Flash butt welding on-line monitoring and quality evaluation method and device based on process parameters

Abstract

This invention relates to a method and apparatus for online monitoring and quality assessment of flash butt welding based on process parameters, belonging to the field of flash butt weld joint quality inspection and assessment. The method consists of parameter acquisition and processing, rapid modeling and simulation calculation, and automatic welding quality assessment. During the flash butt welding process, the method uses a parameter acquisition and processing subsystem to capture process parameters such as welding current, voltage, and displacement in real time, and uses these as input conditions to quickly establish a numerical simulation model of the welded workpiece, calculating the temperature distribution in the joint area. A dedicated algorithm is then used to match the simulation results with a standard temperature model, and finally, the similarity is used as an indicator to perform online assessment of the flash butt weld quality. This improves detection accuracy, reduces detection costs, and can accurately screen out workpieces with poor joint quality.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation of flash butt welding, and particularly to the field of detection and evaluation of flash butt weld joint quality, especially to a method and apparatus for online monitoring and quality evaluation of flash butt welding based on process parameters. Background Technology

[0002] Flash butt welding technology has advantages such as short welding time, good joint quality, and no need to add additional welding materials, so it is widely used in the production process of automobile wheel rims.

[0003] The basic principle of flash butt welding is the coupling of three fields: electricity, heat, and force. It uses the resistance heat generated by the current passing through the workpiece itself and between its end faces as the heat source to assemble the workpieces into a butt joint. Under the combined action of heat, electricity, and mechanical forces, the entire end face is rapidly connected. The basic process principle of flash butt welding is as follows: During flash butt welding, the workpieces to be welded are connected to the secondary circuit of a transformer. One end of the metal plate is fixed to a fixed clamp, and the other end is clamped at a movable clamp. After the two end faces contact, resistance heat is generated under the action of the current. The contact point on the metal end faces is rapidly burned, forming a flash that spatters onto the outside of the joint. Simultaneously, as the heating temperature increases, the movable clamp continuously increases its advancing speed, forming flashes, and the spattering process becomes more intense. When a plastic layer of appropriate depth is formed at both ends of the joint, the physical properties of the joint will change significantly after welding. If the parameters are selected too high or too low, obvious microcracks will appear in the welded joint, posing a significant risk in practical applications.

[0004] Currently, the evaluation methods for flash butt weld joint quality generally involve either direct destructive testing of the joint or online monitoring of process parameters. Direct destructive testing, which analyzes the mechanical properties of the weldment through bending and tensile tests to determine weld quality, only identifies the final weld quality but doesn't reveal the underlying causes of quality issues, thus failing to provide theoretical support for process improvement. In contrast, online monitoring of process parameters provides data for analyzing weld quality problems, but this method doesn't consider temperature variations in different affected zones (interface zone, overheating zone, recrystallization zone, and incomplete recrystallization zone), thus also having limitations. Therefore, evaluating joint quality using the temperature field of the weld joint becomes crucial for solving this problem. Summary of the Invention

[0005] The purpose of this invention is to provide a method and device for online monitoring and quality assessment of flash butt welding based on process parameters, solving the problems of low detection accuracy and low efficiency in traditional detection methods. This invention achieves real-time monitoring of weld quality and the welding process, providing weld quality assessment results immediately after the welding process is completed. The method mainly consists of parameter acquisition and processing, rapid modeling and simulation calculation, and automatic welding quality assessment. During the flash butt welding process, the method uses a parameter acquisition and processing subsystem to capture process parameters such as welding current, voltage, and displacement in real time, and uses these as input conditions to quickly establish a numerical simulation model of the workpiece, calculating the temperature distribution in the workpiece's mating area; then, the simulation results are matched with a standard temperature model using the LCSS algorithm, and finally, the flash butt welding quality is assessed online using similarity as an indicator.

[0006] The above-mentioned objective of the present invention is achieved through the following technical solution:

[0007] A method for online monitoring and quality assessment of flash butt welding based on process parameters includes the following steps:

[0008] Step 1: First, a Rogowski coil current sensor, an isolated Hall voltage sensor, and a triangular laser displacement sensor are used to continuously monitor the secondary current of the welding machine, the voltage between the moving and stationary clamps, and the displacement signal of the moving clamp. The real-time acquisition subsystem automatically determines the start and end of the welding process based on the signal status and stores the instantaneous values ​​of each sensor signal in the time domain sequence in memory. The data processing subsystem automatically calculates the welding parameters to obtain the welding current, voltage RMS value, instantaneous value, flash speed, displacement, and other welding parameters.

[0009] Step 2: The rapid modeling subsystem automatically generates a simulation model based on the workpiece size parameters (radius r, width w, thickness d) input by the user in the rapid finite element modeling module for automobile wheel rims and the material properties selected in the Access material database. Using the welding process parameters obtained by the data processing subsystem as input conditions, the system automatically divides the mesh and performs finite element simulation calculations on the welding heat generation of the workpiece to obtain the temperature field of the workpiece joint and its surrounding area.

[0010] Step 3: The quality assessment subsystem automatically calculates the distribution characteristics of temperature peaks, gradients, and rates of change in the joint and heat-affected zone based on the results of the numerical simulation of the workpiece temperature field. It retains the temperature area 50mm away from both sides of the joint in the numerical simulation results, extracts ten parallel and equidistant temperature curves perpendicular to the joint direction, compares them with the preset standard temperature curves, uses the Longest Common Subsequence (LCSS) algorithm for similarity comparison, and finally evaluates the quality of the welded joint based on the similarity.

[0011] 3.1 The LCSS algorithm calculates the similarity between two curves by using a point matching method. It determines the similarity between the two sets of curves based on the longest common subsequence. The maximum value of the result obtained by the LCSS algorithm is 1, and the minimum value is 0. The larger the value, the higher the similarity.

[0012] 3.2 The steps for performing similarity analysis between the extracted temperature peak curve and the ideal temperature peak curve using the LCSS algorithm are as follows:

[0013] 3.21. A=[a1, a2,…,an], B=[b1, b2,…, bm]

[0014] 3.22. Let A and B represent two sequences, where A is the initially set ideal temperature curve and B is the temperature field curve obtained from numerical simulation. Let a and b be samples (points on the curves) in the sequences, and let m and n be the number of samples in each sequence. Let k points be extracted sequentially from sequences A and B to form sequences AK and BK, such that the Euclidean distance between any two points in AK and BK is equal and less than a threshold ε. The longest AK and BK are the longest common subsequences of the two sets of curves. The similarity between the two curves can be obtained according to the above formula.

[0015] 3.3 The evaluation criteria for the quality of welded joints are as follows: If eight or more temperature curves have a similarity of 0.85 or higher, and the similarity of the remaining temperature curves is 0.8 or higher, the quality evaluation subsystem will evaluate the joint quality as qualified; if one or more temperature curves have a similarity of less than 0.8, the quality evaluation subsystem will evaluate the joint quality as unqualified.

[0016] The real-time acquisition subsystem described in step 1 acquires the displacement signal of the moving clamp in real time. When the displacement of the moving clamp increases, it is determined that the welding process is about to begin. The real-time acquisition subsystem starts acquiring the secondary current of the welding machine and the voltage between the moving and stationary clamps. When either of the two signals suddenly rises to the critical value A, it is determined that the welding process has started. During the welding process, the real-time acquisition subsystem stores the instantaneous values ​​of each sensor signal in memory in the form of a time-domain sequence. The data processing subsystem automatically calculates the welding parameters and obtains welding parameters such as welding current, effective value of voltage, instantaneous value, flash speed, and displacement. When both the secondary current of the welding machine and the voltage between the moving and stationary clamps suddenly drop to the critical value B, it is determined that the welding process has ended, and the real-time acquisition subsystem only acquires the displacement signal of the moving clamp. The critical value A is set as follows: 50% of the preset welding parameters for 5 cycles; the critical value B is set as follows: 10% of the preset welding parameters for 5 cycles.

[0017] Step 2 describes a rapid finite element modeling module for automotive wheel rims. This module embeds the flash butt welding process model of automotive wheel rims into the operating system and sets the contact surfaces and heat dissipation conditions between the workpiece and fixture within each model. First, the user selects the flash butt welding process model and sets the fixture position. Then, based on the user's input of workpiece dimensions, the system automatically assigns these dimensions to the selected model, automating the rapid finite element modeling of automotive wheel rims. The Access material database stores the thermal conductivity, specific heat capacity, resistivity, and other physical properties of commonly used metal materials and fixture materials in automotive wheel rims in text format on the host computer. The meshing process involves the rapid finite element modeling module using the smallest geometric units to divide the interface sides based on the displacement distance measured by the displacement sensor, with the length matching the measured displacement distance. Other parts are divided using the largest geometric units, reducing computational complexity.

[0018] Another objective of this invention is to provide a real-time welding parameter online monitoring device for implementing the above-mentioned flash butt welding online monitoring and quality assessment method based on process parameters. The device includes a host computer, an alarm device, a sensor, and a data acquisition card. The data acquisition card uploads the collected welding parameters to the data acquisition subsystem in the host computer. The host computer includes a data acquisition subsystem, a data processing subsystem, a rapid modeling subsystem, and a quality assessment subsystem. The alarm device is activated when a workpiece fails the quality assessment.

[0019] The beneficial effects of this invention are as follows:

[0020] 1. The accuracy of the detection method is improved, which can accurately detect products with poor welding quality in the joints of the same batch of workpieces produced. This invention fully considers the temperature changes in each affected area of ​​the joint (interface area, overheated area, recrystallization area, and incomplete recrystallization area), which can more accurately simulate the temperature changes of the joint, thereby more accurately judging the quality of the joint.

[0021] 2. Low testing cost: It eliminates the mechanical performance testing required in traditional testing methods, simplifies the testing process, greatly reduces the testing cycle, and significantly frees up labor.

[0022] 3. This method analyzes the temperature field of the joint to assess welding quality, enabling a more thorough and accurate analysis of overheating and burning phenomena in the welded joint. Simulations based on actual parameters during the processing of each workpiece yield highly accurate temperature peak curves, allowing for precise screening of workpieces with defective joint quality. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate the invention and are used to explain it, but do not constitute an undue limitation of the invention.

[0024] Figure 1 This is a flowchart of the flash butt welding online monitoring and quality assessment method of the present invention;

[0025] Figure 2 This is a flowchart of the rapid modeling subsystem of the present invention;

[0026] Figure 3 This is a temperature map of the workpiece joint and its surrounding area obtained by the rapid modeling subsystem of the present invention.

[0027] Figure 4 This is a flowchart of the joint quality assessment method of the present invention;

[0028] Figure 5 This is a structural block diagram of the evaluation device of the present invention. Detailed Implementation

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] See Figures 1 to 5 As shown, the present invention relates to a method and apparatus for online monitoring and quality assessment of flash butt welding based on process parameters. The method comprises parameter acquisition and processing, rapid modeling and simulation calculation, and automatic welding quality assessment. During the flash butt welding process, the method uses a parameter acquisition and processing subsystem to capture process parameters such as welding current, voltage, and displacement in real time. Using these parameters as input, a numerical simulation model of the workpiece is quickly established, and the temperature distribution in the workpiece's mating region is calculated. A dedicated algorithm is then used to match the simulation results with a standard temperature model, and finally, the similarity is used as an indicator to perform online assessment of the flash butt welding quality. The apparatus includes a real-time acquisition subsystem, a data processing subsystem, a rapid modeling subsystem, and a quality assessment subsystem.

[0031] See Figure 1As shown, the flash butt welding online monitoring and quality assessment method based on process parameters of the present invention uses a Rogowski coil current sensor, an isolated Hall voltage sensor, and a triangular laser displacement sensor to continuously monitor the secondary current of the welding machine, the voltage between the moving and stationary clamps, and the displacement signal of the moving clamp during the flash butt welding process. The real-time acquisition subsystem obtains the welding process signals, and then the data processing subsystem automatically calculates the welding parameters, obtaining welding current, voltage RMS values, instantaneous values, flash speed, displacement, and other welding parameters. The rapid modeling subsystem establishes a numerical simulation model of the flash butt welding workpiece and automatically imports the monitored welding parameters as boundary conditions to perform online numerical simulation and obtain the temperature field distribution of the weld joint. The quality assessment subsystem automatically calculates the distribution characteristics of temperature peaks, gradients, and rates of change of the joint and heat-affected zone based on the results of the numerical simulation of the workpiece temperature field, and compares them with the matching characteristics of a preset standard temperature model. Finally, the quality of the weld joint is evaluated based on the similarity.

[0032] The real-time acquisition subsystem acquires the displacement signal of the moving clamp in real time. When the displacement of the moving clamp increases, it is determined that the welding process is about to begin, and the real-time acquisition subsystem starts acquiring the secondary current of the welding machine and the voltage between the moving and stationary clamps. When either of these two signals suddenly rises to a critical value A, the welding process is determined to have started. During the welding process, the real-time acquisition subsystem stores the instantaneous values ​​of each sensor signal in memory in a time-domain sequence. The data processing subsystem automatically calculates the welding parameters, obtaining welding current, voltage RMS values, instantaneous values, flash speed, displacement, and other welding parameters. When both the secondary current of the welding machine and the voltage between the moving and stationary clamps suddenly drop to a critical value B, the welding process is determined to have ended, and the real-time acquisition subsystem only acquires the displacement signal of the moving clamp.

[0033] The critical value A is set as follows: 50% of the preset welding parameters for 5 cycles; the critical value B is set as follows: 10% of the preset welding parameters for 5 cycles.

[0034] See Figure 2 As shown, the rapid modeling subsystem automatically generates a simulation model based on the model selected by the user in the automotive wheel rim rapid finite element modeling module, the input workpiece size parameters (radius r, width w, thickness d), and the material properties selected from the Access material database. It automatically meshes the model based on data monitored by displacement sensors and uses welding process parameters obtained by the data processing subsystem as input conditions to perform finite element simulation calculations on the welding heat generation of the workpiece, obtaining the temperature field of the workpiece joint and its surrounding area. (See [link to relevant documentation]). Figure 3 As shown.

[0035] The rapid finite element modeling module for automotive wheel rims is a secondary development based on the Hyper Mesh platform. Through system integration, it embeds existing flash butt welding process models for automotive wheel rims into the operating system, automating the rapid finite element modeling process. The Access material database stores the physical properties of commonly used metallic materials and fixture materials for automotive wheel rims, such as thermal conductivity, specific heat capacity, resistivity, and other properties, in text format on the host computer. The mesh generation process involves the rapid finite element modeling module using the smallest geometric elements to divide the interface sides based on the displacement distance measured by displacement sensors, with the length matching the measured displacement distance. Other parts are divided using the largest geometric elements to reduce computational complexity.

[0036] Based on the results of numerical simulation of the workpiece temperature field, the quality assessment subsystem automatically calculates the distribution characteristics of temperature peaks, gradients, and rates of change in the joint and heat-affected zone. It retains the temperature area 50mm away from both sides of the joint in the numerical simulation results, extracts ten parallel and equidistant temperature curves perpendicular to the joint direction, compares them with the preset standard temperature curves, uses the Longest Common Subsequence (LCSS) algorithm for similarity comparison, and finally evaluates the quality of the welded joint based on the similarity.

[0037] The LCSS algorithm calculates the similarity between two curves using a point matching method, determining the similarity based on the longest common subsequence of the two sets of curves. The maximum value obtained using the LCSS algorithm is 1, and the minimum value is 0; a higher value indicates a higher similarity.

[0038] The steps for performing similarity analysis between the extracted temperature peak curve and the ideal temperature peak curve using the LCSS algorithm are as follows:

[0039] A=[a1,a2,…,an], B=[b1,b2,…,bm]

[0040] A and B represent two sequences, where A is the initially set ideal temperature curve and B is the temperature field curve obtained from numerical simulation. a and b are samples (points on the curves) in the sequences, and the number of samples in each sequence is set to m and n, respectively. k points are sequentially extracted from sequences A and B to form sequences AK and BK, such that the Euclidean distance between any two points in AK and BK is equal and less than a threshold ε. The longest AK and BK sequences are the longest common subsequences of the two sets of curves. The similarity between the two curves can be calculated using the formula described above.

[0041] See Figure 4As shown, the evaluation standard for the quality of the welded joint is as follows: If, among the ten temperature curves used for similarity comparison, eight or more temperature curves have a similarity score greater than or equal to 0.85, and the remaining temperature curves have a similarity score greater than or equal to 0.8, the quality evaluation subsystem will assess the joint as qualified. If one or more temperature curves have a similarity score less than 0.8, the quality evaluation subsystem will assess the joint as unqualified.

[0042] Example:

[0043] The following explanation uses the flash butt welding production process monitoring of automotive wheel rims as an example: The monitoring system and equipment designed in this invention are connected to the actual production equipment. The welding voltage is set to 3V, flash depth to 5mm, burn-off speed to 1.20mm / s, and tip depth to 6mm via the flash butt welding machine's operating interface. Before welding, the monitoring device is activated, and the workpiece dimension parameters (radius r, width w, thickness d) and material parameters (thermal conductivity, specific heat capacity, resistivity, etc.) are input into the rapid modeling subsystem. Simultaneously with the completion of the welding process, the quality assessment subsystem provides the assessment results. Performance analysis is performed on the workpiece joints that pass the assessment, and the results are consistent with the assessment results. If workpieces produced using excessively large or small welding parameters are deemed unqualified after the welding process, the quality assessment subsystem will issue an assessment result of "unqualified," and the alarm system will be activated.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made to the present invention should be included within the scope of protection of the present invention.

Claims

1. A flash butt welding online monitoring and quality assessment method based on process parameters, characterized in that: Comprising the following steps: Step 1, first using the Rogowski coil current sensor, isolation type Hall voltage sensor, triangular laser displacement sensor continuous monitoring welding machine secondary current, dynamic and static clamp voltage, dynamic clamp displacement signal; real-time acquisition subsystem according to the signal state automatically determine the start and end of the welding process, and the welding process in each sensing signal instantaneous value with time domain sequence way stored in the memory; data processing subsystem automatically calculates the welding parameters, the welding current, voltage effective value, instantaneous value and flash speed, displacement; Step 2, the rapid modeling subsystem according to the user in the automobile wheel rim rapid finite element modeling module input workpiece size parameters, including radius r, width w, thickness d, selected material properties in Access material database, rapid modeling subsystem automatically generates simulation model, and takes the welding process parameters obtained by the data processing subsystem as input conditions, the system automatically divides the grid, and carries out finite element simulation calculation on the workpiece welding heat, to obtain the temperature field of the workpiece and its nearby area; Step 3, the quality evaluation subsystem on the results of the numerical simulation of the workpiece temperature field, automatically calculates the temperature peak, gradient and change rate of the butt joint and the heat affected zone; reserve the temperature area 50mm away from both sides of the butt joint in the numerical simulation results, and cut off ten temperature curves parallel and equidistant to the preset standard temperature curve in the vertical direction of the butt joint, and compare the similarity by using the longest common subsequence LCSS algorithm, and finally evaluate the quality of the welded joint according to the similarity; 3.1, the LCSS algorithm calculates the similarity of two curves by point matching method, and obtains the similarity of two curves according to the longest common subsequence of two curves; the maximum value of the result obtained by using the LCSS algorithm is 1, and the minimum value is 0, and the larger the value is, the higher the similarity is; 3.2, the similarity analysis steps of the temperature peak curve and the ideal temperature peak curve by using the LCSS algorithm are as follows: 3.21, A=[a1, a2, …, an], B=[b1, b2, …, bm] 3.22, A and B represent two sequences respectively, A is the ideal temperature curve set initially, B is the temperature field curve obtained by numerical simulation, a and b are samples in the sequence, and the sample number of each sequence is set as m and n; k points are extracted from sequences A and B respectively to form sequences AK and BK, so that the Euclidean distance of two points in AK and BK is equal and less than a threshold value ε; the longest AK and BK are the longest common subsequence of two curves, and the similarity of two curves is obtained according to the LCSS similarity formula; 3.3, the evaluation standard of the quality of the welded joint is: in the ten temperature curves for similarity comparison, if the similarity of eight or more temperature curves is greater than or equal to 0.85, and the similarity of the other temperature curves is greater than or equal to 0.8, the quality evaluation subsystem will evaluate the joint quality as qualified; if there is one or more temperature curves with similarity less than 0.8, the quality evaluation subsystem will evaluate the joint quality as unqualified.

2. The flash butt welding process parameter based online monitoring and quality assessment method as claimed in claim 1 wherein: The real-time acquisition subsystem of step 1 acquires the displacement signal of the movable clamp in real time, and when the displacement of the movable clamp increases, it is determined that the welding process is about to start, and the real-time acquisition subsystem starts to acquire the secondary current of the welding machine and the voltage between the movable and static clamps, and when either of the two signals suddenly rises to a critical value A, it is determined that the welding process starts; during the welding process, the real-time acquisition subsystem stores the instantaneous values of each sensing signal in the memory in the form of a time sequence; the data processing subsystem automatically calculates the welding parameters to obtain the welding current, voltage effective value, instantaneous value and flash speed, displacement; when the secondary current of the welding machine and the voltage between the movable and static clamps both suddenly drop to a critical value B, it is determined that the welding process is over, and the real-time acquisition subsystem only acquires the displacement signal of the movable clamp; the setting method of the critical value A is: 50% of the preset welding parameter, lasting for 5 cycles; the setting method of the critical value B is: 10% of the preset welding parameter, lasting for 5 cycles.

3. The flash butt welding process parameter based online monitoring and quality assessment method as claimed in claim 1 wherein: The automobile wheel rim rapid finite element modeling module of step 2 is obtained by solidifying the automobile wheel rim flash butt welding process model in the operating system and setting the contact surface between the workpiece and the clamp and the heat dissipation condition in each automobile wheel rim flash butt welding process model; first, the user needs to select the automobile wheel rim flash butt welding process model, set the position of the clamp, and then according to the size parameters of the workpiece input by the user, the system will automatically assign the size parameters to the selected model, realizing the automation of automobile wheel rim rapid finite element modeling; the Access material database stores the thermal conductivity, specific heat capacity, resistance coefficient and resistivity of the metal materials and clamp materials commonly used in automobile wheel rims in the host computer in text form; The meshing process is that the automobile wheel rim rapid finite element modeling module divides the interface on both sides with the minimum geometric unit according to the displacement distance measured by the displacement sensor, and the length is the same as the measured displacement distance; the other parts are divided by the maximum geometric unit to reduce the difficulty of operation.

4. A real-time welding parameter on-line monitoring device for implementing the process parameter based flash butt welding on-line monitoring and quality evaluation method according to any one of claims 1-3, characterized in that: The system includes a host computer, an alarm device, a sensor and a data acquisition card, the data acquisition card uploads the collected welding parameters to the data acquisition subsystem in the host computer, the host computer includes a data acquisition subsystem, a data processing subsystem, a rapid modeling subsystem and a quality evaluation subsystem, and the alarm device is started when the workpiece fails to meet the quality evaluation.

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