Automatic surfacing method for front and back welding of through pipe based on AB PLC (Programmable Logic Controller)

Abstract

The invention provides a through pipe front and back welding automatic surfacing method based on an AB PLC (programmable logic controller), and relates to the technical field of automatic welding and intelligent control, the method comprises the following steps: firstly, acquiring the precise size and microstructure of a through pipe by combining an AB PLC system with laser scanning, analyzing the surface characteristics of a welding area and planning a welding path; during welding, welding parameters are adjusted in real time through an intelligent self-adaptive control system, technologies such as magnetic field assistance and ultrasonic vibration are introduced to improve the welding seam quality, and meanwhile liquid nitrogen cooling is adopted to control the temperature. After welding, new technologies such as terahertz detection are used for evaluating the welding quality. A mining process optimization scheme is analyzed through big data, and centralized management of a cloud platform is supported. A plurality of sets of equipment are uniformly coordinated by AB PLCs, and a virtual simulation technology is adopted to optimize cooperative work. Compared with a traditional method, the method has the advantages that the welding precision, quality and efficiency are remarkably improved, the cost is reduced, defects are reduced, wide applicability and remarkable superiority are achieved, and intelligent development of the welding technology is powerfully promoted.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic welding and intelligent control, and more specifically, particularly relates to an automatic surfacing method for forward and reverse welding of pipes based on ABPLC. Background Art

[0002] In modern industrial production, pipe penetration is widely used, but its welding quality and efficiency have a vital impact on the performance and service life of the product. Traditional pipe penetration welding methods often rely on manual operation and have many problems.

[0003] Manual welding is not only labor-intensive and has a harsh working environment, but the welding quality depends largely on the welder's technical level and experience, and it is difficult to ensure consistency and stability. Inaccurate parameter control during the welding process can easily lead to uneven welds and more welding defects, such as pores, slag inclusions, and lack of fusion, which in turn affect the strength and sealing of the pipe.

[0004] In addition, traditional welding methods are inefficient and difficult to meet the needs of large-scale production. When faced with complex welding tasks and high precision requirements, traditional methods often seem to be unable to cope with them.

[0005] With the continuous development of automation technology and control theory, automated welding methods have gradually emerged. However, the existing automated welding technology still has some limitations in the field of pipe welding. For example, the welding path planning is not accurate enough, the thermal stress distribution during the welding process is not considered enough, it is difficult to achieve real-time intelligent adjustment of welding parameters, and there are certain deficiencies in multi-device collaborative work and welding quality detection.

[0006] In order to overcome the above problems and improve the quality, efficiency and intelligence level of pipe welding, the development of an automatic surfacing method for pipe forward and reverse welding based on ABPLC has important practical significance and application value. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides an automatic surfacing method for forward and reverse welding of pipes based on AB PLC to solve the above problems.

[0008] An automatic surfacing method for forward and reverse welding of pipes based on AB PLC, comprising the following steps:

[0009] S1: Use the AB PLC system to obtain the size parameters of the pipe, including pipe diameter D, pipe length L and pipe wall thickness T. The modeling formula is N = g (D, L, T), where g (D, L, T) = 0.3D 2 L+0.15DT 2 At the same time, laser scanning technology is introduced to scan the microstructure of the pipe and obtain microstructure data for optimization modeling;

[0010] S2: Analyze the surface characteristics of the pipe penetration welding area, set the surface roughness threshold Rz≤3.2μm, and use a high-resolution imaging system to perform real-time imaging analysis of the welding area to detect potential surface defects;

[0011] S3: planning the welding path of forward and reverse welding according to the surface characteristics and microstructure data, with the forward and reverse welding alternating interval K=0.2D-0.05D, and optimizing the welding path to reduce welding deformation in combination with the thermal stress distribution model during welding;

[0012] S4: The movement of the welding equipment is controlled by AB PLC, so that the welding gun moves according to the planned welding path. At the same time, the angle and welding speed of the welding gun are monitored and adjusted in real time. The angle deviation γ ≤ 1.5°, the welding speed deviation Vd ≤ 5%, and an intelligent adaptive control system is introduced to automatically adjust the welding parameters according to the real-time welding status.

[0013] Preferably, the AB PLC system is used to obtain the dimensional parameters of the pipe penetration, specifically: the dimensional data of the pipe penetration is collected by a high-precision measuring device, the pipe diameter measurement accuracy is ±0.1mm, the pipe length measurement accuracy is ±0.5mm, and the pipe wall thickness measurement accuracy is ±0.05mm, and the data is transmitted to the AB PLC system for processing, and the machine learning algorithm is used to perform error correction and prediction on the measurement data.

[0014] Preferably, when planning the welding path for forward and reverse welding, the material of the pipe is considered, the yield strength range of the material is σs[235,355]MPa, and the welding process requirements are taken into account to generate the optimal welding path, the welding current I=150-200A, and the welding current adjustment formula is I'=I+5(T-T0), where T is the real-time welding temperature and T0 is the preset reference temperature. At the same time, magnetic field assisted welding technology is introduced to improve the weld quality by adjusting the magnetic field strength and direction.

[0015] Preferably, during the welding process, AB PLC is used to control the wire feed speed, feed speed S = 2.5-3.5m / min, and arc voltage, voltage range U = 20-25V, and the voltage adjustment formula is U' = U + 0.5log (P-P0), where P is the real-time power and P0 is the preset reference power, and ultrasonic vibration is used to assist welding to reduce welding residual stress.

[0016] Preferably, the temperature sensor in the AB PLC system is used to monitor the temperature of the welding area in real time. The accuracy of the temperature sensor is ±1°C. When the temperature exceeds the preset threshold value Tmax=800°C, the welding current and welding speed are automatically reduced. After the temperature drops to the safe range Tmin=500°C, the normal welding parameters are restored. At the same time, liquid nitrogen cooling technology is used to achieve rapid cooling and improve cooling efficiency.

[0017] Preferably, the movement speed of the welding equipment is controlled by AB PLC, the speed range is V = 0.5-0.8m / min, and the acceleration range is a = 0.1-0.2m / s 2 , and the acceleration adjustment formula is a'=a+0.03cos(ωt), where ω is the angular frequency and t is the time, and multi-axis linkage control technology is used to achieve more complex welding trajectories.

[0018] Preferably, AB PLC is used to record and analyze various parameters in the welding process to form a welding process database. The database storage capacity is not less than 150GB, and a data compression algorithm Z is used with a compression ratio of not less than 2.5:1, which provides a reference for subsequent similar pipe penetration welding. At the same time, big data analysis technology is used to deeply mine the database and extract potential process optimization solutions.

[0019] Preferably, after welding is completed, the quality of the welded joint is automatically inspected using AB PLC-controlled inspection equipment with an inspection accuracy of not less than 0.01 mm. A defect recognition algorithm X is used with an identification accuracy of not less than 90%. If the inspection fails, the welding repair is performed again, and new non-destructive testing technologies, such as terahertz testing, are introduced to improve the accuracy and reliability of the inspection.

[0020] Preferably, the AB PLC system communicates with the remote monitoring terminal, the communication protocol is ModbusTCP, and the communication data is encrypted using encryption algorithm Y with an encryption strength of not less than 128 bits, thereby realizing remote monitoring and operation, and being able to receive external instructions to adjust the welding process in real time. It also supports cloud platform data storage and analysis, thereby realizing centralized management and collaborative optimization of multiple factories and multiple devices.

[0021] Preferably, multiple sets of welding equipment are used to work simultaneously, with the number of equipment being no less than 2 sets, and are uniformly coordinated and controlled by AB PLC to improve welding efficiency, with the efficiency improvement ratio being no less than 40%, and the response time of the equipment collaborative control algorithm M not exceeding 60ms, and virtual simulation technology is used to preview and optimize the collaborative work of multiple devices to reduce debugging time and cost in actual production.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. Significantly improve welding accuracy: AB PLC accurately controls welding path, angle and speed, reduces welding deviation, ensures uniform welds, and improves welding geometric accuracy and dimensional accuracy.

[0024] 2. Improve welding quality: Use a variety of advanced auxiliary technologies, such as magnetic field assisted welding, ultrasonic vibration assisted welding, etc., to refine grains, reduce welding defects, and improve the strength, toughness and density of welds, thereby significantly improving welding quality.

[0025] 3. Optimize welding process: With the help of thermal stress distribution model, the welding path can be reasonably planned, welding deformation and residual stress can be reduced, and a more optimized welding process can be achieved.

[0026] 4. Improve production efficiency: The automatic cladding method combined with multiple sets of equipment to work together greatly shortens the welding time, while reducing the time for manual intervention and adjustment, significantly improving production efficiency.

[0027] 5. Enhanced process adaptability: It can adapt to the pipe welding requirements of different pipe diameters, pipe lengths and pipe wall thicknesses, and meet diversified production tasks by flexibly adjusting welding parameters and path planning.

[0028] 6. Realize intelligent management: Use AB PLC to comprehensively monitor and control the welding process, and combine with big data analysis technology to realize intelligent adjustment and optimization of the welding process, while being able to effectively manage and utilize welding data.

[0029] 7. Ensure data security: The use of advanced encryption algorithms ensures the security of data during the transmission and storage of welding processes, protecting the company's technical secrets and production data.

[0030] 8. Reduce costs: Reduce rework and material waste caused by welding defects, improve equipment utilization, and reduce labor costs and production costs.

[0031] 9. Improve detection accuracy: The introduction of advanced detection technologies such as high-resolution imaging systems and terahertz detection can detect welding defects more accurately and improve the product qualification rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a main flow diagram of the present invention;

[0033] Figure 2 It is a parameter diagram of S1 in the present invention;

[0034] Figure 3 It is a schematic diagram of S2 in the present invention;

[0035] Figure 4 It is a schematic diagram of S3 in the present invention;

[0036] Figure 5 It is a parameter diagram of S4 in the present invention;

[0037] Figure 6 It is a schematic diagram of the size parameters of S1 in the present invention;

[0038] Figure 7 It is a schematic diagram of the contents of S3 in the present invention;

[0039] Figure 8 It is a schematic diagram of the entire process of the present invention;

[0040] Fig. 9 It is a parameter schematic diagram of the temperature sensor in the present invention;

[0041] Fig.10 It is a parameter diagram of the movement speed of the welding equipment in the present invention. DETAILED DESCRIPTION

[0042] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0043] See also Figure 1-Figure 10 The present invention provides an automatic surfacing method for positive and negative welding of pipes based on AB PLC, comprising the following steps:

[0044] S1: Use the AB PLC system to obtain the size parameters of the pipe, including pipe diameter D, pipe length L and pipe wall thickness T. The modeling formula is N = g (D, L, T), where g (D, L, T) = 0.3D 2 L+0.15DT 2 At the same time, laser scanning technology is introduced to scan the microstructure of the perforated pipe and obtain microstructural data. The laser scanning technology emits a laser beam to the surface of the perforated pipe and then uses a sensor to receive the reflected laser. Since the propagation speed of the laser is a known constant, the distance traveled by the laser beam can be calculated by measuring the time difference from the emission to the reception of the laser. When the laser beam scans the surface of the perforated pipe quickly, a large amount of point cloud data can be obtained, which reflects the shape, contour and microstructural characteristics of the perforated pipe surface. By processing and analyzing these point cloud data, a three-dimensional model of the perforated pipe can be accurately constructed, and detailed information on its microstructure, such as surface texture, roughness, tiny cracks or defects, can be obtained. This non-contact measurement method has the characteristics of high precision, high speed and high resolution, and can provide accurate and rich data support for optimizing the modeling of perforated pipes, thereby improving the accuracy and reliability of subsequent welding path planning and process parameter setting;

[0045] S2: Analyze the surface characteristics of the through-tube welding area, set the surface roughness threshold Rz≤3.2μm, and use a high-resolution imaging system to perform real-time imaging analysis of the welding area to detect potential surface defects. The high-resolution imaging system is usually based on the principle of optical imaging. It consists of a high-performance lens, an image sensor, and an image processing unit. The lens is responsible for collecting the light emitted or reflected by the welding area and focusing it on the image sensor. The image sensor can be a charge-coupled device (CCD) or a complementary metal oxide semiconductor (CMOS). Its function is to convert the optical signal into an electrical signal. During the welding process, strong arc light, high temperature, and spattering slag will interfere with the imaging. Therefore, the system usually uses special filtering and shading technology to ensure that only valid welding area images are obtained. The image processing unit processes and analyzes the electrical signals from the image sensor, and improves the image quality and clarity by enhancing contrast, removing noise, adjusting brightness and color, etc. In this way, clear and high-resolution images of the welding area can be obtained in real time, thereby detecting potential surface defects such as tiny cracks, unfused, pores, etc., providing strong support for real-time monitoring and evaluation of welding quality;

[0046] S3: According to the surface characteristics and microstructure data, the welding path of forward and reverse welding is planned, and the alternating interval of forward and reverse welding is K = 0.2D-0.05D. At the same time, combined with the thermal stress distribution model in the welding process, the welding path is optimized to reduce welding deformation. The thermal stress distribution model is a mathematical model used to describe and predict the distribution of thermal stress generated inside the pipe during welding. By predicting the thermal stress distribution under different welding paths, a path that can make the thermal stress distribution more uniform and smaller is selected, thereby reducing welding deformation and residual stress and improving welding quality. Areas where thermal stress is concentrated are prone to welding defects such as cracks and pores. The thermal stress distribution model can help identify these potential defect areas in advance so that corresponding measures can be taken to prevent them during the welding process. According to the thermal stress distribution, the welding current, voltage, welding speed and other parameters are reasonably adjusted to control the heat input during the welding process, thereby optimizing the thermal stress distribution;

[0047] S4: The movement of the welding equipment is controlled by AB PLC so that the welding gun moves according to the planned welding path. At the same time, the angle and welding speed of the welding gun are monitored and adjusted in real time. The angle deviation γ≤1.5°, the welding speed deviation Vd≤5%, and an intelligent adaptive control system is introduced to automatically adjust the welding parameters according to the real-time welding status. The intelligent adaptive control system first collects various parameter information in the welding process in real time through various sensors (such as current sensors, voltage sensors, position sensors, temperature sensors, etc.), including but not limited to welding current, voltage, welding gun position, welding speed, temperature, etc., and then inputs the collected real-time data into the analysis and processing module of the system. This module is usually based on advanced algorithms and models, such as fuzzy logic, neural networks, genetic algorithms, etc., to analyze and process these data, compare and evaluate them with the preset ideal welding state or optimization target, and based on the results of the analysis and evaluation, the system automatically generates corresponding control instructions and adjusts the parameters of the welding equipment (such as current, voltage, speed, etc.) in real time to keep the welding process at the optimal or near-optimal state, so as to adapt to various changes and uncertainties in the welding process and ensure the stability and consistency of the welding quality. For example, if the welding temperature is detected to be too high, the system will automatically reduce the welding current or welding speed to control the heat input; if the welding gun position deviation is found to be large, the position control parameters will be adjusted in time to ensure the accuracy of the welding position.

[0048] The AB PLC system is used to obtain the size parameters of the pipe, specifically: the size data of the pipe is collected through a high-precision measuring device, the pipe diameter measurement accuracy is ±0.1mm, the pipe length measurement accuracy is ±0.5mm, and the pipe wall thickness measurement accuracy is ±0.05mm, and the data is transmitted to the AB The PLC system is used for processing, and the machine learning algorithm is used to correct and predict the errors of the measured data. By establishing a linear relationship between the independent variable (measured data) and the dependent variable (true value), the error of the measured data is corrected and predicted. By building a decision tree to classify and predict the data, error analysis and correction can be performed according to the characteristics and laws of the measured data. The integrated learning algorithm composed of multiple decision trees can integrate the prediction results of multiple decision trees to improve the accuracy of error correction and prediction. It classifies and predicts the data by finding an optimal hyperplane, which is suitable for processing nonlinear relationships in the measured data, simulating the working mode of neurons in the human brain, and learning the patterns and laws in the measured data by training neural networks, so as to correct and predict errors. These machine learning algorithms can discover the error laws in the measured data by learning a large amount of historical measurement data and true values, and make more accurate error correction and prediction for new measurement data.

[0049] When planning the welding path of forward and reverse welding, the material of the pipe is considered, the yield strength range of the material is σs[235,355]MPa, and the welding process requirements are taken into account to generate the optimal welding path, the welding current I=150-200A, and the welding current adjustment formula is I'=I+5(T-T0), where T is the real-time welding temperature and T0 is the preset reference temperature. At the same time, the magnetic field assisted welding technology is introduced to improve the weld quality by adjusting the magnetic field strength and direction. The magnetic field assisted welding technology is a welding technology that applies an external magnetic field during the welding process to improve the welding quality and performance. During welding, by applying a magnetic field of a specific direction and strength in the welding area, the flow and heat transfer of the welding molten pool can be affected, and the formation and growth of the crystal nucleus can be promoted, thereby refining the grain structure of the weld, improving the strength and toughness of the weld, and helping to reduce the generation of defects such as pores and slag inclusions. The magnetic field can change the flow behavior of metal in the molten pool, making it easier for gas and impurities to float out, thereby improving the density of the weld. Reasonable magnetic field effect can control the shape and size of the molten pool, making the weld formation more uniform and beautiful, and improving the geometric accuracy of the weld. The magnetic field can affect the diffusion and distribution of alloy elements during welding, making the chemical composition of the weld more uniform, thereby improving the performance of the weld.

[0050] During the welding process, AB PLC is used to control the wire feed speed, feed speed S = 2.5-3.5m / min, and arc voltage, voltage range U = 20-25V, and the voltage adjustment formula is U' = U + 0.5log (P-P0), where P is the real-time power and P0 is the preset reference power. Ultrasonic vibration assisted welding is used to reduce welding residual stress. Ultrasonic vibration assisted welding is a welding technology that introduces ultrasonic vibration during the welding process. Ultrasonic vibration can break up dendrites in the welding molten pool and promote the formation of equiaxed crystals, thereby refining the grains of the weld and improving the mechanical properties of the weld, such as strength and toughness. Vibration can cause the residual stress generated during the welding process to be released and redistributed, thereby reducing the residual stress level inside the weld, reducing the risk of welding deformation and cracking, and helping to enhance the fluidity of liquid metal in the molten pool, promote uniform mixing of elements, improve the chemical composition uniformity of the weld, and improve the quality of the weld. Ultrasonic vibration can cause gas and impurities to escape more easily from the molten pool and reduce the formation of welding defects such as pores and slag inclusions.

[0051] It also includes using the temperature sensor in the AB PLC system to monitor the temperature of the welding area in real time. The temperature sensor accuracy is ±1°C. When the temperature exceeds the preset threshold Tmax=800°C, the welding current and welding speed are automatically reduced. After the temperature drops to the safe range Tmin=500°C, the normal welding parameters are restored. At the same time, liquid nitrogen cooling technology is used to achieve rapid cooling and improve cooling efficiency.

[0052] The movement speed of the welding equipment is controlled by AB PLC, the speed range is V = 0.5-0.8m / min, and the acceleration range is a = 0.1-0.2m / s 2 , and the acceleration adjustment formula is a'=a+0.03cos(ωt), where ω is the angular frequency and t is the time, and multi-axis linkage control technology is used to achieve more complex welding trajectories. Multi-axis linkage control technology refers to the simultaneous coordinated control of multiple motion axes (usually including linear axes and rotary axes) in the automated processing or manufacturing process, so that they can move accurately and collaboratively according to the predetermined trajectory and speed. In the PLC's automatic surfacing method for forward and reverse welding of pipes, the multi-axis linkage control technology may involve precise control of multiple degrees of freedom such as the position, posture, and movement of the welding gun, for example, in addition to controlling the movement of the welding gun in the three linear directions of X, Y, and Z, it is also possible to simultaneously control the rotation of the welding gun around the X, Y, and Z axes, as well as other possible motion axes, to achieve complex and high-precision welding trajectories. Through multi-axis linkage control, welding of various complex shapes can be achieved, which can adapt to pipe welding tasks of different shapes and requirements, and improve the accuracy, efficiency, and quality of welding. Multi-axis linkage control usually relies on high-performance controllers, drivers, and precise mechanical transmission systems, as well as precise kinematic models and algorithms to achieve coordinated movement between axes.

[0053] AB PLC is used to record and analyze various parameters in the welding process to form a welding process database. The database storage capacity is not less than 150GB. The data compression algorithm Z is used, and the compression ratio is not less than 2.5:1. It provides a reference for subsequent similar pipe welding. At the same time, big data analysis technology is used to deeply mine the database and extract potential process optimization solutions. By analyzing a large amount of welding data, key factors affecting welding quality can be found, such as specific parameter combinations, welding environmental conditions, etc. Based on these findings, process parameters can be optimized to improve the quality of welded joints and reduce the occurrence of defects. Mining the rules in the data can help find the most optimized production process and parameter settings, reduce unnecessary operations and waiting time, improve equipment utilization and production efficiency, and find ways to reduce material consumption, energy consumption, and equipment maintenance costs through big data analysis, thereby reducing overall production costs.

[0054] After welding is completed, the AB PLC-controlled detection equipment is used to automatically detect the quality of the welded joint, with a detection accuracy of not less than 0.01mm. The defect recognition algorithm X is used, and the recognition accuracy is not less than 90%. If the detection is unqualified, the welding repair is carried out again, and new non-destructive testing technologies such as terahertz detection are introduced to improve the accuracy and reliability of the detection. Terahertz waves can penetrate some non-polar and non-metallic materials, such as plastics, ceramics, paper, etc., and have potential for detecting objects covered or wrapped by these materials. In the AB PLC-based automatic surfacing method for forward and reverse welding of pipes, terahertz detection can be used for high-precision, non-contact non-destructive testing of welded joints, and possible internal defects and quality problems can be discovered in time without destroying the welded structure.

[0055] The AB PLC system communicates with the remote monitoring terminal using ModbusTCP as the communication protocol. The encryption algorithm Y is used to encrypt the communication data. The encryption strength is not less than 128 bits, so as to realize remote monitoring and operation, and can receive external instructions to adjust the welding process in real time. At the same time, it supports cloud platform data storage and analysis, and realizes centralized management and collaborative optimization of multiple factories and multiple equipment. The encryption algorithm Y can be a variety of common encryption algorithms, such as:

[0056] AES (Advanced Encryption Standard): A symmetric encryption algorithm that is widely used in various encryption scenarios;

[0057] RSA (Rivest–Shamir–Adleman): an asymmetric encryption algorithm commonly used for digital signatures and key exchange;

[0058] DES (Data Encryption Standard): a symmetric encryption algorithm that is relatively old but still used in some specific scenarios;

[0059] The specific encryption algorithm to be used needs to be determined based on actual security needs, performance requirements and application scenarios.

[0060] Use multiple sets of welding equipment to work simultaneously, with the number of equipment not less than 2 sets, and use AB PLC for unified coordination and control to improve welding efficiency, with the efficiency improvement ratio not less than 40%, and the response time of the equipment collaborative control algorithm M not exceeding 60ms, and use virtual simulation technology to preview and optimize the collaborative work of multiple devices, reducing the debugging time and cost in actual production;

[0061] Equipment collaborative control algorithm M is an algorithm used to coordinate and control multiple sets of welding equipment working simultaneously. According to the characteristics of the welding task and the performance of each device, the welding work is reasonably allocated to different devices to ensure efficient completion of the task and synchronization of the actions of multiple sets of equipment during the welding process to avoid mutual interference and conflict. For example, the start, stop, welding speed and other parameters of each device are controlled to remain consistent or change in a coordinated manner. When a device fails, the working status of other devices can be adjusted in time to ensure that the overall welding work is not greatly affected, or the task can be quickly reallocated. According to the real-time monitored equipment working status and welding progress, the working parameters of each device are dynamically adjusted to achieve the best welding efficiency and quality.

[0062] Embodiment 1:

[0063] Select a pipe with a diameter D of 50mm, a length L of 1000mm, and a wall thickness T of 10mm.

[0064] First, the AB PLC system combined with laser scanning technology was used to obtain the size parameters and microstructure data of the pipe penetration, and an accurate pipe penetration model was obtained. The surface characteristics of the welding area were analyzed, and the surface roughness was 2.8μm. The forward and reverse welding paths were planned, and the alternating interval of forward and reverse welding was 9mm.

[0065] During the welding process, the welding current was set to 160A, the wire feed speed was set to 2.8m / min, and the arc voltage was set to 22V. The temperature of the welding area was monitored in real time using a temperature sensor. When the temperature reached 750°C, the welding current and welding speed were automatically reduced. The movement of the welding equipment was controlled by an AB PLC, with an angle deviation of 1.2° and a welding speed deviation of 3%.

[0066] After welding is completed, a defect recognition algorithm is used for detection, with an accuracy rate of 92%.

[0067] Embodiment 2:

[0068] Select a pipe with a diameter D of 80mm, a length L of 1500mm, and a wall thickness T of 15mm.

[0069] The size parameters and microstructure data were obtained and modeled in the same manner as in Example 1. The surface roughness was 3 μm, and the alternating interval between positive and negative welding was 15 mm.

[0070] The welding parameters were set as follows: welding current 180A, wire feed speed 3m / min, arc voltage 24V. Parameters were adjusted when the temperature exceeded 780°C. The welding equipment motion control angle deviation was 1°, and the welding speed deviation was 4%.

[0071] The test results show that the defect recognition accuracy rate is 93%.

[0072] Embodiment 3:

[0073] The pipe diameter D is 60mm, the pipe length L is 1200mm, and the pipe wall thickness T is 12mm.

[0074] After obtaining parameters and modeling, the surface roughness is 2.5μm, and the alternating interval between positive and negative welding is 11mm.

[0075] Welding current 170A, wire feed speed 2.7m / min, arc voltage 23V, adjust parameters when temperature reaches 760°C. Angle deviation 0.8°, welding speed deviation 2%.

[0076] The defect recognition accuracy of the detection is 95%.

[0077] Comparative Example 1:

[0078] The traditional manual surfacing method is used to weld the pipe with a diameter D of 50 mm, a length L of 1000 mm, and a wall thickness T of 10 mm.

[0079] During the welding process, the welding parameters are adjusted based on manual experience, and technologies such as laser scanning and high-resolution imaging are not used. There is also no intelligent adaptive control and thermal stress distribution model optimization.

[0080] After welding was completed, the detection found that the surface roughness was 4μm, there were many defects, and the defect identification accuracy was only 70%.

[0081] Comparative Example 2:

[0082] Use simple automated welding equipment to weld pipes with a diameter D of 80 mm, a length L of 1500 mm, and a wall thickness T of 15 mm.

[0083] However, the comprehensive control and optimization algorithm of the AB PLC system was not introduced, the welding path planning was not accurate enough, and the temperature control and equipment motion control were not precise enough.

[0084] The detection results show that the surface roughness is 3.5μm and the defect recognition accuracy is 80%.

[0085] In order to more intuitively compare the effects of the method of the present invention with the traditional method, the following experimental examples were conducted and the data were organized into a table:

[0086]

[0087] It can be clearly seen from the data in the above table that the automatic surfacing method for forward and reverse welding of pipes based on AB PLC of the present invention is superior to traditional manual surfacing and simple automated welding methods in terms of welding accuracy, quality control and defect detection, and has significant advantages and good application prospects.

[0088] The differences between Examples 1, 2 and 3 are mainly reflected in:

[0089] The dimensional parameters of the pipes are different: including pipe diameter, pipe length and pipe wall thickness. This leads to differences in welding workload, heat input and welding deformation control.

[0090] The specific parameter settings during welding are different: such as welding current, wire feed speed, arc voltage, etc., to adapt to the requirements of pipe threading and welding of different sizes;

[0091] The control accuracy and detection results during the welding process also vary: for example, welding angle deviation and defect recognition accuracy.

[0092] The significance of setting these three embodiments is:

[0093] The applicability of the present invention is fully demonstrated: through the pipe welding embodiments of different sizes, it is shown that the method of the present invention can be applied to various types of pipe welding and has wide applicability.

[0094] Verify the effects under different parameters: Different welding parameter settings can verify the stability and reliability of the present invention under various working conditions, providing diverse references for practical applications.

[0095] Highlight the flexibility of the method: It can flexibly adjust welding parameters and control strategies according to different pipe penetration characteristics and requirements to achieve the best welding effect.

[0096] Enhanced persuasiveness: The successful results of multiple embodiments together demonstrate the effectiveness and superiority of the method of the present invention, making it more technically credible and persuasive.

[0097] In summary, the present invention proposes an automatic surfacing method for forward and reverse welding of pipes based on AB PLC, which realizes high precision, high efficiency and high quality of the pipe welding process by introducing a series of advanced technologies and innovative algorithms.

[0098] The combination of laser scanning technology and high-resolution imaging systems enables us to fully understand the characteristics of pipe penetration and the real-time status of the welding area from the microscopic and macroscopic levels, providing a solid foundation for accurate modeling and high-quality welding path planning. The application of thermal stress distribution models and intelligent adaptive control systems enables welding parameters to be automatically adjusted according to real-time conditions, greatly improving the stability and adaptability of the welding process and effectively reducing welding deformation and defects.

[0099] The integration of magnetic field assisted welding technology, ultrasonic vibration assisted welding and multi-axis linkage control technology has further improved the quality of welds and the complexity of welding, and expanded the scope of application of the method of the present invention. The application of big data analysis technology provides strong data support for the continuous optimization and innovation of the process, and terahertz detection technology ensures accurate evaluation of welding quality.

[0100] In multiple embodiments, the method of the present invention has shown significantly better performance than the traditional method, which not only improves welding quality and efficiency, reduces costs, but also enhances the adaptability and reliability of the welding process. By comparing with the comparative examples, the advantages of the present invention in welding accuracy, defect control, quality detection, etc. are further highlighted.

[0101] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. An automatic surfacing method for positive and negative welding of pipes based on AB PLC, characterized in that: The following steps are involved: S1: Use the AB PLC system to obtain the dimensional parameters of the pipe, including the pipe diameter D, pipe length L and pipe wall thickness T. The modeling formula is N = g(D, L, T), where g(D, L, T) = 0.3D2 L + 0.15DT2. At the same time, laser scanning technology is introduced to scan the microstructure of the pipe to obtain microstructure data; S2: Analyze the surface characteristics of the pipe penetration welding area, set the surface roughness threshold Rz≤3.2μm, and use a high-resolution imaging system to perform real-time imaging analysis of the welding area to detect potential surface defects; S3: According to the surface characteristics and microstructure data, the welding path of forward and reverse welding is planned, and the alternating interval of forward and reverse welding is K=0.2D-0.05D. At the same time, the thermal stress distribution model during welding is combined to optimize the welding path to reduce welding deformation; S4: The movement of the welding equipment is controlled by AB PLC, so that the welding gun moves according to the planned welding path. At the same time, the angle and welding speed of the welding gun are monitored and adjusted in real time. The angle deviation γ ≤ 1.5°, the welding speed deviation Vd ≤ 5%, and an intelligent adaptive control system is introduced to automatically adjust the welding parameters according to the real-time welding status.

2. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: The AB PLC system is used to obtain the dimensional parameters of the pipe penetration, specifically: the dimensional data of the pipe penetration is collected through a high-precision measuring device, the pipe diameter measurement accuracy is ±0.1mm, the pipe length measurement accuracy is ±0.5mm, and the pipe wall thickness measurement accuracy is ±0.05mm, and the data is transmitted to the AB PLC system for processing, and the machine learning algorithm is used to correct and predict the measurement data.

3. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: When planning the welding path for forward and reverse welding, the material of the pipe is considered, the yield strength range of the material is σs[235,355]MPa, and the welding process requirements are taken into account to generate the optimal welding path, the welding current I=150-200A, and the welding current adjustment formula is I'=I+5(T-T0), where T is the real-time welding temperature and T0 is the preset reference temperature. At the same time, magnetic field assisted welding technology is introduced to improve the weld quality by adjusting the magnetic field strength and direction.

4. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: During the welding process, AB PLC is used to control the wire feed speed, feed speed S = 2.5-3.5m / min, and arc voltage, voltage range U = 20-25V, and the voltage adjustment formula is U' = U + 0.5log (P-P0), where P is the real-time power and P0 is the preset reference power. Ultrasonic vibration is used to assist welding to reduce welding residual stress.

5. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: It also includes using the temperature sensor in the AB PLC system to monitor the temperature of the welding area in real time. The temperature sensor accuracy is ±1°C. When the temperature exceeds the preset threshold value Tmax=800°C, the welding current and welding speed are automatically reduced. After the temperature drops to the safe range Tmin=500°C, the normal welding parameters are restored. At the same time, liquid nitrogen cooling technology is used to achieve rapid cooling and improve cooling efficiency.

6. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: The ABPLC controls the movement speed of the welding equipment, with a speed range of V = 0.5-0.8 m / min, and the acceleration, with an acceleration range of a = 0.1-0.2 m / s 2 , and the acceleration adjustment formula is a'=a+0.03cos(ωt), where ω is the angular frequency and t is the time, and multi-axis linkage control technology is used to achieve more complex welding trajectories.

7. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: ABPLC is used to record and analyze various parameters in the welding process to form a welding process database. The database storage capacity is not less than 150GB. The data compression algorithm Z is used, and the compression ratio is not less than 2.5:

1. It provides a reference for subsequent similar pipe penetration welding. At the same time, big data analysis technology is used to deeply mine the database and extract potential process optimization solutions.

8. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: After welding is completed, the AB PLC-controlled inspection equipment is used to automatically inspect the quality of the welded joints. The inspection accuracy is not less than 0.01mm. The defect recognition algorithm X is used, and the recognition accuracy is not less than 90%. If the inspection fails, the welding repair is performed again. New non-destructive testing technologies, such as terahertz testing, are introduced to improve the accuracy and reliability of the inspection.

9. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: The ABPLC system communicates with the remote monitoring terminal using ModbusTCP as the communication protocol. The encryption algorithm Y is used to encrypt the communication data with an encryption strength of no less than 128 bits to achieve remote monitoring and operation. It can also receive external instructions to adjust the welding process in real time. It also supports cloud platform data storage and analysis to achieve centralized management and collaborative optimization of multiple factories and multiple devices.

10. The method for automatic surfacing of pipe front and back welding based on AB PLC as claimed in claim 1, characterized in that: Use multiple sets of welding equipment to work simultaneously, with no less than 2 sets of equipment, and use AB PLC for unified coordination and control to improve welding efficiency. The efficiency improvement ratio is no less than 40%, and the response time of the equipment collaborative control algorithm M does not exceed 60ms. In addition, virtual simulation technology is used to preview and optimize the collaborative work of multiple devices to reduce the debugging time and cost in actual production.

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