Optical non-contact detection method for tiny double-angle conical structure

Abstract

The invention discloses an optical non-contact detection method for a tiny double-angle conical structure. The method comprises the following steps: S1, carrying device structure detection equipment; s2, carrying out multi-mode scanning on a to-be-detected device structure; s3, carrying out fusion processing on the scanning data, and carrying out feature extraction on the data after fusion processing; and S4, generating a detection report. According to the detection result, the angle detection error is smaller than or equal to + / -5 ', the diameter difference is smaller than or equal to + / -0.01 mm, and the micro-hole detection standard is met; zero-scrap detection is realized, and linear cutting and sectioning are completely avoided; a detection report is automatically generated, SPC statistical analysis is supported, and the data traceability is 100%.

Description

Technical Field

[0001] This invention relates to the field of precision measurement technology, specifically to an optical non-contact detection method using a micro dual-angle conical structure. Background Technology

[0002] In machine manufacturing, conical parts are widely used. Accurately measuring the cone angle is crucial for ensuring product quality when machining these parts. There are two types of cones: internal and external. The measurement methods for these cones depend on the workpiece's precision and the available factory conditions. Traditional methods for inspecting the closely adjacent double-cone structures (53°±15′ and 46°33′±15′) and the diameter of their intersecting circles (Φ1.22±0.02) within the secondary nozzle cavity have the following drawbacks: Destructive testing: requires wire cutting to cut parts, resulting in a 100% scrap rate, and requires repeated processing, grinding, and time-consuming single-piece testing; Insufficient accuracy: Due to interference from the resolution layer and limitations in projector resolution, the shape at the junction of the cones is blurred (angle difference of only 6°27′), making it impossible to accurately measure the diameter of the intersecting circles; Low efficiency: The pass rate is only 60%, requiring multiple repeated tests.

[0003] Current methods for detecting conical structures include contact probe methods, industrial CT inspection methods, and optical projection methods, but these methods still have the following drawbacks: Contact probe method: It is easy to scratch the inner surface of the cavity and cannot be adapted to the Φ2.9mm micro-aperture; Industrial CT inspection method: High cost and insufficient resolution (±0.05mm). Optical projection method: requires cutting the parts and is greatly affected by the grinding quality, with an angle measurement error of ±30′. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides an optical non-contact detection method for a micro dual-angle conical structure, comprising the following steps: S1. mounting a device structure detection device; S2. performing multimodal scanning on the device structure to be detected; S3. fusing the scanned data and extracting features from the fused data; S4. generating a detection report.

[0005] Furthermore, the device structure detection equipment in step S1 specifically includes: a three-dimensional topography scanner equipped with a coaxial illumination module and a miniature flexible probe adapter.

[0006] Further, step S2 specifically includes: end face reference scanning: using a 50× magnification, scanning the device structure to be inspected along a spiral line (accuracy 0.001mm); internal cavity dynamic scanning: scanning the internal cavity of the device structure along a spiral line using optical zoom, with a point cloud density ≥800 points / mm. 2 .

[0007] Further, step S3 specifically includes the following sub-steps: S31. Register the data of the reference plane and the inner cavity surface using the seed point growth method to generate merged point cloud data; S32. Separate and extract the double cone point cloud data; S33. Fit 53° and 46°33′ cone surfaces respectively based on the double cone point cloud data; S34. Calculate the diameter of the intersecting circle through the intersection line of the spatial curved surface.

[0008] Furthermore, the seed point growth method in step S31 specifically involves: determining the initial seed point position, performing point cloud data fusion and merging when the seed growth criteria are met, and terminating when the growth region volume ratio is >98% or the number of iterations is >500.

[0009] Furthermore, the initial seed point position is determined by... The calculation determines that, The gradient of the image is represented by n(x,y); the normal vector is represented by n(x,y). P represents the reference plane normal vector; λ represents the weighting coefficient used to balance the two contributions. seed This indicates the initial seed point position.

[0010] Furthermore, the growth criteria for the seeds are as follows: curvature difference Δk ≤ 0.05, normal vector angle θ ≤ 5° and distance between adjacent points d ≤ 2 × sampling interval; where k is the local average curvature.

[0011] Further, step S32 extracts the double-cone point cloud data by calculating the improved Hausdorff distance; the formula for calculating the improved Hausdorff distance is: In the formula, Let A represent the Hausdorff distance; let A be the set of points a with comparison; and let B be the set of points b with comparison. Let |ab| represent the minimum value in the point set B; |ab| is the Euclidean distance between point a and point b; |ba| is the Euclidean distance between point b and point a; and max is the maximum value among them to ensure symmetry.

[0012] Further, step S34 specifically involves: constructing parametric equations C1 and C2 based on the conical surface: C1: , 0≤z≤H C2: , 0≤z≤H; Then solve equations C1 and C2 simultaneously: ; Calculate the diameter of the intersecting circles: ; In the above formula, α1 is the half-apex angle of cone 1; α2 is the half-apex angle of cone 2; H is the height of the cone; x0 is the offset relative to the x-axis; y0 is the offset relative to the y-axis; z is a constant, 0≤z≤H.

[0013] This invention provides an optical non-contact detection method using a micro dual-angle conical structure, which has the following advantages: The detection results of this invention have an angle detection error of ≤±5′ and a diameter difference of ≤±0.01mm, which meets the micro-hole detection standard; it achieves zero-scrap detection and completely avoids wire cutting; the detection report is automatically generated, supports SPC statistical analysis, and the data traceability is 100%. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0015] Figure 1 A flowchart of the method provided by the present invention; Figure 2 This is a two-dimensional structural diagram of the conical structure provided by the present invention; Figure 3 A three-dimensional scanning schematic diagram of the conical structure provided by the present invention. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0017] The following detailed description of the implementation method of the present invention is in conjunction with the accompanying drawings. The description is only a partial embodiment and not all embodiments. For clarity, representations and descriptions unrelated to the present invention are omitted in the drawings and description.

[0018] To provide a clearer understanding of the technical features, objectives, and beneficial effects of this invention, the following detailed description of the technical solution is provided. Obviously, the described embodiments are only a portion of the embodiments of this invention, not all of them, and should not be construed as limiting the scope of implementation of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the protection scope of this invention.

[0019] like Figure 1 As shown, the present invention provides an optical non-contact detection method for a micro dual-angle conical structure, comprising the following steps: S1. mounting a device structure detection device; S2. performing multimodal scanning on the device structure to be detected; S3. performing fusion processing on the scan data and extracting features from the fused data; S4. generating a detection report.

[0020] The device structure inspection equipment in step S1 specifically includes: a three-dimensional topography scanner equipped with a coaxial illumination module, configured with: optical zoom range: 20×200× continuously adjustable; minimum resolution: 0.1um (Z-axis), 0.5um (X, Y-axis), and a miniature flexible probe adapter.

[0021] like Figure 2 , Figure 3 As shown, step S2 specifically includes: end face reference scanning: using a 50× magnification, scanning the device structure to be inspected along the spiral line (accuracy 0.001mm); internal cavity dynamic scanning: scanning the internal cavity of the device structure along the spiral line using optical zoom, with a point cloud density ≥800 points / mm. 2 .

[0022] Step S3 specifically includes the following sub-steps: S31. Use the seed point growth method to register the data of the reference plane and the inner cavity surface to generate merged point cloud data; S32. Separate and extract the double cone point cloud data; S33. Fit 53° and 46°33′ cone surfaces respectively based on the double cone point cloud data; S34. Calculate the diameter of the intersecting circle through the intersection line of the spatial curved surface.

[0023] The seed point growth method in step S31 is as follows: determine the initial seed point position, perform point cloud data fusion and merging when the seed growth criteria are met, and terminate when the growth region volume ratio is >98% or the number of iterations is >500.

[0024] The initial seed point position is obtained through The calculation determines that, The gradient of the image is represented by n(x,y); the normal vector is represented by n(x,y). P represents the reference plane normal vector; λ represents the weighting coefficient used to balance the two contributions. seedThis indicates the initial seed point position. The specific growth criteria for the seed are: curvature difference Δk ≤ 0.05, normal vector angle θ ≤ 5°, and distance between adjacent points d ≤ 2 × sampling interval; where k is the local average curvature.

[0025] Step S32 extracts the double-cone point cloud data by calculating the improved Hausdorff distance; the formula for calculating the improved Hausdorff distance is: In the formula, Let A represent the Hausdorff distance; let A be the set of points a with comparison; and let B be the set of points b with comparison. Let |ab| represent the minimum value in the point set B; |ab| is the Euclidean distance between point a and point b; |ba| is the Euclidean distance between point b and point a; and max is the maximum value among them to ensure symmetry.

[0026] Specifically, step S34 involves constructing parametric equations C1 and C2 based on the conical surface: C1: , 0≤z≤H C2: , 0≤z≤H; Then solve equations C1 and C2 simultaneously: ; Calculate the diameter of the intersecting circles: ; In the above formula, α1 is the half-apex angle of cone 1; α2 is the half-apex angle of cone 2; H is the height of the cone; x0 is the offset relative to the x-axis; y0 is the offset relative to the y-axis; z is a constant, 0≤z≤H.

[0027] The data were compared and analyzed with the results of sectioning and projection inspection. The analysis focused on aspects such as inspection angle error, intersection circle diameter error, single-piece inspection time, and part scrap rate, as shown in Table 1 below.

[0028] Table 1

[0029] The detection results of this invention have an angle detection error of ≤±5′ and a diameter difference of ≤±0.01mm, which meets the micro-hole detection standard; it achieves zero-scrap detection and completely avoids wire cutting; the detection report is automatically generated, supports SPC statistical analysis, and the data traceability is 100%.

[0030] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. An optical non-contact detection method for a micro dual-angle conical structure, characterized in that, Includes the following steps: S1. Equipped with structural testing equipment; S2. Perform multimodal scanning on the structure of the device to be tested; S3. Perform fusion processing on the scanned data, and extract features from the fused data; S4. Generate a test report.

2. The optical non-contact detection method for a micro dual-angle conical structure according to claim 1, characterized in that, The device structure detection equipment in step S1 specifically includes: a three-dimensional topography scanner equipped with a coaxial illumination module and a miniature flexible probe adapter.

3. The optical non-contact detection method for a micro dual-angle conical structure according to claim 1, characterized in that, The S2 step specifically includes: end face reference scanning: using a 50× magnification, scanning the device structure to be tested along the spiral line; internal cavity dynamic scanning: scanning the internal cavity of the device structure along the spiral line through optical zoom.

4. The optical non-contact detection method for a micro dual-angle conical structure according to claim 1, characterized in that, Step S3 specifically includes the following sub-steps: S31. The seed point growth method is used to register the data of the reference plane and the inner cavity surface to generate merged point cloud data; S32. Separate and extract the double-cone point cloud data; S33. Fit 53° and 46°33′ conical surfaces based on the double-cone point cloud data respectively; S34. Calculate the diameter of the intersecting circle using the intersection line of spatial curved surfaces.

5. The optical non-contact detection method for a micro dual-angle conical structure according to claim 4, characterized in that, The seed point growth method in step S31 is as follows: determine the initial seed point position, perform point cloud data fusion and merging when the seed growth criteria are met, and terminate when the growth region volume ratio is >98% or the number of iterations is >500.

6. The optical non-contact detection method for a micro dual-angle conical structure according to claim 5, characterized in that, The initial seed point position is obtained through The calculation determines that, The gradient of the image is represented by n(x,y); the normal vector is represented by n(x,y). P represents the reference plane normal vector; λ represents the weighting coefficient used to balance the two contributions. seed This indicates the initial seed point position.

7. The optical non-contact detection method for a micro dual-angle conical structure according to claim 5, characterized in that, The specific growth criteria for the seeds are: curvature difference Δk ≤ 0.05, normal vector angle θ ≤ 5° and distance between adjacent points d ≤ 2 × sampling interval; where k is the local average curvature.

8. The optical non-contact detection method for a micro dual-angle conical structure according to claim 4, characterized in that, Step S32 extracts the double-cone point cloud data by calculating the improved Hausdorff distance; the formula for calculating the improved Hausdorff distance is: In the formula, Let A represent the Hausdorff distance; let A be the set of points a with comparison; and let B be the set of points b with comparison. Let |ab| represent the minimum value in the point set B; |ab| is the Euclidean distance between point a and point b; |ba| is the Euclidean distance between point b and point a; and max is the maximum value among them to ensure symmetry.

9. The optical non-contact detection method for a micro dual-angle conical structure according to claim 4, characterized in that, Specifically, step S34 involves constructing parametric equations C1 and C2 based on the conical surface. C1: , 0≤z≤H C2: , 0≤z≤H; Then solve equations C1 and C2 simultaneously: ; Calculate the diameter of the intersecting circles: ； In the above formula, α1 is the half-apex angle of cone 1; α2 is the half-apex angle of cone 2; H is the height of the cone; x0 is the offset relative to the x-axis; y0 is the offset relative to the y-axis; z is a constant, 0≤z≤H.

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