Welding deviation extraction and deformation control method based on stress change at welding site

Abstract

The application discloses a kind of weld deviation extraction and deformation control method based on stress change at welding, it mainly solves the problem of extracting thin stainless steel weld deviation information with large deformation under the condition of no fixture, and the effect of deformation minimization is achieved by adjusting welding parameters through correlation function.The technical solution points are, using infrared light sensing and CCD camera system to collect data and image analysis on both sides of the weld, then combining finite element analysis method and non-contact displacement measurement method to calculate transverse angular deformation and longitudinal bending deformation, both of which are used as twice weld track information extraction, finally, the optimal weld deviation information is extracted as the basis for weld tracking by using the judgment formula weighted fusion method, which provides a new idea for large surface area ultra-thin stainless steel welding process automation weld tracking.

Description

Technical Field

[0001] This invention belongs to the field of weld deviation information extraction technology, specifically relating to a method for extracting weld deviation and controlling deformation based on stress changes at the weld. Background Technology

[0002] In the construction of large surface vessels, high-strength thin steel plates are commonly used to reduce the hull's weight and increase its load-bearing capacity. However, this also presents challenges in controlling welding deformation of thin plates and achieving automated welding processes. To address these issues, most researchers employ welding fixtures and assembly jigs, along with laser welding technology that concentrates heat input for thin plate structures. While welding deformation can be controlled through hardware, real-time control of thermal deformation and efficient automated welding are not feasible. Therefore, research into a method for extracting weld deviations and controlling minimum deformation based on welding stress-strain sensing is urgently needed.

[0003] Patent No. CN107914067B describes an algorithm for obtaining the welding torch tracking point on the joint contour line by analyzing the arc center position in the weld image. This algorithm, combined with visual calibration technology and an image processing system, obtains the welding torch's specific position in world coordinates, thus determining the torch's three-dimensional deviation information. While this method extracts more accurate deviations and allows for torch correction in two directions, visual calibration is difficult to apply to reflective stainless steel, and the algorithm's computational process is cumbersome, making it difficult to meet the real-time requirements of the welding process. Furthermore, the acquisition of coordinates from the arc's geometric center inherently introduces errors, leading to some deviation in the acquired weld image.

[0004] The system, patent number CN104959706B, uses a oscillating device to swing the sensor at different angles. It detects weld deviations by scanning the weld bevel of thin plates. The obtained deviation information is processed by a data conversion and processing module, then further processed by the main controller's arithmetic module. Finally, a tracking execution module compensates for weld deviations, thus achieving automated tracking of butt and lap welds on thin plates. While this invention is simple and compact, inexpensive, and offers high tracking accuracy and wide adaptability, effectively compensating for thermal deformation during welding in real time, the additional capacitance generated by edge effects may directly add to the sensor's capacitance, resulting in non-linear sensor output characteristics and reduced sensor sensitivity. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a weld deviation extraction and deformation control method based on weld strain sensing that can reduce thermal deformation during the welding of thin stainless steel without the need for fixtures and achieve adaptability and stability of weld deviation information extraction for thin stainless steel through a non-contact displacement measurement device system.

[0006] The objective of this invention is achieved through the following technical solution: A method for extracting weld deviations and controlling deformation based on stress changes at the weld joint enables real-time and reliable extraction of weld deviation information from relatively large thin stainless steel plates without the need for fixtures, while also reducing the impact of thermal deformation on weld information extraction. This method primarily utilizes a CCD camera system and infrared sensors installed around the welding torch to collect image data at different stages. This data is then input into a computer image processing center, where finite element analysis and non-contact displacement measurement are used to calculate the lateral and longitudinal deformations. Next, the maximum temperature change difference and the minimum comprehensive variable difference in the temperature distribution field are used as the basis for the weld deviation information, thus realizing a new method for extracting weld deviation information based on the stress change state at the weld joint. Finally, a judgment-based weighted fusion algorithm is used to optimize and output the optimal weld deviation information.

[0007] Furthermore, at the beginning of the welding process, the temperature sensors (7) inside the n pairs of infrared light sensors (3) placed around the welding torch are activated to collect real-time temperature difference data within a circular area on both sides of the weld. The infrared light sensors (3) then aggregate the temperature point signals collected by the temperature sensors (7) to form a model signal from point to line to surface, thereby creating temperature field changes at different levels. The temperature field change data is input to the computational image control center connected to the infrared light sensors. Based on the temperature difference of the temperature distribution field (8), the first weld trajectory image information under normal thermal deformation is obtained. Then, a functional relationship between the arc input heat and welding process parameters is constructed. The heat input change is fed back through the constructed function as a standard quantity for real-time adjustment of welding parameters. Next, a CCD camera (2) parallel to the weld (5) and placed in front of and behind the welding torch (1) is used to collect the longitudinal deflection deformation θ of the weld on both sides under minimum thermal deformation. i and lateral angular deformation ε i The data is used to form two minimum comprehensive deformation amounts on the left and right sides of the weld. Finally, the magnitudes of the minimum comprehensive deformation amounts on both sides of the weld are compared to determine the second weld trajectory image information. The two weld trajectory image information are then fused into the optimal weld deviation information using a judgment-based weighted fusion method. This provides a new method for efficient and automated weld deviation information extraction and minimum deformation control for ultra-large thin stainless steel plates that will generate large deformations during the welding process.

[0008] Furthermore, the device attached to the welding torch includes a pair of CCD cameras (2), an infrared light sensor (3), and an internal temperature sensor (7). The infrared light sensor (3) array is symmetrically distributed on both sides of the weld. The internal temperature sensor (7) performs real-time detection of the temperature field change scene of the weld and extracts the temperature change interval T data at each moment. The three sets of temperature change intervals collected at a certain moment are represented as T. i-1 T i T i+1 When T is satisfied i >T i+1 and T i >T i-1 When both conditions are met, T can be determined. max =T i The temperature range value of the temperature field is used as the first weld trajectory deviation information. At the same time, a pair of CCD cameras (2) arranged vertically to the ground and parallel to the weld will be used to collect the difference in lateral displacement and longitudinal deformation on both sides of the weld.

[0009] Furthermore, the longitudinal bending deformation and transverse angular deformation on both sides of the weld are obtained by capturing images of pre-existing markings on the thin stainless steel using a CCD camera. Image processing methods are then used to obtain the longitudinal deformation information. Finally, the longitudinal displacement Δz and transverse angular deformation displacement Δx are summed using finite element analysis and a non-contact displacement measurement method to calculate the longitudinal bending deformation θ on both sides of the weld. i and angular deformation ε i .like Figure 6 As shown, the object point is thermally deformed from S1(x,y) to S2(x,y). Simultaneously, a certain m·n pixel region A before thermal deformation is taken as a standard block. The corresponding m·n pixel region B after thermal deformation is then compared and calculated as follows:

[0010]

[0011] In the formula, C is the correlation coefficient of the displacement before and after deformation; f A (x i y j ) and f B (x i y j These are the grayscale set functions for regions A and B, respectively. and These are the average gray values ​​of A and B, respectively.

[0012] The maximum value of the region block can be obtained from the aforementioned related functions. Subpixel interpolation is used to calculate the accurate position of point B after thermal deformation, thereby determining the relative displacement between corresponding points on both sides of the weld seam. Differentiation of the relative displacement yields the transverse cross-sectional strain data on both sides of the weld seam at that moment. Figure 4 and 5 As shown, the calculation is as follows:

[0013]

[0014]

[0015] In the formula f 前 (t,z) and f 后 (t, z) are the longitudinal section functions of the weld, respectively; C is the correlation coefficient of the displacement before and after cross-sectional deformation.

[0016] Furthermore, the minimum thermal deformation of the weld angular deformation ε on both sides is described. i and longitudinal flexural deformation θ i The minimum combined deformation of the two quantities can be calculated using the sum of squares formula. The minimum comprehensive deformation is used as the criterion for judging the weighting. Then, the real-time deviation information of the second weld in thin stainless steel is determined by comparing the magnitudes of the minimum comprehensive deformation on both sides. Combined with the first weld trajectory image, a judgment-based weighted fusion method is used to extract the optimal weld deviation information. The main fusion criteria of the judgment-based weighted fusion method are:

[0017] (1) If the minimum comprehensive deformation δ > the standard quantity δ0, calculate the minimum comprehensive deformation change rate. Then, multiply 1-Δδ by the second weld deviation information data value, and multiply Δδ by the first weld deviation information data value. Finally, input both data values ​​into the calculation control center system, and use the judgment weighted fusion method to calculate and obtain the optimal weld deviation information after fusion under minimum deformation.

[0018] (2) If the minimum comprehensive deformation δ≤ the standard value δ0, calculate the minimum comprehensive deformation change rate Δδ, then multiply Δδ by the second weld deviation information data value, multiply 1-Δδ by the first weld deviation information data value, and finally input the two data values ​​into the calculation control center system, and use the judgment weighted fusion method to calculate and obtain the optimal weld deviation information after fusion under the minimum deformation.

[0019] The main features of this invention are: A CCD camera system is used to collect the transverse and longitudinal deformation of the weld seam of the thin stainless steel plate before and after welding. The difference between the deformations before and after welding is used to obtain the transverse angular deformation and longitudinal bending deformation under minimum thermal deformation. Combined with finite element analysis and non-contact displacement measurement, the real-time minimum comprehensive deformation is calculated. Standard deformation is obtained based on parameters such as the thickness and material composition of different thin stainless steels. The minimum comprehensive deformation change rate is further calculated. Then, a judgment-based weighted fusion criterion is used to optimize the weld seam deviation information. Based on the constructed functional relationship between input heat and welding process parameters, combined with the optimized weld seam deviation information, the minimum range of thermal deformation of the thin stainless steel during welding is greatly controlled, improving the accuracy of efficient automated weld seam tracking for thin stainless steel.

[0020] The beneficial effects of this invention are that it provides a method for extracting weld deviations and controlling deformation based on stress changes at the weld joint. It utilizes a CCD camera system and an infrared sensor to collect real-time data on the transverse and longitudinal deformations of the weld surface before and after welding, as well as the temperature difference in the temperature distribution field. This allows for the fusion of transverse and longitudinal deformations with the weld temperature distribution field to output optimized weld deviation information. During welding, a functional relationship between the arc input heat source and welding process parameters is constructed. The feedback from this function on the heat input is used to readjust the welding parameters, ensuring that the input heat source maintains a relatively low minimum thermal deformation range during the weld process. This provides more accurate and adaptable weld information for thin plates subject to significant thermal deformation. Furthermore, the constructed functional relationship greatly reduces the impact of thermal deformation during the welding of thin plates. A non-contact displacement measurement and sensing system allows for flexible acquisition and analysis of real-time data, eliminating the need for additional fixture hardware to assist the welding process, thus significantly improving the sensitivity and automation of the welding process. Attached Figure Description

[0021] Figure 1 Welding Flowchart of a Method for Extracting Weld Information Deviation and Controlling Deformation Based on Welding Stress Variation

[0022] Figure 2 A welding diagram illustrating a method for extracting weld information deviations and controlling deformation based on welding stress variations.

[0023] Figure 3 Temperature distribution field diagram of a method for extracting weld information deviations and controlling deformation based on welding stress variations.

[0024] Figure 4 Longitudinal cross-sectional view of weld seam based on weld seam information deviation extraction and deformation control method according to welding stress variation Figure 5 Transverse cross-section of weld based on weld information deviation extraction and deformation control method according to welding stress variation Figure 6 Transverse displacement variation of weld seam based on weld seam information deviation extraction and deformation control method according to welding stress variation. Detailed Implementation

[0025] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0026] Reference Figure 1 As shown, this implementation case includes a weld deviation extraction and deformation control method based on stress changes at the weld joint, comprising: a welding torch (1), a pair of CCD cameras (2), an infrared light sensor (3), a temperature sensor (7), and a computer image control center system. The temperature sensor inside the infrared light sensor can collect real-time temperature change data on both sides of the weld joint. The computer image control center system can analyze the temperature change data to obtain weld trajectory image information. The CCD cameras placed in front of and behind the welding torch take pictures of the deformation of the thin stainless steel before and after welding at a certain frequency. Then, the two weld trajectory image information are fused into the optimal weld deviation information by a judgment-based weighted fusion method.

[0027] Implementation Case 1, such as Figure 2 and Figure 3 As shown, automated weld seam tracking is implemented in the welding process of large-area ultra-thin stainless steel. First, the infrared light sensor's temperature sensor automatically tracks and collects temperature distribution field change data. This temperature distribution field data is input into a computational image control system for comparison, and the maximum temperature change difference is used to obtain the first weld seam trajectory image information. Then, CCD cameras positioned before and after the welding torch collect the longitudinal deflection and angular deformation on both sides of the weld seam. The minimum combined deformation is used as the extraction standard for the second weld seam trajectory image information. Finally, a judgmental weighted fusion method is used to fuse the two weld seam trajectory image information into optimized weld seam deviation information. This deviation information is input into the welding torch actuator system, causing the welding torch to swing left and right, thus achieving automated weld seam tracking. Especially in scenarios where ultra-large-area stainless steel cannot use fixtures, weld seam tracking can be achieved based on the strain difference of the weld seam, reducing most manual operations and greatly improving welding automation. This provides a new approach for automated weld seam tracking in the welding process of large-area ultra-thin stainless steel.

[0028] Implementation Case 2, such as Figure 1As shown, the welding process parameters and input heat source are optimized to minimize thermal deformation and reduce base material deformation to the greatest extent possible. First, the welding process parameters that most significantly affect the input heat, including welding current, welding voltage, and welding speed, are continuously collected in real time during the welding process. Then, the range of heat source changes at that moment is collected using a temperature sensor. The welding process parameters are used as independent variables, and the heat input source as the dependent variable to construct a functional relationship between the welding process parameters and the heat input source. This functional relationship is then optimized using the least squares method and the method of moments. Each time, the heat source data collected by the temperature sensor is input into the functional relationship, and the welding process parameters are output, thus achieving closed-loop feedback. This optimizes the matching between the welding process parameters and the input heat source, ensuring minimal thermal deformation of the base material during welding, improving workpiece formation and weld quality, and significantly reducing base material waste during the welding process.

Claims

1. A welding deviation extraction and deformation control method based on stress change at a weld, characterized by: First, at the beginning of welding, the temperature sensors (7) inside the n pairs of infrared light sensors (3) placed around the welding torch are activated to collect real-time temperature change range data around the circular surface on both sides of the weld. The temperature change range data is input into the computational image control center connected to the infrared light sensors for comparison to generate the maximum temperature change difference. The maximum temperature change difference of the temperature distribution field (8) is used as the standard for extracting the first weld trajectory image information under normal thermal deformation. Then, the functional relationship between the arc input heat and welding process parameters is constructed. The function feedback of heat input change is used as the standard for adjusting welding parameters. The first method utilizes a pair of CCD cameras (2) parallel to the weld (5) and positioned before and after the welding torch (1) to collect and calculate the longitudinal flexural deformation and angular deformation data of the weld on both sides under minimum thermal deformation, thereby forming two minimum comprehensive deformations of the weld. Finally, by comparing the magnitude of the minimum comprehensive deformations on both sides of the weld, the second weld trajectory image information is determined. Combined with the judgment-based weighted fusion method, the two weld trajectory image information are fused into the optimal weld deviation information, providing a method for efficient and automated weld tracking of ultra-large thin stainless steel plates that will generate large deformations during the welding process, and for extracting weld deviation information and controlling deformation.

2. The weld deviation extraction and deformation control method based on the stress change at the weld according to claim 1, characterized by: Images of pre- and post-deformation points marked on a thin stainless steel plate by a pair of CCD cameras were captured. Image processing methods were then used to obtain lateral and longitudinal displacement information. The longitudinal displacement was then measured using finite element analysis and a non-contact displacement measurement method. and angular deformation displacement By summing the results, the longitudinal bending deformation on both sides of the weld can be further calculated. and lateral angular deformation The minimum comprehensive deformation was calculated using the sum of squares integral formula. The minimum comprehensive deformation before and after the change at the weld is used as the standard for judging the weighted amount and extracting the weld deviation information. The real-time deviation information of the second weld of the thin stainless steel plate is determined by comparing the magnitude of the minimum comprehensive deformation on both sides of the weld.

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