CN112364550B - Fatigue life prediction method and device for thin-wall welding structure - Google Patents
Fatigue life prediction method and device for thin-wall welding structure Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for predicting the fatigue life of a thin-wall welding structure, wherein the method comprises the following steps: receiving fatigue test data of a target standard sample input by a user, wherein the fatigue test data is obtained by performing a fatigue test on the target standard sample, the target standard sample comprises a standard sample with a welding line and a standard sample with two welding lines, the standard sample with one welding line is equivalent to the T-shaped structure of a passenger car framework, and the standard sample with two welding lines is equivalent to the cross-shaped structure of the passenger car framework; carrying out numerical fitting on the fatigue test data to obtain a stress-life curve of the target standard sample piece; and determining the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework, the computer aided engineering CAE simulation theory and the stress-life curve data input by a user. The embodiment of the invention can more accurately predict the fatigue life of the welding seam of the thin-wall structure of the passenger car framework.
Description
Technical Field
The embodiment of the invention relates to the technical field of welding fatigue analysis, in particular to a method and a device for predicting the fatigue life of a thin-wall welding structure.
Background
The passenger car framework is formed by welding various thin-walled section steels and mainly plays a role in bearing and supporting in the use process of the passenger car. One of the important indicators in the reliability design of the welded structure of the passenger car is the fatigue life, and due to the influence of the welding process and under the action of complex service load, the fatigue failure usually starts from the welding seam of the connection of the skeleton-type steel. Therefore, it is necessary to predict and evaluate the fatigue life of the welded structure in order to judge the safety performance of the passenger car skeleton timely and accurately.
In the related technology, the fatigue life prediction and use method of the passenger car skeleton welding structure is to make raw materials (such as welding steel) into a round bar shape, obtain a standard test piece under the specified machining precision grade and heat treatment process, obtain the fatigue life of the standard test piece under the action of pulling, pressing, bending and twisting, obtain a corresponding stress-life (S-N) curve, and obtain the life of the welding structure according to the S-N curve.
However, when the existing passenger car structure fatigue evaluation technology is used for performing reliability fatigue evaluation on a passenger car skeleton structure, only a standard sample piece is subjected to a fatigue test, the fatigue life prediction precision of a welded structure, particularly a welded joint, is very low based on test data of the standard sample piece, the existing welded structure fatigue prediction technology or the existing test data are basically based on steel with the thickness of more than 5mm, the thickness of the steel used for the welded structure of the passenger car skeleton is mainly distributed between 1 mm and 4mm, and the welded structure belongs to welding of thin-wall pipe fittings. For the welding of thin-wall pipe fittings, the influence of welding technological parameters of a welding structure, particularly a welding seam, on the fatigue reliability of a passenger car framework is large, and if the prior art is adopted, the fatigue life prediction precision of the thin-wall type welding structure of the passenger car framework is low.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting fatigue life of a thin-wall welding structure, which are used for solving the problem of low prediction precision of the fatigue life of the thin-wall welding structure of a passenger car framework, especially the fatigue life of a welding seam of the thin-wall welding structure in the prior art.
The first aspect of the embodiments of the present invention provides a method for predicting fatigue life of a thin-wall welded structure, including:
receiving fatigue test data of a target standard sample input by a user, wherein the fatigue test data is obtained by performing a fatigue test on the target standard sample, the target standard sample comprises a standard sample with a welding line and a standard sample with two welding lines, the standard sample with one welding line is equivalent to a T-shaped structure of a passenger car framework, and the standard sample with two welding lines is equivalent to a cross-shaped structure of the passenger car framework;
fitting the fatigue test data to obtain a stress-life curve of a target standard sample piece;
and determining the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework, the CAE (computer aided engineering) simulation theory and the stress-life curve data input by a user.
Optionally, the determining the fatigue life of the welding structure of the passenger car skeleton according to the welding parameters of the welding structure in the passenger car skeleton input by the user, the computer aided engineering CAE simulation theory and the stress-life curve data includes:
establishing a finite element analysis model of the passenger car framework;
analyzing the finite element analysis model under the action of a preset condition to determine stress distribution information of each region in the finite element model;
according to welding parameters of a welding structure input by a user, a welding line is established at a welding seam in the finite element analysis model to obtain a characteristic finite element analysis model, and the corresponding welding parameters at the welding seam in the characteristic finite element analysis model are the same as the actual welding parameters at the welding seam of the passenger car skeleton;
and determining the fatigue life of the passenger car skeleton welding structure according to the characteristic finite element analysis model, the stress distribution information and the stress-life curve data.
Optionally, analyzing the finite element analysis model under the preset condition includes:
performing static analysis on the finite element analysis model according to predetermined working condition information, wherein the working condition information comprises any one or more of the following:
the device comprises a vertical working condition, a braking working condition, a left steering working condition, a right front wheel suspension working condition and a left front wheel suspension working condition.
Optionally, analyzing the finite element analysis model under the preset condition includes:
acquiring a road surface load spectrum;
and under the condition that the road surface load spectrum acts on the finite element analysis model, carrying out dynamic analysis on the finite element analysis model.
Optionally, the determining the fatigue life of the passenger car skeleton welded structure according to the characteristic finite element analysis model and the stress distribution information includes:
correcting the stress distribution information according to welding parameters at the welding seam in the characteristic finite element analysis model, wherein the corrected stress distribution information comprises stress amplitude of each part of the passenger car framework welding structure;
and carrying out interpolation calculation according to the stress amplitude of each part and the stress-life curve data to obtain the fatigue life of each part of the passenger car skeleton welding structure.
Optionally, the establishing a finite element analysis model of the passenger car skeleton includes:
establishing a framework model of the passenger car framework;
extracting a middle surface of the skeleton model, and dividing grids on the extracted middle surface;
and establishing weld joint connection among all pipe fittings in the framework model based on a common node technology to obtain a finite element analysis model of the passenger car framework.
A second aspect of an embodiment of the present invention provides a welded structure fatigue life prediction apparatus, including:
the receiving module is used for receiving fatigue test data of the target standard sample piece input by a user, wherein the fatigue test data is obtained by performing a fatigue test on the target standard sample piece, the target standard sample piece comprises a standard sample piece with one welding line and a standard sample piece with two welding lines, the standard sample piece with one welding line is equivalent to a T-shaped structure of a passenger car framework, and the standard sample piece with two welding lines is equivalent to a cross-shaped structure of the passenger car framework;
the processing module is used for fitting the fatigue test data to obtain a stress-life curve of the target standard sample piece;
and the prediction module is used for determining the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework, the CAE (computer aided engineering) simulation theory and the stress-life curve data input by a user.
A third aspect of an embodiment of the present invention provides an electronic device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes the computer-executable instructions stored in the memory to cause the at least one processor to perform the method for predicting fatigue life of a thin-walled welded structure according to the first aspect of the embodiments of the present invention.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the method for predicting fatigue life of a thin-wall welded structure according to the first aspect of the embodiments of the present invention is implemented.
A fifth aspect of the embodiments of the present invention provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the method for predicting the fatigue life of a thin-wall welded structure according to the first aspect of the embodiments of the present invention.
The method receives fatigue test data of a target standard sample piece input by a user, wherein the fatigue test data is obtained by carrying out a fatigue test on the target standard sample piece; therefore, the test data obtained by performing the fatigue test on the target standard sample piece better conform to the data of the actual welding structure of the passenger car framework, so that the stress-life curve of the target standard sample piece obtained by fitting the fatigue test data can more truly and accurately reflect the actual fatigue state of the passenger car framework welding structure compared with the S-N curve data obtained by the base material standard sample piece; and then, analyzing and determining the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework input by the user and by combining a computer aided engineering CAE simulation theory and the stress-life curve data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a diagram illustrating an application scenario of a fatigue life prediction method for a thin-wall welded structure according to an exemplary embodiment of the present invention;
FIG. 2 is a flow diagram illustrating a method for fatigue life prediction for a thin-walled welded structure in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a schematic flow diagram illustrating a method for fatigue life prediction for a thin-walled welded structure in accordance with another exemplary embodiment of the present invention;
FIG. 4 is an equivalent diagram of a T-shaped structure according to an exemplary embodiment of the present invention;
FIG. 5 is an equivalent schematic diagram of a cross-shaped structure shown in an exemplary embodiment of the invention;
FIG. 6 is a graph illustrating a stress-life curve according to an exemplary embodiment of the present invention;
FIG. 7 is an enlarged partial schematic view of a finite element analysis model according to an exemplary embodiment of the present invention;
FIG. 8 is a diagram illustrating a finite element analysis model according to an exemplary embodiment of the present invention;
FIG. 9 is an enlarged partial schematic view of a finite element analysis model according to another exemplary embodiment of the present invention;
fig. 10 is a schematic structural view showing a fatigue life predicting apparatus of a welded structure according to an exemplary embodiment of the present invention;
FIG. 11 is a block diagram illustrating a prediction module according to an exemplary embodiment of the present invention;
fig. 12 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The passenger car framework is formed by welding various thin-walled section steels and mainly plays a role in bearing and supporting in the use process of the passenger car. One of the important indicators for the reliability design of the welding structure of the passenger car is the fatigue life, and due to the influence of the welding process and under the action of complex service load, the fatigue failure usually starts from the welding seam of the connection of the skeleton section steel. Therefore, it is necessary to predict and evaluate the fatigue life of the welded structure in order to timely and accurately judge the safety performance of the passenger car frame.
In the related technology, the fatigue life prediction and use method of the passenger car skeleton welding structure is to make raw materials (such as welding steel) into a round bar shape, obtain a standard test piece under the specified machining precision grade and heat treatment process, obtain the fatigue life of the standard test piece under the action of pulling, pressing, bending and twisting, obtain a corresponding stress-life (S-N) curve, and obtain the life of the welding structure according to the S-N curve.
However, when the existing passenger car structure fatigue evaluation technology is used for performing reliability fatigue evaluation on a passenger car skeleton structure, only a standard sample piece is subjected to a fatigue test, the fatigue life prediction precision of a welded structure, particularly a welded joint, is very low based on test data of the standard sample piece, the existing welded structure fatigue prediction technology or the existing test data are basically based on steel with the thickness of more than 5mm, the thickness of the steel used for the welded structure of the passenger car skeleton is mainly distributed between 1 mm and 4mm, and the welded structure belongs to welding of thin-wall pipe fittings. For the welding of the thin-wall pipe fitting, the influence of welding technological parameters of a welding structure, particularly a welding seam, on the fatigue reliability of the passenger car framework is large, and if the prior art is adopted, the fatigue life prediction precision of the thin-wall type welding structure of the passenger car framework is also low.
Aiming at the defects, the technical concept provided by the application is as follows: when a welding structure is equivalent to a standard sample piece, the welding seam is taken into consideration, namely, the T-shaped welding structure is equivalently replaced by the standard sample piece with one welding seam, the cross-shaped welding structure is equivalently replaced by the standard sample piece with two welding seams, basic mechanical data obtained by performing a static tensile test on the sample piece with the welding seam are subjected to a fatigue test on a target standard sample piece with the welding seam according to the basic mechanical data to obtain fatigue test data of the target standard sample piece, a user inputs the test data through an operation terminal, and a background server processes the data to obtain an S-N curve. Due to the fact that the welding seam design is added in the standard sample piece, compared with S-N curve data obtained by a base material standard sample piece, the S-N curve data obtained by the method can reflect the actual fatigue state of a passenger car framework welding structure more truly and accurately; and then, according to the welding parameters of the welding structure in the passenger car framework input by the user, the welding parameters are actual welding parameters of the passenger car framework, and then the fatigue life of the welding structure of the passenger car framework is analyzed and predicted by combining a Computer Aided Engineering (CAE) simulation theory and the stress-life curve data.
Fig. 1 is a diagram illustrating an application scenario of a fatigue life prediction method for a thin-wall welded structure according to an exemplary embodiment of the present invention.
As shown in fig. 1, the basic architecture of the application scenario provided by this embodiment mainly includes: the system comprises a static stretching machine 101, a fatigue testing machine 102, an operation terminal 103 and a server 104; the static stretching machine is used for carrying out a static stretching test on the standard sample piece to obtain basic mechanical data of the standard sample piece; the fatigue testing machine performs fatigue test on the standard sample piece according to the basic mechanical data to obtain fatigue test data, a user inputs the fatigue test data through the operation terminal, the server processes the fatigue test data to finally generate an S-N curve of the standard sample piece, and fatigue life analysis is performed on the welding structure according to the S-N curve data.
Fig. 2 is a flowchart illustrating a method for predicting fatigue life of a thin-wall welded structure according to an exemplary embodiment of the present invention, where an execution subject of the method provided in this embodiment may be a server in the embodiment illustrated in fig. 1, or may be other devices having a data processing function.
As shown in fig. 2, the method provided by the present embodiment may include the following steps.
S201, receiving fatigue test data of the target standard sample piece input by a user, wherein the fatigue test data is obtained by performing a fatigue test on the target standard sample piece, the target standard sample piece comprises a standard sample piece with a welding line and a standard sample piece with two welding lines, the standard sample piece with one welding line is equivalent to a T-shaped structure of a passenger car framework, and the standard sample piece with two welding lines is equivalent to a cross-shaped structure of the passenger car framework.
In the step, a target standard sample equivalent to a welding structure of a structural framework of the passenger car is designed, then a physical experiment is carried out on the target standard sample, namely a fatigue test is carried out on the target standard sample to obtain fatigue test data, the fatigue test data of the target standard sample are input through an operation terminal, and the server receives the fatigue test data. The operation terminal can be a mobile phone, a tablet, a keyboard and other devices with an information input function.
Specifically, the structural forms of the whole passenger car frame are divided into various types, such as T-shaped, cross-shaped, Y-shaped and other irregular types, wherein T-shaped and cross-shaped joints are typical, and other complex welding structures can be formed by combining the T-shaped joint and the cross-shaped joint, so that the T-shaped joint and the cross-shaped joint are selected as typical joint forms to be researched in the embodiment. In the concrete implementation, the number and the arrangement form of the welding seams are key factors of the standard sample pieces for carrying out the static test on the equivalent T-shaped welding structure and the cross-shaped welding structure, and the T-shaped welding structure and the cross-shaped welding structure are respectively provided with one welding seam and two welding seams in the same plane, so that the standard sample piece with one welding seam can be used for equivalently replacing the T-shaped welding structure, and the standard sample piece with two welding seams can be used for equivalently replacing the cross-shaped welding structure. For example, as shown in a in fig. 4, it is a T-shaped welded structure, and it is equivalent to a standard sample with a weld as shown in b in fig. 4. As shown in a of fig. 5, is a cross-shaped welded structure, which is equivalent to a standard sample with two welded seams as shown in b of fig. 5.
In one or more possible embodiments, after obtaining the target standard sample piece with the weld joint, performing a static tensile test on the target standard sample piece on a static tensile machine, first performing a static tensile test on a base material standard sample piece (i.e., a standard sample piece without the weld joint) for aligning with a subsequently performed static tensile test on the target standard sample piece with the weld joint; then carrying out static tensile test on the standard sample piece with a welding line; and finally, carrying out static tension test on the standard sample piece with the two welding lines. And comparing the static stretching results of the three standard sample pieces to obtain basic mechanical property data of the standard sample piece with the welding line, such as data of tensile strength limit, elastic modulus, yield strength and the like.
In one or more possible embodiments, after the basic mechanical data of the target standard sample are obtained, several fatigue test conditions can be determined by combining the basic mechanical data, and then the fatigue test is performed on the target standard sample under the several fatigue test conditions to obtain the fatigue test data of the target standard sample.
In the step, because the T-shaped structure equivalence of the standard sample piece with one welding seam and the passenger car skeleton is designed, the cross-shaped structure equivalence of the standard sample piece with two welding seams and the passenger car skeleton is designed; therefore, the test data obtained by carrying out the fatigue test on the target standard sample piece more accords with the data of the actual welding structure of the passenger car framework.
And S202, fitting the fatigue test data to obtain a stress-life curve of the target standard sample.
Specifically, after receiving fatigue test data of a target standard sample input by a user through an operation terminal, the server processes and mathematically fits the fatigue test data to obtain an S-N curve of the target standard sample, wherein S is a vertical coordinate and represents an alternating stress range, N represents a fatigue life, namely, under the action of a transverse amplitude alternating stress with a structural stress range of S, the stress cycle times required for fatigue failure are reached, and the horizontal coordinate is logarithmic lgN of N.
In the step, the test data obtained by performing the fatigue test on the target standard sample piece with one or two welding seams better accords with the data of the actual welding structure of the passenger car framework, so that the stress-life curve of the target standard sample piece obtained by fitting the fatigue test data can more truly and accurately reflect the actual fatigue state of the passenger car framework welding structure compared with the S-N curve data obtained by the base metal standard sample piece.
S203, determining the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework, the CAE (computer aided engineering) simulation theory and the stress-life curve data input by a user.
Specifically, after the S-N curve is obtained, when fatigue analysis is performed on the welding structure of the passenger car framework, a user inputs actual welding parameters and the S-N curve of the welding structure in the passenger car framework into CAE simulation software through an operation terminal, fatigue simulation is performed on the welding structure of the passenger car framework, stress information of each part in the welding structure of the passenger car framework is obtained, and the fatigue life of each part is obtained through calculation of S-N curve data. The actual welding parameters may include, but are not limited to, data including weld type, weld quality, penetration, weld sequence, residual stress, and weld thickness of the weld.
In the embodiment, fatigue test data of the target standard sample piece input by a user are received, wherein the fatigue test data are obtained by performing a fatigue test on the target standard sample piece, and the target standard sample piece comprises a standard sample piece with one welding seam and a standard sample piece with two welding seams, the standard sample piece with one welding seam is equivalent to a T-shaped structure of a passenger car framework, and the standard sample piece with two welding seams is equivalent to a cross-shaped structure of the passenger car framework; therefore, the test data obtained by performing the fatigue test on the target standard sample piece better conform to the data of the actual welding structure of the passenger car framework, so that the stress-life curve of the target standard sample piece obtained by fitting the fatigue test data can more truly and accurately reflect the actual fatigue state of the passenger car framework welding structure compared with the S-N curve data obtained by the base material standard sample piece; and then, analyzing and predicting the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework input by the user and by combining a computer aided engineering CAE simulation theory and the stress-life curve data. In addition, in the embodiment, a method combining a physical test and CAE simulation is used, so that in the prior art, the time for implementing the method by using a test method or a simulation method is shorter, the cost control is lower, and the evaluation accuracy of the welding structure is higher.
In one or more possible embodiments, the target standard sample may be subjected to a tensile-compressive fatigue test by using a fatigue testing machine, for example, for a target standard sample with two welding seams, a strain gauge is firstly attached near the welding seam to monitor the stress change of the area near the welding seam in the fatigue test. Then, according to basic mechanical data (such as yield strength) obtained by a static force tensile test, determining several working conditions of a tension-compression fatigue test by using a lifting method, wherein the fatigue test process can be divided into six or other different test working conditions, the loaded maximum stress amplitude is not more than 80% of the yield strength limit value of a sample, on the basis, the loading stress amplitude of the next group of tests is reduced by 20% compared with that of the previous group, and the loading stress amplitude of each subsequent group of tests is reduced by 20% compared with that of the previous group until the fatigue life is longer than the preset fatigue life. After the condition that the fatigue life is longer than the preset fatigue life occurs, interpolating (namely selecting a stress amplitude between the loading stress amplitude of the last group of tests and the loading stress amplitude of the penultimate group of tests) the loading stress amplitudes of the last group of tests and the penultimate group of tests to perform the last group of tests, and if the fatigue life obtained by the interpolation tests is longer than the preset fatigue life, considering the life corresponding to the interpolation as the infinite life; if the fatigue life is less than the preset fatigue life, interpolating again between the loading stress amplitude and the stress amplitude greater than the preset fatigue life, wherein the test termination times are 500 ten thousand. And finally, obtaining the fatigue life corresponding to each stress amplitude.
In one or more possible embodiments, the fatigue test data is processed and mathematically fitted, and a model summary and parameter estimates for the design fit are determinedComprises the following steps: the independent variable is the cycle number lgN, the dependent variable is the stress amplitude S, the stress amplitude unit is megapascals (MPa), the R square is equal to 0.993, the model abstract degree of freedom 1 is 1, the degree of freedom 2 is 3, the significance is 0.000, the constant is 0.777, and the coefficient b1 is 20.643. The fatigue test data obtained in the above examples were processed and fitted based on this information to obtain an S-N curve as shown in fig. 6, S representing the alternating stress range and N being the number of stress cycles required to achieve fatigue failure under the effect of the amplitude alternating stress in the structural stress range S. The ordinate of the S-N curve is S, the abscissa is logarithm lgN of the stress cycle number N, and the obtained formula corresponding to the S-N curve is y = e (0.777337+20.64300/x) The linearized S-N curve formula obtained by carrying out linearization processing is as follows: y =3.470564-0.270142x, and the fatigue life of the welding structure of the passenger car skeleton can be estimated and predicted by using a CAE simulation method according to the obtained S-N curve and data. Because the sample piece is provided with the welding seam, the obtained fatigue life data are the results obtained by considering the influence of the actual welding process parameters, namely the obtained fatigue life data are more in line with the fatigue state of the actual welding structure, and the obtained S-N curve data also consider various parameter influence factors in the actual welding, so that the method is more accurate and reliable compared with the conventional common S-N curve.
In one or more possible embodiments, as shown in fig. 3, the method for predicting the fatigue life of the welding structure of the passenger car skeleton according to the welding parameters of the welding structure in the passenger car skeleton input by the user, the computer aided engineering CAE simulation theory and the stress-life curve data can comprise the following steps.
S301, establishing a finite element analysis model of the passenger car framework.
Specifically, firstly, geometric modeling is carried out on the passenger car skeleton to obtain a skeleton model. Then, a framework model of the passenger car framework is transmitted into finite element analysis software, a middle surface is automatically extracted from a thin-wall framework digital model in the framework model in the finite element analysis software, grids are divided on the extracted middle surface, as shown in fig. 7, a partial structure enlarged view of the passenger car framework is provided, each middle surface in the drawing is provided with a grid, then, welding seam connection between each pipe fitting in the framework model is established based on a common node technology, the finite element analysis model of the passenger car framework is obtained, and the whole finite element analysis model of the passenger car framework is as shown in fig. 8.
S302, analyzing the finite element analysis model under the action of preset conditions to determine stress distribution information of each region in the finite element model.
In this step, the preset condition may include two conditions, one is static analysis under a given working condition, and the other is dynamic analysis under the action of a load spectrum, which will be described below.
In some embodiments, the analyzing the finite element analysis model under the preset condition includes: performing static analysis on the finite element analysis model according to predetermined working condition information, wherein the working condition information comprises any one or more of the following: the device comprises a vertical working condition, a braking working condition, a left steering working condition, a right front wheel suspension working condition and a left front wheel suspension working condition.
Specifically, the analysis conditions are determined according to factors such as the application market of the passenger car, customer requirements and the like, static analysis under several working conditions such as vertical impact, steering, braking, torsion and the like of the whole car is generally required, and stress distribution conditions of stress danger areas or key attention parts under various working conditions are found out.
For example, six vehicle behavior loads of vehicle strength analysis can be set, as shown in table 1, and g represents the gravity acceleration. The X direction is the direction of the front running of the passenger car, the Y direction is the direction of the left side of the passenger car, and the Z direction is the vertical upward direction. Wherein the working condition 1 is a vertical working condition, and the Z direction applies an acceleration of-3.33 g; the working condition 2 is a braking working condition, the acceleration of minus 0.8g is applied to the X direction, the acceleration of minus 1.0g is applied to the Z direction, the load which is half of the axial load of the front shaft and is 0.8 times of the axial load of the front shaft is respectively applied to the grounding point of the tire of the front wheel, and the load which is half of the axial load of the follow shaft and is 0.8 times of the axial load of the follow shaft is respectively applied to the grounding point of the tire of the follow wheel. The working condition 3 is a left steering working condition, acceleration of +0.8g is applied to the Y direction, and acceleration of-1.0 g is applied to the Z direction; the working condition 4 is a right-turning working condition, the acceleration of minus 0.8g is applied to the Y direction, and the acceleration of minus 1.0g is applied to the Z direction; the working condition 5 is a suspension working condition of the right front wheel, and the Z direction applies acceleration of-1.0 g; the working condition 6 is a left front wheel suspension working condition, and the Z direction is applied with an acceleration of-1.0 g. The six working conditions are respectively acted in the finite element analysis model, and the finite element analysis model is analyzed, so that the stress distribution information of each region of the passenger car framework, namely the stress amplitude of each part under the six working conditions can be obtained.
In a possible case, stress distribution information of each region of the passenger car skeleton may be displayed through a stress cloud diagram, the stress cloud diagram includes a whole passenger car skeleton model, regions with different stress amplitudes in the model are rendered by using different colors, for example, a position where the stress amplitude is greater than a preset threshold (for example, 120 MPa) is rendered by using red, a region with a larger stress amplitude has a deeper color, a region with a stress amplitude smaller than the preset threshold is rendered by using green, and a region with a larger stress amplitude has a darker color.
Further, a key attention area or a stress danger area of the passenger car skeleton may be determined according to stress distribution information (e.g., a stress cloud map) of each area of the passenger car skeleton, and an area having a stress amplitude greater than a preset threshold (e.g., 120 MPa) is determined as the key attention area (stress danger area). In a possible embodiment, position information and stress amplitude of a region with a stress amplitude greater than 120MPa may be marked in the stress cloud graph, and the position information may be represented by numbers, for example, each common node in the skeleton of the passenger car may be numbered in advance, each number corresponding to one common node region, and the common node number with a stress amplitude greater than 120MPa and the corresponding stress amplitude are marked in the stress cloud graph. For example, in the stress distribution information obtained by the finite element analysis model under the condition of working condition 1, two regions have a stress amplitude greater than 120MPa, two positions are respectively a common node region with a number of 2939379, the stress amplitude here is 150.7MPa, and a region with a number of 2930449, the stress amplitude here is 136.5MPa.
In this embodiment, a stress cloud picture mode can be used for a user to visually see the distribution condition of the stress amplitude of each region in the passenger car skeleton, and the region with the stress amplitude larger than the preset threshold value can be quickly found.
In some embodiments, the analyzing the finite element analysis model under the preset condition includes: acquiring a road surface load spectrum; and under the condition that the road surface load spectrum acts on the finite element analysis model, carrying out dynamic analysis on the finite element analysis model.
Specifically, the method can be used for processing road spectrum data to obtain a comprehensive road load spectrum according to the collected road spectrum data of various roads, importing the road load spectrum into finite element analysis software, then automatically applying the road load spectrum to a finite element analysis model by a server, and analyzing the finite element analysis model to obtain the stress distribution information of each region of the passenger car framework.
It should be noted that, the detailed process of acquiring the road load spectrum may refer to the related art, and will not be described in detail here.
In the embodiment, the finite element analysis model is dynamically analyzed by combining the road surface load spectrum, so that the obtained stress distribution information of the passenger car framework is closer to the stress distribution information of each region when the passenger car actually runs on the road surface, and the final fatigue prediction result is more accurate.
And S303, according to welding parameters of the welding structure input by a user, establishing a welding line at the welding seam in the finite element analysis model to obtain a characteristic finite element analysis model, wherein the welding parameters corresponding to the welding seam in the characteristic finite element analysis model are the same as the actual welding parameters at the welding seam of the passenger car framework.
The welding parameters of the welded structure are actual welding parameters of the passenger car skeleton, and include welding types (such as fillet weld, lap weld and butt weld) at each weld in the welded structure, welding quality, penetration, welding sequence, residual stress, welding material thickness and the like, and the welding parameters directly influence fatigue analysis results at the weld of the welded structure, so that before fatigue analysis, a user inputs the welding parameters in an interface of finite element analysis software through an operation terminal, a weld line is established at a common node weld of pipe connection in a finite element analysis model, a local enlarged view of the finite element analysis model after the weld line is established is shown in fig. 9, and the weld line established at the common node weld can be seen in fig. 9. By inputting the welding parameters, the welding process actually used at the welding seam of the finite element analysis model and the welding seam of the passenger car framework is consistent.
S304, correcting the stress distribution information according to the welding parameters at the welding seam in the characteristic finite element analysis model, wherein the corrected stress distribution information comprises the stress amplitude of each part of the passenger car framework welding structure.
Specifically, the weld line is established at the weld joint of the common node according to the actual welding parameters of the characteristic finite element analysis model obtained in the above step, so that the fatigue characteristics of the characteristic finite element analysis model are closer to the actual fatigue characteristics of the passenger car skeleton, and therefore, the stress distribution information obtained in the step S302 is corrected by the finite element analysis software according to the welding parameters at the weld joint in the characteristic finite element analysis model, so that the corrected stress distribution information is closer to the actual stress distribution information of the passenger car skeleton, and thus, a more accurate stress amplitude of each part of the passenger car skeleton welding structure is obtained.
S305, carrying out interpolation calculation according to the stress amplitude of each part and the stress-life curve data to obtain the fatigue life of each part of the passenger car skeleton welding structure.
Specifically, the stress-life curve is imported into the finite element analysis software, and the server performs interpolation calculation on the stress amplitude and the stress-life curve data of each part of the passenger car framework according to a program set in the finite element analysis software, for example, the stress amplitude is substituted into a formula corresponding to the stress-life curve, so as to obtain the fatigue life corresponding to each part.
In one or more possible embodiments, fatigue lives of various parts of the passenger car skeleton may also be displayed by a life cloud graph, and areas corresponding to different lives in the life cloud graph are rendered into different colors, for example, an area where the fatigue life is greater than or equal to a first preset life threshold is rendered into a first color (for example, red), an area where the fatigue life is greater than or equal to a second preset life threshold and less than the first preset life threshold is rendered into a second color (for example, orange), an area where the fatigue life is greater than or equal to a third preset threshold and less than a fourth preset threshold is rendered into a third color (for example, green), an area where the fatigue life is less than the fourth preset threshold is rendered into a fourth color (for example, gray), and for an area where the fatigue life is greater than the first preset threshold, area location information and corresponding fatigue life values are marked in the graph, where the location information may be represented by numbers, and various common nodes in the passenger car skeleton may be numbered in advance, and each common node area corresponds to one common node area. For example, the value of the fatigue life of the common node region is 80702.7 times for the common node number 5776.
In the embodiment, a fatigue life CAE analysis process for a passenger car skeleton welding structure is provided, a finite element analysis model is processed by combining actual welding parameters of a welding seam of a passenger car skeleton in a fatigue analysis process, and obtained stress distribution information of each region of a passenger car stock price is more accurate, so that the finally predicted fatigue life is more accurate.
Fig. 10 is a schematic structural diagram illustrating a fatigue life predicting apparatus for a welded structure according to an exemplary embodiment of the present invention.
As shown in fig. 10, the apparatus provided in this embodiment includes: a receiving module 101, a processing module 102 and a prediction module 103; the receiving module 101 is used for receiving fatigue test data of the target standard sample, which is input by a user, wherein the fatigue test data is obtained by performing a fatigue test on the target standard sample, the target standard sample comprises a standard sample with one welding seam and a standard sample with two welding seams, the standard sample with one welding seam is equivalent to a T-shaped structure of a passenger car framework, and the standard sample with two welding seams is equivalent to a cross-shaped structure of the passenger car framework; the processing module 102 is configured to fit the fatigue test data to obtain a stress-life curve of the target standard sample; the prediction module 103 is configured to determine the fatigue life of the passenger car skeleton welding structure according to the welding parameters of the passenger car skeleton welding structure input by the user, the computer aided engineering CAE simulation theory, and the stress-life curve data.
Further, as shown in fig. 11, the prediction module 103 includes: an establishing unit 1031, configured to establish a finite element analysis model of the passenger car skeleton; an analyzing unit 1032, configured to analyze the finite element analysis model under a preset condition to determine stress distribution information of each region in the finite element model; the processing unit 1033 is configured to establish a weld line at a weld joint in the finite element analysis model according to a welding parameter of the welding structure input by a user, so as to obtain a characteristic finite element analysis model, where a welding parameter corresponding to the weld joint in the characteristic finite element analysis model is the same as an actual welding parameter at the weld joint of the passenger car skeleton; a determining unit 1034, configured to determine the fatigue life of the passenger car skeleton welding structure according to the characteristic finite element analysis model, the stress distribution information, and the stress-life curve data.
Further, the analysis unit is specifically configured to: performing static analysis on the finite element analysis model according to predetermined working condition information, wherein the working condition information comprises any one or more of the following: the device comprises a vertical working condition, a braking working condition, a left steering working condition, a right front wheel suspension working condition and a left front wheel suspension working condition.
Further, the analysis unit is specifically configured to: acquiring a road surface load spectrum; and under the condition that the road surface load spectrum acts on the finite element analysis model, carrying out dynamic analysis on the finite element analysis model.
Further, the determining unit is specifically configured to: correcting the stress distribution information according to the welding parameters at the welding seam in the characteristic finite element analysis model, wherein the corrected stress distribution information comprises the stress amplitude of each part of the passenger car framework welding structure; and carrying out interpolation calculation according to the stress amplitude of each part and the stress-life curve data to obtain the fatigue life of each region of the passenger car skeleton welding structure.
Further, the establishing unit includes: establishing a skeleton model of the passenger car skeleton; extracting a middle surface of the skeleton model, and dividing grids on the extracted middle surface; and establishing weld joint connection among all pipe fittings in the framework model based on a common node technology to obtain a finite element analysis model of the passenger car framework.
For detailed functional description of each module in this embodiment, reference is made to the description of the embodiment of the method, and the detailed description is not provided herein.
Fig. 12 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention. As shown in fig. 12, the electronic device 120 provided in the present embodiment includes: at least one processor 1201 and memory 1202. The processor 1201 and the memory 1202 are connected by a bus 1203.
In a particular implementation, the at least one processor 1201 executes the computer-executable instructions stored by the memory 1202 to cause the at least one processor 1201 to perform the thin-walled weld structure fatigue life prediction methods of the method embodiments described above.
For a specific implementation process of the processor 1201, reference may be made to the above method embodiments, which implement principles and technical effects are similar, and details are not described herein again.
In the embodiment shown in fig. 12, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of hardware and software modules.
The memory may comprise high speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
Another embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the method for predicting the fatigue life of the thin-wall welded structure in the above method embodiment is implemented.
Another embodiment of the present application provides a computer program product comprising a computer program, which when executed by a processor, implements the fatigue life prediction method for a thin-walled welded structure in the above method embodiments.
The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also reside as discrete components in the apparatus.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A fatigue life prediction method for a thin-wall welding structure is characterized by comprising the following steps:
receiving fatigue test data of a target standard sample input by a user, wherein the fatigue test data is obtained by performing a fatigue test on the target standard sample, the target standard sample comprises a standard sample with a welding line in the same plane and a standard sample with two welding lines in the same plane, the standard sample with a welding line in the same plane is equivalent to the T-shaped structure of a passenger car framework, and the standard sample with two welding lines in the same plane is equivalent to the cross-shaped structure of the passenger car framework; carrying out numerical fitting on the fatigue test data to obtain a stress-life curve of the target standard sample;
and determining the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework, the computer aided engineering CAE simulation theory and the stress-life curve data input by a user.
2. The method of claim 1, wherein determining the fatigue life of the welded structure of the passenger car skeleton according to the welding parameters of the welded structure in the passenger car skeleton input by a user, a Computer Aided Engineering (CAE) simulation theory and the stress-life curve data comprises:
establishing a finite element analysis model of the passenger car framework;
analyzing the finite element analysis model under the action of a preset condition to determine stress distribution information of each region in the finite element analysis model;
according to welding parameters of a welding structure input by a user, a welding line is established at a welding seam in the finite element analysis model to obtain a characteristic finite element analysis model, and the corresponding welding parameters at the welding seam in the characteristic finite element analysis model are the same as the actual welding parameters at the welding seam of the passenger car skeleton;
and determining the fatigue life of the passenger car framework welding structure according to the characteristic finite element analysis model, the stress distribution information and the stress-life curve data.
3. The method of claim 2, wherein analyzing the finite element analysis model under predetermined conditions comprises:
performing static analysis on the finite element analysis model according to predetermined working condition information, wherein the working condition information comprises any one or more of the following:
the device comprises a vertical working condition, a braking working condition, a left steering working condition, a right front wheel suspension working condition and a left front wheel suspension working condition.
4. The method of claim 2, wherein analyzing the finite element analysis model under predetermined conditions comprises:
acquiring a road surface load spectrum;
and under the condition that the road surface load spectrum acts on the finite element analysis model, carrying out dynamic analysis on the finite element analysis model.
5. The method of claim 2, wherein determining the fatigue life of the passenger car skeleton weld structure based on the characteristic finite element analysis model and the stress distribution information comprises:
correcting the stress distribution information according to welding parameters at the welding seam in the characteristic finite element analysis model, wherein the corrected stress distribution information comprises stress amplitude of each part of the passenger car framework welding structure;
and carrying out interpolation calculation according to the stress amplitude of each part and the stress-life curve data to obtain the fatigue life of each region of the passenger car skeleton welding structure.
6. The method of any of claims 2-5, wherein the establishing a finite element analysis model of the passenger vehicle skeleton comprises:
establishing a framework model of the passenger car framework;
extracting a middle surface of the skeleton model, and dividing grids on the extracted middle surface;
and establishing weld joint connection among all pipe fittings in the framework model based on a common node technology to obtain a finite element analysis model of the passenger car framework.
7. A welded structure fatigue life prediction apparatus, comprising:
the system comprises a receiving module, a fatigue testing module and a control module, wherein the receiving module is used for receiving fatigue testing data of a target standard sample piece input by a user, the fatigue testing data is obtained by performing fatigue testing on the target standard sample piece, the target standard sample piece comprises a standard sample piece with a welding line in the same plane and a standard sample piece with two welding lines in the same plane, the standard sample piece with a welding line in the same plane is equivalent to the T-shaped structure of a passenger car framework, and the standard sample piece with two welding lines in the same plane is equivalent to the cross-shaped structure of the passenger car framework;
the processing module is used for fitting the fatigue test data to obtain a stress-life curve of the target standard sample piece;
and the prediction module is used for determining the fatigue life of the welding structure of the passenger car framework according to the welding parameters of the welding structure in the passenger car framework, the CAE (computer aided engineering) simulation theory and the stress-life curve data input by a user.
8. An electronic device, comprising: at least one processor and a memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory cause the at least one processor to perform the method of predicting fatigue life of a thin-walled welded structure as recited in any one of claims 1 to 6.
9. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the thin-walled weld structure fatigue life prediction method of any one of claims 1 to 6.
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