A method for online measurement of surface roughness of welded pipe

Abstract

The present invention relates to the technical field of length measurement, and in particular to an online surface roughness measurement method for a welded pipe. The method comprises obtaining a depth information sequence along a welded pipe and dividing the sequence into a plurality of depth information segments. Based on the difference between a minimum value in a depth information segment and its adjacent depth information, and the depth information fluctuation of the depth information segment, an occlusion feature of the depth information segment is obtained. According to the association between the depth information in any two depth information segments, a welding slag feature of each depth information segment is obtained. According to the occlusion feature and the welding slag feature of each depth information segment, the depth information of the welding slag is obtained, the depth information of each depth information segment is updated, and the surface roughness measurement result of the welded pipe is obtained according to the updated depth information of each depth information segment, so as to eliminate the influence of the welding slag on the surface roughness measurement result of the welded pipe, and finally improve the accuracy of measuring the roughness of the welded pipe.

Description

Technical Field

[0001] The invention relates to the technical field of length measurement, and in particular to an online measurement method for the surface roughness of a welded pipe. Background Art

[0002] With the continuous advancement of industrial manufacturing and automation technology, especially in the fields of steel, oil, natural gas, etc., welded pipes, as an important pipeline material, are widely used in pipeline systems for conveying fluids, gases and other materials. The quality of welded pipes is directly related to their reliability in transportation, pressure bearing, corrosion resistance, etc. Therefore, quality control in the production process of welded pipes is particularly important.

[0003] Surface roughness is the roughness of the inner wall of the welded pipe. As an important indicator to measure the quality of the inner wall surface of the welded pipe, it directly affects the mechanical properties, fluid fluidity, corrosion resistance and subsequent welding quality of the welded pipe. Excessive surface roughness of the welded pipe may lead to increased corrosion, increased friction, increased fluid resistance, and even affect the strength and life of the pipeline. Therefore, in the production process of welded pipes, it is very necessary to accurately control and monitor the surface roughness in real time. Laser measurement technology can not only provide higher measurement accuracy and speed, but also realize a comprehensive analysis of the surface morphology of the welded pipe, avoiding the surface damage problem caused by contact measurement.

[0004] In the prior art, laser scanners are usually used to obtain the depth information of the inner wall surface of welded pipes, and then the depth information is directly judged, and the judgment result is used as the basis for evaluating the surface roughness of welded pipes. However, due to some special circumstances, such as the possibility that some welding slag may adhere to the welded pipe during the preparation of the welded pipe, the depth information of the welded pipe surface collected by the laser scanner contains the depth information of the welding slag surface, and thus the roughness of the welded pipe surface cannot be accurately evaluated. Summary of the invention

[0005] In order to solve the technical problem of low accuracy of the existing detection method of the surface roughness of welded pipes, the purpose of the present invention is to provide an online measurement method for the surface roughness of welded pipes. The technical solution adopted is as follows:

[0006] In a first aspect of the present invention, there is provided a method for online measurement of surface roughness of a welded pipe, comprising:

[0007] Acquire depth information of different positions obtained by scanning along the extension direction of the inner wall of the welded pipe to obtain a depth information sequence, and divide the depth information sequence into a plurality of depth information segments according to a maximum value in the depth information sequence;

[0008] Obtaining an occlusion feature of the depth information segment based on a difference between a minimum value in the depth information segment and adjacent depth information and a depth information fluctuation of the depth information segment;

[0009] According to the correlation of the depth information in any two depth information segments, the welding slag characteristics of each depth information segment are obtained;

[0010] The depth information of the welding slag is obtained according to the occlusion characteristics and welding slag characteristics of each depth information segment;

[0011] According to the depth information of the welding slag, the depth information of each depth information segment is updated, and according to the updated depth information of each depth information segment, the surface roughness measurement result of the welded pipe is obtained.

[0012] The present invention has the following beneficial effects: since the presence of welding slag in the welded pipe will result in smaller depth information, and the depression in the welded pipe will result in larger depth information, therefore, according to the maximum value in the depth information sequence, the depth information sequence is divided into multiple depth information segments, then, there is a minimum value in each depth information segment, and the minimum value is used as a suspected welding slag point, and then based on the difference between the minimum value and its adjacent depth information, and the depth information fluctuation of the depth information segment, the occlusion feature of the depth information segment is obtained, and the occlusion feature can characterize the occlusion of the depression by the welding slag, and then according to the correlation of the depth information in any two depth information segments, the welding slag feature of each depth information segment is obtained, and the welding slag feature can characterize the welding slag related situation in the depth information segment, so as to obtain the depth information of the welding slag according to the occlusion feature and welding slag feature of each depth information segment, so as to update the depth information of each depth information segment, eliminate the influence of welding slag on the measurement result of the surface roughness of the welded pipe, and finally improve the accuracy of measuring the roughness of the welded pipe. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart of a method for online measurement of surface roughness of a welded pipe provided by one embodiment of the present invention;

[0014] Figure 2 is the flowchart of step 3;

[0015] Figure 3 is the flowchart of step 3-3;

[0016] Figure 4 is the flowchart for step 4;

[0017] Figure 5 This is the flowchart for step 5. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] This embodiment provides an online measurement method for the surface roughness of a welded pipe. To ensure the feasibility of the detection, the measurement object of the method is a straight welded pipe, and the specification of the welded pipe, that is, the radius of the welded pipe needs to satisfy the requirement that the laser scanner can be set inside the welded pipe and can move along the extension direction of the inner wall of the welded pipe so as to detect the depth information of the welded pipe. The surface in this embodiment refers to the inner surface, that is, the surface of the inner wall of the welded pipe.

[0021] like Figure 1 As shown, the method comprises the following steps:

[0022] Step 1: Obtain depth information of different positions scanned along the extension direction of the inner wall of the welded pipe to obtain a depth information sequence, and divide the depth information sequence into multiple depth information segments according to the maximum value in the depth information sequence.

[0023] The laser scanner is arranged in the weld pipe and moves along the extension direction of the inner wall of the weld pipe. In order to improve the detection accuracy, the laser scanner is arranged on the axis of the weld pipe and moves along the axis of the weld pipe.

[0024] The laser emitting end and receiving end of the laser scanner are arranged toward the inner wall of the weld pipe, and the laser scanner emits laser toward the inner wall of the weld pipe and receives laser reflected from the inner wall of the weld pipe. Moreover, since the laser scanner moves along the extension direction of the inner wall of the weld pipe, the laser scanning of the inner wall of the weld pipe is realized, and the depth information of each position of the inner wall of the weld pipe is obtained. In an exemplary embodiment, the depth information of the weld pipe is obtained after being processed by a relevant depth information processing system, and the processing process of the depth information is: according to the radius specification of the weld pipe, combined with the distance between the weld pipe and the laser scanner, the drift of the depth information caused by the weld pipe contour is eliminated.

[0025] The scanning rate of the laser scanner along the inner wall of the welded pipe is set according to actual needs and can be set by itself without any rigid requirements. In an exemplary embodiment, the scanning rate is set to 2 meters per second. In addition, the sampling frequency of the laser scanner can be set by itself without any rigid requirements. In an exemplary embodiment, the sampling frequency of the laser scanner is set to 1000 Hz.

[0026] A depth information sequence is obtained based on the depth information of different positions obtained by scanning along the extension direction of the inner side wall of the welded pipe.

[0027] When there is a depression or a protrusion on the inner wall of the welded pipe, the depth information will change. When scanning the depression, the depth information will be larger than the normal depth information, and when scanning the protrusion, the depth information will be smaller than the normal depth information.

[0028] Therefore, each maximum and minimum value in the depth information sequence is obtained, and the minimum and maximum values ​​correspond to the local convex position and concave position of the inner wall surface of the welded pipe. However, when the laser scanner collects the depth information at each position in the welded pipe, some convexities will block some concave due to problems such as the scanning angle. Therefore, each minimum value may block the subsequent depth information, so all minimum value points are first recorded as suspected blocking points.

[0029] In order to accurately analyze the minimum value, the depth information sequence is divided into multiple depth information segments according to the maximum value in the depth information sequence. Specifically, the depth information between any two maximum values ​​in the depth information sequence is used as a depth information segment, thereby obtaining multiple depth information segments. Then, each depth information segment contains a minimum value, that is, contains a suspected occlusion point.

[0030] Step 2: Based on the difference between the minimum value in the depth information segment and its adjacent depth information, as well as the depth information fluctuation of the depth information segment, the occlusion feature of the depth information segment is obtained.

[0031] Since some welding slag may adhere to the inner surface of the welded pipe during the processing, the depth information in the laser scanner will be lost due to the occlusion of the welding slag. In order to accurately evaluate the roughness of the welded pipe surface, it is necessary to obtain the occlusion features of the depth information segment.

[0032] For any depth information segment, the maximum point in the depth information segment corresponds to a depression in the weld pipe, and the minimum point corresponds to a protrusion in the weld pipe. Therefore, in the depth information segment, the monotonically increasing part corresponds to the part of the weld pipe from protrusion to depression, and the monotonically decreasing part corresponds to the part of the weld pipe from depression to protrusion. The depression may be blocked by the protrusion, that is, in the depth information segment, the transition part between the minimum value and the maximum value is blocked, which is manifested in the depth information segment as a change between the minimum value and the surrounding adjacent depth information, which is more drastic than the change of the remaining adjacent depth information. The suspected occlusion point in the depth information segment is more likely to block part of the depth information. Based on this, the occlusion feature of each depth information segment is obtained, that is, the occlusion feature of each suspected occlusion point.

[0033] At the same time, the greater the horizontal distance between the inner wall of the weld pipe and the laser scanner, the more the laser scanner will be affected by the angle of the weld pipe contour. Therefore, the greater the horizontal distance between a certain position on the inner wall of the weld pipe and the laser scanner, the more likely it is that obstruction will occur at that position.

[0034] Therefore, since the corresponding depression in the depth information segment is blocked, the minimum value in the depth information segment and the adjacent depth information will change dramatically. Therefore, by comparing the difference in the degree of change between the minimum value in the depth information segment and the adjacent depth information and other adjacent depth information in the depth information segment, and combining the minimum value, the possibility of the depression in the depth information segment being blocked is obtained, that is, the occlusion feature of the depth information segment. In an exemplary embodiment, for any depth information segment, a specific calculation formula for the occlusion feature of the depth information segment is given as follows:

[0035] ;

[0036] In the formula, Represents the occlusion characteristics of the depth information segment, Indicates the average value of the difference between the minimum value in a depth information segment and its two adjacent depth information. The calculation method of can be: respectively obtain the minimum value and its adjacent left depth information, and the right depth information, respectively calculate the absolute value of the difference between the minimum value and the left depth information, and the absolute value of the difference between the minimum value and the right depth information, and then calculate the average of the two absolute values ​​of the difference. The change between the minimum value and the depth information of the surrounding neighbors.

[0037] represents the i-th depth information in the depth information segment, Indicates the i-1th depth information in the depth information segment, and represents the depth information of the non-minimum value in the depth information segment, Indicates the amount of depth information in the depth information segment except for the minimum value. The depth information involved in is all the depth information except the minimum value in the depth information segment. It should be understood that the depth information adjacent to the minimum value on the left and right are not adjacent to each other due to the existence of the minimum value in the middle. However, after removing the minimum value, the depth information adjacent to the minimum value on the left and right become two adjacent depth information, and the two adjacent depth information are calculated. Indicates the average value of the absolute value of the difference between two adjacent depth information except the minimum value in the depth information segment.

[0038] Indicates the horizontal distance between the inner wall of the welded pipe and the laser scanner at the minimum value. Since the horizontal distance between the inner wall of the welded pipe and the laser scanner is proportional to the offset angle between the laser scanner and the contour of the welded pipe itself, It can also be characterized as the offset angle between the inner wall position of the welded pipe at the minimum value and the laser scanner. In an exemplary embodiment, It can also be replaced by the following calculation method: , which represents the inclination angle between the laser scanner and the corresponding position of the minimum value. Indicates the radius of the welded pipe; Represents the inverse sine function.

[0039] In this embodiment, norm is a normalization function, and the normalization method may be: , It represents the object that needs to be normalized, and exp represents the exponential function with the natural constant e as the base.

[0040] Through the above process, the occlusion features of each depth information segment are obtained. The occlusion features of each depth information segment can be equivalent to the occlusion features of each minimum value, that is, the occlusion features of each suspected occlusion point.

[0041] Step 3: According to the association between the depth information in any two depth information segments, the welding slag characteristics of each depth information segment are obtained.

[0042] Since the protrusion of welding slag is much greater than the subtle protrusions and depressions on the surface of the welded pipe, the welding slag part in the welded pipe can be roughly obtained through the depth information value of the depth information sequence as a whole. However, in the industrial processing of the welded pipe, the raw materials used to make the welded pipe may be subjected to uneven forces at various positions, resulting in larger protrusions in the welded pipe. If the welding slag in the welded pipe is simply distinguished based on the depth information, misjudgment may occur, and the roughness of the welded pipe surface cannot be accurately obtained.

[0043] Since the welding slag is formed by the cooling and solidification of the molten metal splashed on the surface of the welding pipe during welding, there is a large difference between the depth information of its surface and the depth information of the welding pipe surface, and there is also a large difference in the depth information of different welding slag surfaces. The depth information of the welding pipe surface is formed during the processing of raw materials into welding pipes, and its depth information is relatively similar, so it can be further distinguished based on this. Therefore, the welding slag characteristics of the depth information segment can be obtained based on the relevant differences between the depth information segment and other depth information segments.

[0044] In an exemplary embodiment, Figure 2 As shown in the figure, the process of obtaining welding slag features includes:

[0045] Step 3-1: The depth information segment and DTW matching is performed on the depth information segments to obtain several DTW matching pairs.

[0046] For the sake of convenience, the The depth information segment and The depth information segments are any two depth information segments, The depth information segment and The depth information segments are not the same depth information segment. The DTW (Dynamic Time Warping) matching algorithm is used to match the The depth information segment and DTW matching is performed on the depth information segments to obtain several DTW matching pairs. For any DTW matching pair, including the A depth information in the depth information segment and the Since the DTW matching algorithm for two sequences belongs to the existing algorithm, it will not be described in detail.

[0047] Step 3-2: Based on The depth information segment and The association between the two depth information in each DTW matching pair corresponding to the depth information segment is obtained. The depth information segment and The difference in depth information between the depth information segments.

[0048] Set The depth information segment and The vth DTW matching pair corresponding to the depth information segment is The depth information segment and Any DTW matching pair corresponding to the depth information segments.

[0049] In an exemplary embodiment, The depth information segment and The difference in depth information of the depth information segments is obtained using the following calculation formula:

[0050] ;

[0051] in, Indicates The depth information segment and The difference in depth information among the depth information segments, v represents the The depth information segment and The vth DTW matching pair corresponding to the depth information segment, Indicates The depth information segment and The number of DTW matching pairs corresponding to the depth information segments.

[0052] Indicates The depth information segment and The average value of the two depth information in the vth DTW matching pair corresponding to the depth information segment. Since the depth information is related to the inclination angle of the laser scanner at the corresponding position, It can also characterize the The depth information segment and The average value of the inclination angles corresponding to the two depth information in the vth DTW matching pair corresponding to the depth information segment.

[0053] Indicates The depth information segment and The absolute value of the difference between the two depth information in the vth DTW matching pair corresponding to the depth information segment indicates the difference between the two depth information.

[0054] Therefore, the The depth information segment and The larger the average value of the two depth information in the vth DTW matching pair corresponding to the depth information segment, that is, the larger the average value of the inclination angle of the two, the larger the depth information segment. The depth information segment and The greater the difference in depth information between the first and second depth information segments, the greater the difference in depth information between the first and second depth information segments. The depth information segment and The larger the absolute value of the difference between the two depth information in the vth DTW matching pair corresponding to the vth depth information segment, the larger the absolute value of the difference between the two depth information in the vth DTW matching pair corresponding to the vth depth information segment. The depth information segment and The greater the difference in depth information between the depth information segments.

[0055] Using the above calculation formula, we can get The depth information segment is different from the other depth information segments in depth information.

[0056] Step 3-3: According to The difference between the depth information segment and the other depth information segments in depth information is obtained. The weld slag characteristics of each depth information segment.

[0057] In getting the The depth information segment can be obtained by comparing the depth information of the first depth information segment with the depth information of the other depth information segments. The weld slag characteristics of each depth information segment.

[0058] In an exemplary embodiment, Figure 3 As shown, The process of obtaining the welding slag characteristics of each depth information segment includes:

[0059] Step 3-3-1: Get the The depth information segment and The spatial distance between the depth information segments in the welded pipe.

[0060] Because the The depth information segment and The depth information segments are distributed in the extension direction of the welded pipe, then, The closest depth information segment to The position of the endpoint of one end of the depth information segment, and obtain the The closest depth information segment to The distance between the two end points in the extension direction of the welded pipe is the distance between the two end points at one end of the depth information segment. The depth information segment and The spatial distance between the depth information segments in the welded pipe.

[0061] Step 3-3-2: According to the spatial distance, obtain the weight coefficient, which is inversely proportional to the spatial distance.

[0062] Since the stress conditions on the inner surface of the welded pipe in a local range are similar during the manufacturing period, that is, the depth information on the surface of the welded pipe in a local range is similar, then The depth information segment and The spatial distance between the first depth information segment in the welded pipe and the The depth information segment and The similarity between the depth information segments is inversely proportional to the spatial distance. The smaller the spatial distance, the greater the similarity.

[0063] Therefore, a negative correlation weight is assigned to the spatial distance to obtain a weight coefficient, which is inversely proportional to the spatial distance. In an exemplary embodiment, the spatial distance is first normalized to negative correlation, such as using Perform negative correlation normalization, b is the object of negative correlation normalization, and we get The result of normalizing the spatial distance between the first depth information segment and other depth information segments in a negative correlation; then, The total result is obtained by adding the negative correlation normalization results of the depth information segment and the spatial distance of each other depth information segment. The depth information segment and The ratio of the result of negative correlation normalization of the spatial distance of the depth information segment in the welded pipe to the total result is taken as the first The depth information segment and The weight coefficient corresponding to the depth information segment is obtained in this way. The depth information segment is a weight coefficient corresponding to each other depth information segment.

[0064] Step 3-3-3: According to The depth information segment and The product of the difference in depth information of the depth information segments and the weight coefficient is obtained The depth information segment and The weighted depth information difference of the depth information segments.

[0065] Since the depth information of the slag surface is formed by the cooling of the molten metal, its depth information is highly random. The depth information segment and The greater the difference in depth information between the depth information segments, the The deeper the information segment, the more likely it is to have welding slag characteristics. The depth information segment and The difference in depth information of the depth information segments is multiplied by the weight coefficient to obtain the The depth information segment and Preferably, the weighted depth information difference of the first depth information segment is The depth information segment and The product of the difference in depth information of the depth information segments multiplied by the weight coefficient is normalized, and the normalized result is used as the first The depth information segment and The weighted depth information difference of the depth information segments.

[0066] Using the above method, we get The weighted depth information difference between the depth information segment and other depth information segments.

[0067] Step 3-3-4: Calculate the The average of the weighted depth information differences between the depth information segment and the other depth information segments is taken as the The welding slag features of each depth information segment are obtained in this way. The welding slag features of each depth information segment are equivalent to the welding slag features of each minimum value, that is, the welding slag features of each suspected occlusion point.

[0068] Step 4: Obtain the depth information of the welding slag according to the occlusion features and welding slag features of each depth information segment.

[0069] After obtaining the occlusion features of each suspected occlusion point and the slag features of each suspected occlusion point through the above steps, the real occlusion point among the suspected occlusion points, namely the slag, can be obtained through the occlusion features and the slag features.

[0070] In an exemplary embodiment, Figure 4 As shown in FIG. 1 , the process of obtaining the depth information of the welding slag includes:

[0071] Step 4-1: Construct a two-dimensional coordinate system using the occlusion feature of the depth information segment as the horizontal coordinate and the welding slag feature of the depth information segment as the vertical coordinate.

[0072] Step 4-2: Based on the occlusion features and slag features of each depth information segment, construct the two-dimensional feature coordinate points corresponding to the minimum values ​​of each depth information segment.

[0073] Step 4-3: Map the two-dimensional feature coordinate points corresponding to each minimum value into a two-dimensional coordinate system and cluster them, obtain the discrete two-dimensional feature coordinate points outside the cluster, determine the position of the minimum value corresponding to the discrete two-dimensional feature coordinate points in the weld pipe as welding slag, and obtain the depth information of the welding slag.

[0074] For any depth information segment, the occlusion feature is used as the horizontal coordinate point of the two-dimensional feature coordinate point, and the slag feature is used as the vertical coordinate point of the two-dimensional feature coordinate point to construct the two-dimensional feature coordinate points of each depth information segment, that is, the two-dimensional feature coordinate points corresponding to the minimum values ​​of each depth information segment, that is, the two-dimensional feature coordinate points of each suspected occlusion point.

[0075] Map the two-dimensional feature coordinate points of each suspected occlusion point into a two-dimensional coordinate system to obtain a suspected occlusion point clustering space. Then cluster the two-dimensional feature coordinate points of each suspected occlusion point in the suspected occlusion point clustering space. It should be understood that the clustering algorithm is set according to actual needs, such as the K-means clustering algorithm, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, etc. If the K-means clustering algorithm is used, the K value needs to be set first.

[0076] In an exemplary embodiment, the DBSCAN clustering algorithm is used to cluster the two-dimensional feature coordinate points of each suspected occlusion point in the suspected occlusion point clustering space. Since the DBSCAN clustering algorithm is a density clustering algorithm, there is no need to set the number of clusters. Since the DBSCAN clustering algorithm is an existing common clustering algorithm, it will not be described in detail.

[0077] After clustering, clusters are obtained. Then, the two-dimensional feature coordinate points outside the clusters are obtained and defined as discrete two-dimensional feature coordinate points. The suspected occlusion points corresponding to the discrete two-dimensional feature coordinate points are determined as the real occlusion points, that is, welding slag. The position of the real occlusion point in the welded pipe is determined as the position of the welding slag. At the same time, the depth information of the welding slag is obtained.

[0078] Step 5: According to the depth information of the welding slag, the depth information of each depth information segment is updated, and according to the updated depth information of each depth information segment, the surface roughness measurement result of the welded pipe is obtained.

[0079] The process of acquiring the depth information of welding slag is essentially: finding a part of the minimum values ​​from each minimum value. Since each depth information segment contains multiple depth information, the depth information of welding slag in each depth information segment is deleted, and the remaining depth information constitutes the target depth information of each depth information segment. It should be understood that if there is no depth information segment of welding slag, the depth information of these depth information segments will not be processed, and the depth information of each depth information segment in these depth information segments will be directly used as the target depth information to update the depth information of each depth information segment.

[0080] Finally, the surface roughness measurement result of the welded pipe is obtained according to the updated depth information of each depth information segment. Figure 5 As shown, a specific process for obtaining the surface roughness measurement results of the welded pipe is given as follows:

[0081] Step 5-1: According to the occlusion feature of each depth information segment, the roughness adjustment weight of each depth information segment is obtained, and the roughness adjustment weight is inversely proportional to the occlusion feature.

[0082] Since the occlusion feature of the depth information segment represents the possibility of the depth information segment being occluded, and the greater the possibility of the depth information being occluded, the less accurate the corresponding depth information segment is in reflecting the true surface roughness of the welded pipe due to the occlusion of the information, that is, the less it can represent the true surface roughness of the welded pipe, so it is necessary to give it a negatively correlated weight according to the occlusion feature. Therefore, the roughness adjustment weight is inversely proportional to the occlusion feature. In an exemplary embodiment, the occlusion features of each depth information segment are first normalized by negative correlation; then, the results of the negatively correlated normalization of the occlusion features of all depth information segments are added to obtain the total result, and finally the ratio of the results of the negatively correlated normalization of the occlusion features of each depth information segment to the total result is used as the roughness adjustment weight of each depth information segment. In this way, the roughness adjustment weight of each depth information segment is obtained.

[0083] Step 5-2: According to the fluctuation of the target depth information of each depth information segment, the weight is adjusted in combination with the roughness of each depth information segment to obtain the surface roughness of the welded pipe.

[0084] For depth information segment, if the The depth information of each target in the depth information segment is consistent with the The greater the difference between the mean values ​​of the target depth information of the depth information segments, the greater the The more serious the fluctuation of the target depth information in each depth information segment, the higher the surface roughness of the welded pipe. The surface roughness of the welded pipe is obtained by combining the roughness adjustment weights of each depth information segment. In an exemplary embodiment, the calculation formula for the surface roughness of the welded pipe is given as follows:

[0085] ;

[0086] in, Indicates the surface roughness of the welded pipe, X indicates the number of depth information segments, and x indicates the depth information segment, Indicates The roughness adjustment weight of each depth information segment, Indicates The depth information segment Target depth information, Indicates The mean value of the target depth information in the depth information segment, Indicates The number of target depth information in a depth information segment.

[0087] The higher the surface roughness of the welded pipe, the rougher the inner surface of the welded pipe.

[0088] In the subsequent step, a roughness threshold may be set, which may be flexibly set by the implementer. If the surface roughness of the welded pipe obtained is higher than the roughness threshold, it means that the surface roughness of the welded pipe is too high, and the welded pipe may be judged as unqualified.

[0089] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for online measurement of surface roughness of welded pipes, characterized in that: include: Acquire depth information of different positions obtained by scanning along the extension direction of the inner wall of the welded pipe to obtain a depth information sequence, and divide the depth information sequence into a plurality of depth information segments according to a maximum value in the depth information sequence; Obtaining an occlusion feature of the depth information segment based on a difference between a minimum value in the depth information segment and adjacent depth information and a depth information fluctuation of the depth information segment; According to the correlation of the depth information in any two depth information segments, the welding slag characteristics of each depth information segment are obtained; The depth information of the welding slag is obtained according to the occlusion characteristics and welding slag characteristics of each depth information segment; According to the depth information of the welding slag, the depth information of each depth information segment is updated, and according to the updated depth information of each depth information segment, the surface roughness measurement result of the welded pipe is obtained; Based on the difference between the minimum value in the depth information segment and the adjacent depth information, and the depth information fluctuation of the depth information segment, the occlusion feature of the depth information segment is obtained, including: ； In the formula, Represents the occlusion characteristics of the depth information segment, represents the average value of the difference between the minimum value in the depth information segment and its two adjacent depth information, wherein the difference is: the absolute value of the difference between the minimum value and the depth information on the left, and the absolute value of the difference between the minimum value and the depth information on the right, represents the i-th depth information in the depth information segment, Indicates the i-1th depth information in the depth information segment, and represents the depth information of the depth information segment which is not the minimum value. Indicates the amount of depth information in the depth information segment except for the minimum value. represents the average value of the absolute value of the difference between two adjacent depth information except the minimum value in the depth information segment, represents the horizontal distance between the inner wall position of the welded pipe at the minimum value and the laser scanner, and norm is a normalized function; According to the correlation of the depth information in any two depth information segments, the welding slag characteristics of each depth information segment are obtained, including: For The depth information segment and depth information segments to perform DTW matching to obtain a number of DTW matching pairs; the first The depth information segment and depth information segments are any two depth information segments; a DTW matching pair includes the A depth information in the depth information segment and the A depth information in a depth information segment; Based on The depth information segment and The association between the two depth information in each DTW matching pair corresponding to the depth information segment is obtained. The depth information segment and The difference in depth information between the depth information segments; According to The difference between the depth information segment and the other depth information segments in depth information is obtained. Weld slag characteristics of each depth information segment; No. The process of obtaining the welding slag characteristics of each depth information segment includes: Get the The depth information segment and The spatial distance between the depth information segments in the welded pipe; According to the spatial distance, a weight coefficient is obtained, wherein the weight coefficient is inversely proportional to the spatial distance; According to The depth information segment and The product of the difference in depth information of the depth information segments and the weight coefficient is obtained. The depth information segment and The weighted depth information difference of the depth information segments; Calculate the The average of the weighted depth information differences between the depth information segment and the other depth information segments is taken as the The weld slag characteristics of each depth information segment.

2. The method for online measurement of surface roughness of welded pipe according to claim 1, characterized in that: According to the maximum value in the depth information sequence, the depth information sequence is divided into multiple depth information segments, including: The depth information between any two maximum values ​​in the depth information sequence is taken as a depth information segment, thereby obtaining multiple depth information segments.

3. The method for online measurement of surface roughness of welded pipe according to claim 1, characterized in that: No. The depth information segment and The difference in depth information of the depth information segments is obtained using the following calculation formula: ； in, Indicates The depth information segment and The difference in depth information among the depth information segments, v represents the The depth information segment and The vth DTW matching pair corresponding to the depth information segment, Indicates The depth information segment and The number of DTW matching pairs corresponding to the depth information segments, Indicates The depth information segment and The average value of the two depth information in the vth DTW matching pair corresponding to the depth information segment, Indicates The depth information segment and The absolute value of the difference between the two depth information in the vth DTW matching pair corresponding to the depth information segment.

4. The method for online measurement of surface roughness of welded pipe according to claim 1, characterized in that: According to the occlusion characteristics of each depth information segment and the welding slag characteristics, the depth information of the welding slag is obtained, including: A two-dimensional coordinate system is constructed by taking the occlusion feature of the depth information segment as the horizontal coordinate and the welding slag feature of the depth information segment as the vertical coordinate; According to the occlusion features and welding slag features of each depth information segment, construct the two-dimensional feature coordinate points corresponding to the minimum value of each depth information segment; The two-dimensional feature coordinate points corresponding to each minimum value are mapped into the two-dimensional coordinate system and clustered, and discrete two-dimensional feature coordinate points outside the cluster are obtained. The position of the minimum value corresponding to the discrete two-dimensional feature coordinate points in the weld pipe is determined as welding slag, and the depth information of the welding slag is obtained.

5. The method for online measurement of surface roughness of welded pipe according to claim 1, characterized in that: According to the depth information of the welding slag, the depth information of each depth information segment is updated, including: The depth information of the welding slag in each depth information segment is deleted, and the remaining depth information constitutes the target depth information of each depth information segment.

6. The method for online measurement of surface roughness of welded pipe according to claim 5, characterized in that: According to the updated depth information of each depth information segment, the surface roughness measurement results of the welded pipe are obtained, including: According to the occlusion feature of each depth information segment, a roughness adjustment weight of each depth information segment is obtained, wherein the roughness adjustment weight is inversely proportional to the occlusion feature; According to the fluctuation of the target depth information of each depth information segment and the roughness adjustment weight of each depth information segment, the surface roughness degree of the welded pipe is obtained.

7. The method for online measurement of surface roughness of welded pipe according to claim 6, characterized in that: According to the fluctuation of the target depth information of each depth information segment, combined with the roughness adjustment weight of each depth information segment, the surface roughness of the welded pipe is obtained, including: ； in, Indicates the surface roughness of the welded pipe, X indicates the number of depth information segments, and x indicates the depth information segment, Indicates The roughness adjustment weight of each depth information segment, Indicates The depth information segment Target depth information, Indicates The mean value of the target depth information in the depth information segment, Indicates The number of target depth information in a depth information segment.

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