3D printing modeling method and system for vehicle parts

Abstract

The invention discloses a 3D printing modeling method and system for vehicle parts, and relates to the technical field of data processing, and the method comprises the following steps: S1, collecting a plurality of two-dimensional images of the parts, and carrying out denoising processing and rotation processing on each two-dimensional image to obtain a plurality of standard two-dimensional images; s2, generating a part contour of the standard two-dimensional image according to the outlier degree of the pixel points in the standard two-dimensional image; s3, cutting each standard two-dimensional image based on the part contour of each standard two-dimensional image, and splicing the plurality of cut standard two-dimensional images to generate a continuous image for the part; and S4, on the basis of the continuous images of the parts, a part three-dimensional model is constructed, and 3D printing is completed through the part three-dimensional model. The method is suitable for parts of various shapes and complexities, the three-dimensional model is rapidly generated, 3D printing manufacturing is carried out, and the product development period is greatly shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a 3D printing modeling method and system for automotive parts. Background Art

[0002] In the field of automotive parts manufacturing, traditional modeling and manufacturing methods usually rely on physical molds and complex mechanical processing processes, which is not only time-consuming and costly, but also particularly limited when faced with complex shapes or customized requirements. In recent years, with the rapid development of 3D printing technology, it has gradually become a new trend in automotive parts manufacturing with its high efficiency, flexibility and low cost.

[0003] However, most existing 3D printing modeling methods rely on CAD (computer-aided design) software, which requires professionals to manually draw 3D models. This process is not only time-consuming and laborious, but may also cause the model accuracy to decrease due to human factors. In addition, for some parts with complex shapes or special textures, traditional CAD modeling methods may find it difficult to accurately capture their detailed features.

[0004] In order to overcome the above shortcomings, the industry has begun to explore image-based 3D modeling methods, especially the technology of reconstructing 3D models using 2D images. This technology collects multiple 2D images of parts and uses image processing algorithms to extract feature information in the images, and then reconstructs the 3D model of the parts. However, the existing image-based 3D modeling methods still have some challenges. For example, due to factors such as shooting conditions, lighting changes, and image noise, the collected 2D images may have quality problems, which will affect the subsequent 3D reconstruction effect. In addition, how to accurately and efficiently extract the contours of parts in the image is also a problem that needs to be solved in current technology. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a 3D printing modeling method and system for automotive parts.

[0006] The technical solution of the present invention is: a 3D printing modeling method for automotive parts comprises the following steps:

[0007] S1, collecting several two-dimensional images of parts, and performing denoising and rotation processing on each two-dimensional image to obtain several standard two-dimensional images;

[0008] S2, generating a component outline of a standard two-dimensional image according to the outlier degree of the pixel points in the standard two-dimensional image;

[0009] S3, based on the component outline of each standard two-dimensional image, cropping each standard two-dimensional image, and splicing the cropped standard two-dimensional images to generate a continuous image for the component;

[0010] S4. Based on the continuous images of the parts, a three-dimensional model of the parts is constructed, and the three-dimensional model of the parts is used to complete 3D printing.

[0011] Furthermore, S2 includes the following sub-steps:

[0012] S21, constructing a pixel fluctuation range for each pixel point in the standard two-dimensional image;

[0013] S22, calculating the outlier degree of each pixel point according to the pixel point and its corresponding pixel fluctuation range;

[0014] S23, generating a component outline of a standard two-dimensional image according to the outlier degree and color value of each pixel point.

[0015] The beneficial effects of the above further scheme are: in the present invention, the pixel fluctuation range is constructed by considering the edge of the standard two-dimensional image and all the neighborhoods of the pixel points, combining the global (edge) and local (neighborhood) information of the image, so that the constructed fluctuation range is more comprehensive and accurate; the edge information usually represents the main features and contours of the image, while the neighborhood information reflects the local environment of the pixel points; the upper and lower limits of the pixel fluctuation range are dynamically determined by comparing the average color value of the edge and the average color value of the neighborhood. In addition, the present invention also introduces random numbers to increase the flexibility of the outlier calculation, so that the result has randomness to a certain extent, which is helpful to better extract the contour of the parts in subsequent processing.

[0016] Further, in S21, a pixel fluctuation range is constructed according to the edge of the standard two-dimensional image and all neighborhoods of the pixel point;

[0017] Upper limit of pixel fluctuation range The calculation formula is:

[0018] ;

[0019] In the formula, Represents the mean color value of all pixels on the edge of a standard two-dimensional image. Represents the mean color value of all neighborhoods of a pixel, Indicates taking the maximum value, Indicates rounding up;

[0020] Lower limit of pixel fluctuation range The calculation formula is:

[0021] ;

[0022] In the formula, Indicates taking the minimum value, Indicates rounding down.

[0023] Furthermore, in S22, the outlier degree of the pixel point The calculation formula is:

[0024] ;

[0025] In the formula, Indicates the lower limit of the pixel fluctuation range, Indicates the upper limit of the pixel fluctuation range. Represents the color value of a pixel. Indicates generating a random number between 0 and 1.

[0026] Further, S23 includes the following sub-steps:

[0027] S231, constructing a feature evaluation function according to the outlier degree and color value of each pixel;

[0028] S232, calculating the relative characteristic value between each pixel point and the characteristic evaluation function;

[0029] S233, taking the sum of all relative characteristic values ​​as the evaluation threshold;

[0030] S234, taking the pixel points whose outlier degree is greater than the evaluation threshold as the component contour of the standard two-dimensional image.

[0031] The beneficial effect of the above further scheme is that in the present invention, the construction of the feature evaluation function is based on the maximum / minimum outlier and the color value of the pixel point, which not only considers the degree of difference between the pixel point and the surrounding pixels (outlier), but also considers its color characteristics, which helps to improve the accuracy of contour extraction. By calculating the relative feature value, the evaluation threshold is set to automatically determine which pixels belong to the component contour.

[0032] Furthermore, in S231, the feature evaluation function The expression is:

[0033] ;

[0034] In the formula, represents the maximum outlier degree, represents the minimum outlier degree, Represents the first The color value of each pixel, Indicates the color value of the pixel corresponding to the maximum outlier in the standard two-dimensional image. Indicates the color value of the pixel corresponding to the minimum outlier in the standard two-dimensional image. Represents the total number of pixels of a standard two-dimensional image.

[0035] Furthermore, in S232, the relative characteristic value between the pixel point and the characteristic evaluation function The calculation formula is:

[0036] ;

[0037] In the formula, Represents the color value of a pixel. represents the feature evaluation function, Represents an exponential function.

[0038] Furthermore, in S4, the continuous images of the parts are converted in format, and the continuous images after the format conversion are modeled using a three-dimensional modeling tool to generate a three-dimensional model of the parts, and the three-dimensional model of the parts is used to complete 3D printing.

[0039] The 3D modeling tools include Kaedim3D tools and DAZ Studio.

[0040] The beneficial effects of the present invention are as follows: the present invention can eliminate noise and interference in the image by performing denoising and rotation processing on the two-dimensional image; when generating the contour of the component, the contour is extracted according to the outlier degree of the pixel points in the standard two-dimensional image, so that the shape and characteristics of the component can be captured more accurately, thereby improving the accuracy of the three-dimensional model; in addition, the present invention also generates continuous images through steps such as cutting and splicing, so that the present invention is suitable for parts of various shapes and complexities, and can quickly generate three-dimensional models and perform 3D printing manufacturing, thereby greatly shortening the product development cycle.

[0041] Based on the above method, the present invention also proposes a 3D printing modeling system for automotive parts, including a two-dimensional image acquisition unit, a part contour acquisition unit, a continuous image acquisition unit and a 3D printing unit;

[0042] The two-dimensional image acquisition unit is used to collect a plurality of two-dimensional images of parts and components, and perform denoising and rotation processing on each two-dimensional image to obtain a plurality of standard two-dimensional images;

[0043] The component contour acquisition unit is used to generate the component contour of the standard two-dimensional image according to the outlier degree of the pixel points in the standard two-dimensional image;

[0044] The continuous image acquisition unit is used to crop each standard two-dimensional image based on the component outline of each standard two-dimensional image, and to splice a plurality of cropped standard two-dimensional images to generate a continuous image for the component;

[0045] The 3D printing unit is used to construct a three-dimensional model of the parts based on the continuous images of the parts, and complete 3D printing using the three-dimensional model of the parts.

[0046] The beneficial effects of the present invention are as follows: the 3D printing modeling system for vehicle parts is suitable for parts of different materials and sizes, and has strong versatility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of a 3D printing modeling method for automotive parts;

[0048] Figure 2 This is a schematic diagram of the structure of the 3D printing modeling system for automotive parts. DETAILED DESCRIPTION

[0049] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0050] like Figure 1 As shown, the present invention provides a 3D printing modeling method for automotive parts, comprising the following steps:

[0051] S1, collecting several two-dimensional images of parts, and performing denoising and rotation processing on each two-dimensional image to obtain several standard two-dimensional images;

[0052] S2, generating a component outline of a standard two-dimensional image according to the outlier degree of the pixel points in the standard two-dimensional image;

[0053] S3, based on the component outline of each standard two-dimensional image, cropping each standard two-dimensional image, and splicing the cropped standard two-dimensional images to generate a continuous image for the component;

[0054] S4. Based on the continuous images of the parts, a three-dimensional model of the parts is constructed, and the three-dimensional model of the parts is used to complete 3D printing.

[0055] In this embodiment of the present invention, S2 includes the following sub-steps:

[0056] S21, constructing a pixel fluctuation range for each pixel point in the standard two-dimensional image;

[0057] S22, calculating the outlier degree of each pixel point according to the pixel point and its corresponding pixel fluctuation range;

[0058] S23, generating a component outline of a standard two-dimensional image according to the outlier degree and color value of each pixel point.

[0059] In the present invention, the pixel fluctuation range is constructed by considering the edge of the standard two-dimensional image and all the neighborhoods of the pixel points, combining the global (edge) and local (neighborhood) information of the image, so that the constructed fluctuation range is more comprehensive and accurate; the edge information usually represents the main features and contours of the image, while the neighborhood information reflects the local environment of the pixel points; the upper and lower limits of the pixel fluctuation range are dynamically determined by comparing the average color value of the edge and the average color value of the neighborhood. In addition, the present invention also introduces random numbers to increase the flexibility of the outlier calculation, so that the result has randomness to a certain extent, which helps to better extract the contour of the parts in subsequent processing.

[0060] In the embodiment of the present invention, in S21, a pixel fluctuation range is constructed according to the edge of the standard two-dimensional image and all neighborhoods of the pixel point;

[0061] Upper limit of pixel fluctuation range The calculation formula is:

[0062] ;

[0063] In the formula, Represents the mean color value of all pixels on the edge of a standard two-dimensional image. Represents the mean color value of all neighborhoods of a pixel, Indicates taking the maximum value, Indicates rounding up;

[0064] Lower limit of pixel fluctuation range The calculation formula is:

[0065] ;

[0066] In the formula, Indicates taking the minimum value, Indicates rounding down.

[0067] In the embodiment of the present invention, in S22, the outlier degree of the pixel point The calculation formula is:

[0068] ;

[0069] In the formula, Indicates the lower limit of the pixel fluctuation range, Indicates the upper limit of the pixel fluctuation range. Represents the color value of a pixel. Indicates generating a random number between 0 and 1.

[0070] In this embodiment of the present invention, S23 includes the following sub-steps:

[0071] S231, constructing a feature evaluation function according to the outlier degree and color value of each pixel;

[0072] S232, calculating the relative characteristic value between each pixel point and the characteristic evaluation function;

[0073] S233, taking the sum of all relative characteristic values ​​as the evaluation threshold;

[0074] S234, taking the pixel points whose outlier degree is greater than the evaluation threshold as the component contour of the standard two-dimensional image.

[0075] In the present invention, the construction of the feature evaluation function is based on the maximum / minimum outlier and the color value of the pixel, which not only considers the degree of difference between the pixel and the surrounding pixels (outlier), but also considers its color characteristics, which helps to improve the accuracy of contour extraction. By calculating the relative feature value, the evaluation threshold is set to automatically determine which pixels belong to the component contour.

[0076] In the embodiment of the present invention, in S231, the feature evaluation function The expression is:

[0077] ;

[0078] In the formula, represents the maximum outlier degree, represents the minimum outlier degree, Represents the first The color value of each pixel, Indicates the color value of the pixel corresponding to the maximum outlier in the standard two-dimensional image. Indicates the color value of the pixel corresponding to the minimum outlier in the standard two-dimensional image. Represents the total number of pixels of a standard two-dimensional image.

[0079] In the embodiment of the present invention, in S232, the relative characteristic value between the pixel point and the characteristic evaluation function The calculation formula is:

[0080] ;

[0081] In the formula, Represents the color value of a pixel. represents the feature evaluation function, Represents an exponential function.

[0082] In the embodiment of the present invention, in S4, the continuous images of the parts are converted into a format, and the continuous images after the format conversion are modeled using a 3D modeling tool to generate a 3D model of the parts, and the 3D printing is completed using the 3D model of the parts.

[0083] The 3D modeling tools include Kaedim3D tools and DAZ Studio.

[0084] Based on the above method, the present invention also proposes a 3D printing modeling system for automotive parts, such as Figure 2 As shown, it includes a two-dimensional image acquisition unit, a component contour acquisition unit, a continuous image acquisition unit and a 3D printing unit;

[0085] The two-dimensional image acquisition unit is used to collect a plurality of two-dimensional images of parts and components, and perform denoising and rotation processing on each two-dimensional image to obtain a plurality of standard two-dimensional images;

[0086] The component contour acquisition unit is used to generate the component contour of the standard two-dimensional image according to the outlier degree of the pixel points in the standard two-dimensional image;

[0087] The continuous image acquisition unit is used to crop each standard two-dimensional image based on the component outline of each standard two-dimensional image, and to splice a plurality of cropped standard two-dimensional images to generate a continuous image for the component;

[0088] The 3D printing unit is used to construct a three-dimensional model of the parts based on the continuous images of the parts, and complete 3D printing using the three-dimensional model of the parts.

[0089] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A 3D printing modeling method for automotive parts, characterized in that: The following steps are involved: S1, collecting several two-dimensional images of parts, and performing denoising and rotation processing on each two-dimensional image to obtain several standard two-dimensional images; S2, generating a component outline of a standard two-dimensional image according to the outlier degree of the pixel points in the standard two-dimensional image; S3, based on the component outline of each standard two-dimensional image, cropping each standard two-dimensional image, and splicing the cropped standard two-dimensional images to generate a continuous image for the component; S4. Based on the continuous images of the parts, a three-dimensional model of the parts is constructed, and the three-dimensional model of the parts is used to complete 3D printing.

2. The 3D printing modeling method for automotive parts according to claim 1, characterized in that: The S2 comprises the following sub-steps: S21, constructing a pixel fluctuation range for each pixel point in the standard two-dimensional image; S22, calculating the outlier degree of each pixel point according to the pixel point and its corresponding pixel fluctuation range; S23, generating a component outline of a standard two-dimensional image according to the outlier degree and color value of each pixel point.

3. The 3D printing modeling method for automotive parts according to claim 2, characterized in that: In S21, a pixel fluctuation range is constructed according to the edge of the standard two-dimensional image and all neighborhoods of the pixel point; The upper limit of the pixel fluctuation range The calculation formula is: ； In the formula, Represents the mean color value of all pixels on the edge of a standard two-dimensional image. Represents the mean color value of all neighborhoods of a pixel, Indicates taking the maximum value, Indicates rounding up; The lower limit of the pixel fluctuation range The calculation formula is: ； In the formula, Indicates taking the minimum value, Indicates rounding down.

4. The 3D printing modeling method for automotive parts according to claim 2, characterized in that: In S22, the outlier degree of the pixel point The calculation formula is: ； In the formula, Indicates the lower limit of the pixel fluctuation range, Indicates the upper limit of the pixel fluctuation range. Represents the color value of a pixel. Indicates generating a random number between 0 and 1.

5. The 3D printing modeling method for automotive parts according to claim 2, characterized in that: The S23 comprises the following sub-steps: S231, constructing a feature evaluation function according to the outlier degree and color value of each pixel; S232, calculating the relative characteristic value between each pixel point and the characteristic evaluation function; S233, taking the sum of all relative characteristic values ​​as the evaluation threshold; S234, taking the pixel points whose outlier degree is greater than the evaluation threshold as the component contour of the standard two-dimensional image.

6. The 3D printing modeling method for vehicle parts according to claim 5, characterized in that: In S231, the feature evaluation function The expression is: ； In the formula, represents the maximum outlier degree, represents the minimum outlier degree, Represents the first The color value of a pixel, Indicates the color value of the pixel corresponding to the maximum outlier in the standard two-dimensional image. Indicates the color value of the pixel corresponding to the minimum outlier in the standard two-dimensional image. Represents the total number of pixels of a standard two-dimensional image.

7. The 3D printing modeling method for vehicle parts according to claim 5, characterized in that: In S232, the relative characteristic value between the pixel point and the characteristic evaluation function The calculation formula is: ； In the formula, Represents the color value of a pixel. represents the feature evaluation function, Represents an exponential function.

8. The 3D printing modeling method for automotive parts according to claim 1, characterized in that: In S4, the continuous images of the parts are converted into a format, and the continuous images after the format conversion are modeled using a three-dimensional modeling tool to generate a three-dimensional model of the parts, and the three-dimensional model of the parts is used to complete 3D printing.

9. A 3D printing modeling system for automotive parts, characterized in that: It includes a two-dimensional image acquisition unit, a component contour acquisition unit, a continuous image acquisition unit and a 3D printing unit; The two-dimensional image acquisition unit is used to collect a plurality of two-dimensional images of the parts, and perform denoising and rotation processing on each two-dimensional image to obtain a plurality of standard two-dimensional images; The component contour acquisition unit is used to generate the component contour of the standard two-dimensional image according to the outlier degree of the pixel points in the standard two-dimensional image; The continuous image acquisition unit is used to crop each standard two-dimensional image based on the component outline of each standard two-dimensional image, and to splice a plurality of cropped standard two-dimensional images to generate a continuous image for the component; The 3D printing unit is used to construct a three-dimensional model of the component based on the continuous images of the component, and complete 3D printing using the three-dimensional model of the component.

Images