Curved surface multi-degree-of-freedom ink jet forming three-dimensional model data processing method

Abstract

The invention belongs to the field of curved surface multi-degree-of-freedom additive manufacturing, and particularly relates to a three-dimensional model data processing method for spatial motion planning in curved surface multi-degree-of-freedom ink jet forming. The method comprises the steps of STL model target space curved surface point cloud extraction, target space curved surface fitting, target space curved surface two-dimensional path generation and target space curved surface ink jet forming space path generation. According to the method, target curved surface space point cloud is rapidly extracted through space point cloud convex hull or concave hull calculation, then the curved surface is fitted based on deep learning to obtain a space function, and then the extracted target curved surface space point cloud is projected to a two-dimensional plane to generate a path and mapped to a three-dimensional space curved surface to obtain a curved surface ink-jet forming path. The method can solve the problems that in the current curved surface additive manufacturing field, path planning difficulty is large, the single-point solving speed is increased along with increase of the number of model vertexes and the number of solving points during STL model data access, a curved surface forming path planning method is single, and curved surface forming complex path planning methods such as curved surface fractal and curved surface contour offset are difficult to achieve. An efficient and convenient data access mode is provided for a curved surface multi-degree-of-freedom ink jet forming complex path planning method, the single-point solving speed is not increased along with the increase of the number of model vertexes and the number of solving points, and the method has important significance for achieving curved surface multi-degree-of-freedom efficient ink jet forming.

Description

Technical Field

[0001] The present invention belongs to the field of curved surface multi-degree-of-freedom additive manufacturing, and relates to a path generation method combining three-dimensional model data processing with deep learning, and specifically is a three-dimensional model data processing method for spatial motion planning in curved surface multi-degree-of-freedom inkjet forming. Background Art

[0002] Curved surface multi-degree-of-freedom inkjet forming technology belongs to additive manufacturing technology. It is a forming technology that uses a multi-axis motion mechanism such as a multi-joint serial robot, parallel robot, etc. as a motion mechanism and loads an inkjet forming device at the end of the multi-axis motion mechanism for follow-up stacking forming. The forming process involves key technologies such as three-dimensional model data processing, multi-axis motion mechanism control, inkjet forming device follow-up control and precision closed-loop control. Among them, three-dimensional model data processing is used to read three-dimensional STL models and plan forming movements. It has outstanding applications in the field of curved surface electronic printing, and has also been expanded in the fields of curved surface wire laying and tape laying, curved surface welding and curved surface spraying.

[0003] During the multi-degree-of-freedom inkjet forming process on curved surfaces, the 3D model data processing process also faces many difficulties, making it difficult to meet complex path planning requirements.

[0004] (1) There is no topological relationship between the model point data stored in the general three-dimensional model file STL for additive manufacturing, and the forming process only focuses on the outer surface of a fixed area without the need for other model data points.

[0005] (2) In order to realize complex motion path planning during the forming process, it is necessary to repeatedly locate data points. The disordered storage of data points requires that all data points be sorted to locate a single point in order to locate the triangle where the target point is located, and then interpolate and calculate the spatial distance z of the target point. As a result, the efficiency of single-point solution in the forming process decreases with the increase of the number of model vertices and the number of solution points.

[0006] (3) All existing methods use methods based on data topology reconstruction and data association, which are difficult to effectively solve the above problems and difficult to implement complex path planning methods. At present, the forming path design method is relatively simple and is still based on the traditional slicing and layering sweeping method, which cannot meet the diverse forming path design requirements of various performances.

[0007] In recent years, with the continuous development of computer technology and the gradual improvement of artificial intelligence frameworks, it has become possible to use neural networks to fit any spatial function. This can effectively avoid the repeated sorting and calculation of point data when locating spatial points, and is robust to the increase in the number of model vertices and solution points. Complex path planning methods can be implemented by repeatedly accessing the model to meet various performance requirements. Today's surface forming motion planning methods are still derived from data-based models and data processing methods, and have not proposed effective solutions to the various difficulties mentioned above. Summary of the invention

[0008] To solve the above problems, the present invention discloses a three-dimensional model data processing method for multi-degree-of-freedom inkjet forming of curved surfaces, so as to solve the limitations of the existing motion planning of multi-degree-of-freedom inkjet forming of curved surfaces, that is, it is difficult to implement complex path planning methods and the efficiency of single-point solution decreases with the increase of the number of model vertices and the number of solution points, thereby providing an effective solution for efficient multi-degree-of-freedom inkjet forming of curved surfaces.

[0009] To achieve the above objectives, the technical solutions adopted in this scheme are as follows:

[0010] A method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model comprises the following steps:

[0011] Read the STL model of the general file in additive manufacturing, obtain the target spatial surface point cloud data in the model through the convex hull or concave hull calculation method, and use the spatial point cloud data as the basic data set for spatial surface fitting;

[0012] Construct a point cloud data processing network model suitable for spatial surface fitting;

[0013] In the established point cloud data processing network model, the plane coordinates and space coordinates of the model points in the basic data set are trained to obtain the trained weights;

[0014] Project the target space surface onto a two-dimensional plane and generate a two-dimensional path based on the two-dimensional point cloud;

[0015] The obtained two-dimensional path is mapped to the point cloud data processing network model and the trained weights are used to output the surface multi-degree-of-freedom inkjet forming spatial path.

[0016] As a further design of this solution, the method for reading STL files and obtaining the target surface is as follows: after reading the spatial coordinates of the unordered points on the surface of the STL model in the ASCII code format, the convex hull or concave hull calculation in the three-dimensional point cloud processing method is used to quickly extract the spatial point cloud data of the target surface in the model and use (x, y) in the point cloud data as input and z as the label as a training data set.

[0017] As a further design of this scheme, a method for constructing a point cloud data processing network model for spatial surface fitting is as follows: a point cloud segmentation module is constructed to segment the target surface point cloud into several block point clouds that are easy to fit; the block point cloud rotation module is used to rotate the block point cloud obtained in the point cloud segmentation module to obtain a rigid rotation matrix with the maximum projection area; and the deep neural network fitting module is used to fit the rigidly rotated block point cloud obtained in the block point cloud rotation module.

[0018] As a further design of this solution, the point cloud segmentation module is used to segment the obtained two-dimensional coordinates into multiple block point clouds according to the projection grid. The construction method is as follows: project the target surface point cloud onto a two-dimensional plane, and use the minimum square enclosure algorithm to calculate the minimum square enclosure of the point cloud, and then use isometric interpolation in the square enclosure to divide the point cloud into multiple block point clouds of equal size.

[0019] As a further design of this scheme, the block point cloud rotation module is used to rotate the block point cloud obtained in the point cloud segmentation module, obtain the rigid rotation matrix of the maximum two-dimensional projection, the rotated point cloud data and the rigid inverse rotation matrix. The construction method is as follows: read in several block point clouds obtained in S2, take the two points with the largest Euclidean distance in each point cloud as the rotation axis and the X vector of the rigid rotation matrix, take the midpoint O of the two points as the coordinate origin, and generate 720 Y vectors passing through point O and perpendicular to the X axis in the plane passing through point 0 and perpendicular to the X axis. The outer product of the X vector and each Y vector is calculated to obtain the Z vector of the rotation matrix, calculate the projection area under the point cloud rotation and iterate the rigid rotation matrix under the maximum projection area, and output the rotated point cloud data and the rigid inverse rotation matrix.

[0020] As a further design of this solution, a deep neural network fitting module is used to fit the rotated point cloud output by the block point cloud rotation module and add a rigid inverse rotation module. The construction method is as follows: read the rotated point cloud data output by the block point cloud rotation module, capture the spatial topological relationship of the point cloud through a deep neural network, and form a mapping relationship between two-dimensional plane points and three-dimensional spatial positions in the surface. The spatial position can be directly output by inputting plane path points.

[0021] As a further design of this scheme, the training of the point cloud data processing network model includes the following steps: reading the target surface point cloud data and processing it through the point cloud segmentation module and the block point cloud rotation module to obtain several rotated block point clouds and their rigid inverse rotation matrices; normalizing the input point cloud data, calculating the spatial distance of the current input (x, y) calculated through the network and multiplying it by the rigid inverse rotation matrix to obtain the calculated spatial distance, comparing the calculated spatial distance with the actual spatial distance in the label, so that the calculated spatial distance is equal to the actual spatial distance; repeating the second step to optimize and adjust the parameters of each network node of the model, and finally obtaining an optimal solution. The optimal solution can make the calculated spatial distance equal to the actual spatial distance. The optimal solution is the weight of the point cloud data processing network model.

[0022] As a further design of this scheme, the method for generating a two-dimensional path for surface forming is as follows: read the target space point cloud and project it into a two-dimensional plane, and calculate the minimum enclosure of the two-dimensional point cloud through a convex hull or concave hull algorithm, and then generate a two-dimensional forming path through calculation methods such as fixed-angle straight line scanning, contour offset, parting curve and grid paving.

[0023] As a further design of this scheme, the method for outputting the curved surface multi-degree-of-freedom inkjet forming spatial path is as follows: read the obtained two-dimensional forming path and the trained point cloud data processing model, and import the two-dimensional forming path points into the point cloud data processing model in batches to generate the curved surface inkjet forming spatial path.

[0024] Beneficial effects of the present invention:

[0025] (1) In the method for processing the data of the curved surface multi-degree-of-freedom inkjet forming three-dimensional model designed by the present invention, the target surface point cloud is segmented and rotated, and then introduced into the network fitting model based on deep learning and then reversely rotated and outputted, which can realize the direct mapping of two-dimensional data to three-dimensional data and effectively avoid the repeated sorting and calculation of point data when locating spatial points;

[0026] (2) In the method for processing the data of the curved surface multi-degree-of-freedom inkjet forming three-dimensional model designed by the present invention, two-dimensional mapping can be used to realize three-dimensional, thereby realizing complex path planning methods for curved surface forming motion that are difficult to realize with traditional calculation methods, such as curved surface contour offset, curved surface fractal and curved surface mesh;

[0027] (3) In the data processing method for curved surface multi-DOF inkjet forming three-dimensional model designed by the present invention, the efficiency of spatial point positioning calculation is robust to the increase in the number of model vertices and the number of solution points, and is suitable for large-scale point cloud computing, which can provide technical support for efficient curved surface multi-DOF inkjet forming. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1This is a schematic diagram of the target surface point cloud extraction of the STL model described in an embodiment of the present invention, wherein the left figure is the initial point cloud three-dimensional image, the middle figure is the surface point cloud annotation calculation three-dimensional image, and the right figure is the surface point cloud three-dimensional image after removing the internal point cloud.

[0029] Figure 2 This is a diagram of the architecture of a point cloud data processing network model for spatial surface fitting according to an embodiment of the present invention.

[0030] Figure 3 Schematic diagram of the point cloud segmentation module described in an embodiment of the present invention.

[0031] Figure 4 This is a schematic diagram of the block point cloud rotation module described in an embodiment of the present invention, wherein the left figure is the projection of the maximum projection area and the minimum envelope of the block point cloud rotation, and the right figure is the curve of the projection area change of the block point cloud rotation.

[0032] Figure 5 This is a schematic diagram of the deep neural network fitting module described in an embodiment of the present invention, wherein the left figure is a three-dimensional image output by fitting the helmet model, and the right figure is an error change curve.

[0033] Figure 6 This is a schematic diagram of three-dimensional path generation described in an embodiment of the present invention.

[0034] Figure 7 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0035] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] like Figure 7 As shown, the embodiment of the present invention discloses a method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model, comprising the following steps:

[0038] (1) Read the STL model, a common file in additive manufacturing, and obtain the target spatial surface point cloud data in the model through the convex hull calculation method, and use the spatial point cloud data as the basic data set for spatial surface fitting;

[0039] (2) Construct a point cloud data processing network model suitable for spatial surface fitting;

[0040] (3) In the established point cloud data processing network model, the plane coordinates and spatial coordinates of the model points in the basic data set are trained to obtain the trained weights;

[0041] (4) Projecting the target space surface onto a two-dimensional plane and generating a two-dimensional path based on the two-dimensional point cloud;

[0042] (5) Mapping the obtained two-dimensional path to the network fitting model and using the trained weights to output the surface multi-degree-of-freedom inkjet forming spatial path.

[0043] As a preferred embodiment of the present invention, refer to Figure 1 ,In step (1), the process of obtaining the data set is as follows:

[0044] (1-1) Read the ASCII code format STL model through the mesh interface;

[0045] (1-2) Calculate the surface point cloud using the convex hull algorithm in the PIL interface;

[0046] (1-3) Take the point cloud data (x, y) as input and z as label as the training data set.

[0047] As a preferred embodiment of the present invention, refer to Figure 2 In step (2), the point cloud data processing network model for spatial surface fitting includes: a point cloud segmentation module for segmenting the target surface point cloud into a number of block point clouds that are easy to fit; a block point cloud rotation module for rotating the block point cloud obtained in the point cloud segmentation module to obtain a rigid rotation matrix with a maximum projection area; and a deep neural network fitting module for fitting the rigidly rotated block point cloud obtained in the block point cloud rotation module.

[0048] As a preferred embodiment of the present invention, refer to Figure 3 The process of the point cloud segmentation module includes: projecting the target surface point cloud onto a two-dimensional plane, calculating the minimum square enclosure of the point cloud using the minimum square enclosure algorithm, and then dividing the point cloud into block point clouds of 50×50 in size using equidistant interpolation with a spacing of 50 in the square enclosure.

[0049] As a preferred embodiment of the present invention, refer to Figure 4The process of the block point cloud rotation module includes: reading in several block point clouds obtained in S2, taking the two points with the largest Euclidean distance in each point cloud as the rotation axis and the X vector of the rigid rotation matrix, taking the midpoint O of the two points as the coordinate origin, generating 720 Y vectors passing through point O and with an angle difference of 0.5° between the planes passing through point 0 and perpendicular to the X axis, calculating the outer product of the X vector and each Y vector to obtain the Z vector of the rotation matrix, calculating the projection area under the point cloud rotation and iterating the rigid rotation matrix under the maximum projection area, and outputting the rotated point cloud data and the rigid inverse rotation matrix. Figure 4 The projection area is the largest when the midpoint cloud rotation angle is 157°.

[0050] As a preferred embodiment of the present invention, refer to Figure 5 The process of the deep neural network fitting module includes: reading the rotated point cloud data output by the block point cloud rotation module, capturing the spatial topological relationship of the point cloud through a multi-layer perceptron, in which the batch size is 256, the epoch is 10, the optimizer is 'adam', the loss_function is 'mse', and the activation function is 'sigmode', which forms a mapping relationship between the two-dimensional plane points in the surface and the three-dimensional spatial position, and the spatial position can be directly output by inputting the plane path point.

[0051] As a preferred embodiment of the present invention, refer to Figure 2 ,In step (3), the point cloud data processing network model training process is as follows:

[0052] (3-1) Reading the target surface point cloud data and processing it through the point cloud segmentation module and the block point cloud rotation module to obtain a number of rotated block point clouds and their rigid inverse rotation matrices;

[0053] (3-2) Normalize the input point cloud data, calculate the spatial distance of the current input (x, y) calculated by the network and multiply it by the rigid inverse rotation matrix to obtain the calculated spatial distance, compare the calculated spatial distance with the actual spatial distance in the label, so that the calculated spatial distance is equal to the actual spatial distance;

[0054] (3-3) Repeat (3-2) to optimize and adjust the parameters of each network node of the model, and finally obtain an optimal solution. The optimal solution can make the calculated space distance equal to the actual space distance. The optimal solution is the weight of the point cloud data processing network model, which is stored as an h5 file together with the network model.

[0055] As a preferred embodiment of the present invention, refer to Figure 6 ,In step (4), the point cloud data processing network model training process is as follows:

[0056] (4-1) Read the target space point cloud and project it into a two-dimensional plane and calculate the minimum enclosing area of ​​the two-dimensional projection point cloud of the radome, aircraft nose and helmet model by using the convex hull algorithm;

[0057] (4-2) Then, a two-dimensional forming path is generated by using fixed-angle linear scanning for the radome and the front nose of the aircraft and a contour offset calculation method for the helmet.

[0058] As a preferred embodiment of the present invention, refer to Figure 6 ,In step (5), the point cloud data processing network model training process is as follows:

[0059] (5-1) Read the two-dimensional forming path of the radome, aircraft nose and helmet model, the trained point cloud data processing model and the corresponding weights;

[0060] (5-2) Importing two-dimensional forming path points in batches into the point cloud data processing model to generate the surface inkjet forming spatial path.

[0061] Finally, 10,000 path points were solved for the radome, aircraft nose and helmet models. The average single-point solution speeds for 20 experiments were 0.0244ms / point, 0.0243ms / point and 0.0246ms / point, respectively. The single-point solution speed did not increase with the increase of the number of vertices but decreased with the increase of the number of solution points.

[0062] The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above-mentioned implementation mode, but also include technical schemes composed of any combination of the above technical features.

Claims

1. A method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model, characterized in that: The steps include: S1: Read the general file STL model in additive manufacturing, obtain the target space surface point cloud data in the model by convex hull or concave hull calculation method, and use the space point cloud data as the basic data set for space surface fitting; S2: Construct a point cloud data processing network model suitable for spatial surface fitting; S3: In the point cloud data processing network model established in S2, the plane coordinates and space coordinates of the model points in the basic data set obtained in S1 are trained to obtain the trained weights; S4: Project the target space surface obtained in S1 onto a two-dimensional plane and generate a two-dimensional path based on the two-dimensional point cloud; S5: Map the two-dimensional path obtained in S4 to the point cloud data processing network model constructed in S2 and use the weights obtained in S3 to output the surface multi-degree-of-freedom inkjet forming spatial path.

2. A method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 1, characterized in that: In S1, the spatial coordinates of the unordered points on the surface of the STL model in ASCII code format are read, and then the spatial point cloud data of the target surface in the model is quickly extracted through the convex hull or concave hull calculation in the three-dimensional point cloud processing method, and the (x, y) in the point cloud data is used as input and z as the label as the training data set.

3. The method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 1, characterized in that: In S2, the point cloud network processing model for spatial surface fitting includes: point cloud segmentation module, block point cloud rotation module and deep neural network fitting module; Among them, the point cloud segmentation module is used to segment the target surface point cloud into several block point clouds that are easy to fit; the block point cloud rotation module is used to rotate the block point cloud obtained in the point cloud segmentation module to obtain a rigid rotation matrix with the maximum projection area; the deep neural network fitting module is used to fit the rigidly rotated block point cloud obtained in the block point cloud rotation module.

4. The method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 3, characterized in that: The point cloud segmentation module segments the obtained two-dimensional coordinates into multiple block point clouds according to the projection grid. The process of generating block point clouds includes: The point cloud obtained in S1 is projected onto a two-dimensional plane, and the minimum square enclosure of the point cloud is calculated using a minimum square enclosure algorithm, and then the point cloud is divided into multiple block point clouds of equal size by isometric interpolation in the square enclosure.

5. The method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 3, characterized in that: The block point cloud rotation module rotates the block point cloud obtained in the point cloud segmentation module, and the process of obtaining the rigid rotation matrix of the maximum two-dimensional projection, the rotated point cloud data and the rigid inverse rotation matrix includes: Read in several block point clouds obtained in S2, take the two points with the largest Euclidean distance in each point cloud as the rotation axis and the X vector of the rigid rotation matrix, take the midpoint O of the two points as the coordinate origin, generate 720 Y vectors passing through point O and with an angle difference of 0.5° between the planes passing through point 0 and perpendicular to the X axis, calculate the outer product of the X vector and each Y vector to get the Z vector of the rotation matrix, calculate the projected area of ​​the point cloud under rotation, iterate the rigid rotation matrix under the maximum projected area, and output the rotated point cloud data and the rigid inverse rotation matrix.

6. The method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 3, characterized in that: The process of the deep neural network fitting module for fitting the rotated point cloud output by the block point cloud rotation module and adding the rigid inverse rotation module includes: The rotated point cloud data output from the block point cloud rotation module is read in, and the spatial topological relationship of the point cloud is captured through a deep neural network to form a mapping relationship between two-dimensional plane points in the surface and three-dimensional spatial positions. The spatial position can be directly output by inputting plane path points.

7. The method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 1, characterized in that: In S3, the process of training the point cloud data processing network model includes the following steps: S3-1, the target surface point cloud data is processed by the point cloud segmentation module and the block point cloud rotation module in S2 to obtain a number of rotated block point clouds and their rigid inverse rotation matrices; S3-2, normalize the input point cloud data, calculate the spatial distance of the current input (x, y) calculated through the network and multiply it by the rigid inverse rotation matrix to obtain the calculated spatial distance, compare the calculated spatial distance with the actual spatial distance in the label, so that the calculated spatial distance is equal to the actual spatial distance; S3-3, repeat S3-2 to optimize and adjust the parameters of each network node of the model, and finally obtain an optimal solution. The optimal solution can make the calculated space distance equal to the actual space distance. The optimal solution is the weight of the point cloud data processing network model.

8. The method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 1, characterized in that: In S4, the target space point cloud obtained in S1 is read and projected into a two-dimensional plane, and the minimum enclosure of the two-dimensional point cloud is calculated by a convex hull or concave hull algorithm. Then, a two-dimensional forming path is generated by calculation methods such as fixed-angle linear scanning, contour offset, parting curve and grid paving.

9. The method for processing data of a curved surface multi-degree-of-freedom inkjet forming three-dimensional model according to claim 1, characterized in that: In S5, the two-dimensional forming path obtained in S4 and the point cloud data processing model trained in S3 are read, and the two-dimensional forming path points are batch-imported into the point cloud data processing model to generate a curved surface inkjet forming spatial path.

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