CN116118189A - 3D printing technology-based rutting test block structure modulus targeting design method - Google Patents
3D printing technology-based rutting test block structure modulus targeting design method Download PDFInfo
- Publication number
- CN116118189A CN116118189A CN202310063941.9A CN202310063941A CN116118189A CN 116118189 A CN116118189 A CN 116118189A CN 202310063941 A CN202310063941 A CN 202310063941A CN 116118189 A CN116118189 A CN 116118189A
- Authority
- CN
- China
- Prior art keywords
- test block
- modulus
- rutting
- overall
- prediction model
- 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.)
- Pending
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 117
- 238000010146 3D printing Methods 0.000 title claims abstract description 27
- 238000013461 design Methods 0.000 title claims abstract description 27
- 238000005516 engineering process Methods 0.000 title claims abstract description 23
- 230000008685 targeting Effects 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 title claims abstract description 17
- 239000000463 material Substances 0.000 claims description 16
- 239000000945 filler Substances 0.000 claims description 12
- 238000012937 correction Methods 0.000 claims description 10
- 238000007689 inspection Methods 0.000 claims description 2
- 239000000203 mixture Substances 0.000 abstract description 3
- 238000002474 experimental method Methods 0.000 description 10
- 238000012417 linear regression Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 238000012856 packing Methods 0.000 description 6
- 238000004088 simulation Methods 0.000 description 5
- 230000008093 supporting effect Effects 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 2
- 238000007639 printing Methods 0.000 description 2
- 239000012779 reinforcing material Substances 0.000 description 2
- 235000017166 Bambusa arundinacea Nutrition 0.000 description 1
- 235000017491 Bambusa tulda Nutrition 0.000 description 1
- 241001330002 Bambuseae Species 0.000 description 1
- 235000015334 Phyllostachys viridis Nutrition 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 239000011425 bamboo Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
- B29C64/20—Apparatus for additive manufacturing; Details thereof or accessories therefor
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
- B29C64/30—Auxiliary operations or equipment
- B29C64/386—Data acquisition or data processing for additive manufacturing
- B29C64/393—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y30/00—Apparatus for additive manufacturing; Details thereof or accessories therefor
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y50/00—Data acquisition or data processing for additive manufacturing
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y50/00—Data acquisition or data processing for additive manufacturing
- B33Y50/02—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
-
- 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
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/10—Additive manufacturing, e.g. 3D printing
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Materials Engineering (AREA)
- Manufacturing & Machinery (AREA)
- Physics & Mathematics (AREA)
- Optics & Photonics (AREA)
- Mechanical Engineering (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
Abstract
The invention provides a rutting test block structure modulus targeting design method based on a 3D printing technology, which comprises the following steps: step 1, classifying filling structures in a rut test block model needing 3D printing; step 2, defining each parameter of a filling structure in the rut test block model in the step 1, and constructing a rut test block overall modulus prediction model function; and 3, testing and correcting the track test block overall modulus prediction model function in the step 2 to obtain an accurate track test block overall modulus prediction model. And (3) taking various filling structures as categories, taking structural composition parameters as variables, and aiming at structural modulus, providing a structural design equation to realize a 3D printing technology taking the structural modulus as a targeting target.
Description
Technical Field
The invention relates to the technical field of 3D printing, in particular to a rutting test block structure modulus targeting design method based on a 3D printing technology.
Background
Today 3D printing technology focuses on a diversified structure, with less consideration of the overall structural modulus: in the london design museum of 7 in 2013, the british designer, catthin Wales, 3D printed a complete set of garments, the burin was successfully printed by Amit zocan at the bureau of miltiorrhizae institute in 2012, the complete structural modulus is not considered by the university of the east of 2016, the calendar of the university of the same kind of science and technology, and the like, which successfully prints out the 'bamboo shoots' from the 'house'.
The existing 3D printing technology prints with filling rate or several existing structures, and has certain limitations: most 3D printers are supported by grids, linear supports, planar supports, tree supports and columnar supports, and the supporting effect is good, but the supporting structure is single, so that many filling structures cannot be realized. The design parameters of the device comprise printing temperature, supporting setting, wall thickness setting, base cushion setting and the like, the printing environment is relatively comprehensive, and the direct design of the whole structural modulus cannot be realized.
Structural design methods that target structural modulus are less common. The general law of mixing holds that the properties of the matrix and the reinforcing material and the size of the components of the matrix and the reinforcing material greatly influence the properties of the composite material, but the internal structure of the material is a condition for influencing the establishment of the law of mixing, and the law of mixing cannot be simply applied when the structural modulus is predicted, and further research is still needed.
Therefore, it is desirable to provide a 3D printing technology-based rutting test block structural modulus targeting design method to solve the above existing problems.
Disclosure of Invention
In view of the above, the invention provides a 3D printing technology-based rutting test block structure modulus targeting design method, which realizes a 3D printing technology with the structure modulus as a targeting target, takes various filling structures as categories, takes structure composition parameters as variables, and provides a structural design equation for the structure modulus.
In order to achieve the technical effects, the invention provides a rutting test block structure modulus targeting design method based on a 3D printing technology, which comprises the following steps:
and 3, testing and correcting the track test block overall modulus prediction model function in the step 2 to obtain an accurate track test block overall modulus prediction model.
Further, the filling structure in the rut test block model in the step 1 comprises a hollow cylinder type, a hollow cube type and a cross-network filling structure, and the cross-network filling structure comprises a horizontal cross-network filling structure and an upright cross-network filling structure.
Further, in the step 2, when the filling structure inside the rut test block model is a hollow cylinder, each parameter is defined as follows: overall modulus y of rutting test block, free parameter beta 0 Outer diameter x of hollow cylinder 1 Wall thickness x 2 Young's modulus x of filler material 3 Height x of cylinder 4 。β i For unknown parameters corresponding to different factors, the overall modulus prediction model function of the rutting test block is as follows:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝275.636-15.11·x 1 +102.147·x 2 +0.519·x 3 -13.859·x 4 。
further, in the step 2, when the filling structure inside the rut test block model is of a hollow cube type, each parameter is defined as follows: the overall modulus of the rutting test block is y, and the free parameter is beta 0 Side length x of hollow cube 1 Wall thickness x between two hollow cubes 2 Young's modulus x of material 3 Height x of hollow cube 4 。β i Unknown parameters corresponding to different factors; the overall modulus prediction model function of the rutting test block is as follows:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝1028.644-39.726·x 1 +34.716·x 2 +0.756·x 3 -33.608·x 4 。
further, in the step 2, when the filling structure inside the rut test block model is a horizontal cross network, each parameter is defined as follows: overall modulus y of rutting test block, free parameter beta 0 Length x of rectangular parallelepiped 1 The deflection angle of the cuboid around the horizontal axis is x 2 Young's modulus x of filler material 3 Height x of rectangular parallelepiped 4 ，β i For unknown parameters corresponding to different factors, the overall modulus prediction model function of the rutting test block is as follows:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝2414.046-15.749·x 1 +0.231·x 2 +0.576·x 3 -35.874·x 4 。
further, in the step 2, when the filling structure inside the rut test block model is in a vertical cross network shape, each parameter is defined as follows: overall modulus y of rutting test block, free parameter beta 0 Length x of rectangular parallelepiped 1 The deflection angle of the cuboid around the vertical axis is x 2 Young's modulus x of filler material 3 ，β i Unknown parameters corresponding to different factors; the overall modulus prediction model function of the rutting test block is as follows:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝2747.819-89.578·x 1 -23.843·x 2 +0.727·x 3 。
further, the overall modulus prediction model of the rut test block subjected to the test correction in the step 3 adopts
After inspection, the deviation meets the requirements.
The technical scheme of the invention at least comprises the following beneficial effects:
1. the invention realizes the 3D printing technology taking the structural modulus as a targeting target;
2. the invention takes material modulus, type, internal structure and shape parameters as variables, refers to the volume (30 cm multiplied by 5 cm) of a rutting test piece, takes the structural modulus of a printed rutting test piece as a prediction target, realizes the target design of the structural modulus of the rutting test piece by means of finite element simulation software, and provides a prediction equation on the basis, thereby realizing a 3D printing technology taking the structural modulus as the target;
3. according to the invention, a plurality of filling structures are used as categories, structural composition parameters are used as variables, and a structural design equation is provided for structural modulus, so that a 3D printing technology with the structural modulus as a target is realized.
Drawings
FIG. 1 is an elevational view of the hollow cylindrical filling structure of the present invention in an upright position;
FIG. 2 is a top view of the hollow cylindrical filling structure of the present invention in an upright position;
FIG. 3 is an elevation view of the hollow cylindrical filling structure of the present invention in a horizontal position;
FIG. 4 is a top view of the hollow cylindrical filling structure of the present invention in a horizontal position;
FIG. 5 is an elevational view of the hollow cylindrical filling structure of the present invention in an upright position;
FIG. 6 is a top view of the hollow cylindrical filling structure of the present invention in an upright position;
FIG. 7 is a graph showing simulated and predicted values of the overall modulus of a rut test block of the hollow cylindrical fill structure of the present invention taken at different sample points;
FIG. 8 is a hollow cylinder outside diameter x of the overall modulus of a rut test block of the hollow cylinder packing structure of the present invention 1 Taking simulation values and predicted values of 94mm at different sample points;
FIG. 9 is a hollow cylinder outside diameter x of the overall modulus of a rut test block of the hollow cylinder packing structure of the present invention 1 Taking simulation values and predicted values of 94mm at different sample points;
FIG. 10 is a front view of a hollow cube-filled structure of the present invention;
FIG. 11 is a top view of a hollow cube-filled structure of the present invention;
FIG. 12 is a graph showing simulated and predicted values of the overall modulus of a rut test block of the hollow cube-filled structure of the present invention taken at different sample points;
FIG. 13 is a front view of a cross-cuboid packing according to the present invention;
FIG. 14 is a top view of a cross-cuboid filler structure according to the present invention;
FIG. 15 is a graph showing simulated and predicted values of the overall modulus of a rut test block of the cross cuboid filler structure of the present invention taken at different sample points;
FIG. 16 is a front view of an upright cross-cuboid packing structure of the present invention;
FIG. 17 is a top view of an upright cross-cuboid packing according to the present invention;
FIG. 18 is a graph showing simulated and predicted values of the overall modulus of a rut test block of the upright intersecting cuboid packing structure of the present invention taken at different sample points;
FIG. 19 is a Young's modulus x of a filler material in the overall modulus of a rut test block of an upright cross-cuboid filler structure of the present invention 3 Taking the simulation value and the predicted value of the values at different sample points when 2000Mpa is taken.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 19 of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
A rutting test block structure modulus targeting design method based on a 3D printing technology comprises the following steps:
and 3, testing and correcting the track test block overall modulus prediction model function in the step 2 to obtain an accurate track test block overall modulus prediction model.
Example 1
As shown in fig. 1 to 7, when the rut test block model is inWhen the filling structure of the portion is a hollow cylinder, first, each parameter is defined: overall modulus y of rutting test block, free parameter beta 0 Outer diameter x of hollow cylinder 1 Wall thickness x 2 Young's modulus x of filler material 3 Height x of cylinder 4 。β i Is an unknown parameter corresponding to different factors. Thereby constructing a multi-element linear model:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
firstly, because experimental samples cannot be listed one by one and the combination among all factors is more, in order to ensure the rigor of the experiment, the orthogonal experiment design is selected for grouping, and the experiment is a four-factor three-level standard orthogonal experiment, so that the following standard orthogonal table is adopted:
and for the aspect of value, x 1 Taking 50mm,60mm and 100mm; x is x 2 Taking 5mm,10mm and 15mm; x is x 3 Taking 500MPa,2000MPa and 3500MPa; x is x 4 10mm,20mm,30mm were taken. Respectively corresponding to column numbers 1-4, and these values are in turn corresponding to test numbers. The relevant modeling and calculation of the pavement (the designated load is 0.7 MPa) are carried out through finite element simulation software. The hollow cylinder has two filling forms, one is in an upright state and the other is in a horizontal state, and the two filling structures are subjected to overall modulus simulation through finite element simulation and a controlled variable method.
The overall modulus of the rut test block is 2515.995Mpa in the upright state.
The overall modulus of the rut test block is 410.117Mpa calculated in the horizontal state.
Thus, the modulus calculated by the hollow cylinder in the upright state at the same time is higher, and only the upright state should be considered from the viewpoint of demand.
The upstanding hollow cylinder is thus modeled with finite element simulation software.
Final resultThe overall modulus of the rut test block is obtained. And the values and function values are subjected to multiple linear regression operation to obtain the relation and the function fitting degree, as shown in fig. 5, the graph shows that the model is not practical due to the negative number in the predicted value, so that the factors can be deduced to have the application range, the cylinder height is analyzed firstly, the influence is small due to the fact that the whole height of the pavement is not high and the set height range is wider, then the material modulus is not considered preferentially due to the fact that the 3500MPa is close to the pavement elastic modulus, the value setting is wider, then the wall thickness is wider for the settable diameter, and the range is not considered any more. The range of the outer diameter of the hollow cylinder is thus modified. The smaller the outer diameter, the closer the filling rate is to 100%, and the more the pavement structure is met, so that the value of the pavement structure has a maximum value. In x 1 Gradually reduce to make trial calculation, when x 1 The fitting result obtained by a linear regression model at 94mm was as follows: as shown in fig. 6, the predicted value of the third set of points is slightly smaller than 0; when x is 1 Taking 93mm, the fitting result obtained by the linear regression model is as follows: as shown in FIG. 7, all predicted values of y are greater than 0, and the goodness of fit R is obtained 2 =0.951 is close to 1, so the fitting degree is better. Thereby obtaining a functional relation
y＝275.636-15.11·x 1 +102.147·x 2 +0.519·x 3 -13.859·x 4 ⑴
And is also provided with
From this, the error=0.15 value is calculated to be smaller and the fitting degree is higher.
Example 2
As shown in fig. 8 to 10, when the filling structure in the rut test block model is a hollow cube type, parameters are defined: the overall modulus of the rutting test block is y, and the free parameter is beta 0 Side length x of hollow cube 1 Wall thickness x between two hollow cubes 2 Young's modulus x of material 3 Height x of hollow cube 4 。β i Is an unknown parameter corresponding to different factors. Thereby constructing a multi-element linear model:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the experiment still adopts a modified four-factor three-level standard orthogonal experiment design table.
For the aspect of value, x 1 Taking 20mm,30mm and 40mm; x is x 2 Taking 20mm,30mm and 40mm; x is x 3 Taking 500MPa,2000MPa and 3500MPa; x is x 4 10mm,20mm,30mm are taken and correspond to column numbers 1-4, respectively, and these values are sequentially corresponding to test numbers. Thus, the road surface is more random and representative, and the related modeling and calculation (the designated load is 0.7 MPa) are carried out through finite element simulation software.
The overall modulus of the rutting test block is finally obtained. And performing multiple linear regression operation on the values and the function values to obtain a relational expression and a function fitting degree: as shown in FIG. 10, all predicted values of y are greater than 0, and the goodness of fit R is obtained 2 =0.983 is close to 1, so the fitting degree is better. Thereby yielding a functional relationship:
y＝1028.644-39.726·x 1 +34.716·x 2 +0.756·x 3 -33.608·x 4 ⑵
and is also provided with
From this, the error= 0.0758 value is calculated to be smaller and the fitting degree is higher.
Example 3
As shown in fig. 11 to 13, when the filling structure in the rut test block model is a horizontal cross net, the width of each rectangular parallelepiped is fixed to 20mm, and first, each parameter is defined: overall modulus y of rutting test block, free parameter beta 0 Length x of rectangular parallelepiped 1 The deflection angle of the cuboid around the horizontal axis is x 2 Filling materialYoung's modulus x of the material 3 Height x of rectangular parallelepiped 4 。β i Is an unknown parameter corresponding to different factors. Thereby constructing a multi-element linear model:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the experiment still adopts a modified four-factor three-level orthogonal experiment design table.
For the aspect of value, x 1 Taking 80mm,100mm and 120mm; x is x 2 45 degrees, 60 degrees, 30 degrees and x are taken 3 Taking 500MPa,2000MPa,3500MPa and x 4 10mm,20mm,30mm are taken and correspond to column numbers 1-4, respectively, and these values are sequentially corresponding to test numbers. Thus, the road surface is more random and representative, and the related modeling and calculation (the designated load is 0.7 MPa) are carried out through finite element simulation software.
The overall modulus of the rutting test block is finally obtained. And performing multiple linear regression operation on the values and the function values to obtain a relational expression and a function fitting degree: as shown in FIG. 13, all predicted values of y are greater than 0 at this time, and the goodness of fit R is obtained 2 =0.944 is close to 1, so the fitting degree is better. Thereby yielding a functional relationship:
y＝2414.046-15.749·x 1 +0.231·x 2 +0.576·x 3 -35.874·x 4 ⑶
and is also provided with
From this, the error=0.144 value is calculated to be smaller and the fitting degree is higher.
Example 4:
as shown in fig. 14 to 17, when the filling structure in the rut test block model is an upright cross cuboid, the width of each cuboid is fixed to 5mm, and first, each parameter is defined: vehicle with a frameThe overall modulus y of the rutting test block and the free parameter beta 0 Length x of rectangular parallelepiped 1 The deflection angle of the cuboid around the vertical axis is x 2 Young's modulus x of filler material 3 ，β i Is an unknown parameter corresponding to different factors. Thereby constructing a multi-element linear model: y=β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 ；
The experiment adopts a three-factor three-level orthogonal experiment design table.
For the aspect of value, the height of the pavement is 5cm, x 1 15mm,20mm and 30mm are taken; x is x 2 45 degrees, 60 degrees, 30 degrees and x are taken 3 500MPa,2000MPa and 3500MPa are respectively corresponding to the column numbers 1-3, and the values are sequentially corresponding to the test numbers.
The relevant modeling and calculation of the pavement (the designated load is 0.7 MPa) are carried out through finite element simulation software.
The overall modulus of the rutting test block is finally obtained. And performing multiple linear regression operation on the values and the function values to obtain a relational expression and a function fitting degree: since the predictive model is ineffective because the predictive value of y is less than 0 as shown in FIG. 16, careful observation of the data reveals that the predictive value of the overall modulus of the road surface at the next-to-last data point is too small, the modulus of filling is found to be too small by comparison with the adjacent points to be 500MPa, and the linear regression analysis table is satisfied when the modulus of filling is found to be increased to 2000MPa by calculation, as shown in FIG. 17, at which time all predictive values of y are greater than 0, and the goodness-of-fit R is obtained 2 =0.904 is close to 1, so the fitting degree is better. Thereby yielding a functional relationship: :
y＝2747.819-89.578·x 1 -23.843·x 2 +0.727·x 3 ⑷
and is also provided with
Thus, err is calculatedor=0.134 is smaller and the fitting degree is higher. />
The foregoing is a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention and are intended to be comprehended within the scope of the present invention.
Claims (7)
1. The track test block structure modulus targeting design method based on the 3D printing technology is characterized by comprising the following steps of:
step 1, classifying filling structures in a rut test block model needing 3D printing;
step 2, defining each parameter of a filling structure in the rut test block model in the step 1, and constructing a rut test block overall modulus prediction model function;
and 3, testing and correcting the track test block overall modulus prediction model function in the step 2 to obtain an accurate track test block overall modulus prediction model.
2. The 3D printing technology-based rut test block structure modulus targeting design method according to claim 1, wherein the filling structure inside the rut test block model in the step 1 comprises a hollow cylinder type, a hollow square type and a cross-web filling structure, and the cross-web filling structure comprises a horizontal cross-web filling structure and an upright cross-web filling structure.
3. The 3D printing technology-based rutting test block structure modulus targeting design method according to claim 2, wherein in the step 2, when the filling structure inside the rutting test block model is a hollow cylinder, each parameter is defined as follows: overall modulus y of rutting test block, free parameter beta 0 Outer diameter x of hollow cylinder 1 Wall thickness x 2 Young's modulus x of filler material 3 Height x of cylinder 4 。β i For unknown parameters corresponding to different factors, the overall modulus prediction module of the track test blockThe type function is:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝275.636-15.11·x 1 +102.147·x 2 +0.519·x 3 -13.859·x 4 。
4. the 3D printing technology-based rut test block structure modulus targeting design method according to claim 2, wherein in the step 2, when the filling structure inside the rut test block model is of a hollow cube type, each parameter is defined as follows: the overall modulus of the rutting test block is y, and the free parameter is beta 0 Side length x of hollow cube 1 Wall thickness x between two hollow cubes 2 Young's modulus x of material 3 Height x of hollow cube 4 。β i Unknown parameters corresponding to different factors; the overall modulus prediction model function of the rutting test block is as follows:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝1028.644-39.726·x 1 +34.716·x 2 +0.756·x 3 -33.608·x 4 。
5. the 3D printing technology-based rutting test block structure modulus targeting design method according to claim 2, wherein in the step 2, when the filling structure inside the rutting test block model is a horizontal cross network, each parameter is defined as follows: overall modulus y of rutting test block, free parameter beta 0 Length x of rectangular parallelepiped 1 The deflection angle of the cuboid around the horizontal axis is x 2 Young's modulus x of filler material 3 Height x of rectangular parallelepiped 4 ，β i Is not equal toUnknown parameters corresponding to the same factors and the overall modulus prediction model function of the rutting test block are as follows:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 +β 4 ·x 4 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝2414.046-15.749·x 1 +0.231·x 2 +0.576·x 3 -35.874·x 4 。
6. the 3D printing technology-based rut test block structure modulus targeting design method according to claim 2, wherein in the step 2, when the filling structure inside the rut test block model is a vertical cross net, each parameter is defined as follows: overall modulus y of rutting test block, free parameter beta 0 Length x of rectangular parallelepiped 1 The deflection angle of the cuboid around the vertical axis is x 2 Young's modulus x of filler material 3 ，β i Unknown parameters corresponding to different factors; the overall modulus prediction model function of the rutting test block is as follows:
y＝β 0 +β 1 ·x 1 +β 2 ·x 2 +β 3 ·x 3 ；
the overall modulus prediction model function of the rutting test block after the test correction in the step 3 is as follows:
y＝2747.819-89.578·x 1 -23.843·x 2 +0.727·x 3 。
7. the 3D printing technology-based rutting test block structural modulus targeting design method according to claim 2, wherein the rutting test block total modulus prediction model subjected to test correction in the step 3 adopts
After inspection, the deviation meets the requirements. />
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310063941.9A CN116118189A (en) | 2023-01-16 | 2023-01-16 | 3D printing technology-based rutting test block structure modulus targeting design method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310063941.9A CN116118189A (en) | 2023-01-16 | 2023-01-16 | 3D printing technology-based rutting test block structure modulus targeting design method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116118189A true CN116118189A (en) | 2023-05-16 |
Family
ID=86307756
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310063941.9A Pending CN116118189A (en) | 2023-01-16 | 2023-01-16 | 3D printing technology-based rutting test block structure modulus targeting design method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116118189A (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103568325A (en) * | 2013-11-08 | 2014-02-12 | 中国科学技术大学 | Three-dimensional printing method |
| CN104792975A (en) * | 2015-04-03 | 2015-07-22 | 山东省交通科学研究所 | Asphalt pavement structure layer modulus inversion method |
| CN107563010A (en) * | 2017-08-08 | 2018-01-09 | 西北工业大学 | Multi-scale model material integrated design method based on shape facility |
| US20180045630A1 (en) * | 2016-08-15 | 2018-02-15 | New York University | Method to estimate strain rate dependent elastic modulus of materials using dynamic mechanical analysis data |
| CN109063324A (en) * | 2018-07-30 | 2018-12-21 | 北京大学 | Finite element crustal stress analogue technique method based on Corner-point Grids |
| US20190339670A1 (en) * | 2017-02-10 | 2019-11-07 | Siemens Product Lifecycle Management Software Inc. | System and method for lattice structure design for additive manufacturing |
| CN110472355A (en) * | 2019-08-20 | 2019-11-19 | 南京航空航天大学 | A kind of 3D printing method for previewing solved based on multi- scenarios method modeling and simulation |
| AU2021100285A4 (en) * | 2021-01-17 | 2021-04-22 | B. Thigale, Somnath DR | Novel 3d printed bionic prosthetic leg |
| CN115049127A (en) * | 2022-06-15 | 2022-09-13 | 杭州电子科技大学 | 3D printing quality prediction method based on BOHB algorithm and neural network |
| CN115203784A (en) * | 2022-06-10 | 2022-10-18 | 石家庄铁道大学 | Optimal design method of squeezed branch pile based on orthogonal test and finite element analysis |
| WO2022247239A1 (en) * | 2021-05-25 | 2022-12-01 | 中国科学院深圳先进技术研究院 | Optimum design method for epoxy molding compound packaging structure |
-
2023
- 2023-01-16 CN CN202310063941.9A patent/CN116118189A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103568325A (en) * | 2013-11-08 | 2014-02-12 | 中国科学技术大学 | Three-dimensional printing method |
| CN104792975A (en) * | 2015-04-03 | 2015-07-22 | 山东省交通科学研究所 | Asphalt pavement structure layer modulus inversion method |
| US20180045630A1 (en) * | 2016-08-15 | 2018-02-15 | New York University | Method to estimate strain rate dependent elastic modulus of materials using dynamic mechanical analysis data |
| US20190339670A1 (en) * | 2017-02-10 | 2019-11-07 | Siemens Product Lifecycle Management Software Inc. | System and method for lattice structure design for additive manufacturing |
| CN107563010A (en) * | 2017-08-08 | 2018-01-09 | 西北工业大学 | Multi-scale model material integrated design method based on shape facility |
| CN109063324A (en) * | 2018-07-30 | 2018-12-21 | 北京大学 | Finite element crustal stress analogue technique method based on Corner-point Grids |
| CN110472355A (en) * | 2019-08-20 | 2019-11-19 | 南京航空航天大学 | A kind of 3D printing method for previewing solved based on multi- scenarios method modeling and simulation |
| AU2021100285A4 (en) * | 2021-01-17 | 2021-04-22 | B. Thigale, Somnath DR | Novel 3d printed bionic prosthetic leg |
| WO2022247239A1 (en) * | 2021-05-25 | 2022-12-01 | 中国科学院深圳先进技术研究院 | Optimum design method for epoxy molding compound packaging structure |
| CN115203784A (en) * | 2022-06-10 | 2022-10-18 | 石家庄铁道大学 | Optimal design method of squeezed branch pile based on orthogonal test and finite element analysis |
| CN115049127A (en) * | 2022-06-15 | 2022-09-13 | 杭州电子科技大学 | 3D printing quality prediction method based on BOHB algorithm and neural network |
Non-Patent Citations (2)
| Title |
|---|
| 刘徽;杨先海;程祥;吕福顺;王飞;孙秋莲;: "人工骨3D打印与铣削复合加工研究", 制造技术与机床, no. 04, 2 April 2018 (2018-04-02), pages 3 - 5 * |
| 周卫峰;: "沥青路面高温车辙仿真分析研究", 武汉理工大学学报(交通科学与工程版), no. 06, 15 December 2013 (2013-12-15), pages 5 - 12 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103604729B (en) | Predication method for macroscopic effective properties of composite material with randomly distributed particles | |
| CN104881582B (en) | The Forecasting Methodology and device of air quality | |
| RU2012152638A (en) | REDUCED ARTIFACTS UNDER ITERATIVE INVERSION OF GEOPHYSICAL DATA | |
| CN103218480A (en) | Method for randomly building bituminous mixture multilayered structure simulation model | |
| CN107742047B (en) | A kind of design method becoming relative density octet lattice structure | |
| CN103984802B (en) | Finite element modeling simplification method of honeycomb sandwich structure | |
| CN112836411A (en) | Method and device for optimizing structure of stiffened plate shell, computer equipment and storage medium | |
| CN109271693A (en) | The multiple dimensioned design method of bi-material layers free damping layer structure | |
| CN108416083B (en) | Two-dimensional dynamic model analysis method and system for towering television tower structure | |
| Feng et al. | Stiffness optimization design for TPMS architected cellular materials | |
| CN108875198B (en) | Fluid finite element-based large-deformation elastomer product static stiffness calculation method | |
| CN114266085B (en) | Mechanical metamaterial annular lattice structure based on bionic level | |
| CN114297779A (en) | Method, system and equipment for quickly predicting aerodynamic coefficient of aircraft | |
| Urbina et al. | Water flow in soils with heterogeneous macropore geometries | |
| CN116118189A (en) | 3D printing technology-based rutting test block structure modulus targeting design method | |
| Laudone et al. | A dual‐porous, inverse model of water retention to study biological and hydrological interactions in soil | |
| CN109271748A (en) | A kind of power transmission tower SSI effect model method for building up, device and storage medium | |
| CN104535040A (en) | Finite element unit partitioning method for blade and detection method of blade | |
| CN108614916A (en) | A kind of method of the large-scale frivolous active phase array antenna wavefront distortion of quick compensation | |
| Sidarta | Neural network-based constitutive modeling of granular material | |
| CN105808802A (en) | Lead core rubber bearing equivalent linearized modeling method and application thereof to bridge seismic isolation | |
| CN114065931A (en) | Land water vapor inversion method and system based on physical model and neural network fusion | |
| CN109518574B (en) | Method for determining equivalent resilience modulus of roadbed top surface under humidification effect | |
| CN103246789A (en) | Computing method of deformation of annular sandwich valve plates of vibration absorber under non-uniform pressure | |
| CN108563856B (en) | Self-adaptive sampling method based on free node B spline modeling |
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 |