CN106827521B - A kind of optimization method of fabrication orientation - Google Patents
A kind of optimization method of fabrication orientation Download PDFInfo
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- CN106827521B CN106827521B CN201710044576.1A CN201710044576A CN106827521B CN 106827521 B CN106827521 B CN 106827521B CN 201710044576 A CN201710044576 A CN 201710044576A CN 106827521 B CN106827521 B CN 106827521B
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- 238000005457 optimization Methods 0.000 title claims abstract description 30
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000013507 mapping Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 11
- 239000000654 additive Substances 0.000 claims description 8
- 230000000996 additive effect Effects 0.000 claims description 8
- 239000002131 composite material Substances 0.000 claims description 5
- 239000000463 material Substances 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 3
- 230000015572 biosynthetic process Effects 0.000 abstract 1
- 230000008901 benefit Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000010146 3D printing Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000011960 computer-aided design Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
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- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000003475 lamination Methods 0.000 description 1
- 238000001459 lithography Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
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- 230000003746 surface roughness Effects 0.000 description 1
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Classifications
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- 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
- B33Y10/00—Processes of additive manufacturing
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- 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
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Abstract
The present invention relates to increases material manufacturing technology field more particularly to a kind of optimization methods of fabrication orientation, comprising: obtains the threedimensional model of manufacturing object, and is converted into the processing model that surface has tri patch；The information of all tri patch in processing model is provided, and obtains the sample interval of fabrication orientation；The k neighbor point formation of each sample of fabrication orientation is found one adjacent to point set, and with each sample of neighbor point set representations fabrication orientation；Each neighbouring point set is reconstructed using a reconstruction and optimization model, determining reconstruction parameter set corresponding with each neighbouring point set；Optimize the multiple characteristic directions for determining the feature that can most reflect fabrication orientation using each sample and reconstruction parameter set of the fabrication orientation after mapping；It is the smallest as fabrication orientation to choose composition error in multiple characteristic directions；The raising of increasing material manufacturing processing efficiency, and high-precision can be promoted to complete the realization of the target of complicated processing, had broad application prospects.
Description
Technical Field
The invention relates to the technical field of additive manufacturing, in particular to a layering direction optimization method.
Background
The additive manufacturing technology (3D printing) has the obvious advantage of reducing environmental pollution and energy consumption over the traditional subtractive manufacturing technology. The additive manufacturing process is to produce 3D products by stacking layer materials, which can achieve rapid verification of engineering design schemes, personalized customization of products, and model processing of complex geometric features and material characteristics.
The slicing direction problem of the model can be classified as a maximization and a minimization problem in manufacturing, and can be realized by an STL (stereo lithography) or cad (computer Aided design) model, and many characteristics are worth considering according to different shapes and applications of workpieces, so that the direction problem of the workpieces is often converted into a solution optimization problem, and the optimization problems are also often multi-objective. Meanwhile, the workpiece orientation problem also influences the time, quality, mechanical characteristics and the like of slice modeling. Depending on the slicing process, certain manufacturing constraints may also need to be considered.
Most research targets of the existing method are in the optimal layering direction. The slicing method and the layering direction are coupled, and the quality of the seal layer and the modeling time are influenced. Some methods of predicting surface roughness attempt to obtain gradient directions, and since the modeling time can be obtained from the number of slices, this method can be used to estimate the manufacturing time of the workpiece. These methods require that all possible slice directions are obtained first and then these different directions are compared, which results in higher computational complexity and a higher computational load if the slicing requirements are multifaceted. In recent years, genetic algorithms have also been introduced into the hierarchical orientation problem to reduce the dimension in solving the reduction optimization problem to reduce the amount of computation. Although the number of layering directions can be reduced, the expansion type in a layering space of a genetic algorithm is poor, the performance in multi-objective optimization is not ideal, and the algorithm does not have obvious advantages in the aspect of finding the effectiveness of the layering directions.
Disclosure of Invention
Aiming at the problems, the invention provides an optimization method of the layering direction, which is applied to a manufacturing object of additive manufacturing; the method comprises the following steps:
step S1, obtaining a three-dimensional model of the manufactured object, and converting the three-dimensional model into a processing model with a triangular patch on the surface;
step S2, providing information of all the triangular patches in the processing model, and obtaining the sample interval of the layering direction according to the information of the triangular patches;
step S3, finding k adjacent points of each sample in the layering direction to form an adjacent point set, and representing each sample in the layering direction by the adjacent point set corresponding to each sample;
step S4, reconstructing each neighboring point set by adopting a reconstruction optimization model, and determining a reconstruction parameter set corresponding to each neighboring point set;
step S5, mapping each sample of the layering direction, and determining a plurality of characteristic directions which can reflect the characteristics of the layering direction most by using each sample of the layering direction and the reconstruction parameter set;
and step S6, selecting the direction with the smallest comprehensive error from the plurality of characteristic directions as the layering direction.
In the above optimization method, in step S5, the number of the feature directions determined to be the most capable of reflecting the feature of the hierarchical direction is 3 to 5.
In the above optimization method, in step S5, the number of the feature directions that are selected to reflect the feature of the hierarchical direction most is 4.
In the above optimization method, in step S4, each reconstruction parameter set includes k reconstruction parameters with a total sum of 1.
In the above optimization method, in step S5, a sum vector of the directions of each sample of the mapped hierarchical directions is a zero vector.
In the above optimization method, in step S6, the composite error includes a volume error of the processed model and the three-dimensional model in each of the feature directions, and a slant height error of the triangular patch.
In the above optimization method, the volume error and the slant height error are weighted and summed to obtain the composite error.
Has the advantages that: the optimization method of the layering direction provided by the invention can promote the improvement of the additive manufacturing processing efficiency and the realization of the goal of finishing complex processing with high precision, and has wide application prospect.
Drawings
FIG. 1 is a flowchart illustrating the steps of a method for optimizing the layering direction according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an error of the equal-layer thickness layered model according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
In a preferred embodiment, as shown in fig. 1, a method for optimizing a lamination direction is proposed, which can be applied to an additive manufactured object; the method can comprise the following steps:
step S1, obtaining a three-dimensional model of the manufactured object, and converting the three-dimensional model into a processing model with a triangular patch on the surface;
step S2, providing information of all triangular patches in the processing model, and obtaining a sample interval in the layering direction according to the information of the triangular patches;
step S3, k adjacent points of each sample in the layering direction are searched to form an adjacent point set, and the adjacent point set corresponding to each sample is used for representing each sample in the layering direction;
step S4, reconstructing each neighboring point set by adopting a reconstruction optimization model, and determining a reconstruction parameter set corresponding to each neighboring point set;
step S5, mapping each sample in the layering direction, and optimizing and determining a plurality of characteristic directions which can reflect the characteristics of the layering direction most by using each sample in the layering direction after mapping and a reconstruction parameter set;
and step S6, selecting the minimum comprehensive error in the multiple characteristic directions as the layering direction.
Wherein the processing model with triangular patches may be an STL model or a CAD model.
In a preferred embodiment, in step S5, the number of feature directions determined to be the most capable of reflecting the hierarchical direction is 3-5.
In a preferred embodiment, in step S5, the number of feature directions determined to be the most characteristic of the hierarchical directions is 4.
In a preferred embodiment, in step S4, each reconstruction parameter set includes k reconstruction parameters with a total of 1.
In a preferred embodiment, in step S5, the sum vector of the directions of each sample of the mapped hierarchical directions is a zero vector.
In a preferred embodiment, in step S6, the composite error includes a volume error of the processed model and the three-dimensional model in each feature direction, and a slant height error of the triangular patch.
In the above embodiment, preferably, the volume error and the slant height error are weighted and summed to obtain the composite error.
Specifically, step 1: converting the three-dimensional model into an STL model format, extracting information (three vertex information and one external normal vector information) of all triangular patches in the STL model, and acquiring a new sample interval according to the informationWherein SmIs the area of a triangle and is,is the normal vector of the triangular patch, wherein m is the total number of the triangular patches.
The number of samples of a triangular patch of a slightly complex model is large, so the candidate slice direction is determined by adopting a dimensionality reduction and data simplification method. The locally linear embedding method (LLE) is a nonlinear dimension reduction algorithm that constructs raw data points by a weighted combination of data of neighboring points. And calculating a local reconstruction weight matrix of the sample through the adjacent points by searching k adjacent points of each sample point, and calculating the output of the sample point through the local reconstruction weight matrix and other points.
Step 2: performing k-type neighboring point classification, i.e. KNN calculation, on each sample point to obtain each sample pointK ofNeighboring pointsIn combination withIs used to represent the original sample point
And step 3: calculating the use of each sample spaceReconstruction coefficients represented by linear combinationsConstructing a target optimization problem (1-1) by minimizing reconstruction errors:
wherein,is shown asIth adjacent point of (2)The reconstruction coefficient can be obtained by solving an optimization problem (such as Lagrange method)
Step 4, setting original sample spaceMapping to a low dimensional spaceIn the method, a characteristic root lambda is obtained by simplifying an optimization problem and performing characteristic decompositionjAnd corresponding feature vectorsThe following formula (1-2) is obtained by solving specifically, and the final formula isWherein M ═ I (I-W)T(I-W)。
The specific calculation formula is as follows:
wherein Q (W) in the formula (1-2) is an optimization objective function related to the reconstruction coefficient, so that the objective function is obtained by minimum optimization
Step 5, selecting four larger (lambda) of the characteristic values1＞λ2＞λ3＞λ4) Corresponding feature vectorAs its alternative slice direction.
And 6, as shown in fig. 2, slicing four slicing directions respectively according to an equal-thickness slicing algorithm based on the STL model, calculating weighted errors of volume errors and slant height errors generated in the four directions, and obtaining the weighted error ξ through a formula (1-3)
cosθ＝MjN/|Mj||N| (1-3)
Step 7, selecting ξ with the smallest weighted error in four directions as the slicing direction M in the slicing algorithmjAnd N is a normal vector of the triangular patch.
In conclusion, the optimization method for the layering direction provided by the invention can promote the improvement of the additive manufacturing processing efficiency and the realization of the goal of finishing complex processing with high precision, and has wide application prospect.
While the specification concludes with claims defining exemplary embodiments of particular structures for practicing the invention, it is believed that other modifications will be made in the spirit of the invention. While the above invention sets forth presently preferred embodiments, these are not intended as limitations.
Various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above description. Therefore, the appended claims should be construed to cover all such variations and modifications as fall within the true spirit and scope of the invention. Any and all equivalent ranges and contents within the scope of the claims should be considered to be within the intent and scope of the present invention.
Claims (5)
1. A method for optimizing a layering direction is applied to a manufactured object of additive manufacturing; it is characterized by comprising:
step S1, obtaining a three-dimensional model of the manufactured object, and converting the three-dimensional model into a processing model with a triangular patch on the surface;
step S2, providing information of all the triangular patches in the processing model, and obtaining the sample interval of the layering direction according to the information of the triangular patches;
step S3, finding k adjacent points of each sample in the layering direction to form an adjacent point set, and representing each sample in the layering direction by the adjacent point set corresponding to each sample;
step S4, reconstructing each neighboring point set by adopting a reconstruction optimization model, and determining a reconstruction parameter set corresponding to each neighboring point set;
step S5, mapping each sample of the layering direction, and determining a plurality of characteristic directions which can reflect the characteristics of the layering direction most by using each sample of the layering direction and the reconstruction parameter set;
step S6, selecting the direction with the smallest composite error in the plurality of characteristic directions as the layering direction;
the comprehensive error comprises a volume error of the processing model and the three-dimensional model in each characteristic direction and a slant height error of the triangular patch;
and weighting and summing the volume error and the slant height error to obtain the comprehensive error.
2. The optimization method according to claim 1, wherein in step S5, the number of the feature directions determined to be the features that can reflect the hierarchical directions most is 3 to 5.
3. The optimization method according to claim 2, wherein in step S5, the number of the feature directions that are selected to reflect the feature of the hierarchical direction most is 4.
4. The optimization method according to claim 1, wherein in the step S4, each reconstruction parameter set includes k reconstruction parameters with a total sum of 1.
5. The optimization method according to claim 1, wherein in the step S5, a resultant vector of directions of each sample of the mapped hierarchical directions is a zero vector.
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