CN113343353A - Multi-leaf steel plate spring model generation system and method - Google Patents
Multi-leaf steel plate spring model generation system and method Download PDFInfo
- Publication number
- CN113343353A CN113343353A CN202110605980.8A CN202110605980A CN113343353A CN 113343353 A CN113343353 A CN 113343353A CN 202110605980 A CN202110605980 A CN 202110605980A CN 113343353 A CN113343353 A CN 113343353A
- Authority
- CN
- China
- Prior art keywords
- target
- module
- candidate value
- parameter
- leaf spring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Abstract
The invention belongs to the technical field of automobile parts and discloses a multi-leaf steel plate spring model generation system and method. The method comprises the following steps: the parameter acquisition module acquires target parameters of a target multi-leaf spring model; the first parameter updating module updates a corresponding first target candidate value according to the target parameter; the constraint relation determining module searches a constraint relation corresponding to the first target candidate value and the target parameter in a preset constraint relation library; the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation; the model generation module generates a target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point line selection rule. By the mode, the target parameters can be obtained as required, and meanwhile, the theoretical relationship among the parameters can be automatically updated, so that the multi-leaf spring model can be generated quickly and closer to a real form.
Description
Technical Field
The invention relates to the technical field of automobile parts, in particular to a multi-leaf steel plate spring model generation system and method.
Background
The steel plate spring suspension is a suspension structure widely used in the field of commercial vehicles in China, and most automobile manufacturers can apply to CATIA software in design and check. However, the CATIA can only simulate the motion process of a rigid model in a motion mechanism module (DMU), and for elastomers with multi-state changes such as a leaf spring, a model generation process with high efficiency, accuracy and close to a real form cannot be performed in modules with high use frequency in two engineering design checks of CATIA assembly design and DMU.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a multi-leaf steel plate spring model generation system and a multi-leaf steel plate spring model generation method, and aims to solve the technical problem that a multi-leaf steel plate spring model cannot be generated efficiently, accurately and close to a real form in the prior art.
In order to achieve the aim, the invention provides a multi-leaf steel plate spring model generation system which comprises a parameter acquisition module, a first parameter updating module, a constraint relation determining module, a second parameter updating module and a model generation module, wherein the first parameter updating module is used for determining the constraint relation of a plurality of leaf springs;
the parameter acquisition module: the method comprises the steps of obtaining target parameters of a target multi-leaf spring model;
the first parameter update module: the target parameter updating module is used for updating a corresponding first target candidate value according to the target parameter;
the constraint relationship determination module: the constraint relation database is used for searching the constraint relation corresponding to the first target candidate value and the target parameter;
the second parameter update module: the constraint relation updating module is used for updating a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation;
the model generation module: and the target multi-leaf spring model is generated based on a preset point-line selection rule according to the target parameter, the first target candidate value and the second target candidate value.
Optionally, the first parameter updating module includes: the device comprises a range determining module and a parameter determining module;
the range determination module: comparing the first initial candidate value with the target parameter to obtain a range of the first initial candidate value;
the parameter determination module: and the device is used for determining a corresponding first candidate value according to the target state and the range, and updating the first candidate value into the first target candidate value.
Optionally, the first parameter updating module further includes: the device comprises an instruction acquisition module and a state selection module;
the instruction acquisition module: the method comprises the steps of obtaining a target multi-leaf spring model generation instruction of a user;
the state selection module: and the target state is selected according to the generation instruction.
Optionally, the second parameter updating module includes: a model building module; the device comprises a parameter input module and a candidate value determination module;
the model building module: the system is used for establishing a preset constraint relation model according to the constraint relation;
the parameter input module is used for: the target parameter and the first target candidate value are input to the preset constraint relation model to obtain a second candidate value;
the candidate value determination module: for updating the second candidate value to a second target candidate value.
Optionally, the model generation module comprises: a positioning constraint module and a model simulation module;
the positioning constraint module: the system is used for carrying out constraint positioning according to the target parameters and the target candidate values to obtain target control points;
the model simulation module: and the simulation module is used for simulating a multi-sheet steel spring model through a sample line according to the target control point so as to generate the target multi-sheet steel spring model.
Further, to achieve the above object, the present invention also provides a multi-leaf spring model generation method applied to the multi-leaf spring model generation system as described above, the multi-leaf spring model generation system including: the multi-leaf steel spring model generation method comprises the following steps of:
the parameter acquisition module acquires target parameters of a target multi-leaf spring model;
the first parameter updating module updates a corresponding first target candidate value according to the target parameter;
the constraint relation determining module searches a constraint relation corresponding to the first target candidate value and the target parameter in a preset constraint relation library;
the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation;
the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point-line selection rule.
Optionally, the first parameter updating module includes: the device comprises a range determining module and a parameter determining module;
the first parameter updating module updates the corresponding first target candidate value according to the target parameter, and comprises:
the range determination module compares a first initial candidate value with the target parameter to obtain a range of the first initial candidate value;
and the parameter determining module determines a corresponding first candidate value according to the target state and the range, and updates the first candidate value into the first target candidate value.
Optionally, the first parameter updating module further includes: the device comprises an instruction acquisition module and a state selection module;
before the range determining module compares the first initial candidate value with the target parameter to obtain the range of the first initial candidate value, the range determining module further includes:
the instruction acquisition module acquires a target multi-leaf spring model generation instruction of a user;
and the state selection module selects the corresponding target state according to the generation instruction.
Optionally, the second parameter updating module includes: a model building module; the device comprises a parameter input module and a candidate value determination module;
the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation, and includes:
the model establishing module establishes a preset constraint relation model according to the constraint relation;
the parameter input module inputs the target parameter and the first target candidate value into the preset constraint relation model to obtain a second candidate value;
the candidate determination module updates the second candidate to a second target candidate.
Optionally, the model generation module comprises: a positioning constraint module and a model simulation module;
the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point line selection rule, and the method comprises the following steps:
the positioning constraint module carries out constraint positioning according to the target parameter, the first target candidate value and the second target candidate value to obtain a target control point;
and the model simulation module simulates a multi-sheet steel spring model through a sample line according to the target control point so as to generate the target multi-sheet steel spring model.
The parameter acquisition module acquires target parameters of a target multi-leaf spring model; the first parameter updating module updates a corresponding first target candidate value according to the target parameter; a constraint relation determining module searches a constraint relation corresponding to the first target candidate value and the target parameter in a preset constraint relation library; the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation; and the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point-line selection rule. By the mode, target parameters can be obtained as required, and meanwhile, the theoretical relationship among the parameters can be updated automatically, so that a plurality of steel plate spring models can be generated quickly and closer to a real form, the steel plate spring suspension can be checked by using the models, and the design checking period of the suspension is shortened.
Drawings
FIG. 1 is a block diagram of a multi-leaf spring model generation system according to a first embodiment of the present invention;
FIG. 2 is a positive bow state diagram of an embodiment of a multi-leaf spring model generation system of the present invention;
FIG. 3 is a diagram illustrating a pressing state of an embodiment of a multi-leaf spring model generation system according to the present invention;
FIG. 4 is a diagram of an anti-bow state of an embodiment of a multi-leaf spring model generation system of the present invention;
FIG. 5 is a diagram of an anti-bow state of an embodiment of a multi-leaf spring model generation system of the present invention;
FIG. 6 is a parameter setting diagram of an embodiment of a multi-leaf spring model generation system of the present invention;
FIG. 7 is a diagram illustrating R values of a multi-leaf spring model generation system according to an embodiment of the present invention;
FIG. 8 is a diagram of an overall leaf spring configuration for an embodiment of a multi-leaf spring model generation system of the present invention;
FIG. 9 is a control point setting diagram of an embodiment of a multi-leaf spring model generation system according to the present invention;
FIG. 10 is a diagram of a positive arch model of an embodiment of a multi-leaf spring model generation system of the present invention;
FIG. 11 is a diagram of a flattening model for an embodiment of a multi-leaf spring model generation system of the present invention;
FIG. 12 is a diagram of an inverse arch model of an embodiment of a multi-leaf spring model generation method of the present invention;
FIG. 13 is a diagram of an inverse arch model of an embodiment of a multi-leaf spring model generation method of the present invention;
FIG. 14 is a block diagram of a multi-leaf spring model generation system according to a second embodiment of the present invention;
FIG. 15 is a schematic flow chart illustrating a multi-leaf spring model generation method according to a first embodiment of the present invention;
FIG. 16 is a flowchart illustrating a multi-leaf spring model generating method according to a second embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a block diagram illustrating a multi-leaf spring model generation system according to a first embodiment of the present invention.
In this embodiment, the multi-leaf spring model generation system includes: the system comprises a parameter acquisition module 10, a first parameter updating module 20, a constraint relation determining module 30, a second parameter updating module 40 and a model generating module 50; the parameter acquisition module 10: the method comprises the steps of obtaining target parameters of a target multi-leaf spring model; the first parameter updating module 20: the target parameter updating module is used for updating a corresponding first target candidate value according to the target parameter; the constraint relation determination module 30: the constraint relation database is used for searching the constraint relation corresponding to the first target candidate value and the target parameter; the second parameter updating module 40: the constraint relation updating module is used for updating a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation; the model generation module 50: and the target multi-leaf spring model is generated based on a preset point-line selection rule according to the target parameter, the first target candidate value and the second target candidate value.
It should be noted that the parameter obtaining module 10 is configured to obtain and set target parameters of a target multi-leaf spring model, where the target parameters refer to key parameters of a multi-leaf spring, namely, a leaf spring eye radius r, an arc height (a connection line from a leaf eye center to a first upper surface or a last lower surface) H, a first main leaf or a last leaf thickness H, and an action length (an arc length and a position of a neutral layer) L, and obtain an expected target value of the key parameters, and use the expected target value as the target parameters.
It should be noted that the first parameter updating module 20 is configured to update a corresponding first target candidate value according to a target parameter, where the first target candidate value is the selected arc height H value, because the plurality of leaf springs have four states, and the four states are respectively a positive spring bow state, as shown in fig. 2; a flattened state, as shown in fig. 3; the reverse bow state when 0< H < r, as shown in fig. 4; when H (numerical value) ≦ 0 and H (absolute value) > r, as shown in fig. 5, the camber H value coexists in four value ranges H > r, H ═ r, 0< H < r, and H <0, so that there are 4 first initial candidate values, and after the target parameter is obtained, it is necessary to obtain a first target candidate value among the 4 first initial candidate values according to the value rule, besides the parameter H, there are two H candidate values, i.e., H candidate value 1 and H candidate value 2, and all three parameters H are the first initial candidate values.
It should be noted that the constraint relation determining module 30 is configured to search a preset constraint relation library preset by the system for a constraint relation between the first target candidate value and the target parameter.
In a specific implementation, because the constraint relationships corresponding to the leaf springs in the four states are different, the corresponding relationship needs to be obtained according to a first target candidate value and a target parameter, the first target candidate value includes not only the arc height H but also the states (flat, positive bow, and negative bow) of the leaf spring model, and the constraint relationships are respectively: positive arch (R-0.5H) × cos (L/2/R) -R + H +0.5H ═ 0; flattening R is 0; (R + R +0.5H) _ cos (L/2/R) -R + _ H | -0.5H ═ 0 when H is not more than 0 and | H | > R; (R +0.5H) × (sin (L/2/R)) × (R +0.5H) × (sin (L/2/R)) ═ 2 × R + H + R + H) × (R-H) when 0< H < R.
The second parameter updating module 40 is configured to obtain a second target candidate value according to the corresponding constraint relationship after obtaining the target parameter and the target candidate value, where the second target candidate value refers to the radius of curvature of the first main neutral layer of the multiple leaf springs, which is R. Since the states of the plurality of leaf springs are different, the curvature R exists in three states, and thus there exist 3 first initial candidates, and after the first target candidate and the target parameter are obtained, a second target candidate is obtained among 3 second initial candidates according to the constraint relationship.
In a specific implementation, in order to make the obtained second target candidate value more accurate, the second parameter updating module further includes: a model building module; the device comprises a parameter input module and a candidate value determination module; the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation, and includes: the model establishing module establishes a preset constraint relation model according to the constraint relation; the parameter input module inputs the target parameter and the first target candidate value into the preset constraint relation model to obtain a second candidate value; the candidate determination module updates the second candidate to a second target candidate.
It should be noted that the model establishing module is configured to establish a preset constraint relationship model according to the obtained constraint relationship, and the preset constraint relationship model is obtained by training according to the constraint relationship and various model parameters in a preset database.
It can be understood that, the parameter entry module is configured to, after obtaining the preset constraint relationship model, enter the target parameter and the first target candidate value into the preset constraint relationship model for calculation to obtain a second candidate value, where the second candidate value is obtained on the basis of 3 second initial candidate values, and except for the parameter R, the candidate value R1 and the candidate value R2 are both second initial candidate values.
It is understood that the candidate value determining module is a module for updating the second candidate value to the second target candidate value, and as shown in fig. 7, the second target candidate value R is obtained according to the current preset constraint relation model and the parameters.
It should be noted that the model generating module 50 is a module configured to generate a target multi-leaf spring model according to a preset point-line selection rule after obtaining a target parameter, a first target candidate value, and a second target candidate value. The preset point line selection rule is obtained based on the central coordinate of the eye, the action length reference point and a neutral layer construction line end point, and is shown as a structural diagram of the integral reed in fig. 8, and is shown as a point line selection rule diagram in fig. 9.
In a specific implementation, in order to obtain a target multi-leaf spring model more accurately, a model needs to be generated based on a more accurate point-line selection rule, and further, the model generation module includes: a positioning constraint module and a model simulation module; the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point line selection rule, and the method comprises the following steps: the positioning constraint module carries out constraint positioning according to the target parameter, the first target candidate value and the second target candidate value to obtain a target control point; and the model simulation module simulates a multi-sheet steel spring model through a sample line according to the target control point so as to generate the target multi-sheet steel spring model.
It should be noted that the positioning constraint module is configured to obtain at least three target control points between the two endpoint and the middle point after obtaining the target parameter, the first target candidate value, and the second target candidate value, and the XYZ coordinate parameter of the target control point refers to the second target candidate value R, the first target candidate value, and the target parameter to constrain positioning.
It can be understood that, the model simulation module is used for simulating a multi-leaf spring model according to a spline line after obtaining a target control point, and the spline line has the following 2 factors: (1) manufacturing errors exist, the actual reed arc is not a pure arc, sample lines or arcs are made through the same three control points, model errors are also in a micro level, and the error requirements can be met in the process of obtaining parts. (2) In the normal generation of the multi-leaf spring model, the pure circular arc cannot simulate the pure flattening state, but can reach the pure flattening state through the sample line, as shown in fig. 10, 11, 12 and 13, respectively, the model diagram is in a state of positive bow, flattening state, state of negative bow height H smaller than r, and state of negative bow height H larger than r.
In this embodiment, the parameter obtaining module 10 obtains target parameters of a target multi-leaf spring model; the first parameter updating module 20 updates the corresponding first target candidate value according to the target parameter; the constraint relation determining module 30 searches a constraint relation corresponding to the first target candidate value and the target parameter in a preset constraint relation library; the second parameter updating module 40 updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation; the model generating module 50 generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point-line selection rule. By the mode, target parameters can be obtained as required, and meanwhile, the theoretical relationship among the parameters can be updated automatically, so that a plurality of steel plate spring models can be generated quickly and closer to a real form, the steel plate spring suspension can be checked by using the models, and the design checking period of the suspension is shortened.
Referring to fig. 14, fig. 14 is a block diagram illustrating a multi-leaf spring model generation system according to a second embodiment of the present invention, which is proposed based on the first embodiment.
In this embodiment, the first parameter updating module 20 includes: a range determination module 201 and a parameter determination module 202;
it should be noted that the range determining module 201 is configured to obtain a magnitude relationship between the first initial candidate value and r in the target parameter, where the multiple leaf springs have four states, and the four states are spring positive bow states respectively; a state of flattening; a reverse bow state when 0< H < r; the reverse bow state when H (numerical) ≦ 0 and H (absolute) > r, requires further determination of the arc height H range when in different states.
For example, as shown in FIG. 8, if H > r, then H first initial candidate value 1 is equal to H, otherwise H first initial candidate value is equal to 125mm (which may be any conventional value). Since the condition behind if is acted on, the first initial candidate value of H in the else case is a value on a parameter table that does not affect the model shape, but the else part must exist in relation to the uniqueness of the state of H. Similarly, the value relationships between the H first initial candidate value and the H first initial candidate value 2, and between the H and the first initial candidate value 3 are set according to the above method, and the equations of the R first initial candidate value 1 and the R first initial candidate value 2 are continuously set.
Further, the first parameter updating module 20 further includes: the device comprises an instruction acquisition module and a state selection module; before the range determining module compares the first initial candidate value with the target parameter to obtain the range of the first initial candidate value, the range determining module further includes: the instruction acquisition module acquires a target multi-leaf spring model generation instruction of a user; and the state selection module selects the corresponding target state according to the generation instruction.
The instruction obtaining module is configured to obtain a target multi-leaf spring model generation instruction sent by a user.
It can be understood that the state selection module is used for identifying a target multi-leaf spring model generation instruction sent by a user to obtain a target state desired by the target user.
It should be noted that the parameter determining module 202 is configured to finally determine the first candidate value after obtaining the target state and the magnitude relationship between the first initial candidate value and the target parameter r, update the first candidate value to the first target candidate value, and select different candidate values according to different states of the multiple leaf springs.
In this embodiment, the range determining module 201 compares a first initial candidate value with the target parameter to obtain a range of the first initial candidate value; the parameter determining module 202 determines a corresponding first candidate value according to the target state and the range, and updates the first candidate value to the first target candidate value. And obtaining a first target candidate value H according to the state and the size relation between the first initial candidate value and the target parameter r, so that the subsequent update of the theoretical relation among the parameters is more accurate.
Referring to fig. 15, fig. 15 is a schematic flow chart of a multi-leaf steel plate spring model generation method according to a first embodiment of the present invention, where the multi-leaf steel plate spring model generation method is applied to a multi-leaf steel plate spring model generation system, and the multi-leaf steel plate spring model generation system includes: the multi-leaf steel spring model generation method comprises the following steps of:
step S10: the parameter acquisition module acquires target parameters of a target multi-leaf spring model.
It should be noted that the execution subject of this embodiment is a multi-leaf spring model generation system, and is capable of setting a target parameter, that is, a target expected value, setting a candidate value after obtaining the target expected value, determining a corresponding constraint relationship according to the state of a target multi-leaf spring, obtaining two target candidate values, and finally generating a target multi-leaf spring model by using the target candidate value and the target parameter, and finally performing model checking by using the target multi-leaf spring, and may also be other devices that can achieve the same function, which is not limited in this embodiment.
The parameter obtaining module is configured to obtain and set target parameters of a target multi-leaf spring model, where the target parameters refer to key parameters of a multi-leaf spring, such as leaf spring eye radius r, arc height (a connection line from an eye center to a first upper surface or a last lower surface), a first main leaf or a last leaf thickness H, and leaf spring action length (an arc length and a position of a neutral layer), and obtain an expected target value of the key parameters, and the expected target value is used as the target parameter.
Step S20: and the first parameter updating module updates the corresponding first target candidate value according to the target parameter.
It should be noted that the first parameter updating module is configured to update a corresponding first target candidate value according to a target parameter, where the first target candidate value is the selected arc height H value, and the multiple leaf springs have four states, where the four states are spring positive bow states respectively; a state of flattening; a reverse bow state when 0< H < r; when H (numerical value) ≦ 0 and H (absolute value) > R, the arc height H values coexist in four value ranges H > R, H ═ R, 0< H < R, and H <0, so there are 4 first initial candidate values, and after obtaining the target parameter, it is necessary to obtain the first target candidate value among the 4 first initial candidate values according to the value rule, except for parameter H, there are two further H candidate values, H candidate value 1 and H candidate value 2, three parameters H are all the first initial candidate values, except for parameter R, and in addition, both R candidate value 1 and R candidate value 2 are the second initial candidate values.
Step S30: and the constraint relation determining module searches a constraint relation corresponding to the first target candidate value and the target parameter in a preset constraint relation library.
It should be noted that the constraint relation determining module is configured to search a preset constraint relation library preset by the system for a constraint relation between the first target candidate value and the target parameter.
In a specific implementation, because the constraint relationships corresponding to the leaf springs in the four states are different, the corresponding relationship needs to be obtained according to a first target candidate value and a target parameter, the first target candidate value includes not only the arc height H but also the states (flat, positive bow, and negative bow) of the leaf spring model, and the constraint relationships are respectively: positive arch (R-0.5H) × cos (L/2/R) -R + H +0.5H ═ 0; flattening R is 0; (R + R +0.5H) _ cos (L/2/R) -R + _ H | -0.5H ═ 0 when H is not more than 0 and | H | > R; (R +0.5H) × (sin (L/2/R)) × (R +0.5H) × (sin (L/2/R)) ═ 2 × R + H + R + H) × (R-H) when 0< H < R.
Step S40: and the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation.
The second parameter updating module is configured to obtain a second target candidate value according to a corresponding constraint relationship after obtaining a target parameter and a target candidate value, where the second target candidate value is obtained by obtaining a radius of curvature of a first main neutral layer of the plurality of leaf springs as R. Since the states of the plurality of leaf springs are different, the curvature R exists in three states, and thus there exist 3 first initial candidates, and after the first target candidate and the target parameter are obtained, a second target candidate is obtained among 3 second initial candidates according to the constraint relationship.
In a specific implementation, in order to make the obtained second target candidate value more accurate, the second parameter updating module further includes: a model building module; the device comprises a parameter input module and a candidate value determination module; the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation, and includes: the model establishing module establishes a preset constraint relation model according to the constraint relation; the parameter input module inputs the target parameter and the first target candidate value into the preset constraint relation model to obtain a second candidate value; the candidate determination module updates the second candidate to a second target candidate.
It should be noted that the model establishing module is configured to establish a preset constraint relationship model according to the obtained constraint relationship, and the preset constraint relationship model is obtained by training according to the constraint relationship and various model parameters in a preset database.
It can be understood that, the parameter entry module is configured to, after obtaining the preset constraint relationship model, enter the target parameter and the first target candidate value into the preset constraint relationship model for calculation to obtain a second candidate value, where the second candidate value is obtained on the basis of 3 second initial candidate values.
It is to be understood that the candidate value determining module is a module for updating the second candidate value to the second target candidate value.
Step S50: the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point-line selection rule.
The model generation module is a module for generating a target multi-leaf spring model according to a preset point-line selection rule after obtaining a target parameter, a first target candidate value and a second target candidate value. The preset point line selection rule is obtained based on the central coordinates of the eye curls, the action length reference point and a neutral layer construction line end point.
In a specific implementation, in order to obtain a target multi-leaf spring model more accurately, a model needs to be generated based on a more accurate point-line selection rule, and further, the model generation module includes: a positioning constraint module and a model simulation module; the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point line selection rule, and the method comprises the following steps: the positioning constraint module carries out constraint positioning according to the target parameter, the first target candidate value and the second target candidate value to obtain a target control point; and the model simulation module simulates a multi-sheet steel spring model through a sample line according to the target control point so as to generate the target multi-sheet steel spring model.
It should be noted that the positioning constraint module is configured to obtain at least three target control points between the two endpoint and the middle point after obtaining the target parameter, the first target candidate value, and the second target candidate value, and the XYZ coordinate parameter of the target control point refers to the second target candidate value R, the first target candidate value, and the target parameter to constrain positioning.
It can be understood that, the model simulation module is used for simulating a multi-leaf spring model according to a spline line after obtaining a target control point, and the spline line has the following 2 factors: (1) manufacturing errors exist, the actual reed arc is not a pure arc, sample lines or arcs are made through the same three control points, model errors are also in a micro level, and the error requirements can be met in the process of obtaining parts. (2) Pure circular arcs in a multi-sheet steel spring model generated normally cannot simulate a pure flattening state, but can be achieved through sample lines.
In the embodiment, a parameter acquisition module acquires target parameters of a target multi-leaf spring model; the first parameter updating module updates a corresponding first target candidate value according to the target parameter; a constraint relation determining module searches a constraint relation corresponding to the first target candidate value and the target parameter in a preset constraint relation library; the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation; and the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point-line selection rule. By the mode, target parameters can be obtained as required, and meanwhile, the theoretical relationship among the parameters can be updated automatically, so that a plurality of steel plate spring models can be generated quickly and closer to a real form, the steel plate spring suspension can be checked by using the models, and the design checking period of the suspension is shortened.
Fig. 16 is a flowchart illustrating a multi-leaf spring model generating method according to a second embodiment of the present invention, which is based on the first embodiment.
In this embodiment, the first parameter updating module includes: a range determination module and a parameter determination module, wherein the step S20 includes:
step S201: the range determination module compares a first initial candidate value to the target parameter to obtain a range of the first initial candidate value.
The range determining module is configured to obtain a magnitude relationship between the first initial candidate value and r in the target parameter, where the plurality of leaf springs have four states, and the four states are spring positive bow states respectively; a state of flattening; a reverse bow state when 0< H < r; the reverse bow state when H (numerical) ≦ 0 and H (absolute) > r, requires further determination of the arc height H range when in different states.
For example, if H > r, then the first initial candidate value of H1 is equal to H, otherwise the first initial candidate value of H is equal to 125mm (which may be any conventional value). Since the condition behind if is acted on, the first initial candidate value of H in the else case is a value on a parameter table that does not affect the model shape, but the else part must exist in relation to the uniqueness of the state of H. Similarly, the value relationships between the H first initial candidate value and the H first initial candidate value 2, and between the H and the first initial candidate value 3 are set according to the above method, and the equations of the R first initial candidate value 1 and the R first initial candidate value 2 are continuously set.
Further, the first parameter updating module further includes: the device comprises an instruction acquisition module and a state selection module; before the range determining module compares the first initial candidate value with the target parameter to obtain the range of the first initial candidate value, the range determining module further includes: the instruction acquisition module acquires a target multi-leaf spring model generation instruction of a user; and the state selection module selects the corresponding target state according to the generation instruction.
The instruction obtaining module is configured to obtain a target multi-leaf spring model generation instruction sent by a user.
It can be understood that the state selection module is used for identifying a target multi-leaf spring model generation instruction sent by a user to obtain a target state desired by the target user.
Step S202: and the parameter determining module determines a corresponding first candidate value according to the target state and the range, and updates the first candidate value into the first target candidate value.
It should be noted that the parameter determining module is configured to finally determine the first candidate value after obtaining the magnitude relationship between the target state and the first initial candidate value and r in the target parameter, update the first candidate value to the first target candidate value, and select different candidate values according to different states of the multiple leaf springs.
In this embodiment, the range determining module compares a first initial candidate value with the target parameter to obtain a range of the first initial candidate value; and the parameter determining module determines a corresponding first candidate value according to the target state and the range, and updates the first candidate value into the first target candidate value. And obtaining a first target candidate value H according to the state and the size relation between the first initial candidate value and the target parameter r, so that the subsequent update of the theoretical relation among the parameters is more accurate.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A multi-leaf spring model generation system, comprising: the system comprises a parameter acquisition module, a first parameter updating module, a constraint relation determining module, a second parameter updating module and a model generating module;
the parameter acquisition module: the method comprises the steps of obtaining target parameters of a target multi-leaf spring model;
the first parameter update module: the target parameter updating module is used for updating a corresponding first target candidate value according to the target parameter;
the constraint relationship determination module: the constraint relation database is used for searching the constraint relation corresponding to the first target candidate value and the target parameter;
the second parameter update module: the constraint relation updating module is used for updating a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation;
the model generation module: and the target multi-leaf spring model is generated based on a preset point-line selection rule according to the target parameter, the first target candidate value and the second target candidate value.
2. The multi-leaf spring model generation system of claim 1, wherein the first parameter update module comprises: the device comprises a range determining module and a parameter determining module;
the range determination module: comparing the first initial candidate value with the target parameter to obtain a range of the first initial candidate value;
the parameter determination module: and the device is used for determining a corresponding first candidate value according to the target state and the range, and updating the first candidate value into the first target candidate value.
3. The multi-leaf spring model generation system of claim 2, wherein the first parameter update module further comprises: the device comprises an instruction acquisition module and a state selection module;
the instruction acquisition module: the method comprises the steps of obtaining a target multi-leaf spring model generation instruction of a user;
the state selection module: and the target state is selected according to the generation instruction.
4. The multi-leaf spring model generation system of claim 1, wherein the second parameter update module comprises: a model building module; the device comprises a parameter input module and a candidate value determination module;
the model building module: the system is used for establishing a preset constraint relation model according to the constraint relation;
the parameter input module is used for: the target parameter and the first target candidate value are input to the preset constraint relation model to obtain a second candidate value;
the candidate value determination module: for updating the second candidate value to a second target candidate value.
5. The multi-leaf spring model generation system of claim 1, wherein the model generation module comprises: a positioning constraint module and a model simulation module;
the positioning constraint module: the system is used for carrying out constraint positioning according to the target parameters and the target candidate values to obtain target control points;
the model simulation module: and the simulation module is used for simulating a multi-sheet steel spring model through a sample line according to the target control point so as to generate the target multi-sheet steel spring model.
6. A multi-leaf spring model generation method applied to the multi-leaf spring model generation system according to any one of claims 1 to 5, the multi-leaf spring model generation system comprising: the multi-leaf steel spring model generation method comprises the following steps of:
the parameter acquisition module acquires target parameters of a target multi-leaf spring model;
the first parameter updating module updates a corresponding first target candidate value according to the target parameter;
the constraint relation determining module searches a constraint relation corresponding to the first target candidate value and the target parameter in a preset constraint relation library;
the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation;
the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point-line selection rule.
7. The multi-leaf spring model generation method of claim 6, wherein the first parameter update module comprises: the device comprises a range determining module and a parameter determining module;
the first parameter updating module updates the corresponding first target candidate value according to the target parameter, and comprises:
the range determination module compares a first initial candidate value with the target parameter to obtain a range of the first initial candidate value;
and the parameter determining module determines a corresponding first candidate value according to the target state and the range, and updates the first candidate value into the first target candidate value.
8. The multi-leaf spring model generation method of claim 7, wherein the first parameter update module further comprises: the device comprises an instruction acquisition module and a state selection module;
before the range determining module compares the first initial candidate value with the target parameter to obtain the range of the first initial candidate value, the range determining module further includes:
the instruction acquisition module acquires a target multi-leaf spring model generation instruction of a user;
and the state selection module selects the corresponding target state according to the generation instruction.
9. The multi-leaf spring model generation method of claim 6, wherein the second parameter update module comprises: a model building module; the device comprises a parameter input module and a candidate value determination module;
the second parameter updating module updates a corresponding second target candidate value according to the target parameter, the first target candidate value and the constraint relation, and includes:
the model establishing module establishes a preset constraint relation model according to the constraint relation;
the parameter input module inputs the target parameter and the first target candidate value into the preset constraint relation model to obtain a second candidate value;
the candidate determination module updates the second candidate to a second target candidate.
10. The multi-leaf spring model generation method of claim 6, wherein the model generation module comprises: a positioning constraint module and a model simulation module;
the model generation module generates the target multi-leaf spring model according to the target parameter, the first target candidate value and the second target candidate value based on a preset point line selection rule, and the method comprises the following steps:
the positioning constraint module carries out constraint positioning according to the target parameter, the first target candidate value and the second target candidate value to obtain a target control point;
and the model simulation module simulates a multi-sheet steel spring model through a sample line according to the target control point so as to generate the target multi-sheet steel spring model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110605980.8A CN113343353B (en) | 2021-05-31 | 2021-05-31 | Multi-leaf steel plate spring model generation system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110605980.8A CN113343353B (en) | 2021-05-31 | 2021-05-31 | Multi-leaf steel plate spring model generation system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113343353A true CN113343353A (en) | 2021-09-03 |
| CN113343353B CN113343353B (en) | 2022-05-20 |
Family
ID=77473712
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110605980.8A Active CN113343353B (en) | 2021-05-31 | 2021-05-31 | Multi-leaf steel plate spring model generation system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113343353B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101476354A (en) * | 2009-01-16 | 2009-07-08 | 北京工业大学 | Frequency-modulation mass anti-flection support |
| CN102609578A (en) * | 2012-01-20 | 2012-07-25 | 重庆长安汽车股份有限公司 | Construction method of car steel plate spring multi-body model |
| CN102937156A (en) * | 2012-11-27 | 2013-02-20 | 东风柳州汽车有限公司 | Plate spring component of heavy-duty truck |
| CN103699703A (en) * | 2012-09-27 | 2014-04-02 | 简式国际汽车设计（北京）有限公司 | Method for establishing balanced suspension plate spring model |
| CN106709204A (en) * | 2017-01-12 | 2017-05-24 | 山东理工大学 | Simulation computation method of deflection characteristics of high-duty two-leveled alterable stiffness leaf spring |
| CN106874527A (en) * | 2015-12-10 | 2017-06-20 | 广州汽车集团股份有限公司 | The method and system of flexible piece motion simulation simulation |
| CN107016214A (en) * | 2017-04-26 | 2017-08-04 | 上海工程技术大学 | A kind of parameter based on finite state machine relies on the generation method of model |
| CN107832543A (en) * | 2017-11-22 | 2018-03-23 | 南京理工大学 | A kind of design method for reducing complex mechanical system dynamic response error |
| CN109340286A (en) * | 2018-09-13 | 2019-02-15 | 北汽福田汽车股份有限公司 | The method that multistage gradual change steel-degree band spring, vehicle and multistage gradual change steel-degree band spring manufacture and design |
| CN111079295A (en) * | 2019-12-20 | 2020-04-28 | 浙江清华柔性电子技术研究院 | Flexible multilayer structure deformability simulation method and device, and computer equipment |
| CN112199767A (en) * | 2020-08-13 | 2021-01-08 | 南京金龙客车制造有限公司 | CATIA-based automobile leaf spring suspension motion modeling method |
-
2021
- 2021-05-31 CN CN202110605980.8A patent/CN113343353B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101476354A (en) * | 2009-01-16 | 2009-07-08 | 北京工业大学 | Frequency-modulation mass anti-flection support |
| CN102609578A (en) * | 2012-01-20 | 2012-07-25 | 重庆长安汽车股份有限公司 | Construction method of car steel plate spring multi-body model |
| CN103699703A (en) * | 2012-09-27 | 2014-04-02 | 简式国际汽车设计（北京）有限公司 | Method for establishing balanced suspension plate spring model |
| CN102937156A (en) * | 2012-11-27 | 2013-02-20 | 东风柳州汽车有限公司 | Plate spring component of heavy-duty truck |
| CN106874527A (en) * | 2015-12-10 | 2017-06-20 | 广州汽车集团股份有限公司 | The method and system of flexible piece motion simulation simulation |
| CN106709204A (en) * | 2017-01-12 | 2017-05-24 | 山东理工大学 | Simulation computation method of deflection characteristics of high-duty two-leveled alterable stiffness leaf spring |
| CN107016214A (en) * | 2017-04-26 | 2017-08-04 | 上海工程技术大学 | A kind of parameter based on finite state machine relies on the generation method of model |
| CN107832543A (en) * | 2017-11-22 | 2018-03-23 | 南京理工大学 | A kind of design method for reducing complex mechanical system dynamic response error |
| CN109340286A (en) * | 2018-09-13 | 2019-02-15 | 北汽福田汽车股份有限公司 | The method that multistage gradual change steel-degree band spring, vehicle and multistage gradual change steel-degree band spring manufacture and design |
| CN111079295A (en) * | 2019-12-20 | 2020-04-28 | 浙江清华柔性电子技术研究院 | Flexible multilayer structure deformability simulation method and device, and computer equipment |
| CN112199767A (en) * | 2020-08-13 | 2021-01-08 | 南京金龙客车制造有限公司 | CATIA-based automobile leaf spring suspension motion modeling method |
Non-Patent Citations (2)
| Title |
|---|
| 朱晓博等: ""基于Pro /Toolkit 的少片变截面钢板弹簧"", 《信息技术》 * |
| 范士琦: ""商用车悬架系统隔振特性分析研究"", 《中国优秀说书学位论文全文数据库 工程科技2辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113343353B (en) | 2022-05-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tong et al. | A hybrid algorithm for reliability analysis combining Kriging and subset simulation importance sampling | |
| JPH0830799A (en) | Automatically laying out method for graph | |
| CN111310318A (en) | Digital twinning-based process margin processing method and system and mechanical manufacturing assembly | |
| CN113240117B (en) | Variable fidelity transfer learning model establishing method | |
| CN113343353B (en) | Multi-leaf steel plate spring model generation system and method | |
| CN112091950A (en) | Robot kinematic parameter identification method based on hybrid genetic simulated annealing algorithm | |
| CN109637580B (en) | Protein amino acid association matrix prediction method | |
| CN114564787A (en) | Bayesian optimization method, device and storage medium for target-related airfoil design | |
| CN107679305B (en) | Road network model creating method and device | |
| CN116521921B (en) | Configuration and query method of data model in three-dimensional visual scene | |
| CN111191321B (en) | Control method, system and storage medium for renaming welding spots | |
| CN113221278A (en) | Light weight method for vehicle-mounted photoelectric platform collimation frame | |
| WO2004097489A1 (en) | Method of designing optical system | |
| CN115329611B (en) | Inertial navigation component simulation method and device, electronic equipment and storage medium | |
| CN115357976B (en) | Iterative calculation method of large-span steel structure construction scheme based on structural performance | |
| CN110096798A (en) | A kind of method of multimode FEM updating | |
| CN111258743B (en) | Cloud task scheduling method, device, equipment and storage medium based on discrete coding | |
| CN115587688A (en) | Scheduling method and system of assembly type building components based on BIM | |
| CN115470557A (en) | Intelligent building design method and system based on BIM | |
| CN111368978B (en) | Precision improving method for offline quantization tool | |
| CN112185416A (en) | AR-based word recitation method | |
| CN113987806B (en) | Atmosphere mode optimization method based on proxy model | |
| CN112109083A (en) | Robot kinematic parameter identification method based on genetic tabu search algorithm | |
| KR100445662B1 (en) | Master pattern designing system and method for the same | |
| CN116842663B (en) | Machine learning-based nonlinear time-varying stress value measuring method for automobile leaf springs |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |