Supercritical boiler support multi-node efficient energy-saving automatic welding process

Abstract

This invention relates to the field of industrial boiler equipment manufacturing technology, and discloses a multi-node, high-efficiency, energy-saving, and automated welding process for supercritical boiler supports, including the following steps: pre-detection and database construction of welding material composition: using a miniature laser-induced breakdown spectroscopy sensor to detect the elemental content of welding materials used in supercritical boiler supports, establishing a basic database of welding material composition, and calculating the deviation values ​​of each element from the standard composition; workpiece pre-treatment and fixture installation; equipment system linkage debugging; pre-adaptation of pre-welding parameters; layered and segmented collaborative welding; time-division and frequency-division four-mode collaborative quality closed-loop control; post-weld treatment and system reset. Through time-division and frequency-division four-mode collaborative quality control, dynamic compensation for welding deformation, and interpass temperature control technology, this invention effectively reduces the incidence of defects such as incomplete fusion and cracks in welds, ensures that the pass rate of weld radiographic inspection meets standards, and improves the stability of welding quality.

Description

Technical Field

[0001] This invention relates to the field of industrial boiler equipment manufacturing technology, specifically to a multi-node, high-efficiency, energy-saving, and automated splicing process for supercritical boiler supports. Background Technology

[0002] Supercritical boilers are boilers whose operating parameters exceed the critical pressure (22.1 MPa) and critical temperature (374℃) of water. With their high energy conversion efficiency and significant energy-saving characteristics, they have become core equipment in modern power plants and are widely used in the power generation field. Because supercritical boilers must withstand temperatures above 540℃, pressures above 25 MPa, and alternating loads during operation, the fixing and support of their main body and auxiliary components rely on specialized steel support structures. These supports must not only provide stable load-bearing capacity but also adapt to the boiler's installation layout and force transmission requirements. To meet the structural strength and stability requirements under complex operating conditions, the supports are typically designed as multi-node structures such as T-type, cross-type, and box-type. These nodes, as key load-bearing parts, require welding processes to achieve a firm connection between the components; therefore, the quality of the multi-node welding directly determines the overall service reliability of the support.

[0003] Currently, welding of supercritical boiler supports in China is mainly done manually and semi-automatically. Although some companies have introduced welding robots, they lack systematic technical solutions adapted to supercritical operating conditions. In the existing welding process, monitoring of welding quality relies heavily on post-weld non-destructive testing, making it difficult to have real-time and effective process control methods. This leads to defects such as incomplete fusion and cracks in the weld due to uneven heat input and insufficient deformation compensation, resulting in large fluctuations in the pass rate of weld radiographic inspection and difficulty in ensuring the stability of welding quality. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-node, high-efficiency, energy-saving, and automated welding process for supercritical boiler supports. This process solves the problem of difficulty in real-time and effective process control, which leads to defects such as incomplete weld fusion and cracks caused by uneven heat input and insufficient deformation compensation during welding.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-node, high-efficiency, energy-saving, and automated splicing process for supercritical boiler supports, comprising the following steps: Pre-detection and database construction of welding material composition: The elemental content of welding materials used in supercritical boiler supports is detected by a miniature laser-induced breakdown spectroscopy sensor, a basic database of welding material composition is established, and the deviation values ​​of each element from the standard composition are calculated. Workpiece pretreatment and fixture installation: Beveling is performed on the T-shaped, cross-shaped and box-shaped nodes of the supercritical boiler support. The workpiece is fixed in the cam-driven adaptive fixture and positioned by electromagnetic adsorption locking. Multi-source sensors are deployed in the key node areas of the workpiece and the preset position of the welding torch. Equipment system linkage debugging: Start the welding system, cam-driven adaptive fixture drive system, time-division frequency four-mode collaborative quality control system and welding material composition detection system, complete the communication connection and synchronous calibration of each system, initialize the detection parameters and time-division switching nodes of the time-division frequency four-mode collaborative quality control system, and verify the coordination of equipment and sensors by no-load trial operation; Pre-welding parameter pre-adaptation: The current welding node type is identified by the visual positioning system, the initial welding parameters and initial stiffness parameters of the fixture are retrieved from the welding thermal cycle database, the initial welding current is corrected based on the element deviation value, and a composition-energy-stress model is established by combining finite element analysis to verify and adjust the initial welding parameters and initial stiffness of the fixture. Layered and segmented collaborative welding: The corresponding detection modes are switched sequentially according to the stages of root pass welding, fill welding, and cover welding. The four-mode detection signals are collected and processed by frequency division. Defect feature parameters are extracted to determine whether there are defects. If a defect or welding abnormality is detected, a shutdown response is immediately triggered. After correcting the parameters according to the defect type, the welding is automatically restarted until the defect is eliminated. Time-division and frequency-division four-mode collaborative quality closed-loop control: During the welding stage, the corresponding detection mode is switched, the four-mode detection signals are collected and processed by frequency division, the defect feature parameters are extracted to determine whether there is a defect, if a defect or welding abnormality is detected, a shutdown response is immediately triggered, the process parameters are corrected according to the defect type and the welding is automatically restarted until the defect is eliminated; Post-weld treatment and system reset: The welded joint is treated with a synergistic process of nanocrystalline coating and post-weld heat treatment. The residual heat during the heat treatment process is recovered, the equipment and sensors are cleaned and maintained, the welding process data is sorted out and the welding thermal cycle database and process parameter optimization model are updated, and the system parameters are reset to the initial state.

[0006] Preferably, the elements include Mn, Si, C, P, and S.

[0007] Preferably, the calculation of the deviation values ​​of each key element from the standard component includes the following steps: Determine the standard component values ​​corresponding to each key element; The actual content values ​​of key elements in the welding material are collected using a miniature laser-induced breakdown spectroscopy sensor. The deviation value is calculated by the difference between the actual content value and the standard component value. The deviation value is expressed as an absolute or relative value.

[0008] Preferably, the multi-source sensor includes a stress sensor, a magnetostrictive sensor, a phase-change resistor array, an eddy current sensor, an acoustic sensor, an electromagnetic sensor, and an infrared thermal imager. All sensors are equipped with an adaptive threshold algorithm to process the signals collected by the sensors.

[0009] Preferably, the pre-adaptation of welding parameters further includes process pre-verification based on a full-element digital twin model of the welding process. The digital twin model includes a geometric twin, a physical twin, a process twin, and a data twin. By simulating the evolution of the temperature field, stress field, and deformation field of the welding process, the rationality of the corrected initial welding parameters is verified. The rationality judgment criteria are welding residual stress ≤150MPa and welding deformation ≤±1mm.

[0010] Preferably, the step of dynamically adjusting the welding parameters based on the verified signal data is as follows: Based on the weld metal resistance change signal collected by the phase change resistor array in the multi-source sensor fusion system, the phase transformation progress of bainite and martensite is determined, and data cross-verification is performed by combining the real-time stress data collected by the stress sensor to adjust the welding current.

[0011] Preferably, the interlayer temperature controlled by the infrared temperature measuring array and the forced air cooling device is controlled at 150~250℃, and the interlayer temperature fluctuation range is controlled within ≤±10℃ by the infrared temperature measuring array and the forced air cooling device.

[0012] Preferably, the steps of the time-division and frequency-division four-mode collaborative quality closed-loop control are as follows: The root pass welding stage uses eddy current detection and acoustic detection modes, the fill pass welding stage uses infrared thermography and electromagnetic detection modes, and the cover pass welding stage uses a four-mode fusion detection mode. When abnormalities such as arc interruption or gas protection failure occur during the welding process, the automatic shutdown response time is ≤1s.

[0013] Preferably, the steps of the synergistic process of nanocrystalline coating and post-weld heat treatment are as follows: A nanocrystalline coating is prepared on the weld surface using physical vapor deposition technology. Then, post-weld heat treatment is performed at a tempering temperature of 600~650℃ and a holding time of 24h. The waste heat recovery is achieved by collecting the heat dissipation waste heat of the workpiece and equipment through a micro waste heat recovery device in the welding material cooling circuit, which is stored in a phase change energy storage unit for preheating the next batch of workpieces.

[0014] Preferably, before the layered and segmented collaborative welding, the workpiece is preheated to 150~200℃; after welding, the workpiece is subjected to a post-heating process of 200℃×1h. Before use, the welding materials are dried at 400℃ for 2 hours. Through the synergistic effect of preheating, post-heating and welding material drying, the diffusible hydrogen content of the weld is controlled to be below 5mL / 100g.

[0015] This invention provides a multi-node, high-efficiency, energy-saving, and automated welding process for supercritical boiler supports. It offers the following advantages: 1. This invention effectively reduces the incidence of defects such as incomplete fusion and cracks in welds by using time-division and frequency-division four-mode collaborative quality control, dynamic compensation for welding deformation, and interpass temperature control technology, ensuring that the pass rate of weld radiographic inspection meets the standards and improving the stability of welding quality.

[0016] 2. This invention significantly improves the automation coverage of multi-node welding of supercritical boiler supports by relying on the rapid changeover capability of cam-driven adaptive fixtures and closed-loop control technology that integrates multi-source sensors, shortens the welding time of a single support, significantly reduces labor costs, and adapts to the needs of multi-variety, small-batch production.

[0017] 3. This invention utilizes a synergistic process of nanocrystalline coating and post-weld heat treatment, along with a heat input and microstructure property mapping model, to optimize the weld microstructure, enhance the high-temperature creep strength and anti-creep properties of the welded joint, and meet the long-term service requirements under supercritical conditions.

[0018] 4. This invention combines digital twin process pre-verification to reduce the number of on-site trial welds, and reduces the energy consumption per weld seam through waste heat recovery and efficient utilization of welding materials, thus achieving green manufacturing while ensuring performance. Attached Figure Description

[0019] Figure 1 This is a process flow diagram of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described 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.

[0021] Please see the appendix Figure 1 This invention provides a multi-node, high-efficiency, energy-saving, and automated welding process for supercritical boiler supports, comprising the following steps: Pre-detection and database construction of welding material composition: The elemental content of welding materials used in supercritical boiler supports is detected by a miniature laser-induced breakdown spectroscopy sensor, a basic database of welding material composition is established, and the deviation values ​​of each element from the standard composition are calculated. Workpiece pretreatment and fixture installation: Beveling is performed on the T-shaped, cross-shaped and box-shaped nodes of the supercritical boiler support. The workpiece is fixed in the cam-driven adaptive fixture and positioned by electromagnetic adsorption locking. Multi-source sensors are deployed in the key node areas of the workpiece and the preset position of the welding torch. Equipment system linkage debugging: Start the welding system, cam-driven adaptive fixture drive system, time-division frequency four-mode collaborative quality control system and welding material composition detection system, complete the communication connection and synchronous calibration of each system, initialize the detection parameters and time-division switching nodes of the time-division frequency four-mode collaborative quality control system, and verify the coordination of equipment and sensors by no-load trial operation; Pre-welding parameter pre-adaptation: The current welding node type is identified by the visual positioning system, the initial welding parameters and initial stiffness parameters of the fixture are retrieved from the welding thermal cycle database, the initial welding current is corrected based on the element deviation value, and a composition-energy-stress model is established by combining finite element analysis to verify and adjust the initial welding parameters and initial stiffness of the fixture. Layered and segmented collaborative welding: The corresponding detection modes are switched sequentially according to the stages of root pass welding, fill welding, and cover welding. The four-mode detection signals are collected and processed by frequency division. Defect feature parameters are extracted to determine whether there are defects. If a defect or welding abnormality is detected, a shutdown response is immediately triggered. After correcting the parameters according to the defect type, the welding is automatically restarted until the defect is eliminated. Time-division and frequency-division four-mode collaborative quality closed-loop control: During the welding stage, the corresponding detection mode is switched, the four-mode detection signals are collected and processed by frequency division, the defect feature parameters are extracted to determine whether there is a defect, if a defect or welding abnormality is detected, a shutdown response is immediately triggered, the process parameters are corrected according to the defect type and the welding is automatically restarted until the defect is eliminated; Post-weld treatment and system reset: The welded joint is treated with a synergistic process of nanocrystalline coating and post-weld heat treatment. The residual heat during the heat treatment process is recovered, the equipment and sensors are cleaned and maintained, the welding process data is sorted out and the welding thermal cycle database and process parameter optimization model are updated, and the system parameters are reset to the initial state.

[0022] By adopting the above technical solution, a miniature laser-induced breakdown spectroscopy sensor is used to detect the content of key elements in welding materials for supercritical boiler supports, establishing a basic database of welding material composition, and using absolute deviation... ,in, The actual content of key elements, Standard composition and relative deviation of key elements ,in, To determine the relative deviation of key elements, the deviation values ​​of each key element from the standard composition are calculated. By capturing the fluctuation data of welding material composition, basic data support is provided for the pre-adaptation of subsequent welding parameters, so as to achieve the matching of welding material composition and welding parameters and avoid the adverse effects of unstable welding material composition on welding quality. Beveling is performed on T-shaped, cross-shaped, and box-shaped nodes of supercritical boiler supports. The workpiece is fixed in a cam-driven adaptive fixture and positioned by electromagnetic adsorption locking. Multi-source sensors are deployed in key node areas of the workpiece and at preset positions of the welding torch. Beveling ensures the basic conditions for welding fusion. The fixture achieves workpiece positioning by electromagnetic adsorption locking. Multi-source sensors realize comprehensive acquisition of temperature, stress, phase transformation, and defect-related signals during the welding process, adapting to the welding requirements of different types of nodes and providing hardware support for subsequent signal processing and parameter adjustment. Start the welding system, cam-driven adaptive fixture drive system, time-division frequency four-mode collaborative quality control system and welding material composition detection system, complete the communication connection and synchronous calibration of each system, initialize the detection parameters and time-division switching nodes of the time-division frequency four-mode collaborative quality control system, verify the coordination of equipment and sensors through no-load test run, ensure smooth signal transmission between equipment through system communication calibration, clarify the working parameters of the detection mode through parameter initialization, verify the functional matching of equipment through no-load test run, and avoid process interruption due to equipment coordination failure or parameter abnormality during welding. The current welding node type is identified using a visual positioning system. Initial welding parameters and fixture initial stiffness parameters are retrieved from the welding thermal cycle database. The initial welding current is corrected based on the deviation values ​​of key elements. A composition-energy-stress model is then established using finite element analysis. ,in, For welding stress, For welding current, The deviation value for the key element. To ensure the stiffness of the fixture, the initial welding parameters and the initial stiffness of the fixture are checked and adjusted. Visual positioning is used to identify the node type. Based on the composition deviation value, the parameters are corrected to make the welding current adapt to the composition state of the welding material. The composition-energy-stress model quantifies the correlation between heat input, composition deviation and stress, and realizes the optimization and adjustment of the initial parameters, providing parameter assurance for welding. The system sequentially switches between the corresponding detection modes for the root pass, fill pass, and cover pass stages, collects and processes the four-mode detection signals by frequency division, extracts defect feature parameters to determine the presence of defects, and immediately triggers a shutdown response if a defect or welding abnormality is detected. After correcting the parameters according to the defect type, the system automatically restarts the welding process until the defect is eliminated. The phased switching of detection modes enables targeted quality monitoring for different welding stages, frequency division processing avoids interference between signals from different modes, defect feature extraction and judgment enable the identification of welding defects, and abnormal shutdown and parameter correction enable real-time control of welding quality, ensuring the quality of weld formation. During the welding stage, the corresponding detection mode is switched, the four-mode detection signals are collected and processed by frequency division, and the defect feature parameters are extracted to determine whether there is a defect. If a defect or welding abnormality is detected, a shutdown response is immediately triggered. After the process parameters are corrected according to the defect type, the welding is automatically restarted until the defect is eliminated. The time-division and frequency-division four-mode collaborative detection covers the internal and surface quality monitoring dimensions of the weld. The rapid response mechanism enables timely handling of welding abnormalities, and the process parameter correction ensures the quality of the welding and avoids the continuous generation of unqualified welds. A synergistic process of nanocrystalline coating and post-weld heat treatment is employed to process the welded joint. This process recovers residual heat during heat treatment, cleans and maintains equipment and sensors, organizes and updates the welding thermal cycle database and process parameter optimization model, and resets system parameters to their initial state. The synergistic optimization of the welded joint microstructure by nanocrystalline coating and post-weld heat treatment, along with residual heat recovery for energy reuse, equipment maintenance and data updates to ensure process continuity and subsequent optimization of welding parameters, improves the high-temperature performance of the welded joint, and reduces energy consumption during the welding process.

[0023] The elements include Mn, Si, C, P, and S.

[0024] By adopting the above technical solution, a micro laser-induced breakdown spectroscopy sensor is used to detect the content of Mn, Si, C, P, and S in the welding materials used for supercritical boiler supports, establishing a basic database of welding material composition. By capturing the content fluctuation data of these five elements, compositional basis is provided for pre-adaptation of welding parameters. Combined with the composition-energy-stress model σ=f(I,ΔC,K), where σ is the welding stress, I is the welding current, ΔC is the deviation value of Mn, Si, C, P, and S, and K is the fixture stiffness, the correlation between element content changes and heat input and welding stress is quantified, enabling targeted correction of welding current. Among these, the control of C content can help suppress the generation of cold cracks in steel, the adaptation of Mn and Si content can optimize the mechanical properties of weld metal, and the monitoring of P and S content can avoid the increase of weld brittleness or defects caused by their excess. Ultimately, compositional guarantee is provided for the uniformity of weld microstructure and high-temperature service performance.

[0025] Calculating the deviation values ​​of each key element from the standard composition includes the following steps: Determine the standard component values ​​corresponding to each key element; The actual content values ​​of key elements in the welding material are collected using a miniature laser-induced breakdown spectroscopy sensor. The deviation value is calculated by the difference between the actual content value and the standard component value. The deviation value is expressed as an absolute or relative value.

[0026] By adopting the above technical solution, the standard composition values ​​corresponding to each key element are determined. These standard composition values ​​are set according to the industry standard for welding materials used in supercritical boiler support welding and the welding process requirements of Q345R and Q460GJD steel, providing a benchmark for composition deviation calculation. The actual content values ​​of Mn, Si, C, P, and S in the welding material are collected by a micro laser-induced breakdown spectroscopy sensor to capture the content of key elements. The deviation value is calculated by the difference between the actual content value and the standard composition value. The composition fluctuation data of key elements in the welding material are obtained through system collection and calculation, providing direct data support for welding current correction in the pre-welding parameter pre-adaptation stage. Combined with the composition-energy-stress model, a quantitative correlation between the welding material composition and welding process parameters and welding stress is established to achieve the matching of welding process parameters and welding material composition, laying the compositional foundation for the uniformity of weld microstructure. At the same time, monitoring the deviation of C element helps to suppress the generation of cold cracks in steel, and monitoring the deviation of P and S elements avoids the increase of weld brittleness, thus ensuring the high-temperature service performance of the welded joint.

[0027] The multi-source sensors include stress sensors, magnetostrictive sensors, phase-change resistor arrays, eddy current sensors, acoustic sensors, electromagnetic sensors, and infrared thermal imagers. All sensors are equipped with adaptive threshold algorithms to process the signals acquired by the sensors.

[0028] By employing the above technical solution, a stress sensor acquires stress signals during the welding process, a magnetostrictive sensor acquires workpiece deformation signals, a phase change resistor array acquires phase change-related resistance signals of the weld metal, an eddy current sensor acquires signals related to internal weld defects, an acoustic sensor acquires acoustic signals during the welding process, an electromagnetic sensor acquires arc-related electromagnetic signals, and an infrared thermal imager acquires temperature signals of the welding area. The combination of multiple sensors achieves comprehensive acquisition of multi-dimensional physical signals such as stress, deformation, phase change, defects, temperature, and electromagnetic fields during the welding process, avoiding the detection blind spots of a single sensor. All sensors are configured with an adaptive threshold algorithm, which is implemented using the formula... ,in, The adjusted signal recognition threshold, As the initial threshold, This is the environmental impact factor. To mitigate interference from factors such as arc intensity and temperature fluctuations in the welding environment, the signal recognition threshold is dynamically adjusted. All sensors are equipped with electromagnetic shielding structures. The algorithm processes the raw signal data collected by each sensor using a formula. ,in, To integrate the standardized signal data after algorithmic processing, For the first Signal weighting coefficients for various sensors For the first The raw signal data from various sensors, processed by algorithms, achieves preliminary standardization of each signal. Through multi-dimensional signal acquisition, it covers changes in key physical quantities during the welding process. An adaptive threshold algorithm counteracts interference from complex working conditions such as strong arc light and high-temperature radiation, ensuring the reliability of signal data. This provides basic data support for data cross-verification of multi-source sensor fusion, dynamic adjustment of welding parameters, optimization of welding torch path and fixture stiffness, and time-division and frequency-division four-mode collaborative quality control. It establishes the correlation between signal data and welding stress, deformation, and defects, ensuring the realization of closed-loop control for visual positioning, arc tracking, and deformation compensation, and providing data assurance for stable weld quality.

[0029] Pre-fitting of welding parameters also includes process pre-verification based on a full-element digital twin model of the welding process. The digital twin model includes a geometric twin, a physical twin, a process twin, and a data twin. By simulating the evolution of the temperature field, stress field, and deformation field during the welding process, the rationality of the corrected initial welding parameters is verified. The rationality judgment criteria are welding residual stress ≤150MPa and welding deformation ≤±1mm.

[0030] By adopting the above technical solution, a geometric twin is constructed based on the structural parameters of the T-shaped, cross-shaped, and box-shaped nodes of the supercritical boiler support to build a three-dimensional model; a physical twin integrates the temperature-dependent performance parameters of Q345R and Q460GJD steel; a process twin imports the corrected initial welding parameters and welding sequence; and a data twin synchronizes the welding material composition deviation value and sensor basic data. The four twins work together to form a digital twin model of all elements of the welding process, realizing the mapping of the welding process. The model simulates the evolution of the temperature field, stress field, and deformation field during the welding process. The temperature field simulation is based on the heat conduction equation. ,in, For temperature, For time, For welding heat input, stress field simulation is based on stress-strain relationship. ,in, For welding residual stress, In response, For temperature, the deformation field simulation is based on the geometric deformation equation. ,in, This refers to the amount of welding deformation. The feature length of the workpiece is determined by the formula. ,in, For the welding residual stress or deformation data copied from the model, The simulation accuracy is quantified using preset reference data for rationality assessment. The rationality assessment criterion is welding residual stress. Welding deformation If the simulation results meet the standard, the corrected initial welding parameters are deemed reasonable. The multi-physics evolution of the welding process is reproduced through virtual simulation, and the control effect of the corrected initial welding parameters on the residual stress and deformation of the weld is verified in advance. This avoids on-site welding quality problems caused by unreasonable parameters, reduces the resource consumption of on-site trial welding, provides virtual verification support for pre-fitting of welding parameters, and ensures that the final determined welding parameters can meet the welding quality requirements of the supercritical boiler support steel structure.

[0031] The steps for dynamically adjusting welding parameters based on the verified signal data are as follows: Based on the weld metal resistance change signal collected by the phase change resistor array in the multi-source sensor fusion system, the phase transformation progress of bainite and martensite is determined, and data cross-verification is performed by combining the real-time stress data collected by the stress sensor to adjust the welding current.

[0032] By employing the above technical solution, the multi-source sensor fusion system receives the weld metal resistance change signal collected by the phase change resistor array. This signal is correlated with the phase transformation process of bainite and martensite in the weld metal, and the phase transformation progress of bainite and martensite is determined by the resistance change pattern. Simultaneously, it receives real-time stress data from the welding process collected by the stress sensor, and uses a formula... ,in For the verified fused data, The weighting coefficients for the phase-change resistor signal are: This is the signal data of the change in weld metal resistance. These are the weighting coefficients for the stress signal. For real-time stress data, cross-validation is performed to eliminate measurement errors from single signals; based on the validated fused data, the formula is used... ,in, For the adjusted welding current, The initial welding current, This is the current regulation coefficient. Based on the preset target fusion data, corresponding to the target phase transformation progress and target stress state, the welding current is adjusted. The phase transformation progress provides feedback on the evolution state of the weld metal microstructure, and the real-time stress data provides feedback on the mechanical state of the welding process. The fusion data formed after mutual verification of the two provides a basis for the adjustment of welding current, realizes the dynamic matching of welding current with weld microstructure evolution and welding stress, controls the ratio of bainite to martensite in weld metal, reduces welding residual stress, ensures the uniformity of weld microstructure, and provides support for the high-temperature creep strength and creep resistance of welded joints.

[0033] The interlayer temperature is controlled at 150~250℃ by the coordinated control of infrared temperature measurement array and forced air cooling device, and the interlayer temperature fluctuation range is controlled within ≤±10℃ by the coordinated control of infrared temperature measurement array and forced air cooling device.

[0034] By adopting the above technical solution, the infrared temperature measuring array collects interlayer temperature data in real time. The temperature deviation is calculated using the formula ΔT = |T_measured - T_set|, where ΔT is the interlayer temperature deviation, T_measured is the actual interlayer temperature collected by the infrared temperature measuring array, and T_set is a preset interlayer temperature within the range of 150~250℃. This deviation signal is then transmitted to the control module of the forced air cooling device. The cooling airflow is adjusted using the formula F = k × ΔT, where F is the cooling airflow adjustment amount of the forced air cooling device, and k is the airflow adjustment coefficient. This achieves the desired temperature deviation. The external temperature measurement array and the forced air cooling device work together to control the interpass temperature between 150 and 250°C through real-time temperature acquisition and dynamic cooling adjustment, while controlling the interpass temperature fluctuation range within ≤±10°C. This adapts to the welding thermal cycle requirements of steel and avoids problems such as coarse weld grains and uneven hardness caused by excessively high or fluctuating interpass temperatures in multi-layer welding of thick plates, as well as incomplete fusion caused by excessively low temperatures. It provides temperature assurance for the control of welding residual stress and the uniformity of weld microstructure, and supports the stability of high-temperature creep strength and creep resistance of welded joints.

[0035] The steps for time-division and frequency-division four-mode collaborative quality closed-loop management are as follows: The root pass welding stage uses eddy current detection and acoustic detection modes, the fill pass welding stage uses infrared thermography and electromagnetic detection modes, and the cover pass welding stage uses a four-mode fusion detection mode. When abnormalities such as arc interruption or gas protection failure occur during the welding process, the automatic shutdown response time is ≤1s.

[0036] By adopting the above technical solutions, the root pass welding stage employs eddy current detection and acoustic detection modes. Eddy current detection collects electrical signals related to internal weld defects, while acoustic detection collects acoustic signals during the welding process, adapting to the detection needs of defects such as incomplete fusion and slag inclusions in this stage. The fill pass welding stage employs infrared thermography and electromagnetic detection modes. Infrared thermography collects temperature distribution signals in the welding area, while electromagnetic detection collects arc-related electromagnetic signals, specifically monitoring interpass temperature fluctuations and arc stability in this stage. The cap pass welding stage employs a four-mode fusion detection mode, integrating four detection signals to achieve comprehensive coverage of the weld surface and internal quality. The detection signals from each stage are frequency-divided using a bandpass filter, and the results are processed using the formula... ,in, This is the standardized signal after frequency division. To detect the original signal, The frequency division coefficients for the corresponding detection mode are used to eliminate interference between signals of different frequencies: This is achieved through the formula... Where Def represents the fused defect feature parameters. For eddy current detection, a standardized signal is provided. To standardize the signal for acoustic detection, For infrared thermal imaging detection standardized signals, To standardize the electromagnetic detection signal, extract defect feature parameters and set defect judgment thresholds. ,when When a defect is detected, or when abnormalities such as arc interruption or gas shield failure occur during welding, an automatic shutdown command is triggered via the signal transmission link. The shutdown response follows the formula. ,in For automatic shutdown response time, For signal transmission time, To ensure the system's semi-judgment processing time. By switching detection modes in a time-division and frequency-division manner, the detection methods for different welding stages can be adapted to the quality control priorities of that stage, avoiding blind spots of a single detection mode. Frequency division processing ensures the reliability of signal data, and four-mode fusion achieves comprehensive quality monitoring. Rapid shutdown response in case of anomalies can promptly terminate unqualified welding processes. Combined with subsequent repair welding operations after correcting process parameters according to defect type, a closed-loop quality control is formed, reducing the continuous generation of weld defects and ensuring the stability of welding quality of the supercritical boiler support steel structure, thus providing quality assurance for the high-temperature service reliability of the welds.

[0037] The steps of the synergistic process of nanocrystalline coating and post-weld heat treatment are as follows: A nanocrystalline coating is prepared on the weld surface using physical vapor deposition technology. Then, post-weld heat treatment is performed at a tempering temperature of 600~650℃ and a holding time of 24h. Waste heat recovery is achieved by collecting the heat dissipation of the workpiece and equipment through a micro waste heat recovery device in the welding material cooling circuit and storing it in a phase change energy storage unit for preheating the next batch of workpieces.

[0038] By adopting the above technical solution, a nanocrystalline coating is prepared on the weld surface using physical vapor deposition technology, and the coating is obtained through the formula... ,in, To enhance the bonding strength between the coating and the weld substrate, This is the maximum load for the coating peel test. To ensure the bonding area between the coating and the substrate and guarantee the stability of the bond, this coating can block the erosion of oxidizing media in high-temperature environments, while also improving the surface hardness of the weld. Subsequently, post-weld heat treatment is performed at a tempering temperature of 600~650℃ and a holding time of 24 hours, according to the formula... ,in, To enhance the high-temperature creep strength of welded joints, This is the tempering temperature. To determine the holding time, a correlation was established between heat treatment parameters and high-temperature service performance. The heat treatment process refines weld grains and eliminates residual welding stress, synergistically optimizing the weld microstructure with a nanocrystalline coating to improve the high-temperature creep strength and anti-creep properties of the weld joint. Waste heat recovery collects residual heat from the workpiece and equipment via a miniature waste heat recovery device in the welding torch cooling circuit, and calculates the heat using a formula. ,in For waste heat recovery efficiency, To recover waste heat, The total heat emitted by the workpiece and equipment is quantified to improve the recovery effect. The recovered waste heat is stored in a phase change energy storage unit for preheating the next batch of workpieces, realizing the recycling of thermal energy, reducing energy consumption in the welding process, forming a high-temperature performance-oriented green welding process, and adapting to the reliability requirements and green manufacturing requirements of supercritical boiler support in high-temperature service.

[0039] Before layered and segmented collaborative welding, the workpiece is preheated to 150~200℃; after welding, the workpiece is subjected to a post-heating process of 200℃×1h. Before use, the welding materials are dried at 400℃ for 2 hours. Through the synergistic effect of preheating, post-heating and welding material drying, the diffusible hydrogen content of the weld is controlled to be below 5mL / 100g.

[0040] By adopting the above technical solution, the welding materials are dried at 400℃ for 2 hours before use to remove the moisture adsorbed inside the welding materials, reducing the source of hydrogen generation during welding. Before layered and segmented collaborative welding, the workpiece is preheated to 150~200℃ to reduce the temperature gradient between the welding area and the base material, slowing down the cooling rate of the weld metal and creating conditions for the escape of diffusing hydrogen. After welding, a post-heating process of 200℃ for 1 hour is performed on the workpiece to continuously maintain the temperature of the weld area, further promoting the precipitation of diffusing hydrogen in the weld metal. The synergistic effect of these components controls the diffusible hydrogen content in the weld. Its working principle is quantified by the formula H0=H-(H1+H2+H3), where H is the final diffusible hydrogen content of the weld, H1 is the initial diffusible hydrogen content of the weld before treatment, H2 is the hydrogen content removed during the drying process of the welding material, H3 is the hydrogen content removed during the preheating process of the workpiece, and H4 is the hydrogen content removed during the post-heating process of the workpiece. This suppresses the generation of cold cracks in the weld, ensures the integrity of the welded joints of the steel structure of the supercritical boiler support, and provides a basis for the stability of its mechanical properties during high-temperature service.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-node, high-efficiency, energy-saving, automated splicing process for supercritical boiler supports, characterized in that: Includes the following steps: Pre-detection and database construction of welding material composition: The elemental content of welding materials used in supercritical boiler supports is detected by a miniature laser-induced breakdown spectroscopy sensor, a basic database of welding material composition is established, and the deviation values ​​of each element from the standard composition are calculated. Workpiece pretreatment and fixture installation: Beveling is performed on the T-shaped, cross-shaped and box-shaped nodes of the supercritical boiler support. The workpiece is fixed in the cam-driven adaptive fixture and positioned by electromagnetic adsorption locking. Multi-source sensors are deployed in the key node areas of the workpiece and the preset position of the welding torch. Equipment system linkage debugging: Start the welding system, cam-driven adaptive fixture drive system, time-division frequency four-mode collaborative quality control system and welding material composition detection system, complete the communication connection and synchronous calibration of each system, initialize the detection parameters and time-division switching nodes of the time-division frequency four-mode collaborative quality control system, and verify the coordination of equipment and sensors by no-load trial operation; Pre-welding parameter pre-adaptation: The current welding node type is identified by the visual positioning system, the initial welding parameters and initial stiffness parameters of the fixture are retrieved from the welding thermal cycle database, the initial welding current is corrected based on the element deviation value, and a composition-energy-stress model is established by combining finite element analysis to verify and adjust the initial welding parameters and initial stiffness of the fixture. Layered and segmented collaborative welding: The corresponding detection modes are switched sequentially according to the stages of root pass welding, fill welding, and cover welding. The four-mode detection signals are collected and processed by frequency division. Defect feature parameters are extracted to determine whether there are defects. If a defect or welding abnormality is detected, a shutdown response is immediately triggered. After correcting the parameters according to the defect type, the welding is automatically restarted until the defect is eliminated. Time-division and frequency-division four-mode collaborative quality closed-loop control: During the welding stage, the corresponding detection mode is switched, the four-mode detection signals are collected and processed by frequency division, the defect feature parameters are extracted to determine whether there is a defect, if a defect or welding abnormality is detected, a shutdown response is immediately triggered, the process parameters are corrected according to the defect type and the welding is automatically restarted until the defect is eliminated; Post-weld treatment and system reset: The welded joint is treated with a synergistic process of nanocrystalline coating and post-weld heat treatment. The residual heat during the heat treatment process is recovered, the equipment and sensors are cleaned and maintained, the welding process data is sorted out and the welding thermal cycle database and process parameter optimization model are updated, and the system parameters are reset to the initial state.

2. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The elements include Mn, Si, C, P, and S.

3. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The calculation of the deviation values ​​of each key element from the standard component includes the following steps: Determine the standard component values ​​corresponding to each key element; The actual content values ​​of key elements in the welding material are collected using a miniature laser-induced breakdown spectroscopy sensor. The deviation value is calculated by the difference between the actual content value and the standard component value. The deviation value is expressed as an absolute or relative value.

4. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The multi-source sensors include stress sensors, magnetostrictive sensors, phase-change resistor arrays, eddy current sensors, acoustic sensors, electromagnetic sensors, and infrared thermal imagers. All sensors are equipped with adaptive threshold algorithms to process the signals collected by the sensors.

5. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The pre-adaptation of welding parameters also includes process pre-verification based on a full-element digital twin model of the welding process. The digital twin model includes a geometric twin, a physical twin, a process twin, and a data twin. By simulating the evolution of the temperature field, stress field, and deformation field of the welding process, the rationality of the corrected initial welding parameters is verified. The rationality judgment criteria are welding residual stress ≤150MPa and welding deformation ≤±1mm.

6. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The steps for dynamically adjusting welding parameters based on the verified signal data are as follows: Based on the weld metal resistance change signal collected by the phase change resistor array in the multi-source sensor fusion system, the phase transformation progress of bainite and martensite is determined, and data cross-verification is performed by combining the real-time stress data collected by the stress sensor to adjust the welding current.

7. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The interlayer temperature is controlled at 150~250℃ by the coordinated control of the infrared temperature measuring array and the forced air cooling device, and the interlayer temperature fluctuation range is controlled within ≤±10℃ by the coordinated control of the infrared temperature measuring array and the forced air cooling device.

8. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The steps of the time-division and frequency-division four-mode collaborative quality closed-loop control are as follows: The root pass welding stage uses eddy current detection and acoustic detection modes, the fill pass welding stage uses infrared thermography and electromagnetic detection modes, and the cover pass welding stage uses a four-mode fusion detection mode. When abnormalities such as arc interruption or gas protection failure occur during the welding process, the automatic shutdown response time is ≤1s.

9. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, The steps of the synergistic process of nanocrystalline coating and post-weld heat treatment are as follows: A nanocrystalline coating is prepared on the weld surface using physical vapor deposition technology. Then, post-weld heat treatment is performed at a tempering temperature of 600~650℃ and a holding time of 24h. The waste heat recovery is achieved by collecting the heat dissipation waste heat of the workpiece and equipment through a micro waste heat recovery device in the welding material cooling circuit, which is stored in a phase change energy storage unit for preheating the next batch of workpieces.

10. The multi-node high-efficiency energy-saving automated splicing process for supercritical boiler supports according to claim 1, characterized in that, Before the layered and segmented collaborative welding, the workpiece is preheated to 150~200℃; after welding, the workpiece is subjected to a post-heating process of 200℃×1h. Before use, the welding materials are dried at 400℃ for 2 hours. Through the synergistic effect of preheating, post-heating and welding material drying, the diffusible hydrogen content of the weld is controlled to be below 5mL / 100g.

Images