Risk point prediction method for vehicle frame assembly

Abstract

The present invention relates to the field of vehicle testing technology, and in particular to a method for predicting risk points of a frame assembly, by constructing a virtual test bench, a rigid-flexible coupling model of a frame assembly, and a suspension template in dynamics software and assembling them to form a virtual model of the frame assembly. The road load data of the whole vehicle is collected based on the actual vehicle, and the key channels that characterize the load of the frame assembly are distinguished; the road load data of the whole vehicle collected from the actual vehicle is iteratively calculated in the virtual model of the frame assembly, and the load spectra of all attachment points of the frame assembly under various loading conditions are extracted; according to the fatigue damage distribution results, the service life of the assembly is calculated and the risk is predicted in combination with the damage theory, and the frame optimization design is completed. The present invention can reproduce the real load of the global attachment points of the frame assembly, and extract the load history of typical loading conditions to calculate the service life of each design scheme, determine the risk points, improve the optimization efficiency of each design scheme, and shorten the development cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle testing, and in particular to a method for predicting risk points of a vehicle frame assembly. Background Art

[0002] The frame is the base platform of the entire vehicle. It supports and connects various components of the vehicle and bears various random loads from inside and outside the vehicle. This random load is coupled with the multi-degree-of-freedom frame system to produce complex alternating loads, which directly affect the service life of the frame assembly. Therefore, at all stages of the entire frame product development, quantified load data is needed to evaluate the design and guide the iterative optimization of the frame structure.

[0003] In the prior art, the quantitative load data at each stage of vehicle frame product development is mainly obtained through road simulation tests and torsional fatigue bench tests. Road simulation tests can only obtain the load spectrum of some attachment points of the frame, but cannot obtain the load of the global attachment points of the frame assembly, that is, they cannot accurately predict the service life and risk points of the structure; torsional fatigue bench tests can only simulate a single working condition of frame torsion, and cannot perform fatigue tests on typical working conditions such as bending, torsion, braking, and steering. Moreover, road simulation tests and torsional fatigue bench tests are both verified on physical objects, which cannot be verified during the product development stage and cannot provide guidance for forward research and development. Summary of the invention

[0004] The present invention provides a method for predicting risk points of a vehicle frame assembly, which is used to solve the defect that the quantitative load data in each stage of vehicle frame product development in the prior art is not fully obtained, which is not conducive to providing guidance for forward research and development. The method can reproduce the real load of the global attachment point of the vehicle frame assembly, and extract the load history of typical loading conditions to calculate the service life and risk points of each design scheme, thereby improving the optimization efficiency of each design scheme and shortening the development cycle.

[0005] The present invention provides a method for predicting risk points of a vehicle frame assembly, comprising: constructing a virtual test bench of a vehicle frame assembly based on dynamics software; constructing a network model of the vehicle frame assembly, generating a flexible body of the vehicle frame assembly, and generating a rigid-flexible coupling model of the vehicle frame assembly in the dynamics software based on the flexible body of the vehicle frame assembly; constructing a suspension template and establishing a communicator adapted to the virtual test bench on the axle of the suspension template; assembling the rigid-flexible coupling model of the vehicle frame assembly with the suspension template to form an assembly, and then assembling the assembly with the virtual test bench to form a virtual model of the vehicle frame assembly; adjusting the virtual model of the vehicle frame assembly based on the measured axle load of the vehicle frame assembly, so that the virtual test bench is in an actual preload state state, perform standard working conditions on the virtual test bench to clarify the load characteristics of the frame assembly; based on the load characteristics of the frame assembly, clarify the strain measurement points of the frame assembly under different standard working conditions, arrange sensors at the corresponding strain measurement points on the actual vehicle, and collect the whole vehicle road load data of the actual vehicle; analyze the whole vehicle road load data collected from the actual vehicle, distinguish the key channels that characterize the load of the frame assembly, and clarify the real load conditions of the frame assembly; load the real load conditions on the virtual model of the frame assembly, extract the frame attachment point load data of the virtual model of the frame assembly after the loading conditions, perform risk assessment, predict the frame failure points, and complete the frame optimization design.

[0006] According to a vehicle frame assembly risk point prediction method provided by the present invention, the standard working conditions include bending conditions, torsion conditions, braking conditions and cornering conditions.

[0007] According to a method for predicting risk points of a vehicle frame assembly provided by the present invention, strain measurement points of the vehicle frame assembly under different standard working conditions are obtained in the following manner: after executing each standard working condition on the virtual test bench, the stress distribution result on the virtual model of the vehicle frame assembly is obtained, the principal stress direction is analyzed, and the strain sensitive area and the principal stress direction under different standard working conditions are clarified; a position with a gentle stress distribution gradient is selected in the strain sensitive area as a strain measurement point, and a unidirectional strain gauge is arranged at the strain measurement point on the actual vehicle, and the direction is consistent with the principal stress direction.

[0008] According to a vehicle frame assembly risk point prediction method provided by the present invention, collecting vehicle road load data for an actual vehicle includes: building a data acquisition system based on a test plan, the data acquisition system including a suspension displacement measurement module, an axle head and a three-dimensional acceleration measurement module of the frame near the axle head, a frame torsion angle measurement module, a strain measurement module and a data collector; conducting actual vehicle tests on a durable road surface and a performance road surface reinforced in a test field, and collecting load spectra based on the data acquisition system; inspecting and correcting the collected load spectrum data to obtain accurate load data, and performing segmentation and extraction according to different characteristic road surfaces to obtain vehicle road load data under different characteristic road surfaces.

[0009] According to a method for predicting risk points of a vehicle frame assembly provided by the present invention, before loading the virtual model of the vehicle frame assembly with the actual loading condition, the virtual bench is first verified and the number of virtual bench cycles is formulated, including: driving the virtual bench with white noise stimulated by an actuator, taking the key channel of the vehicle frame assembly as the target signal, and calculating the transfer function and correlation coefficient of the virtual bench; formulating the virtual bench working condition based on the typical working condition of the vehicle frame assembly and the road load data of the whole vehicle, the excitation loading method of the virtual bench includes the road simulation test pavement excitation of the whole vehicle and the standard working condition excitation of the virtual bench; based on the key channels of the actual frame load, the key channels of the frame assembly as the target signal, and the transfer function and correlation coefficient of the virtual bench as the target signal; formulating the virtual bench working condition based on the typical working condition of the vehicle frame assembly and the road load data of the whole vehicle, the excitation loading method of the virtual bench includes the road simulation test pavement excitation of the whole vehicle and the standard working condition excitation of the virtual bench; The virtual test bench iteration is performed according to the load condition to obtain the virtual test bench actuator excitation, which mainly includes the whole vehicle road simulation test pavement excitation and the test bench standard condition excitation; the excitation signal is input into the virtual test bench actuator, the test bench response signal is obtained, and the ratio of the response signal to the target signal is calculated whether it is within a reasonable range; if the ratio is reasonable, the drive is the real test bench excitation, and its response signal is the test bench response signal; otherwise, the actuator drive needs to be corrected; the total damage value of the key channel of the whole vehicle road load data is calculated based on the amplitude of the virtual test bench response signal; based on the damage equivalence principle, the number of virtual test bench cycles is determined based on the total damage of the key channel of the whole vehicle road load data.

[0010] According to a method for predicting risk points of a vehicle frame assembly provided by the present invention, the load data of the frame attachment points of the virtual model of the vehicle frame assembly is extracted, and risk assessment is performed, including: extracting the load spectra of all attachment points of the vehicle frame assembly, driving the virtual fatigue model according to the working conditions and the corresponding test bench cycles, and then predicting the risk of the vehicle frame assembly based on the damage theory, and statistically analyzing the stress amplitude-cycle number matrix of the load spectra of all attachment points, and calculating the relative damage of the vehicle frame assembly by the following formula 1 to evaluate the risk of all attachment points: Formula 1: , where D is the relative damage of the frame assembly, is the number of cycles under stress amplitude, is the life under stress amplitude, and c is the allowable damage of the frame assembly.

[0011] According to a vehicle frame assembly risk point prediction method provided by the present invention, the transfer function and the correlation function of the virtual test bench are calculated by the following formula 2: Formula 2: , where is the transfer function of the virtual gantry, is the actuator excitation white noise, The response of the actuator to white noise excitation; is the correlation coefficient of the virtual bench, is the covariance of x and y, is the variance of x, is the variance of y.

[0012] The road surface excitation of the whole vehicle road simulation test is calculated by the following formula three: Formula three: , where is the driving of the ith actuator, is the driving response of the ith actuator, is the inverse function of the transfer function, G is the weighting coefficient, is the response signal amplitude under the driving of the i-th actuator, is the key channel signal of the frame assembly load, and a and b are the accuracy ranges of the response signal.

[0013] The total damage value of the key channel of the vehicle road load data is calculated by the following formula 4: Formula 4: , where is the total damage value of the key channels of the vehicle road load data, is the response signal amplitude under the driving of the i-th actuator, is the fatigue strength index.

[0014] The method for predicting risk points of a frame assembly provided by the present invention forms a virtual model of the frame assembly by constructing a virtual test bench, a rigid-flexible coupling model of the frame assembly and a suspension template in dynamics software and assembling them. The initial posture of the actuator is adjusted based on the measured axle load minus the weight of the unassembled parts on the virtual test bench, so that the frame is in an actual preload state, the working condition of the virtual test bench is calibrated, and the accuracy and rationality of the virtual test bench are verified. The whole vehicle road load data is collected based on the actual vehicle to characterize the key channels for the frame assembly to be loaded. The whole vehicle road load data collected from the actual vehicle is iteratively calculated in the virtual model of the frame assembly to obtain the real excitation of the virtual test bench, extract the load spectrum of all attachment points of the frame assembly, and then the risk prediction of the assembly is carried out based on the damage theory. According to the fatigue damage distribution results, the failure point of the frame is predicted to complete the frame optimization design. The frame assembly risk point prediction method of the present invention can reproduce the actual load of the global attachment point of the frame assembly, and extract the load history of typical loading conditions to calculate the service life and risk points of each design scheme, thereby improving the optimization efficiency of each design scheme and shortening the development cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic flow chart of the vehicle frame assembly risk point prediction method provided by the present invention.

[0017] Figure 2 It is a structural schematic diagram of the vehicle frame assembly virtual model provided by the present invention.

[0018] Figure 3 It is a schematic structural diagram of the front vertical loading device of the frame assembly provided by the present invention.

[0019] Figure 4 It is a schematic structural diagram of the vertical loading device at the rear of the vehicle frame assembly provided by the present invention.

[0020] Figure 5 It is a schematic structural diagram of the front side loading device of the vehicle frame assembly provided by the present invention.

[0021] Figure 6 It is a schematic structural diagram of the rear side loading device of the frame assembly provided by the present invention.

[0022] Figure 7 It is a structural schematic diagram of the longitudinal loading device of the frame assembly provided by the present invention.

[0023] Figure 8 It is a structural schematic diagram of the suspension wheel end loading device provided by the present invention.

[0024] Fig. 9 It is a structural schematic diagram of the support member provided by the present invention.

[0025] Fig.10 It is a structural schematic diagram of the rigid-flexible coupling model of the vehicle frame assembly provided by the present invention.

[0026] Fig.11 It is a structural schematic diagram of the front suspension dynamics model provided by the present invention.

[0027] Fig.12 It is a structural schematic diagram of the rear suspension dynamics model provided by the present invention.

[0028] Fig.13 It is a schematic diagram of a measurement point scheme for testing a real road load spectrum of a vehicle frame provided by the present invention.

[0029] Fig.14 It is a schematic diagram of the time domain signal of the road simulation data provided by the present invention.

[0030] Figure numerals: 1. vertical loading device at the front of the frame assembly; 101. front vertical actuator; 102. front cross beam; 2. vertical loading device at the rear of the frame assembly; 201. rear vertical actuator; 202. rear cross beam; 3. lateral loading device at the front of the frame assembly; 301. front lateral actuator; 302. steering axle; 303. front suspension stabilizer bar; 304. front leaf spring; 305. front connecting member; 4. lateral loading device at the rear of the frame assembly; 401. rear lateral actuator; 5. longitudinal loading device of the frame assembly; 501. longitudinal actuator; 6. suspension wheel end loading device; 601. vertical displacement actuator; 602. wheel; 603. rear leaf spring; 7. support member; 8. rigid-flexible coupling model of the frame assembly; 9. saddle. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0033] In the embodiments of the present invention, unless otherwise clearly specified and limited, the first feature being "above" or "below" the second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "above" and "above" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. The first feature being "below", "below" and "below" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0034] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0035] Combine the following Figures 1 to 14 The specific implementation process of the vehicle frame assembly risk point prediction method of the present invention is described.

[0036] The present invention provides a method for predicting risk points of a vehicle frame assembly, see Figure 1 As shown, the process includes the following steps S1 to S8.

[0037] S1. Build a virtual test bench for the frame assembly based on dynamics software.

[0038] S2. Construct a network model of the frame assembly, generate a flexible body of the frame assembly, and based on the flexible body of the frame assembly, generate a rigid-flexible coupling model of the frame assembly in the dynamics software.

[0039] S3, constructing a suspension template and establishing a communicator adapted to the virtual test bench on the axle of the suspension template.

[0040] S4. Assemble the rigid-flexible coupling model of the vehicle frame assembly with the suspension template to form an assembly, and then assemble the assembly with the virtual bench to form a virtual model of the vehicle frame assembly.

[0041] S5. Adjust the virtual model of the frame assembly based on the measured axle load of the frame assembly, put the virtual test bench in an actual preload state, perform standard working conditions on the virtual test bench, and clarify the load characteristics of the frame assembly.

[0042] S6. Based on the load characteristics of the frame assembly, the strain measurement points of the frame assembly under different standard working conditions are clarified, sensors are arranged at the corresponding strain measurement points on the actual vehicle, and the road load data of the actual vehicle is collected.

[0043] S7. Analyze the vehicle road load data collected from the actual vehicle, distinguish the key channels that characterize the load of the frame assembly, and clarify the actual load conditions of the frame assembly.

[0044] S8. Load the virtual model of the frame assembly with real excitation (real loading condition), extract the load data of the frame attachment points of the virtual model of the frame assembly after the loading condition, conduct risk assessment, predict the failure point of the frame, and complete the frame optimization design.

[0045] It can be understood that the risk point prediction method of the vehicle frame assembly in this embodiment is to construct a virtual test bench and a rigid-flexible coupling model of the vehicle frame assembly in the dynamics software ( Fig.10 As shown) and the suspension template and assemble to form Figure 2 The virtual model of the frame assembly shown is based on the measured axle load minus the weight of the unassembled parts on the virtual test bench, and the initial posture of the actuator is adjusted to put the frame in the actual preload state, calibrate the virtual test bench working conditions, and verify the accuracy and rationality of the virtual test bench. The whole vehicle road load data is collected based on the actual vehicle, and the key channels of the frame assembly are characterized. The whole vehicle road load data collected by the actual vehicle is iteratively calculated in the virtual model of the frame assembly to obtain the real excitation of the virtual test bench, and the load spectrum of all attachment points of the frame assembly is extracted. The virtual fatigue model is driven according to the loading conditions and the corresponding test bench cycles, and then the risk of the assembly is predicted based on the damage theory. According to the fatigue damage distribution results, the failure point of the frame is predicted to complete the frame optimization design. The risk point prediction method of the frame assembly of this embodiment can reproduce the real load of the global attachment point of the frame assembly, and extract the load history of the typical loading conditions to calculate the service life and risk points of each design scheme, improve the optimization efficiency of each design scheme, and shorten the development cycle.

[0046] In some embodiments of the vehicle frame assembly risk point prediction method of the present invention, see Figure 2 As shown, the virtual test bench includes a front vertical loading device 1 of the frame assembly, a rear vertical loading device 2 of the frame assembly, a front lateral loading device 3 of the frame assembly, a rear lateral loading device 4 of the frame assembly and a longitudinal loading device 5 of the frame assembly.

[0047] It can be understood that the front vertical loading device 1 of the frame assembly is used to apply vertical loads to the cab, engine and gearbox. Figure 2 and Figure 3 As shown, the front vertical loading device 1 of the frame assembly includes three front vertical actuators 101. The upper end of each front vertical actuator 101 is connected to a front cross beam 102. The front cross beam 102 is overlapped on the rigid-flexible coupling model 8 of the frame assembly. The three front vertical actuators 101 are respectively used to apply vertical loads to the cab, the engine and the gearbox.

[0048] The front vertical loading devices 1 of the frame assembly are three arranged at intervals along the first direction, the first direction is the front-to-back direction of the frame, the front vertical actuators 101 are arranged along the third direction, the third direction is the vertical direction, the upper end of the front vertical actuator 101 is connected to the front crossbeam 102 through a rotating pair, the lower end of the front vertical actuator 101 is connected to the ground through a rotating pair, the two ends of the front crossbeam 102 are overlapped on the upper wing surface of the rigid-flexible coupling model of the frame assembly, and the end of the front crossbeam 102 and the upper wing surface are connected through a bushing. It should be noted here that the front vertical actuators 101 can be one, two or more, and the specific number is reasonably designed according to the front size of the rigid-flexible coupling model 8 of the frame assembly; in addition, the spacing between the front vertical actuators 101 can be evenly distributed, and the specific spacing can also be reasonably designed according to the front size of the rigid-flexible coupling model 8 of the frame assembly.

[0049] It is understandable that the vertical loading device 2 at the rear of the frame assembly is used to apply a vertical load acting on the saddle or the subframe. Figure 2 and Figure 4 As shown, the rear vertical loading device 2 of the frame assembly includes two rear vertical actuators 201, and the upper ends of the two rear vertical actuators 201 are connected to a rear cross beam 202, and the rear cross beam 202 is overlapped on the frame assembly rigid-flexible coupling model 8. For trucks and engineering vehicles, the rear vertical loading device 2 of the frame assembly can apply a vertical load mounted on the subframe.

[0050] Specifically, for the tractor type, the rear vertical loading device 2 of the frame assembly on the saddle 9 of the frame assembly rigid-flexible coupling model 8 includes two rear vertical actuators 201 and a rear cross beam 202. The two rear vertical actuators 201 are distributed on the left and right sides of the saddle along the second direction, and the second direction is the left and right direction of the frame, that is, the two rear vertical actuators 201 are distributed on the left and right sides of the saddle 9; the rear cross beam 202 is fixedly connected to the top of the saddle, and the two ends of the rear cross beam 202 are connected to the upper ends of the two rear vertical actuators 201 through a ball joint, and the lower end of the rear vertical actuator 201 is connected to the ground through a ball joint. For trucks and engineering vehicles, the rear vertical loading device 2 of the frame assembly can adopt the same structure as the front vertical loading device 1 of the frame assembly. The rear vertical loading device 2 of the frame assembly on the frame assembly rigid-flexible coupling model 8 includes a rear vertical actuator and a rear cross beam. The upper end of the rear vertical actuator is connected to the rear cross beam through a rotating pair, and the lower end of the rear vertical actuator is connected to the ground through a rotating pair. The rear cross beam is erected on the upper wing surface at the rear of the frame assembly rigid-flexible coupling model 8; there can be two rear vertical loading devices 2 of the frame assembly, and the spacing between the two rear vertical loading devices 2 of the frame assembly is distributed according to the size of the rear of the frame.

[0051] It is understandable that the front side loading device 3 of the frame assembly is used to apply the side load of the steering axle. Figure 2 and Figure 5 As shown, the front lateral loading device 3 of the frame assembly includes a front lateral actuator 301 , which is connected to a steering axle 302 , and the steering axle 302 acts on the rigid-flexible coupling model 8 of the frame assembly.

[0052] Specifically, the suspension template includes a front suspension dynamics model, on which a steering axle 302 is arranged, and a front lateral actuator 301 can apply a lateral load to the steering axle 302. It should be noted that the number of the front lateral actuators 301 is equal to the number of the steering axles 302 of the front suspension dynamics model, and the positions correspond one to one. Fig.11 As shown, the front suspension dynamics model includes a front suspension stabilizer bar 303, a front leaf spring 304 and a steering axle 302, a front lateral actuator 301 is arranged along the second direction, one end of the front lateral actuator 301 is connected to the axle head, and the other end of the front lateral actuator 301 is connected to the ground through a ball joint. Further, when the installation space of the front lateral actuator 301 is limited, the front lateral actuator 301 is also connected to a front connecting member 305, and the front lateral actuator 301 is connected to the axle head through the front connecting member 305.

[0053] It is understood that the side loading device 4 at the rear of the frame assembly is used to apply a side load acting on the saddle or the subframe. Figure 2 and Figure 6 As shown, the rear lateral loading device 4 of the vehicle frame assembly includes a rear lateral actuator 401 , which is connected to the end of the rear cross beam 202 .

[0054] Specifically, the rear lateral actuator 401 is arranged along the second direction, one end of the rear lateral actuator 401 is connected to the end of the rear cross beam 202, the rear cross beam 202 acts on the saddle 9, and the other end of the rear lateral actuator 401 is connected to the ground through a ball joint.

[0055] It is understandable that the frame assembly longitudinal loading device 5 is used to apply the longitudinal load of the frame assembly. Figure 2 and Figure 7 As shown, the longitudinal loading device 5 of the frame assembly includes a longitudinal actuator 501 , and the longitudinal actuator 501 is connected to the middle part of the rear cross beam 202 .

[0056] Specifically, the longitudinal loading device 5 of the frame assembly includes a longitudinal actuator 501, which is arranged along a first direction, one end of the longitudinal actuator 501 is connected to the rear cross beam 202, the rear cross beam 202 acts on the saddle, and the other end of the longitudinal actuator 501 is connected to the ground through a ball joint.

[0057] In other embodiments of the vehicle frame assembly risk point prediction method of the present invention, the virtual test bench also includes a suspension wheel end loading device 6 and a support member 7; the suspension wheel end loading device 6 is used to apply a forced displacement load to the suspension; the support member 7 is used to establish vertical support for the wheels not connected to the vertical actuator, to establish lateral support for the axles not connected to the lateral actuators, to establish longitudinal support for the axles of the rear suspension assembly, to increase vertical support for the tractor on the saddle, and to increase vertical support for the truck / engineering vehicle on the subframe.

[0058] It is understandable that, combined with Figure 2 and Figure 8 As shown, the suspension wheel end loading device 6 includes a plurality of vertical displacement actuators 601 arranged along the third direction, the two ends of the steering bridge 302 are connected to the upper ends of the vertical displacement actuators 601 through ball joints, and the lower ends of the vertical displacement actuators 601 are connected to the ground through ball joints. The suspension template also includes a rear suspension dynamics model, see Fig.12 As shown, the rear suspension dynamics model includes wheels 602 and rear leaf springs 603. Both ends of the rear axle of the suspension structure are also connected to vertical displacement actuators 601 through ball joints. It should be noted that the number of vertical displacement actuators 601 depends on the number of steering axles 302 and rear axles of the suspension structure. Figure 2 and Fig. 9 As shown, the support member 7 mainly includes auxiliary supports for simulating tire reaction force, roll force, and self-aligning moment, and simulated supports for simulating overturning moments of working conditions such as axle encountering obstacles and braking. The support member 7 is used to vertically support the wheels 602 of the frame. It should be noted that the support member 7 in the virtual test bench can optimize the test bench boundary and avoid redundant loading with the actuator; the number, position, function, installation position, and constraint method of the support member 7 in the virtual test bench can be adaptively increased, decreased, and moved according to different types of frame assemblies.

[0059] In other embodiments of the vehicle frame assembly risk point prediction method of the present invention, after the virtual test bench is established in the dynamics software through the above process, a frame assembly mesh model is established in the finite element software, and the frame assembly includes all longitudinal beams, cross beams, saddles, suspension mounting brackets, related accessories and corresponding connecting parts. External connection points are established, the number of which is adapted to the vertical loading device of the virtual test bench, and the position corresponds to the vertical loading device of the virtual test bench. External connection points are established at the suspension mounting bracket. The frame assembly mesh model is generated into a flexible body for the multi-body dynamics model. The flexible body only needs to retain the outer contour. After the creation is completed, it is imported into the multi-body dynamics software to generate a rigid-flexible coupling model of the frame assembly. Then, in the multi-body dynamics software, templates for the suspension systems of tractors, trucks, and engineering vehicles are established, and a communicator is established on the axle to adapt to the virtual test bench loading device. A matching mechanism is established in the virtual test bench, the rigid-flexible coupling model of the frame assembly, the suspension template and other dynamic models. During assembly, the corresponding communicators of each part can be automatically matched. The rigid-flexible coupling model of the frame assembly is assembled with the suspension template, and then the assembly is adapted to the virtual test bench to form a virtual model of the frame assembly.

[0060] After the virtual model of the vehicle frame assembly is established, in some embodiments of the vehicle frame assembly risk point prediction method of the present invention, the initial posture of the actuator is adjusted based on the measured axle load minus the weight of unassembled parts on the virtual test bench, so that the frame is in an actual preload state; the virtual test bench working conditions are calibrated with standard working conditions to verify the accuracy and rationality of the virtual test bench, wherein the standard working conditions include bending conditions, torsion conditions, braking conditions and turning conditions.

[0061] a. Bending conditions

[0062] The actuator is driven to move, so that the vertical loading mechanism (front vertical loading mechanism, rear vertical loading mechanism) increases the appropriate downward load, and the wheel actuator generates an increased upward load. The size and distribution of each load can be adjusted according to the actual bending working condition mode.

[0063] b. Reversal conditions

[0064] The vertical loading mechanism is kept preloaded, and the wheel actuator is driven to load the wheels on the same axle in different phases. It should be noted here that when the rigid-flexible coupling assembly of the frame assembly has multiple axle arrangement actuators, the diagonal loading between the axles is maintained.

[0065] c. Braking conditions

[0066] The rear vertical loading mechanism is kept preloaded, and the front vertical loading mechanism is added with a downward vertical load, and a load in the driving direction is applied to the frame through the longitudinal actuator (front lateral loading mechanism, rear lateral loading mechanism).

[0067] d. Turning conditions

[0068] Keep the vertical loading mechanism preloaded, drive the lateral actuator, and load the front and rear actuators (front lateral loading mechanism, rear lateral loading mechanism) in the same phase.

[0069] It should be noted that the virtual test bench includes but is not limited to the reproduction of bending conditions, torsion conditions, braking conditions and cornering conditions. The virtual model of the frame assembly is adjusted based on the measured axle load of the frame assembly, so that the virtual test bench is in an actual preload state, and standard conditions are performed on the virtual test bench to clarify the load characteristics of the frame assembly.

[0070] Based on the load characteristics of the frame assembly, the strain measurement points of the frame assembly under different standard working conditions are clarified to facilitate the collection of vehicle road load data for the actual vehicle. In some embodiments of the frame assembly risk point prediction method of the present invention, the strain measurement points of the frame assembly under different standard working conditions are obtained by the following method: Fig.13 As shown in the figure, after executing each standard working condition on the virtual test bench, the stress distribution results on the virtual model of the frame assembly are obtained, the principal stress direction is analyzed, and the strain sensitive area and principal stress direction under different standard working conditions are clarified; the position with a gentle stress distribution gradient in the strain sensitive area is selected as the strain measurement point, and the unidirectional strain gauge is arranged at the strain measurement point on the actual vehicle, and the direction is consistent with the principal stress direction.

[0071] Furthermore, during the implementation of the above-mentioned frame assembly risk point prediction method, the road load data of the actual vehicle is collected, including: building a data collection system based on the test plan, the data collection system includes a suspension displacement measurement module, an axle head and a three-dimensional acceleration measurement module of the frame near the axle head, a frame torsion angle measurement module, a strain measurement module and a data collector; arranging loading data for the actual vehicle, the loading data includes the frame acceleration, frame strain and torsion angle data of the front, middle and rear axle heads of the frame assembly and the corresponding positions of the axle heads, and the loading data also includes suspension displacement; conducting actual vehicle tests on the reinforced durable road surface and performance road surface of the test site, and collecting load spectra based on the data collection system; inspecting and correcting the collected load spectrum data to obtain accurate load data, and segmenting and extracting them according to different characteristic road surfaces to obtain the road load data of the whole vehicle under different characteristic road surfaces.

[0072] Specifically, firstly, the suspension displacement, frame front, middle and rear axle axle heads and frame acceleration at the corresponding positions of the axle heads, frame strain and torsion angle data during the vehicle driving process are arranged, wherein the frame strain data should include the wing and belly strain of the frame longitudinal beam, the frame cross beam strain, and the strain gauge arrangement direction is determined according to the simulation analysis results. Then, the data acquisition system is built according to the test plan, and the data acquisition system includes the suspension displacement measurement module, the axle head and the frame three-way acceleration measurement module near the axle head, the frame torsion angle measurement module, the strain measurement module and the data collector; after the sensor and the data collector are connected, check whether the channel signal can work normally, eliminate the test data abnormality caused by problems such as line disconnection, strain gauge, acceleration, and torsion angle sensor parameter settings, and record the sensor installation position in detail before the formal test begins. Secondly, the durable road surface and performance road surface are strengthened in the test field, and the actual vehicle road test is carried out. The road load spectrum is collected, and the sampling frequency is set to 10 times the maximum frequency of the signal of interest. Data collection is carried out according to the test specifications, and at least 3 sets of valid data are guaranteed for each characteristic road surface. Finally, the road load spectrum data analysis is performed to verify and correct the load data obtained from the test. The data verification includes the data time domain (see Fig.14 As shown, Ch203, Ch204, Ch208, and Ch209 represent different test channels, SG02, SG03, SG07, and SG08 represent different strain measurement points, le represents the left side, ri represents the right side, and mi represents the middle), statistical domain validity, repeatability, and consistency tests, etc. Correction includes eliminating signal drift, eliminating singular points, filtering, etc., to obtain accurate load data, and segment and extract according to different characteristic road surfaces. Based on the vehicle road load data, the key channels that characterize the load of the frame assembly are distinguished, and the actual load conditions of the frame assembly are clarified.

[0073] In some embodiments of the vehicle frame assembly risk point prediction method of the present invention, before loading the real load condition on the virtual model of the vehicle frame assembly, the virtual test bench is first verified and the number of virtual test bench cycles is formulated, including: driving the virtual test bench by means of actuator-excited white noise, taking the key channel of the frame assembly as the target signal, and calculating the transfer function and correlation coefficient of the virtual test bench; formulating the virtual test bench working condition based on the typical working condition of the frame assembly and the road load data of the whole vehicle, and the excitation loading method of the virtual test bench includes road simulation test pavement excitation of the whole vehicle and standard working condition excitation of the virtual test bench; inputting the excitation signal into the virtual test bench actuator, obtaining the test bench response signal, and calculating whether the ratio of the response signal to the target signal is within a reasonable range; if the ratio is reasonable, the drive is the real excitation of the test bench, and its response signal is the test bench response signal; otherwise, the actuator drive needs to be corrected; calculating the total damage value of the key channel of the whole vehicle road load data based on the amplitude of the virtual test bench response signal; and formulating the number of virtual test bench cycles based on the total damage of the key channel of the whole vehicle road load data based on the damage equivalence principle.

[0074] It can be understood that the transfer function of the virtual test bench is calculated, the virtual test bench is driven by the actuator to excite the white noise, and the key channel of the frame assembly load is used as the target signal , through the following formula 2: Formula 2: , where is the transfer function of the virtual gantry, is the actuator excitation white noise, The response of the actuator to white noise excitation; is the correlation coefficient of the virtual bench, is the covariance of x and y, is the variance of x, is the variance of y.

[0075] The test bench working conditions are formulated based on the typical working conditions of the frame and the road load data of the whole vehicle. The test bench excitation loading methods mainly include: road simulation test pavement excitation of the whole vehicle and standard working condition excitation of the test bench.

[0076] Among them, the test bench standard working condition excitation is to synthesize multiple groups of multi-axis sine waves with phase characteristics, and the test bench excitation input is performed by analyzing the standard working conditions. The road surface excitation of the whole vehicle road simulation test selects a signal with a relatively high correlation with the whole vehicle response signal as the target signal, and performs virtual iteration to obtain the test bench actuator excitation for the whole vehicle road simulation test. The excitation signal is input into the virtual test bench actuator to obtain the test bench response signal, and calculate whether the ratio of the response signal to the target signal is within a reasonable range. If the ratio is reasonable, the drive is the real test bench excitation, otherwise, the actuator drive needs to be corrected. The road surface excitation of the whole vehicle road simulation test is calculated by the following formula three: Formula three: , where is the driving of the ith actuator, is the driving response of the ith actuator, is the inverse function of the transfer function, G is the weighting coefficient, is the response signal amplitude under the driving of the i-th actuator, is the key channel signal of the frame assembly load, and a and b are the accuracy ranges of the response signal.

[0077] The total damage value of the key channel of the vehicle road load data is calculated by the following formula 4: Formula 4: , where is the total damage value of the key channels of the vehicle road load data, is the response signal amplitude under the driving of the i-th actuator, is the fatigue strength index, typical automotive parts Based on the damage equivalence principle, the number of virtual test bench cycles is determined based on the total damage of key channels of the vehicle road load data.

[0078] After virtual bench verification and the number of virtual bench cycles are obtained, the load spectra of all attachment points of the frame assembly are extracted, and the virtual fatigue model is driven according to the working conditions and the corresponding number of bench cycles. Then, the risk of the assembly is predicted based on the damage theory, and the stress amplitude-cycle number matrix of the load spectrum of each attachment point is statistically calculated. The relative damage of the frame assembly is calculated according to the Miner damage theory formula, and the risk points are evaluated. Specifically, the relative damage of the frame assembly is calculated by the following formula 1 to evaluate the risk of all attachment points: Formula 1: , where D is the relative damage of the frame assembly, is the number of cycles under stress amplitude, is the life under stress amplitude, and C is the allowable damage of the frame assembly. According to the fatigue damage distribution results, the failure point of the frame is predicted and the frame optimization design is completed.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting risk points of a vehicle frame assembly, characterized in that: include: Build a virtual test bench for the frame assembly based on dynamics software; Constructing a finite element model of the frame assembly, generating a flexible body of the frame assembly, and generating a rigid-flexible coupling model of the frame assembly in a dynamics software based on the flexible body of the frame assembly; Constructing a suspension template and establishing a communicator adapted to the virtual bench on the axle of the suspension template; Assembling the rigid-flexible coupling model of the vehicle frame assembly with the suspension template to form an assembly, and then assembling the assembly with the virtual bench to form a virtual model of the vehicle frame assembly; Adjusting the virtual model of the vehicle frame assembly based on the measured axle load of the vehicle frame assembly, so that the virtual test bench is in an actual preload state, performing standard working conditions on the virtual test bench, and clarifying the load characteristics of the vehicle frame assembly; Based on the load characteristics of the frame assembly, the strain measurement points of the frame assembly under different standard working conditions are identified, sensors are arranged at the corresponding strain measurement points on the actual vehicle, and the road load data of the actual vehicle is collected; Analyze the vehicle road load data collected from actual vehicles, identify the key channels that characterize the load of the frame assembly, and clarify the actual load conditions of the frame assembly; The real loading condition is loaded on the virtual model of the frame assembly, the load data of the frame attachment points of the virtual model of the frame assembly under the loading condition is extracted, and a risk assessment is performed to predict the failure point of the frame, thereby completing the frame optimization design.

2. The method for predicting risk points of a vehicle frame assembly according to claim 1, characterized in that: The standard working conditions include bending conditions, torsion conditions, braking conditions and cornering conditions.

3. The method for predicting risk points of a vehicle frame assembly according to claim 2, characterized in that: The strain measurement points of the frame assembly under different standard working conditions are obtained in the following ways: After executing each standard working condition on the virtual test bench, the stress distribution result on the virtual model of the frame assembly is obtained, the principal stress direction is analyzed, and the strain sensitive area and the principal stress direction under different standard working conditions are clarified; a position with a gentle stress distribution gradient is selected in the strain sensitive area as a strain measurement point, and a unidirectional strain gauge is arranged at the strain measurement point on the actual vehicle, and the direction is consistent with the principal stress direction.

4. The method for predicting risk points of a vehicle frame assembly according to claim 3, characterized in that: The collection of vehicle road load data for actual vehicles includes: Build a data acquisition system based on the test plan. The data acquisition system includes a suspension displacement measurement module, an axle head and axle head near the three-axis acceleration measurement module of the frame, a frame torsion angle measurement module, a strain measurement module and a data collector; Conduct actual vehicle tests on durable and performance roads at the test site, and collect load spectra based on the data acquisition system; The collected load spectrum data is checked and corrected to obtain accurate load data, and segmented and extracted according to different characteristic road surfaces to obtain the vehicle road load data under different characteristic road surfaces.

5. The method for predicting risk points of a vehicle frame assembly according to claim 4, characterized in that: Before the virtual model of the vehicle frame assembly is loaded with the actual loading condition, the virtual bench is verified and the number of virtual bench cycles is set, including: The virtual test bench is driven by white noise excited by an actuator, and a key channel of the frame assembly being loaded is used as a target signal to calculate a transfer function and a correlation coefficient of the virtual test bench; The virtual bench working condition is formulated based on the typical working condition of the frame assembly and the road load data of the whole vehicle, and the excitation loading mode of the virtual bench includes the road simulation test pavement excitation of the whole vehicle and the standard working condition excitation of the virtual bench; Input the excitation signal into the virtual bench actuator, obtain the bench response signal, and calculate whether the ratio of the response signal to the target signal is within a reasonable range; if the ratio is reasonable, the drive is the real bench excitation, and its response signal is the bench response signal; otherwise, the actuator drive needs to be corrected; The total damage value of the key channel of the vehicle road load data is calculated based on the amplitude of the virtual test bench response signal; based on the damage equivalence principle, the number of virtual test bench cycles is determined based on the total damage of the key channel of the vehicle road load data.

6. The method for predicting risk points of a vehicle frame assembly according to claim 5, characterized in that: Extracting the frame attachment point load data of the frame assembly virtual model and performing risk assessment includes: Extract the load spectra of all attachment points of the frame assembly, drive the virtual fatigue model according to the working conditions and the corresponding test bench cycles, and then predict the risk of the frame assembly based on the damage theory. Statistically calculate the stress amplitude-cycle number matrix of the load spectra of all attachment points, and calculate the relative damage of the frame assembly through the following formula 1 to evaluate the risk of all attachment points; Formula 1: , where D is the relative damage of the frame assembly, is the number of cycles under stress amplitude, is the life under stress amplitude, and c is the allowable damage of the frame assembly.

7. The method for predicting risk points of a vehicle frame assembly according to claim 5, characterized in that: The transfer function and correlation function of the virtual gantry are calculated by the following formula 2: Formula 2: , where is the transfer function of the virtual gantry, is the actuator excitation white noise, The response of the actuator to white noise excitation; is the correlation coefficient of the virtual bench, is the covariance of x and y, is the variance of x, is the variance of y; For the vehicle road simulation test, the road surface excitation of the virtual test bench is calculated by the following formula 3: Formula 3: , where is the driving of the ith actuator, is the driving response of the ith actuator, is the inverse function of the transfer function, G is the weighting coefficient, is the response signal amplitude under the driving of the i-th actuator, is the key channel signal of the frame assembly load, a and b are the accuracy ranges of the response signal; The total damage value of the key channel of the vehicle road load data is calculated by the following formula 4: Formula 4: , where is the total damage value of the key channels of the vehicle road load data, is the response signal amplitude under the driving of the i-th actuator, is the fatigue strength index.

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