Quality inspection methods applicable to titanium alloy bars

Abstract

This invention relates to the field of bar material inspection, specifically a quality inspection method for titanium alloy bars, comprising the following steps: S1, rotating the titanium alloy bar at a uniform speed and emitting detection light towards the surface of the titanium alloy bar; S2, capturing the detection light reflected from the surface of the titanium alloy bar to obtain a detection image, and extracting the first 3D point cloud data of the surface of the titanium alloy bar based on the detection image; S3, acquiring the surface temperature and vibration information of the titanium alloy bar at each moment, the vibration information including vibration acceleration and vibration angular velocity; This invention calculates the filtering parameters and gain at different temperatures through experiments. Compared with other point cloud data compensation methods, it not only considers the individual effects of temperature and vibration on the accuracy of point cloud data, but also the mutual influence between temperature and vibration, which can improve the accuracy of subsequent point cloud data compensation.

Description

Technical Field

[0001] This invention relates to the field of bar testing, specifically to a quality testing method applicable to titanium alloy bars. Background Technology

[0002] Titanium alloy bars are metallic materials with titanium and titanium alloys as the main components. During the production and processing of titanium metal bars, surface defects may occur, including surface cracks, inclusions, porosity, scratches, and pits. Each defect affects the quality of the titanium alloy bar. Existing surface inspection methods mainly use laser scanning of the titanium alloy bar to receive reflected light and construct three-dimensional point cloud data, and then analyze the surface defects of the titanium alloy bar based on the three-dimensional point cloud data. However, during the laser scanning process of the titanium alloy bar, the titanium alloy bar is in a rotating state, which will cause a certain degree of vibration and temperature change. Vibration and temperature change will affect the accuracy of the three-dimensional point cloud data. If this factor is not considered, the quality inspection accuracy of the titanium alloy bar will be significantly reduced. Therefore, this invention proposes a quality inspection method suitable for titanium alloy bars. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a quality inspection method for titanium alloy bars, thus solving the technical problems mentioned in the background section.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] The quality inspection method applicable to titanium alloy bars specifically includes the following steps:

[0006] S1. Rotate the titanium alloy rod at a constant speed and emit detection light towards the surface of the titanium alloy rod;

[0007] S2. Capture the detection light reflected from the surface of the titanium alloy rod to obtain a detection image, and extract the first 3D point cloud data of the surface of the titanium alloy rod based on the detection image;

[0008] S3. Obtain the surface temperature and vibration information of the titanium alloy rod at each moment. The vibration information includes vibration acceleration and vibration angular velocity.

[0009] S4. Correct the first 3D point cloud data based on surface temperature and vibration information to generate the second 3D point cloud data;

[0010] S5. Construct the target model by inputting the second 3D point cloud data into the target model and outputting the types and sizes of all surface defects.

[0011] Furthermore, the incident angle of the detected light is between 30 degrees and 60 degrees, and the receiving angle of the detected light receiver is between 30 degrees and 60 degrees.

[0012] Furthermore, step S2 specifically includes the following steps:

[0013] S21. Construct an image coordinate system with the top left corner of the detected image as the origin 0, and the top and left sides of the detected image as the x-axis and y-axis, respectively.

[0014] S22. Preprocess the detected image to obtain the image to be used. ;

[0015] S23. Use the grayscale centroid method on the image to be processed. Each row of pixels is scanned to extract the center position of the laser line. The calculation formula is as follows:

[0016]

[0017] In the formula, This represents the pixel intensity of the pixel in the i-th row of the c-th column; This represents the maximum number of rows of pixels in the image to be used;

[0018] S24. With the camera position as the origin O1, construct a camera coordinate system with the east-west direction, the north-south direction, and the vertical direction as the X-axis, Y-axis, and Z-axis, respectively. Then, determine the center position of the laser line. Convert to 3D coordinates in camera coordinate system ;

[0019] S25. Repeat S21-S24 for each frame of the detected image to obtain the first 3D point cloud data of the titanium alloy rod surface.

[0020] Furthermore, step S22 specifically includes the following steps:

[0021] S221. Obtain a control image of the titanium alloy rod that is not affected by the detection light, and find the control image that corresponds to the detection image.

[0022] S222. Subtract the control image from the detected image to obtain the first preprocessed image. Its expression is:

[0023]

[0024] In the formula, Indicates the detection of pixels in the image Pixel intensity; Represents the pixels in the comparison image Pixel intensity;

[0025] S223, Process the first preprocessed image Perform Gaussian filtering to obtain the image to be used. Its expression is:

[0026]

[0027] In the formula, This represents the pixel coordinates of a pixel within the Gaussian kernel relative to its center point. Indicates standard deviation; Represents the natural constant.

[0028] Furthermore, step S24 specifically includes the following steps:

[0029] S241. Obtain the camera's intrinsic parameter matrix. Its expression is:

[0030]

[0031] In the formula, and These represent the camera focal length in pixels; and These represent the intersection points of the camera's optical axis and the detection image plane, respectively.

[0032] S242. Construct the plane equation under the detection ray plane to calculate the camera's extrinsic parameters, including the plane normal vector. and constant term The equation of the plane is expressed as:

[0033]

[0034] S243, via the intrinsic parameter matrix Calculate the center position of the laser line In the three-dimensional coordinates of the camera coordinate system Its expression is:

[0035]

[0036] in, In the formula, Indicates the center position of the laser line One pixel; This represents the direction vector of a 3D ray in the camera coordinate system; Represents a point on a three-dimensional ray; Indicates the scale factor;

[0037] S244, Point Substitute into the plane equation to calculate the scale factor. The calculation formula is as follows:

[0038]

[0039] In the formula, Represents the scale factor.

[0040] Furthermore, step S4 specifically includes the following steps:

[0041] S41. Set up a parameter detection test to calculate the filter parameters at each temperature. and gain ;

[0042] S42. Obtain the linear thermal expansion coefficient of the titanium alloy rod and calculate its actual length at the current temperature. The calculation formula is as follows:

[0043]

[0044] In the formula, This indicates the standard length of the titanium alloy bar at the reference temperature; The linear thermal expansion coefficient of the titanium alloy rod; Indicates reference temperature;

[0045] S43. Extract filter parameters based on the current temperature. and gain The instantaneous deflection angle is calculated based on it. and instantaneous displacement The calculation formulas are as follows:

[0046]

[0047]

[0048] In the formula, Indicates the current moment The vibration angular velocity of the titanium alloy rod; Indicates at time The instantaneous deflection angle; Indicates the current moment Vibration velocity of the titanium alloy rod; Indicates at time The instantaneous displacement;

[0049] S44, based on the instantaneous deflection angle and instantaneous displacement Constructing the vibration transformation matrix Its expression is:

[0050]

[0051] In the formula, Represents the rotation matrix;

[0052] S45. According to the vibration transformation matrix The first 3D point cloud data is corrected to generate vibration-compensated point cloud data. The calculation formula is as follows:

[0053]

[0054] In the formula, This represents the first 3D point cloud data; This represents vibration-compensated point cloud data;

[0055] S46. Based on actual length Vibration compensation point cloud data The correction is performed to generate a second 3D point cloud data, and its calculation formula is as follows:

[0056]

[0057] In the formula, This represents the second 3D point cloud data.

[0058] Furthermore, step S41 specifically includes the following steps:

[0059] S411. Install a standard titanium alloy rod, set several temperatures, and rotate the standard titanium alloy rod to detect the test parameters of the standard titanium alloy rod at each temperature. The test parameters include the actual vibration displacement and vibration velocity.

[0060] S412, for each temperature Calculate linear acceleration based on vibration acceleration The calculation formula is as follows:

[0061]

[0062] In the formula, Indicates vibration acceleration; Represents gravitational acceleration;

[0063] S413, Define filter parameters and gain The range of values ​​for;

[0064] S414, Select any pair of filter parameters and gain And estimate the vibration displacement based on its calculation. ;

[0065] S415. Calculate and estimate vibration displacement. The root mean square error between the actual vibration displacement and the actual vibration displacement The calculation formula is as follows:

[0066]

[0067] In the formula, This indicates the total number of times the experimental parameter was tested; Indicates the first The actual vibration displacement of the test parameters was detected in the second experiment. No. Estimated vibration displacement based on the parameters of the second test experiment;

[0068] S416. Use optimization algorithms to find the root mean square error. Minimum filter parameters and gain .

[0069] Further, in step S414, the vibration displacement is estimated. The calculation formula is:

[0070]

[0071] In the formula, Indicates in Estimated vibration displacement at time; Indicates in The vibration velocity of the titanium alloy rod at any given moment; Indicates at time The vibration acceleration; This indicates the time interval between adjacent sampling moments.

[0072] Furthermore, in step S5, the PointNet neural network is selected as the basis, and other titanium alloy rods of the same size and model are selected to generate corresponding point cloud data as training samples. Then, the size and type of each surface defect are manually labeled as sample labels. The neural network is trained using the training samples and sample labels to obtain the target model.

[0073] Compared with the prior art, the present invention provides a quality inspection method suitable for titanium alloy bars, which has the following beneficial effects:

[0074] 1. This invention calculates the filtering parameters and gain at different temperatures through experiments. Compared with other point cloud data compensation methods, it not only considers the individual effects of temperature and vibration on the accuracy of point cloud data, but also the mutual influence between temperature and vibration, which can improve the accuracy of subsequent point cloud data compensation.

[0075] 2. This invention uses a laser to irradiate the surface of a rotating titanium alloy rod, and then constructs 3D point cloud data based on the pattern formed by laser reflection. Compared with the method of directly analyzing surface defects based on 2D images, it can obtain the three-dimensional dimensions of surface defects such as depth, length, and width more accurately, and is not affected by false defect signals such as surface color difference, oxidation marks, and oil stains, and has strong anti-interference ability. Attached Figure Description

[0076] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0077] Figure 1 This is a schematic diagram of the quality inspection method for titanium alloy bars according to the present invention. Detailed Implementation

[0078] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0079] Those skilled in the art will understand that all or part of the steps in the methods of the following embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] Titanium alloy bars are metallic materials with titanium and titanium alloys as the main components. During the production and processing of titanium metal bars, some defects may appear on their surface, including surface cracks, inclusions, porosity, scratches, and pits. Each defect will affect the quality of the titanium alloy bar, and the impact of each defect on the quality of the titanium alloy bar is as follows:

[0081] 1. Surface cracks: Cracks are sharp notches that generate huge stress concentrations at their tips. When components operate under alternating loads (such as engine rotor rotation or landing gear retraction and extension), the stress concentrates at the crack tip, causing the crack to gradually propagate and become a fatigue source, thus significantly reducing fatigue strength.

[0082] 2. Inclusions: Due to their significant difference in hardness from the aggregate, they generate extremely high stress concentrations around them, making them a very dangerous source of fatigue and easily leading to fatigue failure.

[0083] 3. Porosity: The sharp edges of pores are like micro-cracks, which can become fatigue sources. In particular, aggregated porosity can significantly reduce the strength and plasticity of materials. Furthermore, under impact loads, pores may become channels for rapid crack propagation, reducing impact toughness.

[0084] 4. Scratches and dents: Although not as sharp as cracks, deep scratches and dents can also disrupt the smoothness and continuity of the surface, becoming potential sources of fatigue and leading to the initiation of microcracks. In addition, for parts with dimensional accuracy requirements, these damages may lead to their direct scrapping.

[0085] To avoid the aforementioned problems in the production and practical application of titanium alloy bars, please refer to [link / reference needed]. Figure 1 As shown, a quality inspection method suitable for titanium alloy bars is proposed, which specifically includes the following steps:

[0086] S1. Rotate the titanium alloy rod at a constant speed and emit a detection light beam onto the surface of the titanium alloy rod; specifically, the incident angle of the detection light beam is between 30 degrees and 60 degrees, and the receiving angle of the detection light beam receiver is also between 30 degrees and 60 degrees; specifically, in actual industrial systems, a laser scanner is typically used to emit a laser (i.e., the detection light beam), and a camera is used to detect the laser beam. The laser scanner and the camera are symmetrically distributed with respect to the normal of the titanium alloy rod surface; it should be noted that the detection light beam is a row of laser beams, and its arrangement direction is...

[0087] S2. Capture the detection light reflected from the surface of the titanium alloy rod to obtain a detection image, and extract the first 3D point cloud data of the titanium alloy rod surface based on the detection image. Specifically, common surface defect detection methods calculate the location and size of surface defects based on pixel change information in 2D images. However, this method heavily relies on pixel changes in the image. If the surface of the titanium alloy rod is affected by false defect signals such as color difference, oxidation marks, and oil stains, the detection accuracy of this method will be severely affected. Therefore, step S2 specifically includes the following steps:

[0088] S21. Construct an image coordinate system with the top left corner of the detected image as the origin 0, and the top and left sides of the detected image as the x-axis and y-axis, respectively.

[0089] S22. Preprocess the detected image to obtain the image to be used. Specifically, step S22 includes the following steps:

[0090] S221. Obtain a reference image of the titanium alloy rod without the influence of the detection light, and find a reference image corresponding to the detection image; specifically, when rotating the titanium alloy rod, match the corresponding reference image according to the rotation angle of the clamp used to fix the titanium alloy rod (or the rotation angle of the motor driving the clamp).

[0091] S222. Subtract the control image from the detected image to obtain the first preprocessed image. Its expression is:

[0092]

[0093] In the formula, Indicates the detection of pixels in the image Pixel intensity; Represents the pixels in the comparison image Pixel intensity;

[0094] S223, Process the first preprocessed image Perform Gaussian filtering to obtain the image to be used. Its expression is:

[0095]

[0096] In the formula, This represents the pixel coordinates of a pixel within the Gaussian kernel relative to its center point. Indicates standard deviation; This represents the natural constant. Specifically, when performing Gaussian filtering, a Gaussian kernel is first defined, and then the image is convolved using the Gaussian kernel to complete the filtering operation. The pixel coordinates of the center point of the Gaussian kernel are (0, 0). The distance from the center point of the Gaussian kernel is determined. For example, if the size of the Gaussian kernel is 3×3, then the pixel coordinates of the pixel in the upper left corner are (-1, -1). For custom parameters;

[0097] S23. Use the grayscale centroid method on the image to be processed. Each row of pixels is scanned to extract the center position of the laser line. The calculation formula is as follows:

[0098]

[0099] In the formula, This represents the pixel intensity of the pixel in the i-th row of the c-th column; This represents the maximum number of rows of pixels in the image to be used;

[0100] S24. With the camera position as the origin O1, construct a camera coordinate system with the east-west direction, the north-south direction, and the vertical direction as the X-axis, Y-axis, and Z-axis, respectively. Then, determine the center position of the laser line. Convert to 3D coordinates in camera coordinate system Specifically, step S24 includes the following steps:

[0101] S241. Obtain the camera's intrinsic parameter matrix. Its expression is:

[0102]

[0103] In the formula, and These represent the camera focal length in pixels; and These represent the intersection points of the camera's optical axis and the detection image plane, respectively.

[0104] S242. Construct the plane equation under the detection ray plane to calculate the camera's extrinsic parameters, including the plane normal vector. and constant term The equation of the plane is expressed as:

[0105]

[0106] Specifically, in solving the plane equation, a calibration block with a defined shape and size can be set up and fixed on a fixture of a titanium alloy rod. A laser is emitted onto the titanium alloy rod, and several images are captured. For each image, the pixel coordinates of the laser line's center point are extracted. Since the shape and size of the calibration block are known, and the distances between the calibration block and the camera and laser scanner are also determined, the intrinsic parameter matrix of the camera can be used as a reference. The pixel coordinates of the laser line center point in the camera coordinate system are obtained. The three-dimensional coordinates of the laser line center point in these images are then fitted using a plane fitting algorithm (such as the least squares method) to obtain the camera's extrinsic parameters.

[0107] S243, via the intrinsic parameter matrix Calculate the center position of the laser line In the three-dimensional coordinates of the camera coordinate system Its expression is:

[0108]

[0109] in, In the formula, Indicates the center position of the laser line One pixel; This represents the direction vector of a 3D ray in the camera coordinate system; Represents a point on a three-dimensional ray; Indicates the scale factor;

[0110] S244, Point Substitute into the plane equation to calculate the scale factor. The calculation formula is as follows:

[0111]

[0112] Specifically, the equation of the detection ray plane in the camera coordinate system is: Substitute the three-dimensional ray calculation formula into the above equation to obtain the scale factor.

[0113] S25. Repeat S21-S24 for each frame of the detected image to obtain the first 3D point cloud data of the titanium alloy rod surface.

[0114] In this invention, a laser is used to irradiate the surface of a rotating titanium alloy rod, and then 3D point cloud data is constructed based on the pattern formed by laser reflection. Compared with the method of directly analyzing surface defects based on 2D images, the three-dimensional dimensions of surface defects such as depth, length, and width can be obtained more accurately, and it is not affected by false defect signals such as surface color difference, oxidation marks, and oil stains, and has strong anti-interference ability.

[0115] S3. Acquire the surface temperature and vibration information of the titanium alloy rod at each moment. The vibration information includes vibration acceleration and vibration angular velocity. Specifically, since the detection image is taken during the rotation of the titanium alloy rod, the vibration caused by the rotation of the titanium alloy rod will reduce the accuracy of the detection image. In addition, the titanium alloy rod will expand or contract with temperature changes. If the above factors are not considered, the first 3D point cloud data will not be accurate enough. Therefore, it is necessary to detect the vibration and surface temperature of the titanium alloy rod. The vibration of the titanium alloy rod is obtained by an acceleration sensor, and the surface temperature is obtained by a temperature sensor.

[0116] S4. Correct the first 3D point cloud data based on surface temperature and vibration information to generate the second 3D point cloud data. Specifically, common point cloud data correction methods only consider the individual effects of component vibration and temperature changes on the point cloud data, without considering the combined effects of vibration and temperature on the titanium alloy rod. For example, the elastic modulus (stiffness) of the titanium alloy rod will decrease slightly with increasing temperature. Therefore, under the same rotational driving force or external disturbance, the titanium alloy rod with lower stiffness (at high temperature) will produce a larger amplitude and different vibration frequency. In other words, temperature changes will change the dynamic characteristics of the entire titanium alloy rod vibration system, thereby affecting the generation accuracy of the first 3D point cloud data. Therefore, step S4 specifically includes the following steps:

[0117] S41. Set up a parameter detection test to calculate the filter parameters at each temperature. and gain Specifically, step S41 includes the following steps:

[0118] S411. Install a standard titanium alloy rod, set several temperatures, and rotate the standard titanium alloy rod to detect the test parameters of the standard titanium alloy rod at each temperature. The test parameters include the actual vibration displacement and vibration velocity. Specifically, reflective marks can be attached to the standard titanium alloy rod, and its motion trajectory can be directly tracked in three dimensions using an optical motion capture system such as Vicon or OptiTrack.

[0119] S412, for each temperature Calculate linear acceleration based on vibration acceleration The calculation formula is as follows:

[0120]

[0121] In the formula, Indicates vibration acceleration; Represents gravitational acceleration;

[0122] S413, Define filter parameters and gain The range of values; specifically, The value range is (0.01, 0.5). The value range is (0.8, 1.2).

[0123] S414, Select any pair of filter parameters and gain And estimate the vibration displacement based on its calculation. The calculation formula is as follows:

[0124]

[0125] In the formula, Indicates in Estimated vibration displacement at time; Indicates in The vibration velocity of the titanium alloy rod at any given moment; Indicates at time The vibration acceleration; Indicates the time interval between adjacent sampling moments;

[0126] S415. Calculate and estimate vibration displacement. The root mean square error between the actual vibration displacement and the actual vibration displacement The calculation formula is as follows:

[0127]

[0128] In the formula, This indicates the total number of times the experimental parameter was tested; Indicates the first The actual vibration displacement of the test parameters was detected in the second experiment. No. Estimated vibration displacement based on the parameters of the second test experiment;

[0129] S416. Use optimization algorithms to find the root mean square error. Minimum filter parameters and gain .

[0130] In this invention, the filtering parameters and gain at different temperatures are calculated experimentally. Compared with other point cloud data compensation methods, this method not only considers the individual effects of temperature and vibration on the accuracy of point cloud data, but also the mutual influence between temperature and vibration, which can improve the accuracy of subsequent point cloud data compensation.

[0131] S42. Obtain the linear thermal expansion coefficient of the titanium alloy rod and calculate its actual length at the current temperature. The calculation formula is as follows:

[0132]

[0133] In the formula, This indicates the standard length of the titanium alloy bar at the reference temperature; The linear thermal expansion coefficient of the titanium alloy rod; Indicates a reference temperature; specifically, It is 25 degrees Celsius; for ; and The dimensions are consistent;

[0134] S43. Extract filter parameters based on the current temperature. and gain The instantaneous deflection angle is calculated based on it. and instantaneous displacement The calculation formulas are as follows:

[0135]

[0136]

[0137] In the formula, Indicates the current moment The vibration angular velocity of the titanium alloy rod; Indicates at time The instantaneous deflection angle; Indicates the current moment Vibration velocity of the titanium alloy rod; Indicates at time The instantaneous displacement; specifically, It can be calculated based on vibration acceleration; when When the value is 1, it indicates that the titanium alloy rod was at rest at the previous moment. and All are 0;

[0138] S44, based on the instantaneous deflection angle and instantaneous displacement Constructing the vibration transformation matrix Its expression is:

[0139]

[0140] In the formula, Represents the rotation matrix;

[0141] Specifically, , ; ; ;in, , and These represent the deflection angles of the titanium alloy rod around the X, Y, and Z axes of the camera coordinate system, respectively; it should be noted that... , and It can be based on the instantaneous deflection angle Calculated; and The dimensions are consistent;

[0142] S45. According to the vibration transformation matrix The first 3D point cloud data is corrected to generate vibration-compensated point cloud data. The calculation formula is as follows:

[0143]

[0144] In the formula, This represents the first 3D point cloud data; This represents vibration-compensated point cloud data;

[0145] S46. Based on actual length Vibration compensation point cloud data The correction is performed to generate a second 3D point cloud data, and its calculation formula is as follows:

[0146]

[0147] In the formula, This represents the second 3D point cloud data.

[0148] S5. Construct the target model by inputting the second 3D point cloud data into the target model and outputting the types and sizes of all surface defects. Specifically, in step S5, a PointNet neural network is selected as the basis, and other titanium alloy rods of the same size and model are selected to generate corresponding point cloud data as training samples. Then, the size and type of each surface defect are manually labeled as sample labels. The neural network is trained using the training samples and sample labels to obtain the target model. Alternatively, PointNet++ and DGCNN, which can process point cloud data, can be used as the basis for constructing the target model. These are all existing technologies and will not be elaborated here.

[0149] The target model outputs the type and size of surface defects and feeds them back to the control terminal so that the operator can analyze the cause of the surface defects, adjust the production process, and remove unqualified titanium alloy rods.

[0150] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for quality detection of titanium alloy bars, characterized in that, Specifically comprising the following steps: S1, rotate the titanium alloy rod at a constant speed, and emit detection light to the surface of the titanium alloy rod; S2, capture the detection light reflected from the surface of the titanium alloy rod to obtain a detection image, and extract first 3D point cloud data of the surface of the titanium alloy rod according to the detection image; S3, obtain the surface temperature and the vibration information of the titanium alloy rod at each moment, the vibration information including vibration acceleration and vibration angular velocity; S4, correct the first 3D point cloud data according to the surface temperature and the vibration information, and generate second 3D point cloud data; In step S4, specifically comprising the following steps: S41, set a parameter detection test to calculate the filtering parameter at each temperature and gain ; In step S41, specifically comprising the following steps: S411, install a standard titanium alloy rod, set several temperatures, rotate the standard titanium alloy rod, and detect test parameters of the standard titanium alloy rod at each temperature, the test parameters including actual vibration displacement and vibration speed; S412、For each temperature linear acceleration from the vibration acceleration The formula is: wherein, denotes the vibration acceleration; denotes the gravitational acceleration; S413、define filter parameters and gain value range; S414, optionally a pair of filter parameters and gain and from which an estimate of the vibration displacement is calculated ; In step S414, the vibration displacement is estimated The calculation formula is: wherein, represents an estimated vibration displacement at time; represents a vibration velocity of the titanium alloy rod at time; represents a vibration acceleration at time; represents a time interval of adjacent sampling times S415, calculating a root mean square error between the estimated vibration displacement and the actual vibration displacement which is calculated by the formula: wherein, denotes the total number of detection experiment parameters; denotes the actual vibration displacement of the th detection experiment parameter; denotes the estimated vibration displacement of the th detection experiment parameter; S416, find root mean square error using optimization algorithm Minimum filter parameter And gain ; S42, obtain the linear thermal expansion coefficient of the titanium alloy rod, and calculate the actual length thereof at the current temperature The calculation formula is: wherein, Lref represents the standard length of the titanium alloy rod at the reference temperature; α represents the linear thermal expansion coefficient of the titanium alloy rod; Tref represents the reference temperature; S43, extracting filtering parameters from the current temperature and gain from which the instantaneous deflection angle and the instantaneous displacement are calculated, respectively , wherein, denotes the instantaneous deflection angle at the current time the angular velocity of the titanium alloy rod; denotes the instantaneous displacement at the time t of the instantaneous deflection angle; denotes the instantaneous displacement at the current time the velocity of the titanium alloy rod; denotes the instantaneous displacement at the time t of the instantaneous displacement; S44. Constructing a vibration transformation matrix and instantaneous displacement S44. Constructing a vibration transformation matrix whose expression is wherein denotes a rotation matrix; S45、According to the vibration transformation matrix The first 3D point cloud data is corrected to generate vibration compensation point cloud data, and the calculation formula is: wherein, denotes the first 3D point cloud data; denotes the vibration-compensated point cloud data; S46、According to the actual length Compensating the point cloud data correction, the second 3D point cloud data is generated, and the calculation formula is: wherein, denotes the second 3D point cloud data; S5, construct a target model, input the second 3D point cloud data into the target model, and output types and sizes of all surface defects.

2. The method for quality detection of titanium alloy bar according to claim 1, characterized in that, The interval of the incidence angle of the detection light is between 30 degrees and 60 degrees, and the interval of the receiving angle of the detection light receiver is between 30 degrees and 60 degrees.

3. The method for quality detection of titanium alloy bar according to claim 1, characterized in that, In step S2, specifically comprising the following steps: S21, taking the upper left corner of the detection image as the coordinate origin 0, and taking the upper edge and the left edge of the detection image as the x-axis and the y-axis respectively to construct an image coordinate system; S22, pre-process the detection image to obtain a to-be-used image ; S23, scanning each row of pixel points of the to-be-used image using a gray gravity center method to extract a laser line center position The calculation formula is: ​ wherein, represents the pixel intensity of the pixel point in the i-th row of the c-th column; represents the maximum value of the row number of the pixel point in the image to be used. S24, with the camera position as the origin 01, and with the east-west direction, the north-south direction, and the vertical direction as the X-axis, the Y-axis, and the Z-axis, respectively, a camera coordinate system is constructed, and the laser line center position is converted into a three-dimensional coordinate in the camera coordinate system ; S25, repeating S21-S24 for each frame of detection image to obtain the first 3D point cloud data of the surface of the titanium alloy rod.

4. The method for quality detection of titanium alloy bar according to claim 3, characterized in that, In step S22, specifically comprising the following steps: S221, obtain a control image of the titanium alloy rod without the influence of the detection light, and find the control image corresponding to the detection image; S222, subtract the control image from the detection image to obtain a first pre-processed image The expression is: wherein, represents the pixel intensity of a pixel point in the detection image; represents the pixel intensity of a pixel point in the reference image; S223、to the first pre-processed image performing Gaussian filtering to obtain a to-be-used image whose expression is wherein, represents the pixel coordinate of the kernel pixel relative to the center point; represents the standard deviation; represents the natural constant.

5. The method for quality detection of titanium alloy bar according to claim 3, characterized in that, In step S24, specifically comprising the following steps: S241、acquire the intrinsic matrix of the camera The expression is: wherein, and respectively represent the camera focal length in pixel unit; and respectively represent the intersection of the camera optical axis and the detection image plane; S242、constructing a plane equation under the detection light plane to calculate the extrinsic parameter of the camera, the extrinsic parameter including a plane normal vector and a constant term , an expression of the plane equation is: ; S243、by the internal reference matrix Computing the laser line center position In the three-dimensional coordinates of the camera coordinate system The expression is: wherein, wherein, represents the laser line center position one pixel point; represents the direction vector of the three-dimensional ray in the camera coordinate system; represents the point on the three-dimensional ray; represents the scale factor; S244, the point Substitute the plane equation to calculate the scale factor The calculation formula is: In the formula, denotes the scale factor.

6. The method for quality detection of titanium alloy bar according to claim 1, characterized in that, In step S5, PointNet neural network is selected as the basis, then other titanium alloy rods of the same size and model are selected to generate corresponding point cloud data as training samples, then the size and type of each surface defect are manually labeled as sample labels, the training samples and sample labels are used to train the above neural network, and the target model is obtained.

Images