Curvature compensation type metal material geometric dimension image measurement method and system

Abstract

The invention discloses a curvature compensation type metal material geometric dimension image measurement method and system, and relates to the technical field of dimension measurement. The curvature compensation type metal material geometric dimension image measurement method comprises the following steps: A1, sample dimension identification and outer contour test; a2, calculating and correcting the cross section area; a3, establishing a correlation model by considering influence factors; a laser measurement light curtain and a displacement sensor are combined to realize synchronous high-precision acquisition of the contour, the thickness and the length; a curvature compensation image algorithm is used for correcting measurement errors caused by curvature characteristics, and the measurement precision is improved; dimensional deviation, shape deviation and parallel section thickness and length verification are integrated, and it is ensured that the detection result is comprehensive and reliable; different sample expansion conditions caused by environmental factors and material factors are considered, a relation model is established, the sample expansion conditions can be directly predicted according to the environmental factors and the material factors, and then sample parameters are calculated more accurately; the whole process does not need manual intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of dimension measurement, and in particular to a curvature-compensated metal material geometric dimension image measurement method and system. Background Art

[0002] Traditional planar testing technology faces multiple challenges when inspecting tubular materials such as steel pipes and fittings. First, the curved surface characteristics of tubular materials cause the measurement reference plane to shift, and traditional methods cannot effectively compensate for the errors introduced by curvature deformation. Second, a single sensor struggles to simultaneously acquire multi-dimensional data such as profile, thickness, and length, and lacks the ability to fuse multi-source data, limiting measurement accuracy. Furthermore, the inspection process is highly manual, making it difficult to meet the high-speed, high-precision requirements of industrial online testing. These challenges hinder the development of intelligent metal pipe inspection, and a high-precision testing method that integrates multi-sensor technology and features curvature compensation is urgently needed. Summary of the Invention

[0003] In view of the deficiencies of the prior art, the present invention provides a curvature-compensated metal material geometric dimension image measurement method and system, which solves the problems of the prior art.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A curvature-compensated metal material geometric dimension image measurement method, comprising the following steps: A1. Specimen size identification and outer contour testing: Use a laser measuring light curtain to test the outer contour of the parallel section of the longitudinal arc specimen, obtain the outer contour light curtain array data, determine the number of columns and rows in the array, and record the environmental conditions and specimen material properties during the test; The light curtain array is divided into multiple sub-arrays according to the number of rows, and the zero norm of each sub-array is calculated to obtain the size deviation and shape deviation of the parallel section of the arc specimen to verify whether the outer contour is compliant; A2. Cross-sectional area calculation and correction: Perform contour correction and thickness verification. When the outer contour is not in compliance, use the normal distribution function to verify the data, eliminate outliers, calculate the cross-sectional similarity width and thickness, and preliminarily calculate the cross-sectional area. A3. Establish a correlation model considering influencing factors: Introduce the correlation formula model of the influence of environmental factors and material properties on the cross-sectional area to modify it, and then obtain the predicted cross-sectional area based on the influencing factors.

[0005] Preferably, the step A1 specifically includes: A1.1. Use a laser measuring light curtain to measure the outer contour of the parallel segment of the longitudinal arc specimen. Obtain the outer contour light curtain array to determine the number of columns and rows to be displayed. Record the environmental conditions during the test and the material properties of the specimen to consider the impact of environmental factors on the measurement results during data analysis in step A3. A1.2. Divide the light curtain array into multiple sub-arrays according to the number of rows and determine the zero norm of each sub-array , B i Represents the width vector of the parallel segment of the arc specimen at the i-th discrete part; A1.3. Use formula and , the size deviation M and shape deviation N of the parallel section of the arc specimen are obtained, where H is the nominal width of the specimen, n is the total number of discrete points, and i∈1 to n; A1.4. Use M and N to check whether the outer contour of the parallel section of the longitudinal arc specimen is compliant after processing.

[0006] Preferably, A2 includes: A2.1. Contour correction: A2.1.1. When the outer contour of the parallel section of the longitudinal arc specimen is judged to be non-compliant, use the normal distribution function to verify The conformity of (excluding data other than 3 times the standard deviation), corrected to get the average value ; When the outer contour of the parallel section of the longitudinal arc specimen is judged to be compliant, , H is the nominal width; A2.1.2. Use formula Calculate and obtain the similar width of the cross section of the longitudinal arc specimen ,in is the minimum measured thickness of the specimen, is the nominal outside diameter.

[0007] Preferably, A2 further includes: A2.2, Thickness calibration: A2.2.1. Use a high-precision displacement sensor to measure the length and thickness of the parallel section of the longitudinal arc specimen and obtain 、 、 、 ,Pick ; A2.2.2. Using the formula and , verify the compliance of the longitudinal arc specimen, where A is the original wall thickness of the steel pipe and z is the allowable deviation coefficient of the wall thickness.

[0008] formula The calculation process is: ; but: ; in, and is the known value measured.

[0009] Preferably, A2 further includes: A2.3. Calculation of cross-sectional area: A2.3.1 Area calculation: Using , calculate the cross-sectional area of the longitudinal arc specimen.

[0010] Preferably, the specific step of A3 is: considering the influence of environmental factors such as temperature T and humidity H on the expansion or contraction of the material, and the potential influence of material properties such as elastic modulus E and Poisson's ratio ν on the cross-sectional area measurement, to establish a correlation formula model: Temperature effect correction term: ΔS T =β T × (T - T0) × S', where β T is the temperature expansion coefficient, T0 is the reference temperature; Humidity effect correction term: ΔS P =β P ×(P - P0)×S', where β P is the humidity influence coefficient, P0 is the reference humidity; Correction term for material properties: ΔS Q = f(E, ν, ...) × S', where f is a function determined by the material properties, and the parameters within the f function are determined according to the properties to be considered; Comprehensively corrected cross-sectional area: S final = S' + ΔS T + ΔS P + ΔS Q .

[0011] The present invention also discloses a measurement system, comprising: Laser measuring light curtain, used to measure the outer contour array of the parallel section of the specimen; High-density displacement sensor, used to measure the length and thickness of the parallel section of the specimen; Multi-source sensor acquisition system, including a sample size recognition module and a cross-sectional area calculation module; The cross-sectional area calculation module includes a contour correction module, a thickness verification module and a calculation module. The contour correction module processes the data of the sample outer contour array to obtain the chord length of the cross-sectional outer circle; the thickness verification module verifies the compliance of the length and thickness of the parallel section of the sample; and the cross-sectional area calculation module uses a curvature compensation image algorithm to correct the cross-sectional area of the sample.

[0012] Preferably, the multi-source sensor acquisition system further includes a temperature sensor and a humidity sensor, which are used to measure the temperature and humidity environmental data in real time during the measurement process and record the time data accordingly.

[0013] The present invention provides a curvature-compensated metal material geometric dimension image measurement method and system. Compared with the existing technology, it has the following advantages: (1) Multi-sensor fusion: combining laser measurement light curtain and displacement sensor to achieve synchronous high-precision acquisition of contour, thickness and length; (2) Curvature compensation algorithm: The curvature compensation imaging algorithm is used to correct the measurement error caused by the curvature feature, thereby improving the measurement accuracy; (3) Compliance verification: integrated size deviation, shape deviation, parallel section thickness and length verification to ensure comprehensive and reliable test results; (4) Analysis of influencing factors: Considering the differences in sample expansion caused by environmental factors and material factors, a relationship model is established to facilitate the direct prediction of sample expansion based on environmental factors and material factors, thereby more accurately calculating sample parameters; (5) Automated testing: The entire process does not require human intervention. It is a high-precision and high-efficiency testing technology solution suitable for tubular materials such as steel pipes and pipe fittings, promoting the development of industrial testing towards intelligence and labeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the original steel pipe specimen; Figure 2 It is the side view of the original steel pipe specimen after cutting and processing; Figure 3 It is a schematic diagram of the longitudinal arc specimen; Figure 4 It is a schematic diagram of the calculation process of the cross-sectional area S0 of the longitudinal arc specimen; Figure 5 This is the working principle diagram of the laser test light curtain; Figure 6 It is a formula Schematic diagram of the calculation process. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0016] See Figures 1-6 The present invention discloses a curvature-compensated metal material geometric dimension image measurement method and provides the following three technical solutions: The first implementation method includes the following steps: A1. Specimen size identification and outer contour testing: Use a laser measuring light curtain to test the outer contour of the parallel section of the longitudinal arc specimen, obtain the outer contour light curtain array data, determine the number of columns and rows in the array, and record the environmental conditions and specimen material properties during the test; The light curtain array is divided into multiple sub-arrays according to the number of rows, and the zero norm of each sub-array is calculated to obtain the size deviation and shape deviation of the parallel section of the arc specimen to verify whether the outer contour is compliant; A2. Cross-sectional area calculation and correction: Perform contour correction and thickness verification. When the outer contour is not in compliance, use the normal distribution function to verify the data, eliminate outliers, calculate the cross-sectional similarity width and thickness, and preliminarily calculate the cross-sectional area. A3. Establish a correlation model considering influencing factors: Introduce the correlation formula model of the influence of environmental factors and material properties on the cross-sectional area to modify it, and then obtain the predicted cross-sectional area based on the influencing factors.

[0017] Combining the laser measurement light curtain with the displacement sensor, it realizes the synchronous high-precision acquisition of the contour, thickness and length. The curvature compensation imaging algorithm is used to correct the measurement error caused by the curvature characteristics, thereby improving the measurement accuracy. The integrated size deviation, shape deviation, parallel section thickness and length calibration ensures that the test results are comprehensive and reliable. Taking into account the different sample expansion conditions caused by environmental factors and material factors, a relationship model is established to facilitate the direct prediction of the sample expansion conditions based on environmental factors and material factors, and thus more accurately calculate the sample parameters. The entire process does not require human intervention and is suitable for high-precision and high-efficiency detection technology solutions for tubular materials such as steel pipes and pipe fittings, promoting the development of industrial detection towards intelligence and labeling.

[0018] The second embodiment differs from the first embodiment mainly in that the step A1 specifically includes: A1.1. Use a laser measuring light curtain to measure the outer contour of the parallel segment of the longitudinal arc specimen. Obtain the outer contour light curtain array to determine the number of columns and rows to be displayed. Record the environmental conditions during the test and the material properties of the specimen to consider the impact of environmental factors on the measurement results during data analysis in step A3. A1.2. Divide the light curtain array into multiple sub-arrays according to the number of rows and determine the zero norm of each sub-array , B i Represents the width vector of the parallel segment of the arc specimen at the i-th discrete part; A1.3. Use formula and , the size deviation M and shape deviation N of the parallel section of the arc specimen are obtained, where H is the nominal width of the specimen, n is the total number of discrete points, and i∈1 to n; A1.4. Use M and N to check whether the outer contour of the parallel section of the longitudinal arc specimen is compliant after processing.

[0019] The A2 includes: A2.1. Contour correction: A2.1.1. When the outer contour of the parallel section of the longitudinal arc specimen is judged to be non-compliant, use the normal distribution function to verify The conformity of (excluding data other than 3 times the standard deviation), corrected to get the average value ; When the outer contour of the parallel section of the longitudinal arc specimen is judged to be compliant, , H is the nominal width; A2.1.2. Use formula Calculate and obtain the similar width of the cross section of the longitudinal arc specimen ,in is the minimum measured thickness of the specimen, is the nominal outer diameter; A2.2, Thickness calibration: A2.2.1. Use a high-precision displacement sensor to measure the length and thickness of the parallel section of the longitudinal arc specimen and obtain 、 、 、 ,Pick ; A2.2.2. Using the formula and , verify the compliance of the longitudinal arc specimen, where A is the original wall thickness of the steel pipe and z is the allowable deviation coefficient of the wall thickness; A2.3. Calculation of cross-sectional area: A2.3.1 Area calculation: Using , calculate the cross-sectional area of the longitudinal arc specimen.

[0020] refer to Figure 6 ,formula The calculation process is: ; but: ; in, and is the known value measured.

[0021] In terms of measurement and verification, laser measurement screens and high-precision displacement sensors enable more accurate acquisition of data such as the outer contour, length, and thickness of the parallel segments of longitudinal arc specimens. Formulas and algorithms are used to determine dimensional and shape deviations, and their compliance is verified, improving measurement accuracy and verification reliability. For contour correction, a normal distribution function is used to eliminate abnormal data, correct it to the average value, and calculate similar cross-sectional widths to ensure more realistic data. Thickness verification uses test data to verify compliance and ensure specimen quality.

[0022] The third embodiment differs from the first embodiment mainly in that the specific steps of A3 are: considering the influence of environmental factors such as temperature T and humidity H on material expansion or contraction, as well as the potential influence of material properties such as elastic modulus E and Poisson's ratio ν on cross-sectional area measurement, a correlation formula model is established: Temperature effect correction term: ΔS T =β T × (T - T0) × S', where β T is the temperature expansion coefficient, T0 is the reference temperature; Humidity effect correction term: ΔS P =β P ×(P - P0)×S', where β P is the humidity influence coefficient, P0 is the reference humidity; Correction term for material properties: ΔS Q = f(E, ν, ...) × S', where f is a function determined by the material properties, and the parameters within the f function are determined according to the properties to be considered; Comprehensively corrected cross-sectional area: S final = S' + ΔS T + ΔS P + ΔS Q .

[0023] The impact of environmental factors and material properties on cross-sectional area measurement is fully considered. By establishing a correlation formula model, correction terms are set for the influence of temperature and humidity, and correction terms are also set for material properties such as elastic modulus and Poisson's ratio. This multi-factor comprehensive correction method can more accurately eliminate measurement errors caused by external factors and the inherent characteristics of the material, making the final comprehensive correction cross-sectional area closer to the true value, improving the accuracy and reliability of cross-sectional area measurement, and providing a more solid data foundation for subsequent related research and applications.

[0024] The present invention also discloses a measurement system, comprising: Laser measuring light curtain, used to measure the outer contour array of the parallel section of the sample, Figure 5 This is based on the principle of a laser measurement light curtain. Static light emitted by the laser is reflected by a high-speed rotating prism, scanned and collimated to form a moving parallel beam, which is then focused and imaged onto a phototube. When an object is placed in the measurement area, it partially blocks the beam, creating a shadow. The phototube then generates a corresponding dark level. Calculating the width of the dark level provides the object's diameter. High-density displacement sensor, used to measure the length and thickness of the parallel section of the specimen; Multi-source sensor acquisition system, including a sample size recognition module and a cross-sectional area calculation module; The cross-sectional area calculation module includes a contour correction module, a thickness verification module and a calculation module. The contour correction module processes the data of the sample outer contour array to obtain the chord length of the cross-sectional outer circle; the thickness verification module verifies the compliance of the length and thickness of the parallel section of the sample; and the cross-sectional area calculation module uses a curvature compensation image algorithm to correct the cross-sectional area of the sample.

[0025] The multi-source sensor acquisition system also includes a temperature sensor and a humidity sensor, which are used to measure temperature and humidity environmental data in real time during the measurement process and record time data accordingly.

[0026] The present invention will be further described below in conjunction with the embodiments: Example 1: Online detection of seamless steel pipes Scenario: A construction company sent a batch of seamless steel pipes (GB / T 8162-2018) for inspection. The nominal outer diameter D0 is 42 mm, and the nominal width H of the parallel section of the longitudinal arc specimen after processing is 10 mm (e.g. Figure 1 and 2 As shown in the figure), check the compliance of the outer contour of the longitudinal arc specimen after processing (as shown in the figure). Figure 3 shown in red); Steps: 1) Use laser to measure the light curtain and test the outer contour of the sample to obtain a 263-column × 543-row light curtain array; 2) Split by the number of rows to obtain 543 row vectors ;3) Calculate the size tolerance M and shape tolerance N values; ; ; According to the table, after machining on both sides, the dimensional tolerance M and shape tolerance N of the nominal transverse dimension (i.e., nominal width H=10mm) are required to be ±0.03mm and 0.04mm respectively. The actual measurements are 0.187mm and 0.027mm, which means that after machining, the "outer contour of the parallel section of the longitudinal arc specimen" is non-compliant. Example 2: Online detection of seamless steel pipes Scenario: A construction company sent a batch of seamless steel pipes (GB / T 8162-2018) for inspection. The nominal outer diameter D0 and the original wall thickness A were 42mm×4mm (e.g. Figure 1 and Figure 2 As shown), the nominal width H of the parallel section of the longitudinal arc specimen after processing is 10 mm (as shown Figure 3 As shown), the wall thickness tolerance z=0.875, calculate the cross-sectional area of the longitudinal arc specimen (as shown Figure 4 shown); step: 1) Use high-precision displacement sensors to measure the length and thickness of the parallel section of the longitudinal arc specimen to obtain 、 、 、 ,Pick ; 2) Using a laser to measure the light curtain, the outer contour of the specimen was tested, resulting in a 263-column × 543-row light curtain array. This was then split by the number of rows to obtain 543 row vectors {B}. The dimensional tolerance M and form tolerance N were calculated to verify the conformity of the outer contour of the processed longitudinal arc specimen. 3) Normal distribution function verification The conformity of (excluding data other than 3 times the standard deviation), corrected to get the average value ; 4) Use the formula Calculate and obtain the similar width of the cross section of the longitudinal arc specimen ,in is the minimum value of the measured thickness of the specimen; 5) Utilize , calculate the cross-sectional area of the longitudinal arc specimen; 6) Using formulas and , verify the compliance of the longitudinal arc specimen, where A is the original wall thickness of the steel pipe (A=4mm).

[0027] Calculation process: 1) ,calculate ; 2) This step uses the results of Example 1 to conclude that the "outer contour of the parallel section of the longitudinal arc specimen" after processing is non-compliant; therefore, the actual measurement results must be used to calculate B; 3) Normal distribution function verification The conformity of (excluding data other than 3 times the standard deviation), corrected to get the average value ; 4) ; 5) Calculation ,because ,according to Calculate and get ; 6) Requirements , Actual measurement results , , we can conclude The results are accurate.

[0028] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0029] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0030] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A curvature-compensated metal material geometric dimension image measurement method, characterized in that: The following steps are involved: A1. Specimen size identification and outer contour testing: Use a laser measuring light curtain to test the outer contour of the parallel section of the longitudinal arc specimen, obtain the outer contour light curtain array data, determine the number of columns and rows in the array, and record the environmental conditions and specimen material properties during the test; The light curtain array is divided into multiple sub-arrays according to the number of rows, and the zero norm of each sub-array is calculated to obtain the size deviation and shape deviation of the parallel section of the arc specimen to verify whether the outer contour is compliant; A2. Cross-sectional area calculation and correction: Perform contour correction and thickness verification. When the outer contour is not in compliance, use the normal distribution function to verify the data, eliminate outliers, calculate the cross-sectional similarity width and thickness, and preliminarily calculate the cross-sectional area. A3. Establish a correlation model considering influencing factors: Introduce the correlation formula model of the influence of environmental factors and material properties on the cross-sectional area to modify it, and then obtain the predicted cross-sectional area based on the influencing factors.

2. The curvature-compensated metal material geometric dimension image measurement method according to claim 1, characterized in that: The A1 step specifically includes: A1.

1. Use a laser measuring light curtain to measure the outer contour of the parallel segment of the longitudinal arc specimen. Obtain the outer contour light curtain array to determine the number of columns and rows to be displayed. Record the environmental conditions during the test and the material properties of the specimen to consider the impact of environmental factors on the measurement results during data analysis in step A3. A1.

2. Divide the light curtain array into multiple sub-arrays according to the number of rows and determine the zero norm of each sub-array , B i Represents the width vector of the parallel segment of the arc specimen at the i-th discrete part; A1.

3. Use formula and , the size deviation M and shape deviation N of the parallel section of the arc specimen are obtained, where H is the nominal width of the specimen, n is the total number of discrete points, and i∈1 to n; A1.

4. Use M and N to check whether the outer contour of the parallel section of the longitudinal arc specimen is compliant after processing.

3. The curvature-compensated metal material geometric dimension image measurement method according to claim 1, characterized in that: The A2 includes: A2.

1. Contour correction: A2.1.

1. When the outer contour of the parallel section of the longitudinal arc specimen is judged to be non-compliant, use the normal distribution function to verify The conformity of the correction is obtained by ; When the outer contour of the parallel section of the longitudinal arc specimen is judged to be compliant, , H is the nominal width; A2.1.

2. Use formula Calculate and obtain the similar width of the cross section of the longitudinal arc specimen ,in is the minimum measured thickness of the specimen, is the nominal outside diameter.

4. The curvature-compensated metal material geometric dimension image measurement method according to claim 3, characterized in that: Said A2 also includes: A2.2, Thickness calibration: A2.2.

1. Use a high-precision displacement sensor to measure the length and thickness of the parallel section of the longitudinal arc specimen and obtain 、 、 、 ,Pick ; A2.2.

2. Using the formula and , verify the compliance of the longitudinal arc specimen, where A is the original wall thickness of the steel pipe and z is the allowable deviation coefficient of the wall thickness.

5. The curvature-compensated metal material geometric dimension image measurement method according to claim 3, characterized in that: formula The calculation process is: ； but: ; in, and is the known value measured.

6. The curvature-compensated metal material geometric dimension image measurement method according to claim 4, characterized in that: Said A2 also includes: A2.

3. Calculation of cross-sectional area: A2.3.1 Area calculation: Using , calculate the cross-sectional area of the longitudinal arc specimen.

7. The curvature-compensated metal material geometric dimension image measurement method according to claim 1, characterized in that: The specific steps of A3 are: considering the influence of environmental factors such as temperature T and humidity H on the expansion or contraction of the material, as well as the potential influence of material properties such as elastic modulus E and Poisson's ratio ν on the cross-sectional area measurement, and establishing a correlation formula model: Temperature effect correction term: ΔS T =β T × (T - T0) × S', where β T is the temperature expansion coefficient, T0 is the reference temperature; Humidity effect correction term: ΔS P =β P ×(P - P0)×S', where β P is the humidity influence coefficient, P0 is the reference humidity; Correction term for material properties: ΔS Q = f(E, ν, ...) × S', where f is a function determined by the material properties, and the parameters within the f function are determined according to the properties to be considered; Comprehensively corrected cross-sectional area: S final = S' + ΔS T + ΔS P + ΔS Q .

8. A measurement system for implementing the curvature-compensated metal material geometric dimension image measurement method according to any one of claims 1 to 7, characterized in that: include: Laser measuring light curtain, used to measure the outer contour array of the parallel section of the specimen; High-density displacement sensor, used to measure the length and thickness of the parallel section of the specimen; Multi-source sensor acquisition system, including a sample size recognition module and a cross-sectional area calculation module; The cross-sectional area calculation module includes a contour correction module, a thickness verification module and a calculation module. The contour correction module processes the data of the sample outer contour array to obtain the chord length of the cross-sectional outer circle; the thickness verification module verifies the compliance of the length and thickness of the parallel section of the sample; and the cross-sectional area calculation module uses a curvature compensation image algorithm to correct the cross-sectional area of the sample.

9. A measurement system according to claim 8, characterized in that: The multi-source sensor acquisition system also includes a temperature sensor and a humidity sensor, which are used to measure temperature and humidity environmental data in real time during the measurement process and record time data accordingly.

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