A wear-resistant dredging pipeline welding method and welding process

Abstract

This application relates to the field of welding technology, and particularly to a welding method and welding process for wear-resistant dredging pipelines. The method includes: during the welding process of the dredging pipeline, obtaining welding parameter data; obtaining an instantaneous welding difference coefficient according to the distribution difference characteristics of the welding parameter data at different times; obtaining an asynchronous difference coefficient; dividing the entire welding process to obtain several asynchronous comparison intervals; obtaining the comprehensive comparison scores of each type of welding parameter data in each asynchronous comparison interval; obtaining the asynchronous feedback significant value of each type of welding parameter data; optimizing and adjusting the welding control based on the asynchronous feedback characteristics of each welding parameter during the welding process to obtain a dredging pipeline with preliminary welding completed; docking the dredging pipeline with a flange and welding a reinforcing rib plate on the back of the flange to obtain a dredging pipeline with final welding completed. This application aims to improve the control accuracy of the pipeline welding process and achieve high-quality welding with high pipeline fitting degree.

Description

Technical Field

[0001] The present application relates to the field of welding technology, and particularly relates to a welding method and welding process for wear-resistant dredging pipelines. Background Art

[0002] During dredging operations, the pipeline faces a harsh environment with pressure inside the pipe. A large amount of sand and gravel, especially irregularly impacting stones, cause great wear to the pipeline. During the welding process of wear-resistant dredging pipelines, flash butt welding and butt welding methods are usually used for welding, and process parameters such as welding current, voltage, and welding speed are strictly controlled to ensure welding quality. However, in the actual welding process, the two ends of the pipeline usually present an oval shape, and such a shape results in gaps at different positions. From the perspective of the welding process, the existence of such gaps will affect the welding quality and the overall performance of the pipeline; when there are gaps at both ends of the pipeline, direct welding may lead to uneven welds and affect the welding quality.

[0003] When using a traditional automatic welding control system to weld the pipeline, the welding parameters should be kept consistent. If asynchronous disorder characteristics appear among the welding parameters during the welding process, defects such as incomplete fusion and the presence of air bubbles will occur, making the shape and size of the molten pool unstable during welding, affecting the uniformity of the weld, and thus reducing the welding quality of the wear-resistant dredging pipeline. Summary of the Invention

[0004] In view of the above, it is necessary to provide a welding method and welding process for wear-resistant dredging pipelines to solve the above problems.

[0005] The first aspect of the present application provides a welding method for wear-resistant dredging pipelines, and the method includes:

[0006] During the preliminary welding of the dredging pipeline, optimize and adjust the welding control.

[0007] Among them, the specific process of optimizing and adjusting the welding control is as follows:

[0008] A1: During the welding process of the dredging pipeline, obtain various welding parameter data at each moment.

[0009] A2: According to the distribution difference characteristics of all kinds of welding parameter data at each moment and other moments, obtain the instantaneous welding difference coefficient at each moment; according to the similarity degree between all kinds of welding parameter data at all moments during the welding process and the instantaneous welding difference coefficient, and combining the distribution of extreme points in all kinds of welding parameter data, obtain the asynchronous difference coefficient.

[0010] A3: Based on the asynchronous difference coefficient, divide the entire welding process to obtain several asynchronous comparison intervals; use the objective weighting method to obtain the comprehensive comparison scores of each type of welding parameter data in each asynchronous comparison interval; by analyzing the comprehensive comparison scores and the similarity characteristics of data distribution of each type of welding parameter data in all pairwise-combined asynchronous comparison intervals, obtain the asynchronous feedback significant value of each type of welding parameter data;

[0011] A4: Optimize and adjust the welding control based on the asynchronous feedback characteristics of each type of welding parameter data to obtain a dredging pipeline with preliminary welding completed;

[0012] Use a hydraulic device to dock the dredging pipeline with the flange, and weld a reinforcing rib plate on the back of the flange to obtain a dredging pipeline with final welding completed.

[0013] Among them, during the welding process of the dredging pipeline, the welding current is 100A, the welding speed is 58 cm / min, the argon gas flow rate during the welding process is 10 L / min, and the angle between the welding torch and the pipeline surface is kept between 70 and 80 degrees.

[0014] Among them, the process of obtaining the instantaneous welding difference coefficient at each moment is as follows:

[0015] Take the sequence of various welding parameter data arranged in chronological order as each row vector in the matching feature matrix;

[0016] For each column vector in the matching feature matrix, calculate the Manhattan distance between each column vector and every other column vector, and take the mean of all the Manhattan distances as the instantaneous welding difference coefficient corresponding to each column vector at each moment.

[0017] Among them, the steps of obtaining the asynchronous difference coefficient are as follows:

[0018] Take the normalized result of the Pearson correlation coefficient between all the welding parameter data at all moments during the welding process and the instantaneous welding difference data as the matching difference consistency of each type of welding parameter data;

[0019] Analyze the extreme value change characteristics of each type of welding parameter data during the welding process to obtain the variation mean of each type of welding parameter data;

[0020] Calculate the absolute value of the product of the matching difference consistency and the variation mean of each type of welding parameter data, and take the sum of the absolute values of all such products of each type of welding parameter data as the asynchronous difference coefficient.

[0021] Among them, the specific process of obtaining the variation mean of each type of welding parameter data is as follows:

[0022] For each welding parameter data, obtain the extreme values in the welding parameter data, and form a sequence of all the extreme values in chronological order as the interference interval data sequence of each welding parameter data. Calculate the mean of all elements in the first-order difference sequence of the interference interval data sequence, and denote it as the variation mean of each welding parameter data.

[0023] Among them, the number of moments included in the asynchronous comparison interval is equal to the ceiling value of the asynchronous difference coefficient.

[0024] Among them, the formula for obtaining the asynchronous feedback significant value of each welding parameter data is specifically: Among them, p x represents the asynchronous feedback significant value of the x-th welding parameter data; p c,d represents the mean of the comprehensive comparison scores of the x-th welding parameter data in the c-th and d-th asynchronous comparison intervals; k x,c and k x,d respectively represent the x-th welding parameter data in the c-th and d-th asynchronous comparison intervals; hd(k x,c , k x,d ) represents the Hausdorff distance between k x,c and k x,d ; m represents the number of asynchronous comparison intervals.

[0025] Among them, the process of optimizing and adjusting the welding control is as follows:

[0026] Take the normalization result of the asynchronous feedback significant value of various welding parameter data as the adjustment variable p, and combine it with the initial proportional parameter P o of the welding parameter data control to obtain the adjusted proportional parameter P of various welding parameter data. The formula form is: P = P o + P o × p;

[0027] According to the adjusted proportional parameters of various parameter data, combined with PID control, optimize and adjust the welding control.

[0028] Among them, the diameter of the flange is smaller than the diameter of the concentric circle of the dredging pipeline, and the gap is within 1 millimeter.

[0029] In a second aspect, an embodiment of the present application further provides a wear-resistant dredging pipeline welding process, and the welding process implements the steps of any one of the above-mentioned wear-resistant dredging pipeline welding methods.

[0030] The present application has at least the following beneficial effects:

[0031] In this application, considering that during the welding process of pipelines, the welding parameters should be kept consistent. If the welding parameters show asynchronous disorders, it will lead to defects such as incomplete fusion and bubbles during welding, making the shape and size of the molten pool unstable. Therefore, during the welding process of dredging pipelines, various welding parameter data at each moment are obtained; according to the distribution difference characteristics of all types of welding parameter data at each moment and other moments, the instantaneous welding difference coefficient at each moment is obtained, which helps to describe the differences in the change characteristics of welding parameter data at different moments; according to the similarity degree between various welding parameter data at all moments during the welding process and the instantaneous welding difference coefficient, combined with the distribution of extreme points in various welding parameter data, the asynchronous difference coefficient is obtained, providing a basis for accurately dividing the time intervals with asynchronous comparison characteristics during the welding process in the subsequent stage; by analyzing the comprehensive comparison scores and the similarity characteristics of data distribution of each type of welding parameter data in all pairwise asynchronous comparison intervals, the asynchronous feedback significant value of each type of welding parameter data is obtained, which can compare the asynchronous response deviation characteristics of each type of welding parameter data and adjust the automatic welding control system. Its beneficial effect lies in fully combining the differences in parameter matching characteristics during the welding process, accurately analyzing the asynchronous response deviation of welding process parameters, improving the control accuracy of the pipeline welding process, and achieving high-quality welding with high pipeline fitting degree. Brief Description of the Drawings

[0032] Figure 1 It is a step flowchart of a wear-resistant dredging pipeline welding method provided by an embodiment of this application;

[0033] Figure 2 It is a schematic diagram for obtaining the asynchronous feedback significant value provided by an embodiment of this application;

[0034] Figure 3 It is a flowchart for optimizing and adjusting the welding control provided by an embodiment of this application. Detailed Embodiments

[0035] In the description of the embodiments of this application, words such as "exemplary", "or", "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "exemplary", "or", "for example" is intended to present relevant concepts in a specific manner.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art belonging to the technical field of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0037] In addition, it should be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. For the methods disclosed in the embodiments of this application or the methods shown in the flowcharts, which include one or more steps for implementing the methods, without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0039] The following specifically describes the specific solutions of a wear-resistant dredging pipeline welding method and welding process provided by this application in conjunction with the accompanying drawings.

[0040] Please refer to Figure 1 , which shows a step flowchart of a wear-resistant dredging pipeline welding method provided by an embodiment of this application. The method includes:

[0041] The pipeline made of Q355B steel plate has toughness but poor wear resistance, while the pipeline made of high-strength wear-resistant steel plate has three times the wear resistance of Q355B steel plate, but poor toughness and is prone to cracking. Therefore, the material of the dredging pipeline in this application at least includes Q355B steel plate and high-strength wear-resistant steel plate, which can have both the effects of toughness and high-strength wear resistance, while reducing procurement costs and improving work efficiency.

[0042] The first step: During the initial welding of the dredging pipeline, optimize and adjust the welding control.

[0043] Clean the oil stains, rust, oxides and other impurities in the area to be welded of the dredging pipeline and at least 20 - 30 mm on both sides thereof, that is, use stainless steel wire brushes and sandpaper tools to polish until the metal luster is exposed. Among them, the reason for cleaning the impurities is that the impurities will reduce the bonding strength and wear resistance of the weld.

[0044] Weld the pipeline using submerged arc welding equipment according to the set welding parameters. Among them, the welding current is 100 A, the welding speed is 58 cm / min, the argon flow rate during the welding process is 10 L / min, the angle between the welding torch and the pipeline surface is maintained between 70 - 80 degrees, and the welding process is controlled by an automatic welding control system during the welding process. The specific welding control process is as follows:

[0045] A1: During the welding of the dredging pipeline, obtain various welding parameter data at each moment.

[0046] Under normal circumstances, during the welding process, the automatic welding control system can maintain the stability of different welding parameters to ensure the welding quality. However, due to the complexity of the environment during the actual welding process, the influence degrees of interference factors at different stages on the consistency deviation of welding parameter data vary. Therefore, during the welding process, sensors are used to collect the welding parameter data. Specifically, a current sensor, a displacement sensor, a flow sensor, and an angle sensor are respectively used to collect the current data, welding speed data, argon gas flow data, and the included angle data between the welding torch and the pipe surface during the welding process. Among them, the time interval for data collection is 0.1 second, and the implementer can adjust it according to the actual situation. Each type of collected data is used as input, and a median filter is used to perform noise reduction processing on each type of collected data. It should be noted that using a median filter for noise reduction processing is a well-known technology to those skilled in the art and will not be elaborated further.

[0047] A2: According to the distribution difference characteristics of all types of welding parameter data at each moment and other moments, obtain the instantaneous welding difference coefficient at each moment; according to the similarity degree between all types of welding parameter data at all moments during the welding process and the instantaneous welding difference coefficient, and combining the distribution of extreme value points in all types of welding parameter data, obtain the asynchronous difference coefficient.

[0048] In the welding operation of wear-resistant dredging pipelines, flanges are usually used to assemble and fix the pipelines to achieve uniform distribution of pipeline gaps and reduce the gaps at both ends of the pipelines. During the welding process, it is necessary to control the welding direction and the stability of various welding parameters. Once the change of welding parameters shows the characteristics of asynchronous disorder, it is very likely to cause the shape and size of the molten pool during welding to lose stability, thereby triggering defects such as incomplete fusion and bubble generation during welding, seriously reducing the welding quality.

[0049] Traditional welding control methods often only make a single adjustment for the deviation between the actual detected value and the preset value of each welding parameter, but ignore the matching relationship between the changes of different influencing factors on welding parameters during the actual welding process. For example, when the welding current shows the characteristics of asynchronous disorder, it will increase the heat output of the arc, thereby causing the penetration depth and width of the weld to increase accordingly. At this time, if the welding speed remains unchanged, too much heat will cause the weld metal to overheat, which may cause coarse grains and deteriorated microstructure, having an adverse impact on the mechanical properties of the weld.

[0050] Based on the above analysis, during the welding process of wear-resistant dredging pipelines, the control of each type of welding parameter data should fully consider the matching influence relationship between different welding parameters, and combined with the consistency change difference of the actual welding process, deeply analyze the asynchronous disorder characteristics of different welding parameters during the welding progress.

[0051] During the welding process, if the stability of the welding quality is to be maintained, the matching relationship of the numerical values between the welding parameters at different times should be consistent; therefore, in order to further compare the differences in the matching states of the welding parameters at different times, for the collected various welding parameter data, the sequence formed by the various welding parameter data in chronological order is used as each row in the matching feature matrix, that is, the matching feature matrix is a matrix composed of all types of welding parameter data, and the respective row vectors of the matching feature matrix are arranged according to the current data, welding speed data, argon gas flow data, and the angle data between the welding torch and the pipe surface; it should be noted that the implementer can adjust the arrangement method of the row vectors of the matching feature matrix according to the actual situation, and this application does not limit it.

[0052] During the welding process, if some of the parameters change due to environmental interference factors, the matching difference will not change significantly within a short time range; therefore, for each column element in the matching feature matrix, calculate the Manhattan distance between each column element and every other column element, and take the mean of all the Manhattan distances as the instantaneous welding difference coefficient of each column element, and reflect the matching state difference of the welding parameters at different times compared to the overall welding process through the instantaneous welding difference coefficient.

[0053] Furthermore, the sequence formed by the instantaneous welding difference coefficients corresponding to all times in chronological order is used as the welding difference coefficient sequence; if the change of one type of welding parameter data due to interference is more consistent with the changes of the matching features of other welding parameters, then the asynchronous disorder feature of this type of welding parameter data is more significant; therefore, for each type of welding parameter data during the welding process, calculate the Pearson correlation coefficient between the welding parameter data and the welding difference data sequence, and the larger the Pearson correlation coefficient, the stronger the consistency between the data change characteristics of the welding parameter data in time series and the change characteristics of the matching state among multiple parameters; take the Pearson correlation coefficients corresponding to all types of welding parameter data as the input, and use the Softmax function to obtain the normalized result of the Pearson correlation coefficient corresponding to each type of welding parameter data, and obtain the matching difference consistency of various welding parameter data, and reflect the relative characteristics of the consistency of each type of welding parameter data compared to the matching relationship difference among all welding parameters through the normalized result.

[0054] Further, the analysis interval of the asynchronous characteristics during the welding process is divided by the relative characteristics of the consistency of the welding parameter data compared with the matching relationship difference; specifically, for each type of welding parameter data, the extreme values in the welding parameter data at all times are obtained, and the sequence composed of all the extreme values in chronological order is used as the interference interval data sequence of each type of welding parameter data, and the mean value of all elements in the first-order difference sequence of the interference interval data sequence is calculated, which is denoted as the variation mean value of each type of welding parameter data; the variation mean value reflects the fluctuation range of the response change of the welding parameter data in different stages.

[0055] Based on the analysis results of the stage response change range of each type of welding parameter data and the consistency difference compared with the welding parameter matching characteristic difference, an asynchronous difference coefficient is obtained. The specific calculation formula is: where h represents the asynchronous difference coefficient in the dredging pipeline welding control process; a x represents the matching difference consistency of the x-th type of welding parameter data, and b x represents the variation mean value of the x-th type of welding parameter data; n represents the number of types of welding parameter data; the larger the asynchronous difference coefficient, the more significant the influence of different welding parameters being interfered with over time during the welding process; that is to say, the asynchronous characteristics of the welding parameters show obvious differences in a relatively long time range.

[0056] A3: Based on the asynchronous difference coefficient, the entire welding process is divided to obtain several asynchronous comparison intervals; the entropy weight method is used to obtain the comprehensive comparison scores of each type of welding parameter data in each asynchronous comparison interval; by analyzing the comprehensive comparison scores of each type of welding parameter data in all pairwise asynchronous comparison intervals and the similarity characteristics of the data distribution, the asynchronous feedback significant value of each type of welding parameter data is obtained.

[0057] Based on the asynchronous difference coefficient, the time interval of the welding parameter data obtained during the current welding process is evenly divided. Among them, the number of moments corresponding to each divided time interval is equal to the ceiling value of the asynchronous difference coefficient. Each divided time interval is used as an asynchronous comparison interval. It should be noted that the number of moments corresponding to the last time interval can be less than the ceiling value of the asynchronous difference coefficient; the purpose of dividing the interval based on the asynchronous difference coefficient is to consider that due to different interference factors during the welding process, there are differences in the asynchronous response interval ranges between welding parameters. If the comparison is carried out in an interval range that is too small or too large, the matching characteristic relationship of the responses between welding parameters cannot be accurately analyzed.

[0058] Therefore, according to the divided asynchronous comparison intervals, each type of welding parameter data is divided. Specifically, for each type of welding parameter data, the elements of the welding parameter data on each asynchronous comparison interval are arranged in chronological order to form a set as the asynchronous comparison sequence of the welding parameter data. The asynchronous comparison sequences of all types of parameter data under the same asynchronous comparison interval are used as inputs, and the topsis algorithm based on the entropy weight method is used for multi-dimensional data comparison. Specifically: the standard deviation of each asynchronous comparison sequence is used as an extremely small index, and the entropy weight method is used to obtain the weight of each type of welding parameter data, and the comprehensive comparison score of each type of welding parameter data on the asynchronous comparison interval is output. The significant characteristics of the asynchronous response of each type of parameter data of the pipeline compared with other parameters are reflected through the comprehensive comparison score; the asynchronous feedback significant value of each type of welding parameter data is calculated based on the comprehensive comparison score. The specific calculation formula is: where p x represents the asynchronous feedback significant value of the x-th type of welding parameter data; p c,d represents the mean value of the comprehensive comparison scores of the x-th type of welding parameter data on the c-th and d-th asynchronous comparison intervals; k x,c and k x,d respectively represent the asynchronous comparison sequences of the x-th type of welding parameter data on the c-th and d-th asynchronous comparison intervals, that is, the x-th type of welding parameter data on the c-th and d-th asynchronous comparison intervals; hd(k x,c , k x,d ) represents the Hausdorff distance between k x,c and k x,d ; m represents the number of asynchronous comparison intervals; the larger the calculated asynchronous feedback significant value, the more significant the difference in the matching characteristics of the welding parameter data compared with other parameters caused by interference factors during the welding process, that is, the greater the degree of possible asynchronous deviation.

[0059] Among them, the schematic diagram for obtaining the asynchronous feedback significant value is as shown in Figure 2 .

[0060] A4: Optimize and adjust the welding control based on the asynchronous feedback characteristics of each type of welding parameter data to obtain a dredging pipeline with preliminary welding completed.

[0061] If the asynchronous feedback characteristics are more significant, it means that the possibility of the welding parameters showing continuous asynchronous characteristics due to interference during the welding process is greater, that is, asynchronous deviation may occur during the welding control process, resulting in deviation of the welding control; therefore, optimize and adjust the welding control based on the asynchronous feedback characteristics of each type of welding parameter during the welding process.

[0062] Specifically, the control of the dredging pipeline welding process by the automatic welding control system is mainly achieved through a Programmable Logic Controller (PLC). Among them, the feedback control for each welding parameter is implemented using the PID control algorithm. Taking the asynchronous feedback significant value calculated from all welding parameter data collected up to the current moment as the input, the Z-score normalization algorithm is used to obtain the normalization result of all asynchronous feedback significant values, denoted as the adjustment variable, and the control parameters of the PID response are adjusted. Specifically, the adjustment method for the control parameters of the PID response for one of the welding parameters is as follows: P x = P o + P o × p x , where P x represents the proportional parameter adjusted for the x-th type of welding parameter data, P o is the initial proportional parameter controlled by the x-th type of welding parameter data, and the initial proportional parameter is determined by the decay curve method; p x represents the adjustment variable of the x-th type of welding parameter data. Therefore, through the above method, based on the input adjustment variable, the control parameters in the automatic welding control system are adjusted to obtain the dredging pipeline with the initial welding completed.

[0063] Among them, the flow chart for optimizing and adjusting the welding control is as shown in Figure 3 .

[0064] The second step: Use the hydraulic device to dock the dredging pipeline with the flange, and weld the reinforcing rib plate on the back of the flange to obtain the finally welded dredging pipeline.

[0065] Align the head of the prepared low-pressure hydraulic device with one end of the dredging pipeline, slowly start the hydraulic device, make the pressure act evenly on the pipeline, dock the end of the pipeline onto the flange plate, and gradually press the pipeline end into the flange through a certain pressure. During the pressing process, ensure that the pipeline axis is consistent with the flange axis to prevent deviation or inclination. The pressure should be controlled at 13 MPa to avoid deformation and damage of the pipeline or flange due to excessive pressure, and the pressure can be adjusted according to factors such as pipeline material, specification, and flange model. When the pipeline is completely pressed into the flange and reaches the predetermined position, maintain the pressure for 10 minutes to make the pipeline and the flange fit fully, and then slowly release the pressure of the hydraulic device to complete the installation of the pipeline flange, making the welding gap more uniform, and controlling the pipeline concentricity within 10 mm to improve the stability and uniformity of the pipeline system.

[0066] It should be noted that the diameter of the flange is smaller than the diameter of the pipe concentric circle, and the difference is within 1 mm. In this embodiment, the value is 1 mm. The smaller diameter of the flange than the pipe concentric circle helps to ensure that the pipe end can be more tightly connected to the flange. The back of the flange is welded by submerged arc welding to ensure its connection strength and sealing performance. Before submerged arc welding, the welding surfaces of the flange and the pipe are thoroughly cleaned and pretreated to remove impurities such as oil stains and scale to ensure the welding quality. Reinforcing rib plates are evenly welded on the back of the flange. The thickness, height and number of the rib plates are determined according to factors such as the diameter of the pipe, the pressure rating and the operating conditions. The thickness of the rib plates is 5 - 10 mm, and the height is 0.6 - 0.8 times the thickness of the flange. The welding of the rib plates and the flange uses continuous fillet welds, and the weld height is not less than 0.7 times the thickness of the rib plates.

[0067] Thus, the welding of the dredging pipe is completed, and the finally welded dredging pipe is obtained.

[0068] Based on the same inventive concept as the above method, an embodiment of the present application also provides a welding process for a wear-resistant dredging pipe, and the welding process implements the steps of any one of the above-mentioned welding methods for a wear-resistant dredging pipe.

[0069] In summary, considering that during the welding of the pipe in the welding process, the welding parameters should be kept consistent. If the welding parameters show asynchronous disorder, it will lead to defects such as lack of fusion and generation of bubbles during welding, making the shape and size of the molten pool unstable. Therefore, during the welding process of the dredging pipe, various welding parameter data at each moment are obtained; according to the distribution difference characteristics of all kinds of welding parameter data at each moment and other moments, the instantaneous welding difference coefficient at each moment is obtained, which helps to describe the difference in the change characteristics of welding parameter data at different moments; according to the similarity degree between all kinds of welding parameter data at all moments during the welding process and the instantaneous welding difference coefficient, combined with the distribution of extreme points in all kinds of welding parameter data, the asynchronous difference coefficient is obtained, which provides a basis for accurately dividing the time interval with asynchronous comparison characteristics during the welding process; by analyzing the comprehensive comparison scores and the similarity characteristics of data distribution of each kind of welding parameter data in all pairwise asynchronous comparison intervals, the asynchronous feedback significant value of each kind of welding parameter data is obtained, and the asynchronous response deviation characteristics of each kind of welding parameter data can be compared to adjust the automatic welding control system. The beneficial effect is to fully combine the difference in parameter matching characteristics during the welding process, accurately analyze the asynchronous response deviation of the welding process parameters, improve the control accuracy of the pipe welding process, and achieve high-quality welding with high pipe fitting degree.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0071] For those skilled in the art, it is obvious that the present application is not limited to the details of the above-described exemplary embodiments, and without departing from the basic features of the present application, the present application can be implemented in other specific forms. Therefore, from any perspective, the above-described embodiments of the present application should be regarded as exemplary and non-restrictive; modifying the technical solutions described in the foregoing embodiments, or equivalently replacing some of the technical features, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A wear-resistant dredging pipeline welding method, characterized in that: The method comprises the following steps: During the dredging pipeline welding process, the welding current was 100A, the welding speed was 58cm / min, the argon gas flow rate during the welding process was 10L / min, the angle between the welding gun and the pipeline surface was maintained between 70 and 80 degrees, and the welding control was optimized and adjusted; The specific process of optimizing and adjusting the welding control is as follows: A1: During the dredging pipeline welding process, obtain various welding parameter data at each moment; A2: The sequence of various welding parameter data in chronological order is used as each row vector in the matching feature matrix; the instantaneous welding difference coefficient at each moment is obtained according to the similarity distribution between each column vector and the remaining column vectors in the matching feature matrix; the extreme value change characteristics of each welding parameter data during the welding process are analyzed to obtain the variation mean of each welding parameter data; the asynchronous difference coefficient is obtained according to the similarity between various welding parameter data at all moments in the welding process and the instantaneous welding difference coefficient, combined with the variation mean of each welding parameter data; A3: Based on the asynchronous difference coefficient, the entire welding process is divided to obtain several asynchronous comparison intervals; an objective weighting method is used to obtain the comprehensive comparison score of each welding parameter data in each asynchronous comparison interval; by analyzing the comprehensive comparison score of each welding parameter data in all pairwise combinations of asynchronous comparison intervals and the similarity characteristics of data distribution, the asynchronous feedback significance value of each welding parameter data is obtained; A4: Optimize and adjust the welding control based on the asynchronous feedback characteristics of each welding parameter data to obtain the dredged pipeline with preliminary welding completed; The dredging pipe is butt-jointed with the flange using a hydraulic device, and a reinforcing rib plate is welded on the back of the flange to obtain the final welded dredging pipe.

2. A wear-resistant dredging pipeline welding method as claimed in claim 1, characterized in that: The process of obtaining the instantaneous welding difference coefficient at each moment is as follows: For each column vector in the matching feature matrix, the Manhattan distance between each column vector and each other column vector is calculated, and the mean of all the Manhattan distances is used as the instantaneous welding difference coefficient of each column vector at the corresponding moment.

3. A wear-resistant dredging pipeline welding method as claimed in claim 1, characterized in that: The variation mean of each welding parameter data is obtained as follows: For each welding parameter data, the extreme values ​​in the welding parameter data are obtained, and the sequence composed of all the extreme values ​​in chronological order is used as the interference interval data sequence of each welding parameter data. The mean of all elements in the first-order difference sequence of the interference interval data sequence is calculated and recorded as the variation mean of each welding parameter data.

4. A wear-resistant dredging pipeline welding method as claimed in claim 1, characterized in that: The steps of obtaining the asynchronous difference coefficient are: The normalized result of the Pearson correlation coefficient between various welding parameter data at all moments in the welding process and the instantaneous welding difference data is used as the matching difference consistency of various welding parameter data; The absolute value of the product of the matching difference consistency and the variation mean of each welding parameter data is calculated, and the cumulative sum of the absolute values ​​of the products of all welding parameter data is taken as the asynchronous difference coefficient.

5. A wear-resistant dredging pipeline welding method as claimed in claim 1, characterized in that: The number of moments included in the asynchronous comparison interval is equal to the rounded-up value of the asynchronous difference coefficient.

6. A wear-resistant dredging pipeline welding method as claimed in claim 1, characterized in that: The formula for obtaining the asynchronous feedback significance value of each welding parameter data is specifically: Among them, p x represents the asynchronous feedback significance value of the xth welding parameter data; p c,d represents the mean of the comprehensive comparison scores of the x-th welding parameter data in the c-th and d-th asynchronous comparison intervals; k x,c and k x,d represents the xth welding parameter data in the cth and dth asynchronous comparison intervals respectively; hd(k x,c ,k x,d ) represents k x,c and k x,d The Hausdorff distance between them; m represents the number of asynchronous contrast intervals.

7. A wear-resistant dredging pipeline welding method as claimed in claim 1, characterized in that: The process of optimizing and adjusting the welding control is as follows: The normalized results of the asynchronous feedback significant values ​​of various welding parameter data are used as the adjustment variable p, combined with the initial proportional parameter P controlled by the welding parameter data o , and obtain the proportional parameter P after adjusting various welding parameter data. The formula is: P = P o +P o ×p; The welding control is optimized and adjusted according to the proportional parameters adjusted according to various parameter data in combination with PID control.

8. A wear-resistant dredging pipeline welding method as claimed in claim 1, characterized in that: The diameter of the flange is smaller than the diameter of the concentric circle of the dredging pipe, and the difference is within 1 mm.

9. A wear-resistant dredging pipeline welding process, characterized in that: The welding process implements the steps of a wear-resistant dredging pipeline welding method as described in any one of claims 1-8.

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