Titanium alloy VAR smelting molten pool edge-reaching control method based on machine vision

Abstract

The invention provides a machine vision-based titanium alloy VAR smelting molten pool edge-reaching control method, which comprises the following steps: after calibration and correction by using an industrial camera, collecting the molten pool morphology of a vacuum arc remelting furnace VAR; the obtained molten pool landform picture is processed, and the molten pool topography surface gray level image is divided into a bright area, a gradual change area and a dark area; extracting a sub-pixel edge by using a Canny operator and a Zernike moment, and fitting a molten pool edge by using a least square circle fitting algorithm; subtracting the obtained crucible edge from the molten pool morphology edge, and calculating to obtain the distance from the molten pool edge to the crucible; and comparing the obtained distance from the edge of the molten pool to the crucible with a threshold value, and judging whether the molten pool reaches the edge or not. Visual judgment of operators is replaced with machine vision, dependence on artificial experience is reduced, titanium alloy VAR smelting control is simpler and more convenient, VAR smelting is more intelligent and automatic, product stability is improved, cost is reduced, and product competitiveness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of titanium alloy smelting, and in particular to a titanium alloy VAR smelting molten pool to edge control method based on machine vision. Background Art

[0002] As a lightweight and high-strength metal material, titanium alloy is widely used in aerospace, medical equipment and other fields. As the main method for melting titanium alloy, vacuum consumable arc furnace (VAR) has the advantages of low power consumption, high melting speed and good quality reproducibility. However, during the VAR melting process, the control of the molten pool to the edge often depends on the experience and skills of the operator, resulting in certain fluctuations in the surface quality, uniformity of elemental composition and yield rate of titanium alloy ingots. Machine vision is a technology that uses computers to recognize and analyze images. It can simulate the human visual system to achieve image understanding and processing. Using machine vision instead of the operator's naked eye recognition and judgment to control the molten pool to the edge is beneficial to the stability of the quality and surface quality of titanium alloy ingots. Therefore, it is of great practical significance to develop a method based on machine vision to control the molten pool to the edge of titanium alloy VAR melting. Summary of the invention

[0003] According to the above technical problem of controlling the molten pool to the edge in titanium alloy VAR smelting, a method for controlling the molten pool to the edge in titanium alloy VAR smelting based on machine vision is provided. The present invention mainly uses an industrial camera to obtain a molten pool topography image, and divides the image into a bright area, a gradient area and a dark area after processing, calculates the distance between the edge of the crucible and the edge of the molten pool, and judges whether the molten pool has reached the edge after comparing it with a threshold.

[0004] The technical means adopted by the present invention are as follows:

[0005] A titanium alloy VAR smelting molten pool to edge control method based on machine vision, comprising:

[0006] S1. After calibration and correction using an industrial camera, collect the molten pool morphology of a vacuum consumable arc furnace VAR;

[0007] S2, processing the molten pool topography image obtained in S1, and dividing the grayscale image of the molten pool topography surface into a bright area, a gradient area and a dark area;

[0008] S3, using the Canny operator and Zernike moment to extract the sub-pixel edge, and using the least squares circle fitting algorithm to fit the edge of the molten pool;

[0009] S4, subtracting the edge of the crucible obtained in S3 from the edge of the molten pool morphology, and calculating the distance between the edge of the molten pool and the crucible;

[0010] S5. Compare the distance from the edge of the molten pool to the crucible obtained in S4 with the threshold value to determine whether the molten pool has reached the edge.

[0011] Furthermore, the step S2 specifically includes:

[0012] The molten pool topography image obtained in S1 is converted into a grayscale image, and the molten pool topography grayscale image is divided into a bright area, a gradient area, and a dark area by calculation:

[0013] I ld =K d I l cosθ

[0014] Among them, I ld is the reflected light intensity, K d is the reflection coefficient of the material, I l is the incident light intensity, and θ is the angle between the incident light direction and the surface normal at the point of incidence.

[0015] Furthermore, the step S3 specifically includes:

[0016] The Canny operator and Zernike moment are used to extract the sub-pixel edge, the center of the crucible is set as the coordinate origin, and the point on the edge of the crucible is expressed as:

[0017] D = x 2 +y 2

[0018] Where D is the crucible radius, and the interval of x is (x1, x2);

[0019] The least squares circle fitting algorithm is used to fit the edge of the molten pool. The calculation formula of the least squares method is:

[0020]

[0021] Among them, (α, β) is the center of the circle fitted by the edge of the molten pool morphology, R is the radius of the fitting circle, (x i ,y i ) is the point at the edge of the molten pool, x i The interval is (x1, x2).

[0022] Furthermore, the step S4 specifically includes:

[0023] Subtract the edge of the crucible obtained in S3 from the edge of the molten pool morphology, and calculate the distance between the edge of the molten pool and the crucible. The calculation formula is:

[0024] r min =y 坩埚 -y 熔池边缘

[0025] The points at the edge of the crucible and the points at the edge of the molten pool have the same interval (x1, x2).

[0026] Furthermore, the step S5 specifically includes:

[0027] The distance r from the edge of the molten pool to the crucible obtained by S4 min Compare with threshold value;

[0028] If r min <threshold, the molten pool has reached the edge; if r min > threshold, increase the melting current and re-operate S1-S4 until it is determined that the molten pool has reached the edge.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] The titanium alloy VAR smelting molten pool to edge control method based on machine vision provided by the present invention replaces the operator's visual judgment with machine vision, reduces the reliance on manual experience, makes the titanium alloy VAR smelting control simpler and more convenient, and makes VAR smelting more intelligent and automated, thereby improving product stability, reducing costs, and improving product competitiveness.

[0031] The titanium alloy VAR smelting pool to edge control method based on machine vision provided by the present invention can increase the intelligence and automation of the production line, improve production efficiency, improve the surface quality of titanium alloy ingots, increase the ingot yield rate, and reduce costs.

[0032] Based on the above reasons, the present invention can be widely promoted in the field of titanium alloy smelting technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 This is a flow chart of the titanium alloy VAR smelting pool-to-edge control method based on machine vision in the present invention.

[0035] Figure 2 It is a schematic diagram of image processing of the titanium alloy VAR smelting pool-to-edge control method based on machine vision of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] like Figure 1 As shown, the present invention provides a titanium alloy VAR smelting molten pool to edge control method based on machine vision, comprising:

[0039] S1. After calibration and correction using an industrial camera, collect the molten pool morphology of a vacuum consumable arc furnace VAR;

[0040] S2, processing the molten pool topography image obtained in S1, and dividing the grayscale image of the molten pool topography surface into a bright area, a gradient area and a dark area;

[0041] In specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0042] The molten pool topography image obtained in S1 is converted into a grayscale image, and the molten pool topography grayscale image is divided into a bright area, a gradient area, and a dark area by calculation:

[0043] I ld =K d I l cosθ

[0044] Among them, I ld is the reflected light intensity, K d is the reflection coefficient of the material, I l is the incident light intensity, and θ is the angle between the incident light direction and the surface normal at the point of incidence.

[0045] S3, using the Canny operator and Zernike moment to extract the sub-pixel edge, and using the least squares circle fitting algorithm to fit the edge of the molten pool;

[0046] In specific implementation, as a preferred embodiment of the present invention, step S3 specifically includes:

[0047] The Canny operator and Zernike moment are used to extract the sub-pixel edge, the center of the crucible is set as the coordinate origin, and the point on the edge of the crucible is expressed as:

[0048] D = x 2 +y 2

[0049] Where D is the crucible radius, and the interval of x is (x1, x2);

[0050] The least squares circle fitting algorithm is used to fit the edge of the molten pool. The calculation formula of the least squares method is:

[0051]

[0052] Among them, (α, β) is the center of the circle fitted by the edge of the molten pool morphology, R is the radius of the fitting circle, (x i ,y i ) is the point at the edge of the molten pool, x i The interval is (x1, x2).

[0053] S4, subtracting the edge of the crucible obtained in S3 from the edge of the molten pool morphology, and calculating the distance between the edge of the molten pool and the crucible;

[0054] In specific implementation, as a preferred embodiment of the present invention, step S4 specifically includes:

[0055] Subtract the edge of the crucible obtained in S3 from the edge of the molten pool morphology, and calculate the distance between the edge of the molten pool and the crucible. The calculation formula is:

[0056] r min =y 坩埚 -y 熔池边缘

[0057] The points at the edge of the crucible and the points at the edge of the molten pool have the same interval (x1, x2).

[0058] S5. Compare the distance from the edge of the molten pool to the crucible obtained in S4 with the threshold value to determine whether the molten pool has reached the edge.

[0059] In specific implementation, as a preferred embodiment of the present invention, step S5 specifically includes:

[0060] The distance r from the edge of the molten pool to the crucible obtained by S4 minCompare with threshold value;

[0061] If r min <threshold, the molten pool has reached the edge; if r min > threshold, increase the melting current and re-operate S1-S4 until it is determined that the molten pool has reached the edge.

[0062] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0063] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0064] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0065] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0066] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0067] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A titanium alloy VAR smelting pool to edge control method based on machine vision, characterized in that: include: S1. After calibration and correction using an industrial camera, collect the molten pool morphology of a vacuum consumable arc furnace VAR; S2, processing the molten pool topography image obtained in S1, and dividing the grayscale image of the molten pool topography surface into a bright area, a gradient area and a dark area; S3, using the Canny operator and Zernike moment to extract the sub-pixel edge, and using the least squares circle fitting algorithm to fit the edge of the molten pool; S4, subtracting the edge of the crucible obtained in S3 from the edge of the molten pool morphology, and calculating the distance between the edge of the molten pool and the crucible; S5. Compare the distance from the edge of the molten pool to the crucible obtained in S4 with the threshold value to determine whether the molten pool has reached the edge.

2. The titanium alloy VAR smelting pool-to-edge control method based on machine vision according to claim 1 is characterized in that: The step S2 specifically includes: The molten pool topography image obtained in S1 is converted into a grayscale image, and the molten pool topography grayscale image is divided into a bright area, a gradient area, and a dark area by calculation: I ld =K d I l cosθ Among them, I ld is the reflected light intensity, K d is the reflection coefficient of the material, I l is the incident light intensity, and θ is the angle between the incident light direction and the surface normal at the point of incidence.

3. The titanium alloy VAR smelting pool-to-edge control method based on machine vision according to claim 1 is characterized in that: The step S3 specifically includes: The Canny operator and Zernike moment are used to extract the sub-pixel edge, the center of the crucible is set as the coordinate origin, and the point on the edge of the crucible is expressed as: D=x 2 +y 2 Where D is the crucible radius, and the interval of x is (x1, x2); The least squares circle fitting algorithm is used to fit the edge of the molten pool. The calculation formula of the least squares method is: Among them, (α, β) is the center of the circle fitted by the edge of the molten pool morphology, R is the radius of the fitting circle, (x i ,y i ) is the point at the edge of the molten pool, x i The interval is (x1, x2).

4. The titanium alloy VAR smelting pool-to-edge control method based on machine vision according to claim 1 is characterized in that: The step S4 specifically includes: Subtract the edge of the crucible obtained in S3 from the edge of the molten pool morphology, and calculate the distance between the edge of the molten pool and the crucible. The calculation formula is: r min =y 坩埚 -y 熔池边缘 The points at the edge of the crucible and the points at the edge of the molten pool have the same interval (x1, x2).

5. The titanium alloy VAR smelting pool-to-edge control method based on machine vision according to claim 1 is characterized in that: The step S5 specifically includes: The distance r from the edge of the molten pool to the crucible obtained by S4 min Compare with threshold value; If r min <threshold, the molten pool has reached the edge; if r min > threshold, increase the melting current and re-operate S1-S4 until it is determined that the molten pool has reached the edge.

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