction welding operation, and stress data of the welding surface in a normal direction;
[0008] A weak bonding defect prediction result is determined based on the temperature data and the stress data.
[0009] In one embodiment, determining a weak bonding defect prediction result according to the temperature data and the stress data includes:
[0010] Determine the welding ratio of the welding surface after the friction welding process is completed according to the temperature data and the stress data;
[0011] According to the welding ratio, the weak bonding defect prediction result is determined.
[0012] In one embodiment, determining the weak bonding defect prediction result according to the welding ratio includes:
[0013] If the welding ratio is not less than the preset value, the weak bonding defect prediction result is determined to be that there is no weak bonding defect.
[0014] In one embodiment, determining the weak bonding defect prediction result according to the welding ratio includes:
[0015] If the welding ratio is less than the preset value, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0016] In one embodiment, determining the welding ratio of the welding surface after the friction welding process is completed according to the temperature data and the stress data includes:
[0017] The welding ratio is determined according to the temperature data, the stress data, the welding time, and the material coefficients of the two welding workpieces.
[0018] In one embodiment, the temperature data of the welding surface between two welding workpieces during the friction welding operation and the stress data of the welding surface in the normal direction are determined based on the finite element thermal simulation model, including:
[0019] Simulating the temperature field and stress field during the friction welding operation based on the finite element thermal simulation model;
[0020] Determine the temperature data according to the temperature field; the temperature data includes temperature data of the temperature of the welding surface changing with time during the friction welding operation;
[0021] The stress data is determined according to the stress field; the stress data includes stress data of stress of the welding surface in the normal direction changing with time during the friction welding operation.
[0022] In a second aspect, the present application also provides a weak binding defect simulation prediction device. The device comprises:
[0023] A model building module is used to build a finite element thermal simulation model according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters;
[0024] A first determination module is used to determine temperature data of a welding surface between two welding workpieces during the friction welding operation and stress data of the welding surface in a normal direction based on the finite element thermal simulation model;
[0025] The second determination module is used to determine a weak bonding defect prediction result according to the temperature data and the stress data.
[0026] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.
[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.
[0028] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of any of the above methods when executed by a processor.
[0029] The above-mentioned weak bond defect simulation prediction method, device, equipment, storage medium and product establish a finite element thermal simulation model according to preset model parameters, simulate the friction welding operation process under the preset model parameters, and then determine the temperature data of the welding surface between the two welding workpieces during the friction welding operation, as well as the stress data of the welding surface in the normal direction based on the finite element thermal simulation model. According to the temperature data and stress data, the weak bond defect prediction result is determined, thereby being able to replace a large number of manual process tests with simulation experiments, saving costs, and simulating the friction welding operation process under the preset model parameters with the finite element thermal simulation model can also improve the accuracy of weak bond defect prediction. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 is an internal structure diagram of a computer device provided in an embodiment of the present application;
[0031] Figure 2 It is a flow chart of a weak binding defect simulation prediction method provided in an embodiment of the present application;
[0032] Figure 3 It is a flow chart of a method for determining a weak binding defect prediction result provided in an embodiment of the present application;
[0033] Figure 4 It is a flow chart of a method for determining temperature data and stress data provided in an embodiment of the present application;
[0034] Figure 5 It is a flow chart of a friction welding weak bonding defect simulation prediction method provided in an embodiment of the present application;
[0035] Figure 6 It is a schematic diagram comparing the weak bonding defects of friction welding in a simulation experiment and an actual experiment provided in an embodiment of the present application;
[0036] Figure 7 It is a structural block diagram of a weak binding defect simulation prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0038] In the fields of aerospace, petrochemicals, etc., friction welding and other welding processes are usually used in the production and manufacturing of parts such as aircraft engine rotor components, automobile transmission shafts, and oil drill pipes. During the welding process, the material will undergo a complex and intense thermal process, which may lead to incomplete bonding on the interface, that is, weak bonding defects, resulting in huge economic losses and even a significant impact on the safety of people's lives and property.
[0039] At present, it is often necessary to manually perform process experiments to detect weak bonding defects. In traditional welding process optimization, process parameters without weak bonding defects are usually determined based on a large number of process experiments, so there is a problem of high cost.
[0040] The weak binding defect simulation prediction method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Figure 1 is an internal structure diagram of a computer device provided in an embodiment of the present application. The computer device may be a server, and its internal structure diagram may be as follows Figure 1 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a weak binding defect simulation prediction method is implemented.
[0041] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0042] In one embodiment, Figure 2 As shown, Figure 2 is a flow chart of a weak binding defect simulation prediction method provided in an embodiment of the present application, which can be applied to Figure 1The computer device comprises the following steps:
[0043] S201, establishing a finite element thermal simulation model according to preset model parameters.
[0044] Among them, the finite element thermal simulation model is used to simulate the friction welding process under preset model parameters.
[0045] Exemplarily, the preset model parameters may include, for example, preset dimensions and materials of the welding workpiece, and welding process parameters, wherein the welding process parameters may include, for example, moment of inertia, initial rotation speed, axial load, and the like.
[0046] In one embodiment, a finite element thermal simulation model under the working conditions corresponding to the preset model parameters can be established by the finite element method, and the friction welding operation process under the working conditions corresponding to the preset model parameters can be simulated by the finite element thermal simulation model.
[0047] Optionally, establishing a finite element thermal simulation model may include, for example, steps such as geometric modeling, meshing, material constitutive model, interface friction and heat generation model, and boundary condition application.
[0048] S202, based on the finite element thermal simulation model, determining the temperature data of the welding surface between the two welding workpieces during the friction welding operation, and the stress data of the welding surface in the normal direction.
[0049] In one embodiment, the temperature field and stress field experienced by the welded workpiece during the friction welding operation can be simulated based on the finite element thermal simulation model. Based on the temperature field and stress field, the temperature evolution data and stress evolution data at a preset position on the welding surface are determined. The temperature evolution data is the above-mentioned temperature data, and the stress evolution data is the above-mentioned stress data.
[0050] The temperature evolution data include temperature data of the temperature of the welding surface changing with time during the friction welding operation, and the stress evolution data include stress data of the stress of the welding surface in the normal direction changing with time during the friction welding operation.
[0051] It should be noted that the preset position can be any position on the welding surface and is not limited here.
[0052] S203, determining a weak bonding defect prediction result according to the temperature data and the stress data.
[0053] Optionally, the welding ratio of the welding surface after the friction welding process is completed can be calculated based on the temperature data and the stress data, and then the weak bonding defect prediction result can be determined based on the welding ratio.
[0054] Alternatively, the morphology of the weld under the working conditions corresponding to the temperature data and stress data can be determined by macroscopic metallographic testing and other methods, thereby determining the weak bond defect prediction result.
[0055] In an embodiment of the present application, a finite element thermal simulation model is established according to preset model parameters to simulate the friction welding process under the preset model parameters. Then, based on the finite element thermal simulation model, the temperature data of the welding surface between the two welding workpieces during the friction welding process and the stress data of the welding surface in the normal direction are determined. According to the temperature data and the stress data, the weak bond defect prediction result is determined, thereby replacing a large number of manual process tests with simulation experiments, saving costs. In addition, by simulating the friction welding process under the preset model parameters with the finite element thermal simulation model, the accuracy of the weak bond defect prediction can be improved.
[0056] Reference Figure 3 , Figure 3 1 is a flow chart of a method for determining a weak bonding defect prediction result provided by an embodiment of the present application. This embodiment involves a possible implementation method of determining a weak bonding defect prediction result based on temperature data and stress data. Based on the above embodiment, the above S203 includes the following steps:
[0057] S301, determining the welding ratio of the welding surface after the friction welding process is completed according to the temperature data and the stress data.
[0058] Optionally, the welding ratio of the welding surface after the friction welding process is completed can be determined according to the temperature data and stress data by the following formula (1):
[0059] (1)
[0060] In formula (1), is the welding ratio, is the compressive stress in the normal direction of the weld surface, is the temperature on the welding surface, is the welding time, , , is the material coefficient of the welding workpiece, is the ideal gas constant, is the activation energy of plastic deformation, It is the additional stress loss caused by the friction between the micro-convex bodies on the interface during the actual contact and welding process of the surface when the micro-convex bodies are deformed in an uncoordinated manner. Among them, the compressive stress in the normal direction of the welding surface can be determined based on the stress data, and the temperature on the welding surface can be determined based on the temperature data.
[0061] Alternatively, the morphology of the weld under the working conditions corresponding to the temperature data and stress data can be determined by macroscopic metallographic testing and other methods, and then the welding ratio of the weld surface after the friction welding process is completed can be determined based on the morphology of the weld.
[0062] S302, determining a weak bonding defect prediction result according to the welding ratio.
[0063] Optionally, a preset value may be set according to actual needs, and then the welding ratio is compared with the preset value. If the welding ratio is not less than the preset value, the weak bonding defect prediction result is determined to be that there is no weak bonding defect. Alternatively, if the welding ratio is less than the preset value, the weak bonding defect prediction result is determined to be that there is a weak bonding defect.
[0064] Exemplarily, the welding ratio is usually less than or equal to 1, so the preset value can be set to 1. If the welding ratio is equal to 1, the weak bonding defect prediction result is determined to be the absence of a weak bonding defect; if the welding ratio is less than 1, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0065] In another possible implementation, if the welding condition is less demanding, the preset value can be set to 0.95. If the welding ratio is not less than 0.95, the prediction result of the weak bonding defect is determined to be that there is no weak bonding defect; if the welding ratio is less than 0.95, the prediction result of the weak bonding defect is determined to be that there is a weak bonding defect.
[0066] In the embodiment of the present application, the welding ratio of the welding surface after the friction welding process is completed is determined based on the temperature data and stress data, and the weak bond defect prediction result is determined based on the welding ratio, thereby replacing a large number of manual process experiments with simulation experiments, saving costs, and simulating the friction welding process under preset model parameters with a finite element thermal simulation model can also improve the accuracy of weak bond defect prediction.
[0067] Based on the above embodiment, the above S302 includes the following steps:
[0068] If the welding ratio is not less than the preset value, the weak bonding defect prediction result is determined to be that there is no weak bonding defect.
[0069] Exemplarily, the welding ratio is usually less than or equal to 1, so the preset value can be set to 1. If the welding ratio is equal to 1, the weak bonding defect prediction result is determined to be that there is no weak bonding defect.
[0070] Alternatively, if the requirements for the welding condition are relatively low, the preset value may be set to 0.95. If the welding ratio is not less than 0.95, the weak bonding defect prediction result is determined to be that there is no weak bonding defect.
[0071] In the embodiment of the present application, if the welding ratio is not less than the preset value, the weak bond defect prediction result is determined to be that there is no weak bond defect, so that the weak bond defect prediction result can be determined based on the welding ratio obtained by simulation, thereby replacing a large number of manual process experiments with simulation experiments, saving costs, and simulating the friction welding operation process under preset model parameters with a finite element thermal simulation model can also improve the accuracy of weak bond defect prediction.
[0072] Based on the above embodiment, the above S302 further includes the following steps:
[0073] If the welding ratio is less than the preset value, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0074] Exemplarily, the welding ratio is usually less than or equal to 1, so the preset value can be set to 1. If the welding ratio is less than 1, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0075] Alternatively, if the welding condition is less demanding, the preset value may be set to 0.95. If the welding ratio is less than 0.95, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0076] In the embodiment of the present application, if the welding ratio is less than the preset value, the weak bonding defect prediction result is determined to be the existence of a weak bonding defect, so that the weak bonding defect prediction result can be determined based on the welding ratio obtained by simulation, thereby replacing a large number of manual process experiments with simulation experiments, saving costs, and simulating the friction welding process under preset model parameters with a finite element thermal simulation model can also improve the accuracy of weak bonding defect prediction.
[0077] Based on the above embodiment, the above S301 includes the following steps:
[0078] The welding ratio is determined based on the temperature data, stress data, welding time, and material coefficients of the two welding workpieces.
[0079] For example, the welding ratio of the welding surface after the friction welding process is completed can be determined according to the temperature data and the stress data by the following formula (1):
[0080] (1)
[0081] In formula (1), is the welding ratio, is the compressive stress in the normal direction of the weld surface, is the temperature on the welding surface, is the welding time, , , is the material coefficient of the welding workpiece, is the ideal gas constant, is the activation energy of plastic deformation, It is the additional stress loss caused by the friction between the micro-convex bodies on the interface during the actual contact and welding process of the surface when the micro-convex bodies are deformed in an uncoordinated manner. Among them, the compressive stress in the normal direction of the welding surface can be determined based on the stress data, and the temperature on the welding surface can be determined based on the temperature data.
[0082] In the embodiment of the present application, the welding ratio is determined according to the temperature data, stress data, welding time, and the material coefficient of the two welding workpieces, so that the welding ratio can be determined based on the temperature data and stress data obtained by simulation, and then the weak bond defect prediction result is determined, which realizes the replacement of a large number of manual process experiments by simulation experiments, saves costs, and simulates the friction welding operation process under preset model parameters by a finite element thermal simulation model, which can also improve the accuracy of weak bond defect prediction.
[0083] Reference Figure 4 , Figure 4 1 is a flow chart of a method for determining temperature data and stress data provided in an embodiment of the present application. This embodiment involves a possible implementation method of determining the temperature data of the welding surface between two welding workpieces during the friction welding operation and the stress data of the welding surface in the normal direction based on a finite element thermal simulation model. Based on the above embodiment, the above S202 includes the following steps:
[0084] S401, simulating the temperature field and stress field during the friction welding operation based on the finite element thermal simulation model.
[0085] S402, determining temperature data according to the temperature field.
[0086] S403, determining stress data according to the stress field.
[0087] The temperature data includes temperature data of the temperature of the welding surface changing with time during the friction welding operation, and the stress data includes stress data of the stress of the welding surface in the normal direction changing with time during the friction welding operation.
[0088] In one embodiment, the temperature field and stress field experienced by the welded workpiece during the friction welding operation can be simulated based on the finite element thermal simulation model. Based on the temperature field and stress field, the temperature evolution data and stress evolution data at a preset position on the welding surface are determined. The temperature evolution data is the above-mentioned temperature data, and the stress evolution data is the above-mentioned stress data.
[0089] The temperature evolution data include temperature data of the temperature of the welding surface changing with time during the friction welding operation, and the stress evolution data include stress data of the stress of the welding surface in the normal direction changing with time during the friction welding operation.
[0090] It should be noted that the preset position can be any position on the welding surface and is not limited here.
[0091] In a possible implementation, based on the temperature evolution data and stress evolution data at a preset position on the welding surface, a curve of temperature variation over time and a curve of normal stress variation over time can be drawn to analyze the temperature and stress variation.
[0092] In the embodiment of the present application, the temperature field and stress field during the friction welding operation are simulated based on the finite element thermal simulation model. According to the temperature field, the temperature data is determined, and according to the stress field, the stress data is determined. Thus, the welding ratio can be determined based on the temperature data and stress data obtained by simulation, and then the weak bond defect prediction result can be determined. This realizes the replacement of a large number of manual process tests with simulation experiments, saves costs, and the simulation of the friction welding operation process under preset model parameters with the finite element thermal simulation model can also improve the accuracy of weak bond defect prediction.
[0093] Reference Figure 5 , Figure 5 1 is a flow chart of a method for simulating and predicting weak bonding defects in friction welding provided in an embodiment of the present application. The method comprises the following steps:
[0094] S501, establishing a finite element thermal simulation model according to preset model parameters.
[0095] S502, simulating the temperature field and stress field during the friction welding operation based on the finite element thermal simulation model.
[0096] S503, determining temperature data according to the temperature field, and determining stress data according to the stress field.
[0097] S504, determining the welding ratio of the welding surface after the friction welding process is completed according to the temperature data and the stress data.
[0098] S505, if the welding ratio is equal to 1, it is determined that the weak bonding defect prediction result is that there is no weak bonding defect.
[0099] S506, if the welding ratio is less than 1, determining that the weak bonding defect prediction result is that a weak bonding defect exists.
[0100] In order to introduce the embodiments of the present application more clearly, Figure 6 An exemplary description is given. Figure 6It is a schematic diagram comparing the weak bonding defects of friction welding in a simulation experiment and an actual experiment provided in an embodiment of the present application.
[0101] The size is Φ30mm and the welding process parameters are as follows: the moment of inertia is 4kg·m 2 Taking the inertia friction welding process of GH4169 high-temperature alloy under the working conditions of initial speed of 2700rpm and axial load of 24t as an example, the finite element method is used to establish a finite element thermal simulation model of the inertia friction welding process. For the established finite element thermal simulation model, the temperature field and stress field experienced by the welded workpiece during the welding process are simulated and calculated. Based on the temperature field, the temperature data at the positions of 1mm, 4mm, 7mm, 10mm, and 13mm away from the core on the welding surface are determined, and based on the stress field, the stress data at the five positions are determined. Then, based on the above formula (1), the changes in the simulated welding ratio over time at the five positions under this working condition are determined, as follows: Figure 6 (a) shown.
[0102] Reference Figure 6 (a) It can be seen that at the four positions on the welding surface 1mm, 4mm, 7mm, and 10mm away from the core, the welding ratio after welding is completed , indicating that the bonding is good and no weak bonding defects are formed; and at a position 13mm away from the core on the welding surface, the welding ratio after welding is , indicating that the binding is incomplete and a weak binding defect is formed at this position.
[0103] Under the above working conditions, actual inertia friction welding experiments were carried out, and the welding conditions of the obtained welding surfaces were analyzed. Figure 6 (b) shows a 1 / 2 cross section of the joint. The welding conditions near five positions on the welding surface, namely 1mm, 4mm, 7mm, 10mm, and 13mm from the core, were observed. The black arrows c and d are respectively near the positions 10mm and 13mm from the core. The welding conditions are shown as follows: Figure 6 As shown in (c) and (d), it can be seen that the bonding at the 10mm position is good and no weak bonding defects are formed; however, at the 13mm position, discontinuous chain holes are observed, indicating that the bonding is incomplete and a weak bonding defect is formed at this position.
[0104] Therefore, the simulation prediction results are consistent with the actual experimental results, verifying the accuracy of the simulation prediction method for weak binding defects.
[0105] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0106] Based on the same inventive concept, the embodiment of the present application also provides a weak binding defect simulation prediction device for implementing the weak binding defect simulation prediction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more weak binding defect simulation prediction device embodiments provided below can refer to the limitations of the weak binding defect simulation prediction method above, and will not be repeated here.
[0107] In one embodiment, Figure 7 As shown, Figure 7 700 is a structural block diagram of a weak binding defect simulation prediction device provided in an embodiment of the present application. The device 700 includes:
[0108] The model building module 701 is used to build a finite element thermal simulation model according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters.
[0109] The first determination module 702 is used to determine the temperature data of the welding surface between two welding workpieces and the stress data of the welding surface in the normal direction during the friction welding operation based on the finite element thermal simulation model.
[0110] The second determination module 703 is used to determine the weak bonding defect prediction result according to the temperature data and the stress data.
[0111] In one embodiment, the second determining module 703 includes:
[0112] The first determination unit is used to determine the welding ratio of the welding surface after the friction welding process is completed according to the temperature data and the stress data.
[0113] The second determination unit is used to determine the weak bonding defect prediction result according to the welding ratio.
[0114] In one embodiment, the second determining unit includes:
[0115] The first determination subunit is used to determine that the weak bonding defect prediction result is that there is no weak bonding defect if the welding ratio is not less than a preset value.
[0116] In one embodiment, the second determining unit further includes:
[0117] The second determination subunit is used to determine that the weak bonding defect prediction result is that a weak bonding defect exists if the welding ratio is less than a preset value.
[0118] In one embodiment, the first determination unit is specifically configured to determine the welding ratio according to temperature data, stress data, welding time, and material coefficients of two welding workpieces.
[0119] In one embodiment, the first determining module 702 includes:
[0120] The simulation unit is used to simulate the temperature field and stress field during the friction welding operation based on the finite element thermal simulation model.
[0121] The third determining unit is used to determine temperature data according to the temperature field; the temperature data includes temperature data of the temperature of the welding surface changing with time during the friction welding operation.
[0122] The fourth determination unit is used to determine stress data according to the stress field; the stress data includes stress data of stress of the welding surface in the normal direction changing with time during the friction welding operation.
[0123] Each module in the above-mentioned weak bonding defect simulation prediction device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.
[0124] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:
[0125] A finite element thermal simulation model is established according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters;
[0126] Based on the finite element thermal simulation model, determine the temperature data of the welding surface between the two welding workpieces during the friction welding operation, as well as the stress data of the welding surface in the normal direction;
[0127] Based on the temperature data and stress data, the weak bonding defect prediction results are determined.
[0128] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:
[0129] According to the temperature data and stress data, determine the welding ratio of the welding surface after the friction welding process is completed;
[0130] According to the welding ratio, the weak bonding defect prediction result is determined.
[0131] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:
[0132] If the welding ratio is not less than the preset value, the weak bonding defect prediction result is determined to be that there is no weak bonding defect.
[0133] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:
[0134] If the welding ratio is less than the preset value, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0135] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:
[0136] The welding ratio is determined based on the temperature data, stress data, welding time, and material coefficients of the two welding workpieces.
[0137] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:
[0138] Simulate the temperature and stress fields during friction welding based on the finite element thermal simulation model;
[0139] Determine temperature data according to the temperature field; the temperature data includes temperature data of the welding surface changing with time during the friction welding operation;
[0140] Stress data are determined according to the stress field; the stress data include stress data of stress on the welding surface in the normal direction changing with time during the friction welding operation.
[0141] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
[0142] A finite element thermal simulation model is established according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters;
[0143] Based on the finite element thermal simulation model, determine the temperature data of the welding surface between the two welding workpieces during the friction welding operation, as well as the stress data of the welding surface in the normal direction;
[0144] Based on the temperature data and stress data, the weak bonding defect prediction results are determined.
[0145] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0146] According to the temperature data and stress data, determine the welding ratio of the welding surface after the friction welding process is completed;
[0147] According to the welding ratio, the weak bonding defect prediction result is determined.
[0148] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0149] If the welding ratio is not less than the preset value, the weak bonding defect prediction result is determined to be that there is no weak bonding defect.
[0150] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0151] If the welding ratio is less than the preset value, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0152] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0153] The welding ratio is determined based on the temperature data, stress data, welding time, and material coefficients of the two welding workpieces.
[0154] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0155] Simulate the temperature and stress fields during friction welding based on the finite element thermal simulation model;
[0156] Determine temperature data according to the temperature field; the temperature data includes temperature data of the welding surface changing with time during the friction welding operation;
[0157] Stress data are determined according to the stress field; the stress data include stress data of stress on the welding surface in the normal direction changing with time during the friction welding operation.
[0158] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:
[0159] A finite element thermal simulation model is established according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters;
[0160] Based on the finite element thermal simulation model, determine the temperature data of the welding surface between the two welding workpieces during the friction welding operation, as well as the stress data of the welding surface in the normal direction;
[0161] Based on the temperature data and stress data, the weak bonding defect prediction results are determined.
[0162] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0163] According to the temperature data and stress data, determine the welding ratio of the welding surface after the friction welding process is completed;
[0164] According to the welding ratio, the weak bonding defect prediction result is determined.
[0165] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0166] If the welding ratio is not less than the preset value, the weak bonding defect prediction result is determined to be that there is no weak bonding defect.
[0167] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0168] If the welding ratio is less than the preset value, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
[0169] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0170] The welding ratio is determined based on the temperature data, stress data, welding time, and material coefficients of the two welding workpieces.
[0171] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:
[0172] Simulate the temperature and stress fields during friction welding based on the finite element thermal simulation model;
[0173] Determine temperature data according to the temperature field; the temperature data includes temperature data of the welding surface changing with time during the friction welding operation;
[0174] Stress data are determined according to the stress field; the stress data include stress data of stress on the welding surface in the normal direction changing with time during the friction welding operation.
[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.
[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0177] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A weak binding defect simulation prediction method, characterized in that: The method comprises: Establishing a finite element thermal simulation model according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters; Based on the finite element thermal simulation model, determining temperature data of a welding surface between two welding workpieces during the friction welding operation, and stress data of the welding surface in a normal direction; A weak bonding defect prediction result is determined according to the temperature data and the stress data.
2. The method according to claim 1, characterized in that: Determining the weak bonding defect prediction result according to the temperature data and the stress data includes: Determining the welding ratio of the welding surface after the friction welding process is completed according to the temperature data and the stress data; The weak bonding defect prediction result is determined according to the welding ratio.
3. The method according to claim 2, characterized in that Determining the weak bonding defect prediction result according to the welding ratio includes: If the welding ratio is not less than a preset value, it is determined that the weak bonding defect prediction result is that there is no weak bonding defect.
4. The method according to claim 2, characterized in that: Determining the weak bonding defect prediction result according to the welding ratio includes: If the welding ratio is less than a preset value, the weak bonding defect prediction result is determined to be the presence of a weak bonding defect.
5. The method according to any one of claims 2 to 4, characterized in that: Determining the welding ratio of the welding surface after the friction welding process is completed according to the temperature data and the stress data includes: The welding ratio is determined according to the temperature data, the stress data, the welding time, and the material coefficients of the two welding workpieces.
6. The method according to claim 1, characterized in that Determining the temperature data of the welding surface between the two welding workpieces during the friction welding operation and the stress data of the welding surface in the normal direction based on the finite element thermal simulation model includes: Simulating the temperature field and stress field during the friction welding operation based on the finite element thermal simulation model; Determining the temperature data according to the temperature field; the temperature data includes temperature data of the temperature of the welding surface changing with time during the friction welding operation; The stress data is determined according to the stress field; the stress data includes stress data of stress of the welding surface in the normal direction changing with time during the friction welding operation.
7. A weak binding defect simulation prediction device, characterized in that: The device comprises: A model building module, used to build a finite element thermal simulation model according to preset model parameters; the finite element thermal simulation model is used to simulate the friction welding operation process under the preset model parameters; A first determination module is used to determine temperature data of a welding surface between two welding workpieces during the friction welding operation and stress data of the welding surface in a normal direction based on the finite element thermal simulation model; The second determination module is used to determine a weak bonding defect prediction result according to the temperature data and the stress data.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.