Steel truss joint welding deformation control method based on first-piece deformation law feedback

Abstract

The application discloses a steel truss node welding deformation control method based on first-piece deformation law feedback, belongs to the technical field of welding deformation control, and comprises the following steps: selecting a representative node as a first-piece test object; building a special welding jig, and arranging displacement and temperature sensors at key positions of the first-piece test object; performing welding operation on the first-piece test object according to initial welding process parameters and a welding sequence; collecting welding deformation data and temperature field data in real time, and forming a first-piece welding deformation database; analyzing welding deformation dominant factors, and summarizing welding deformation laws of nodes of the same type; optimizing the initial welding process parameters, selecting a verification node of the same type as the first-piece test object to perform a welding test, and verifying the effectiveness of the optimized welding process parameters; and if the verification is qualified, applying the optimized welding process parameters to batch production of main truss nodes. The method can solve the problems of difficult control of main truss node welding deformation, geometric precision out-of-tolerance and unqualified weld quality.

Description

Technical Field

[0001] This invention belongs to the field of steel truss node welding deformation control technology, and more specifically, relates to a steel truss node welding deformation control method based on feedback of the deformation law of the first piece. Background Technology

[0002] In the construction of long-span steel truss bridges, the main truss nodes, as the core load-bearing components of the steel truss, are structurally complex and have dense connections. They typically integrate multiple components such as flat joints, transverse joints, and web member joints, exhibiting significant characteristics of numerous welds and large amounts of weld metal. The welding quality and geometric accuracy of these nodes directly determine the overall load-bearing capacity and service safety of the bridge, and controlling welding deformation is a core technical challenge in the manufacturing process of main truss nodes.

[0003] In existing technologies, the control of welding deformation of main truss nodes largely relies on preset welding process parameters (such as fixed welding sequence and general heat input) or conventional jig constraints. This approach fails to fully consider the impact of structural differences in different types of main truss nodes (such as arch foot embedded section nodes and chord-web member connection nodes) on welding deformation patterns, and also lacks dynamic feedback and process adjustment mechanisms for deformation data during welding. Specifically, in engineering scenarios such as the Guniuhe Grand Bridge, the main truss nodes are manufactured using high-strength thick steel plates (thickness can reach 22~50mm). During welding, the rigid constraints cause rapid weld cooling, easily forming hardened structures, leading to a decrease in weld impact toughness. Simultaneously, the concentration of constraint stress easily triggers welding cracks. Furthermore, high-altitude welding operations are limited by the environment (such as wind, rain, and temperature fluctuations) and structural form, further increasing the difficulty of welding deformation control.

[0004] In actual construction, due to the lack of analysis and dynamic adjustment methods for welding deformation patterns of specific nodes, problems such as deviations in node geometric accuracy, diagonal misalignment, and unqualified weld quality (such as cold cracks and porosity) often occur. These problems require repeated repairs or even rework, which not only prolongs the manufacturing cycle but may also weaken the mechanical properties of the nodes, making it difficult to meet the stringent requirements for the manufacturing accuracy and quality of main truss nodes in large-span steel truss bridges.

[0005] Therefore, there is an urgent need for a method that can dynamically optimize the process based on the welding deformation law of the first piece node to achieve precise control of the welding deformation of the main truss node. Summary of the Invention

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method for controlling welding deformation of steel truss nodes based on feedback of the first-piece deformation pattern. By constructing a closed-loop control system of "first-piece testing - pattern analysis - process optimization - batch application," and based on deformation and temperature field data collected during the welding process of the first-piece node, this method analyzes the dominant factors of welding deformation through comparison with actual data and finite element simulation verification, summarizes the welding deformation patterns of similar main truss nodes, and specifically optimizes process parameters such as welding sequence, heat input parameters, post-preheating heat treatment, and jig constraints. It is also adaptable to high-altitude welding and closure segment nodes. By adjusting the process in special scenarios and dynamically fine-tuning the welding current and speed through deformation deviation calculation correction coefficients during mass production, the existing technologies can effectively solve problems such as difficult control of welding deformation of main truss nodes, out-of-tolerance geometric accuracy, and unqualified weld quality. This significantly improves the accuracy and stability of welding deformation control of main truss nodes, ensures the mechanical properties and manufacturing quality of the nodes, and can adapt to the manufacturing needs of main truss nodes of different structural types. While expanding the application scope, it reduces rework and repair costs, shortens the manufacturing cycle, and can meet the stringent requirements of the main truss nodes for manufacturing precision and quality in large-span steel truss bridges.

[0007] To achieve the above objectives, one aspect of the present invention provides a method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece, comprising the following steps:

[0008] S1. Based on the structural type of the main truss node of the steel truss bridge, select a representative node as the first test object; build a special welding jig and install displacement sensors and temperature sensors at key positions of the first test object;

[0009] S2. Welding of the first test piece is carried out on the special welding jig according to the initially preset welding process parameters and the determined welding sequence; deformation data and temperature field data during the welding process are collected in real time through displacement sensors and temperature sensors to form a first piece welding deformation database.

[0010] S3. Process the welding deformation database data of the first piece, analyze the dominant factors of welding deformation of the first piece test object, and summarize the welding deformation law of the main truss nodes that are consistent with or highly similar to the first piece test object in terms of structural composition, connection form, steel plate specifications and stress characteristics; based on the welding deformation law, adjust and optimize the initially preset welding process parameters to obtain optimized welding process parameters.

[0011] S4. Select a verification node of the same type as the first test piece, conduct a welding test using the optimized welding process parameters, collect verification deformation data and compare it with the data in the first piece welding deformation database to verify the effectiveness of the optimized welding process parameters; if the verification is qualified, apply the optimized welding process parameters to the mass production of the main truss node.

[0012] Furthermore, the structural types of the main truss nodes of the steel truss bridge described in step S1 include arch foot embedded section nodes, chord nodes, web member nodes, connecting strut nodes, horizontal bracing nodes, closure section nodes, column nodes, and cap beam nodes; chord nodes include upper chord nodes and lower chord nodes; web member nodes include box-type web member nodes and I-type web member nodes; closure section nodes include closure nodes with upper chord, closure nodes with lower chord, and closure nodes with web members; column nodes include standard column nodes and auxiliary tower column nodes on the arch.

[0013] The special welding jig is an independent grid frame made of steel profiles, and the independent grid frame is equipped with adjustable-elevation columns and limit stops.

[0014] Furthermore, the key locations mentioned in step S1 include the connection weld between the node plate and the chord, the joint of the longitudinal ribs, and the weld between the partition plate and the top and bottom plates.

[0015] Further, step S2 includes:

[0016] S21. Determine the initial preset welding process parameters: Select welding wire and flux that are suitable for the main truss steel material, set the welding current to 180~220A, control the arc voltage at 24~28V, maintain the welding speed at 15~20cm / min, and preheat the welding area with induction heating equipment before welding, with the preheating temperature set at 80~120℃.

[0017] S22. The first piece node welding operation shall be carried out in a symmetrical and balanced welding sequence; the principle of "multi-layer and multi-pass welding" shall be strictly followed during the welding process, and the welding slag shall be cleaned in time after each weld is completed; at the same time, the interpass temperature shall be ensured not to be lower than the lower limit of the preheating temperature.

[0018] S23. After the welding operation begins, the sensor data acquisition system is activated to simultaneously collect two core data types: deformation data and temperature field data. At the same time, the welding sequence, the welding time of each weld, and the actual operating parameters of the welding equipment are recorded to form a complete first-piece welding process record document. The deformation data includes the transverse shrinkage, longitudinal elongation, and angular deformation during the welding process of each weld segment. The temperature field data includes the real-time temperature change curves of the preheating stage, the welding stage, and the post-heating stage, with key annotations on the preheating temperature reaching the standard, the interpass temperature fluctuation range, and the post-weld slow cooling temperature decrease rate.

[0019] S24. During the welding process, the weld quality is monitored by a combination of real-time visual inspection and intermittent non-destructive testing.

[0020] S25. After all welding operations of the first piece are completed, stop the acquisition of sensor data, classify and organize the collected deformation data and temperature field data according to "welding stage" and "weld location", and remove invalid data; at the same time, associate the parameters in the welding process record document with the sensor data to form a first piece welding deformation database containing "process parameters - process data - quality record".

[0021] Further, step S3 includes:

[0022] The first piece welding deformation database constructed in step S2 is filtered and classified and labeled according to "weld type", "welding stage" and "structural part"; at the same time, the corresponding temperature field data and welding process parameters are associated to form a structured data matrix.

[0023] The core influencing factors of the welding deformation of the first piece node were analyzed by combining actual data comparison with finite element simulation verification in the first piece welding deformation database.

[0024] Based on the analysis results, the welding deformation patterns of the main truss nodes that are consistent with or highly similar to the first test object in terms of structural composition, connection form, steel plate specifications, and stress characteristics were extracted. The patterns were summarized as follows: "The deformation increases when the weld is far from the support point of the jig" and "In multi-layer welding, the later weld is prone to cause secondary deformation of the earlier weld." For the arch foot embedded section node, the pattern was summarized as "When welding the upper chord using the inverted method, the weld connecting the web plate and the bottom plate is prone to excessive angular deformation." At the same time, combined with the requirements of anti-corrosion coating and geometric accuracy, the allowable deformation thresholds of welds in different parts were clarified.

[0025] Based on the deformation pattern and allowable deformation threshold, the initial welding process is adjusted and optimized accordingly.

[0026] The adjusted process parameters, operating requirements, and quality inspection standards were compiled into standardized process documents.

[0027] Furthermore, the core influencing factors of welding deformation mentioned in step S3 include welding heat input, jig constraint strength, welding sequence, and steel plate thickness.

[0028] Furthermore, the adjustment and optimization measures mentioned in step S3 include: adjusting the welding sequence to symmetrical welding in the same direction; using multi-layer and multi-pass welding for thick steel plate welds, adjusting the welding current to 160~190A and using induction heating equipment; raising the preheating temperature to 100~150℃, and keeping the weld warm and cooling slowly for ≥2h; and adding H-beam temporary supports at easily deformable parts of the joints.

[0029] Furthermore, the deformation comparison in step S4 includes: checking whether the deformation of each key part of the verification node is significantly reduced compared with the first piece test, and whether it meets the geometric accuracy requirements of the main truss node; the geometric accuracy requirements of the main truss node are: inter-node length deviation ≤1mm / m, diagonal misalignment ≤0.5mm;

[0030] The quality compliance verification includes: visual inspection and non-destructive testing of the weld at the verification node according to the weld quality inspection standard to ensure that the weld quality meets the acceptance standard;

[0031] Verifying the effectiveness of the optimized welding process also includes: if both the deformation comparison and quality compliance verification indicators meet the standards, the optimized welding process parameters are deemed to be qualified; if they do not meet the standards, return to step S3 to re-analyze the deformation pattern and further adjust the process parameters.

[0032] Furthermore, step S4 also includes periodically sampling 5% to 10% of batch nodes for deformation detection during the batch production process, continuously collecting batch deformation data, and fine-tuning the optimized welding process parameters based on the batch deformation data.

[0033] The second aspect of the present invention provides a steel truss node welding deformation control system based on the first piece deformation law feedback, used to implement the steel truss node welding deformation control method based on the first piece deformation law feedback, including: a first piece node selection and test preparation module, a first piece node welding and data acquisition module, a welding deformation law analysis and process optimization module, and an optimized process parameter verification and batch application module;

[0034] The first test node selection and test preparation module is used to select representative nodes as the first test objects based on the structural type of the main truss nodes of the steel truss bridge; to build a special welding jig and to install displacement sensors and temperature sensors at key positions of the first test objects;

[0035] The first piece node welding and data acquisition module is used to perform welding operations on the first piece test object on the special welding jig according to the initially preset welding process parameters; and to collect deformation data and temperature field data in real time during the welding process through the displacement sensor and temperature sensor to form a first piece welding deformation database.

[0036] The welding deformation law analysis and process optimization module is used to process the welding deformation database data of the first piece, analyze the dominant factors of welding deformation of the first piece test object, and summarize the welding deformation laws of the main truss nodes that are consistent with or highly similar to the first piece test object in terms of structural composition, connection form, steel plate specifications and stress characteristics; based on the welding deformation laws, the initially preset welding process parameters are adjusted and optimized to obtain optimized welding process parameters.

[0037] The optimized process parameter verification and batch application module is used to select a verification node of the same type as the first test piece, conduct welding tests using the optimized welding process parameters, collect verification deformation data and compare it with the data in the first piece welding deformation database to verify the effectiveness of the optimized welding process parameters; if the verification is qualified, the optimized welding process parameters are applied to the batch production of the main truss node.

[0038] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0039] (1) The steel truss node welding deformation control method and system based on the first piece deformation law feedback of the present invention realizes the dynamic optimization of the process based on the welding deformation law of the first piece node by constructing a closed-loop control system of "first piece test - law analysis - process optimization - batch application". It can accurately control the welding deformation of the main truss node, effectively solve the problems of node geometric accuracy deviation and weld quality failure caused by the lack of deformation law analysis and dynamic adjustment means in the prior art, ensure the manufacturing accuracy and mechanical properties of the main truss node, and meet the stringent requirements of the main truss node manufacturing quality of large-span steel truss bridges.

[0040] (2) The steel truss node welding deformation control method and system based on the first piece deformation law feedback of the present invention analyzes the dominant factors of welding deformation and summarizes the deformation law for different types of main truss nodes according to their structural differences. On this basis, the welding process parameters are adjusted and optimized in a targeted manner so that the optimized welding process can be adapted to main truss nodes of different structural types, thereby improving the versatility and applicability of the welding process.

[0041] (3) The steel truss node welding deformation control method and system based on the feedback of the first piece deformation law of the present invention collects deformation data and temperature field data in real time during the welding process of the first piece node to form a first piece welding deformation database, which can provide detailed data support for the analysis of welding deformation law, so that the process optimization has a clear basis and improves the scientificity and accuracy of the process optimization; by optimizing the welding sequence, heat input parameters, preheating and postheating measures and jig constraints, the welding residual stress and restraint stress concentration are reduced, the generation of hardened structure is reduced, the tendency of cold cracking of weld is reduced, the impact toughness and internal quality of weld are improved, and the welding quality of the main truss node is guaranteed; the optimized welding process is applied to mass production, and the process parameters are continuously monitored and fine-tuned during the mass production process to achieve the stability and continuity of the main truss node welding deformation control, improve production efficiency, reduce the cost of repeated repairs or even rework caused by welding deformation, and shorten the manufacturing cycle.

[0042] (4) The steel truss node welding deformation control method and system based on the first piece deformation law feedback of the present invention adds a windproof shed and increases the preheating temperature for high-altitude welding scenarios, collects the impact data of the cutting amount at the closure section node and optimizes the process, which can realize the effective control of the welding deformation of the main truss node under special scenarios, expand the application scope of the present invention and improve its practicality in complex construction environments. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the welding deformation control method for steel truss nodes based on the feedback of the deformation law of the first piece, according to an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram showing the arrangement of the arch foot pre-embedded section on the special welding jig in an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of the steel truss node welding deformation control system based on the feedback of the first piece deformation law in an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0048] To address the challenge of controlling welding deformation at main truss joints in long-span steel truss bridges, this invention uses deformation data from the welding process of the first joint as the core feedback basis. Combined with the structural characteristics of the joint and the construction environment, it constructs a closed-loop control system of "first-piece testing - pattern analysis - process optimization - batch application." Figure 1 As shown, one aspect of the present invention provides a method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece, comprising the following steps:

[0049] S1. Selection of the first test node and preparation for the test: Based on the structural type of the main truss node of the steel truss bridge, a representative node is selected as the first test object; a special welding jig is built, and displacement sensors and temperature sensors are installed at the key positions of the first test object.

[0050] S2. First-piece node welding and data acquisition: Welding operation of the first-piece test object is carried out on the special welding jig according to the initially preset welding process parameters; deformation data and temperature field data during the welding process are collected in real time through the displacement sensor and temperature sensor to form a first-piece welding deformation database.

[0051] S3. Welding Deformation Law Analysis and Process Optimization: The welding deformation database data of the first piece is processed to analyze the dominant factors of welding deformation of the first piece test object, and the welding deformation law of the main truss nodes that are consistent with or highly similar to the first piece test object in terms of structural composition, connection form, steel plate specifications and stress characteristics is summarized; based on the welding deformation law, the initially preset welding process parameters are adjusted and optimized to obtain optimized welding process parameters;

[0052] S4. Optimization of process parameters verification and batch application: Select a verification node of the same type as the first test piece, conduct welding tests using the optimized welding process parameters, collect verification deformation data and compare it with the data in the first piece welding deformation database to verify the effectiveness of the optimized welding process parameters; if the verification is qualified, apply the optimized welding process parameters to the batch production of the main truss node.

[0053] Furthermore, step S1 specifically includes:

[0054] S11: Based on the structural types of the main truss nodes of the steel truss bridge, nodes with high structural complexity, high welding difficulty, and representative characteristics are preferentially selected as the first test objects; the structural types of the main truss nodes of the steel truss bridge include arch foot embedded section nodes, chord nodes, web member nodes, connecting system strut nodes, horizontal bracing nodes, closure section nodes, column nodes, and cap beam nodes; chord nodes include upper chord nodes and lower chord nodes; web member nodes include box-type web member nodes and I-type web member nodes; closure section nodes include closure nodes with upper chord, closure nodes with lower chord, and closure nodes with web members; column nodes include standard column nodes and auxiliary tower column nodes on the arch;

[0055] S12: An independent, grid-type welded jig is made of structural steel (e.g., ...). Figure 2 As shown, the jig must meet the support and positioning requirements of the first node; an adjustable column 3 is set on the top of the jig to accurately adjust the installation height of each component of the node; at the same time, a limiting stop 4 (such as an H-beam side limiting structure) is set at the corresponding position of the jig to prevent the node from shifting laterally during welding; the jig base is fixed to the concrete ground to ensure the overall stability of the jig and avoid the deformation of the jig from affecting the welding accuracy of the node.

[0056] S13: Based on the welding deformation sensitive area of ​​the first node, displacement sensors and temperature sensors are deployed at key locations; these key locations include the connection weld between the node plate and the chord, the joint of the longitudinal ribs, and the weld between the partition plate and the top and bottom plates; displacement sensors are mainly deployed at the connection weld between the node plate and the chord, the joint of the longitudinal ribs, and the weld between the partition plate and the top and bottom plates to collect the transverse shrinkage, longitudinal elongation, and angular deformation during the welding process in real time; temperature sensors are installed close to the surface of the steel plate in the weld area to collect the real-time temperature change curve of the weld and the heat-affected zone during the welding process, and record key temperature parameters such as preheating temperature, interpass temperature, and post-heating temperature; after the sensors are deployed, pre-adjustment is required to ensure the accuracy and continuity of data acquisition;

[0057] S14: According to the processing technology flow of the main truss components, pre-process each component of the first node; including rust removal of steel surface (reaching Sa2.5 grade and above), edge beveling (meeting welding process requirements), and re-inspection of component geometry (ensuring that the accuracy of individual components meets design standards); after pre-processing, assemble the first node on a special welding jig according to the assembly sequence (e.g., the arch foot embedded section adopts the "lower chord positive installation method + upper chord inverted installation method", and the chord adopts the "first assemble the groove type and then weld the box type" sequence). During the assembly process, adjust the relative position of each component of the node through the jig column and limit stop to ensure that the geometric parameters such as node center distance, diagonal difference, and flatness meet the manufacturing rule requirements. After assembly, use temporary clamps to fix each component;

[0058] Furthermore, the dedicated welding jig includes a base platform 1, an independent grid frame body 2, adjustable-height columns 3, limiting stops 4, and lateral stiffening supports 5. The base platform 1 is made of thick steel plate and fixed to the ground with anchor bolts, providing stable support for the jig body and ensuring no overall displacement of the jig during welding. The independent grid frame body 2 is welded from I-beams, channel steel, and other structural steels, forming a grid-like structure with high rigidity, effectively resisting deformation caused by welding thermal stress and providing a uniform and stable support surface for the steel truss nodes. The adjustable-height columns 3 are evenly distributed along the longitudinal and transverse directions of the grid frame body, with the bottom of the columns rigidly connected to the grid frame body. The system includes a top-mounted screw adjustment mechanism with an adjustment range adapted to the elevation requirements of various parts of the steel truss node. This mechanism is used to precisely adjust the geometric posture of the first test piece to meet welding reference requirements. Limiting stops 4 are located at the ends of the jig, formed by welding steel profiles and fixedly connected to the main body of the grid frame. They conform to the side contours of the steel truss node and limit the horizontal displacement and rotation of the first test piece during welding, thus achieving positional constraints. Lateral stiffening supports 5 are diagonally arranged between the main body of the grid frame and the base platform. Made of angle steel or channel steel, they enhance the overall rigidity of the jig, preventing lateral deformation during welding stress and ensuring the stability of constraint strength.

[0059] Further, step S2 includes:

[0060] S21. Determine the initial preset welding process parameters: Select welding wire and flux suitable for the main truss steel material, set the welding current to 180~220A, control the arc voltage at 24~28V, maintain the welding speed at 15~20cm / min, and preheat the welding area using induction heating equipment before welding, with the preheating temperature set at 80~120℃; at the same time, check the stability of the special welding jig and the connection status of the sensors to ensure that the data acquisition channel is unobstructed, and clean the impurities (such as rust and oil) at the assembly interface of the first piece node to ensure the welding area is clean;

[0061] S22. Welding sequence determination and execution: The first node welding operation shall be carried out in a symmetrical and balanced welding sequence; when welding the upper chord node, the connection weld between the diaphragm and the top and bottom plates shall be welded first, then the weld between the node plate and the web plate shall be welded, and finally the butt weld of the longitudinal rib shall be welded. The same weld shall be welded in symmetrical segments in the same direction to reduce the concentration of welding restraint stress; during the welding process, the principle of "multi-layer multi-pass welding" shall be strictly followed. After each weld is completed, the weld slag shall be cleaned in time to avoid slag inclusion defects, and at the same time, the interpass temperature shall be ensured not to be lower than the lower limit of the preheating temperature.

[0062] S23. Real-time Data Acquisition and Recording: After the welding operation begins, the sensor data acquisition system is activated to simultaneously collect two core data types: deformation data and temperature field data. Deformation data is obtained through displacement sensors deployed at key nodes, specifically including the lateral shrinkage, longitudinal elongation, and angular deformation of each weld segment during the welding process. The acquisition frequency is no less than once per minute to ensure complete recording of the deformation dynamics throughout the welding process. Temperature field data is obtained through temperature sensors in the weld area, specifically including real-time temperature change curves during the preheating, welding, and post-heating stages. Key features include the time required to reach the preheating temperature target, the interpass temperature fluctuation range, and the rate of temperature decrease during post-weld slow cooling. Simultaneously, designated personnel record the welding sequence, the welding time for each weld, and the actual operating parameters of the welding equipment (including actual current and voltage deviations) to form a complete first-piece welding process record document.

[0063] S24. Welding Process Quality Monitoring and Anomaly Handling: During the welding process, weld quality is monitored using a combination of real-time visual inspection and intermittent non-destructive testing. Real-time visual inspection includes visually checking the weld for surface defects such as weld beads, lack of fusion, and porosity. Intermittent non-destructive testing includes magnetic particle testing of the welded sections after every 3-5 welds to check for surface and near-surface cracks. If abnormal welding parameters or weld defects are found, welding operations are immediately suspended, the cause of the abnormality is analyzed, and corrective measures are taken (including equipment repair and welding material replacement). Welding is restarted after rectification is completed, and the abnormality and handling process are recorded in detail to ensure the continuity and authenticity of the first piece welding data.

[0064] S25. Data Processing and Database Construction: After all welding operations of the first piece are completed, stop sensor data acquisition, classify and organize the collected deformation data and temperature field data according to "welding stage" and "weld location", and remove invalid data; at the same time, associate the parameters in the welding process record document (including welding sequence, actual process parameters, and quality anomaly handling) with the sensor data to form a first piece welding deformation database containing "process parameters - process data - quality record".

[0065] Further, step S3 includes:

[0066] S31. Preprocessing of the first piece welding deformation database: The first piece welding deformation database constructed in step S2 is systematically sorted out, effective data is screened, and classified and labeled according to "weld type", "welding stage" and "structural part"; at the same time, the corresponding temperature field data and welding process parameters are associated to form a structured data matrix; weld types include gusset plate and web plate welds, top plate and diaphragm welds; welding stages include preheating, multi-layer welding, and post-heating; temperature field data includes the preheating temperature compliance rate and interpass temperature fluctuation value of different weld segments; welding process parameters include actual welding current, voltage, and speed;

[0067] S32. Analysis of Dominant Factors of Welding Deformation: This analysis combines actual data comparison from the first piece welding deformation database with finite element simulation verification to analyze the core influencing factors of the first piece node welding deformation. It compares the correlation between deformation data of different weld segments and corresponding process parameters, including determining whether the large shrinkage of high-strength thick steel plate welds is related to excessive heat input, and whether the deformation in densely packed areas of the node plate is affected by stress concentration. Simultaneously, considering the structural characteristics of the main truss node, it uses finite element simulation to reconstruct the stress-strain distribution during welding, verifying the hypotheses of "unreasonable welding sequence leading to deformation superposition" and "insufficient jig constraint causing displacement," clarifying the influence weight of each factor on deformation, and forming the analysis results. The deformation data of the weld segment includes transverse shrinkage and angular deformation. The core influencing factors of welding deformation include welding heat input, jig constraint strength, welding sequence, and steel plate thickness.

[0068] S33. Summary of Welding Deformation Laws of Main Truss Nodes: Based on the analysis results, the welding deformation laws of main truss nodes that are consistent with or highly similar to the first test piece in terms of structural composition, connection form, steel plate specifications, and stress characteristics are extracted. The laws are summarized as follows: "The deformation increases when the weld is far from the support point of the jig" and "In multi-layer welding, the subsequent weld is prone to secondary deformation of the preceding weld." For the arch foot embedded section nodes, the law is summarized as follows: "When welding the upper chord using the inverted method, the weld connecting the web plate and the bottom plate is prone to excessive angular deformation." At the same time, combined with the requirements of anti-corrosion coating and geometric accuracy, the allowable deformation thresholds of welds in different parts are clarified.

[0069] S34. Initial Welding Process Parameter Adjustment and Optimization: Based on the deformation law and allowable deformation threshold, the initial welding process parameters are adjusted and optimized in a targeted manner, specifically including:

[0070] Welding sequence optimization: The initial single-direction welding was adjusted to "symmetrical segmented welding in the same direction" to reduce the concentration of restraint stress;

[0071] Heat input parameter optimization: For thick steel plate welds (plate thickness 30~50mm), the initial welding current of 180~220A is adjusted to 160~190A, and "multi-layer multi-pass welding" is used instead of "few-layer multi-pass welding" to reduce the heat input of a single weld and reduce the generation of hardened structure;

[0072] Optimization of preheating and postheating measures: Induction heating equipment is used to increase the initial preheating temperature from 80~120℃ to 100~150℃ and extend the preheating and holding time to 30~60min; after welding, additional heat preservation and slow cooling measures (such as covering with heat preservation cotton) are added, and the heat preservation time is ≥2h to promote the overflow of diffuse hydrogen in the weld metal and reduce the tendency of cold cracking.

[0073] Strengthening the constraint of the frame: Temporary H-beam supports are added to easily deformable parts of the joints to limit free deformation during the welding process;

[0074] S35: Optimize the preparation of welding process parameter documents: Compile the adjusted process parameters (such as welding current, preheating temperature, welding sequence), operation requirements (such as interpass temperature control range, temporary support installation position), and quality inspection standards (such as appearance inspection requirements after each weld) into standardized process documents, clarify the responsible parties and operation procedures for each process, and ensure that subsequent verification tests can be carried out according to the standardized procedures;

[0075] Furthermore, in step S32, the core influencing factors of the welding deformation of the first piece are analyzed by combining actual data comparison with finite element simulation verification in the first piece welding deformation database, including:

[0076] The deformation data (such as transverse shrinkage and angular deformation) of different weld segments in the first piece welding deformation database are screened with the corresponding welding process parameters (such as welding current, voltage, speed, and preheating temperature) and structural parameters (such as steel plate thickness and node constraint form) to form multiple sets of comparison samples.

[0077] By comparing the differences in deformation among different samples, the correlation between welding heat input (determined by welding current, voltage, and speed) and deformation was analyzed to determine whether the large shrinkage of high-strength thick steel plate welds was caused by excessive heat input; the relationship between deformation and constraint stress concentration in dense areas of the gusset plate was also analyzed.

[0078] Based on the structural dimensions, material properties, and welding process parameters of the first test object, a finite element model was established to simulate the temperature field, stress field, and deformation process during welding.

[0079] The stress-strain distribution under different welding sequences (such as unidirectional welding and symmetrical welding) was simulated to verify the hypothesis that "unreasonable welding sequence leads to superposition of deformation"; the nodal displacement under different jig constraint strengths was simulated to verify the hypothesis that "insufficient jig constraint causes displacement".

[0080] By comparing the finite element simulation results with the actual data in the first piece welding deformation database, the influence weights of factors such as welding heat input, jig constraint strength, welding sequence, and steel plate thickness on deformation are clarified. Through analysis of variance (ANOVA), the degree of significant influence of different factors (such as heat input, welding sequence, jig constraint, and steel plate thickness) and their interactions on the deformation is analyzed, key control factors are screened, and the core influencing factors of the first piece node welding deformation are determined.

[0081] The meaning of the "significant influence" is as follows: Statistical analysis of welding deformation data using ANOVA is used to determine whether a certain factor (such as welding heat input, welding sequence, jig constraint, and steel plate thickness) truly exists and is sufficiently large in its influence on welding deformation, rather than being caused by random errors or accidental fluctuations; ANOVA is used to determine which factors truly dominate deformation (significant influence); which factors have negligible influence (insignificant influence); and which factors, when combined, will have a cumulative effect (significant interaction effect); ultimately, the key process parameters that must be optimized are precisely identified to ensure effective, reliable, and repeatable deformation control.

[0082] The process of screening key control factors is as follows: Multiple sets of comparative analyses are conducted on deformation data, temperature field data, and welding process parameters from the first piece welding deformation database. Combined with the temperature field, stress field, and deformation distribution results obtained from finite element simulation, analysis of variance (ANOVA) is used to test the significance and quantify the influence weights of potential influencing factors such as welding heat input, jig constraint strength, welding sequence, and steel plate thickness. This determines whether the influence of each factor on welding deformation is statistically significant, and identifies the contribution and degree of influence of each factor on the deformation. Key control factors that play a dominant role and have the most significant impact on welding deformation are identified, while non-key factors with weak or no significant influence are eliminated. Based on the identified key control factors, the focus of process optimization and control points are clarified. Subsequent improvements are only made around the key control factors, such as adjusting the welding sequence, optimizing heat input parameters, and strengthening jig constraints, avoiding ineffective adjustments to non-key parameters. This achieves precise control of welding deformation at steel truss nodes and efficient and stable production.

[0083] Furthermore, the core of temperature field simulation is solving the transient heat conduction equation, which considers the welding heat source input, the latent heat of material phase transformation, and boundary heat dissipation; the finite element model expression for the temperature field is:

[0084] (1)

[0085] in, Density of steel; Specific heat capacity of steel; The instantaneous temperature of the node; The time variable is the time of the welding process; The thermal conductivity of steel; This is the term for heat conduction; Intensity of the internal heat source;

[0086] The key boundary conditions of the temperature field finite element model include: convective heat dissipation boundary, radiative heat dissipation boundary, and welding heat source boundary.

[0087] The convection heat dissipation boundary is the heat exchange between the model surface and the air, expressed as:

[0088] (2)

[0089] in, The convective heat transfer coefficient is approximately 5~25 W / (m²) during natural air convection. 2 ·K)), For ambient temperature, The boundary normal vector;

[0090] The radiative heat dissipation boundary represents the heat radiation loss of the model surface at high temperatures, expressed as:

[0091] (3)

[0092] in, The emissivity of steel is approximately 0.6 to 0.8. It is the Stefan-Boltzmann constant;

[0093] The welding heat source boundary adopts a double ellipsoidal heat source model to represent the equivalent arc heat input, and the heat source intensity... Calculate according to formula (4):

[0094] (4)

[0095] Thermal efficiency (approximately 0.7~0.9 for arc welding). The welding current is (A). The arc voltage is (V). The volume of the heat source's operating area (m³) 3 );

[0096] The stress field and deformation are described by the thermo-elastoplastic constitutive equations, based on the temperature field calculation results, taking into account thermal expansion, plastic yielding, and welding residual stress; the finite element model expressions for the stress field and deformation are as follows:

[0097] (5)

[0098] in, For stress increment tensors; the stress change within a certain time step. Corresponding to the x and yz directions; Let be the elastic stiffness tensor, and let be the stress that varies with the current stress. and temperature The changing fourth-order tensor reflects the material's elastic deformation capacity; This is the total strain increment tensor, which includes elastic strain, thermal strain, and plastic strain components. For thermal strain increment tensor, represents the free expansion / contraction strain caused by temperature change; is the plastic strain increment tensor, which is the permanent strain increment generated after the material yields;

[0099] Thermal strain increment tensor The expression is:

[0100] (6)

[0101] in, The coefficient of thermal expansion; This represents the temperature increment. The Kronecker function;

[0102] The key boundary conditions for the stress field and deformation finite element model are:

[0103] Displacement constraint boundary: Simulate the limiting effect of the special welding jig and apply displacement constraints to the jig support position;

[0104] Contact boundary: The welding joint surfaces between the constituent units of the node are designed with binding contact to ensure coordinated deformation;

[0105] Initial conditions: Before welding begins, the overall temperature of the model is uniformly at ambient temperature. The initial stress is 0;

[0106] During operation, based on the structural dimensions of the first piece node and the thermophysical parameters of the material (thermal conductivity, specific heat capacity, density), the transient heat conduction equation is solved using finite element software to simulate the temperature distribution and variation of the weld and heat-affected zone during the welding process, and the node temperature distribution at each time step is obtained.

[0107] The temperature field results are used as thermal loads, and combined with material mechanical parameters (elastic modulus, Poisson's ratio, yield strength), the distribution of thermal stress caused by temperature gradient during welding is calculated.

[0108] Substitute the thermo-elastic-plastic constitutive equations to solve for the stress field at each time step; based on the stress field results, calculate the overall and local deformation of the nodes under thermal stress and restraint stress by integrating the plastic strain increment; and calculate the error with the actual deformation data of the first test piece.

[0109] The constraint reaction force under different jig constraint strengths was simulated, and the quantitative relationship between constraint strength and deformation was analyzed to verify the accuracy of the model and provide support for the analysis of the dominant factors of welding deformation.

[0110] Further, step S4 includes:

[0111] S41: Verification Node Selection and Test Preparation: Select nodes that are completely consistent with the structural composition, steel plate specifications, and connection form of the first piece test object in step S1 as verification nodes. Reuse the special welding jig built in step S1, and re-lay out displacement sensors and temperature sensors (ensure that the sensor positions and models are consistent with the first piece test). Prepare suitable welding materials, heating equipment, and temporary support components (such as H-beam diagonal braces) according to the optimized welding process parameter file compiled in step S3.

[0112] S42: Conduct verification node welding according to optimized welding process parameters; during the welding process, collect deformation data (lateral shrinkage, longitudinal elongation, angular deformation) and temperature field data (preheating temperature, interpass temperature, slow cooling temperature), record the deviation between the actual process parameters and the optimized parameters of the first piece test (such as current fluctuation range, preheating temperature compliance rate), and form a verification welding data document.

[0113] S43: Compare the welding data of the verification node with the test data of the first piece, and verify the effect of the optimized process from two aspects: deformation comparison and quality compliance verification. If both the deformation comparison and quality compliance verification indicators meet the standards, the optimized welding process parameters are deemed to be qualified. If they do not meet the standards, return to step S3 to re-analyze the deformation pattern and further adjust the process parameters.

[0114] The deformation comparison includes: checking whether the deformation of each key part of the verification node is significantly reduced compared with the first piece test, and whether it meets the geometric accuracy requirements of the main truss node; the section length deviation is ≤1mm / m, and the diagonal misalignment is ≤0.5mm;

[0115] The quality compliance verification includes: visual inspection (including no weld beads, porosity, and lack of fusion) and non-destructive testing (including ultrasonic testing for internal defects and magnetic particle testing for surface cracks) of the verification node welds according to the weld quality inspection standards, to ensure that the weld quality meets the GB50205-2020 "Standard for Acceptance of Construction Quality of Steel Structures";

[0116] S44: Transform the verified optimized welding process parameters into standard operating procedures for mass production, clearly defining the operational details for each process (including welding sequence steps, sensor placement specifications, and temporary support installation locations), quality control points (including interpass temperature monitoring frequency and weld inspection timing), and anomaly handling procedures (including adjustment methods for parameter deviations); organize process training for mass production operators, ensuring that personnel master the core points of the optimized process through practical exercises, and simultaneously train quality inspection personnel to be familiar with the deformation amount and weld quality judgment standards under optimized process parameters;

[0117] S45: Start mass production of this type of main truss node according to the standardized process parameter document; select 5% to 10% of the nodes in each batch as monitoring samples, and deploy sensors to collect deformation and temperature data during the welding process, and conduct weld appearance and non-destructive testing simultaneously; periodically (e.g., every 10 nodes produced) summarize the monitoring sample data, analyze the fluctuation trend of deformation in mass production, and if abnormal data is found, immediately stop production, investigate the cause and take corrective measures;

[0118] S46: Based on monitoring data from mass production, dynamically fine-tune welding process parameters; in high temperature and high humidity environments, appropriately increase the preheating temperature by 5~10℃ to ensure weld toughness; in low temperature environments in winter, extend the post-weld heat preservation time by 30~60min to reduce the risk of cold cracking; after the process parameters stabilize (the deformation and quality of 3 consecutive batches of monitored samples meet the standards), solidify the final process parameters into the enterprise standard process for long-term production of similar main truss nodes, and establish process archives to record the parameter adjustment process and basis;

[0119] Step S46, fine-tuning of process parameters, includes:

[0120] Calculate the preheating temperature adjustment amount based on the ambient temperature and humidity at the site;

[0121] Based on the deviation between the batch deformation amount and the target value, correction coefficients for welding current and welding speed are calculated to achieve dynamic fine-tuning of process parameters.

[0122] Furthermore, when the main truss nodes need to be welded at high altitudes, based on the optimized welding process of mass production in step S4: a windproof canopy is added to block strong winds (wind speeds > 5 m / s will affect the stability of the welding arc and the weld formation), to avoid environmental factors such as wind and rain interfering with the welding process and to ensure the stability of welding parameters; the preheating temperature is increased by 20~30℃: heat dissipation is fast at high altitudes, and increasing the preheating temperature can ensure that the welding area reaches sufficient heat input, prevent the weld from generating hardened structures and cold cracks due to excessive cooling, and ensure the impact toughness and internal quality of the weld.

[0123] Furthermore, when the first test object is the closure section node (the splicing node when the main truss is closed): step S2 also includes collecting data on the impact of the cutting amount at the closure opening: the closure section node needs to be "cut" (cut to adjust the length) according to the actual length on site, and data on the impact of the cutting amount on welding deformation should be collected to clarify the rule of "how the size of the cutting amount causes differences in welding deformation";

[0124] Step S3 also includes adjusting the initial welding process parameters (such as welding sequence, heat input, and jig constraints) based on the impact data of the closure section cutting amount, so that the process is adapted to the special structure of the closure section node, and ensures that the main truss alignment and geometric accuracy meet the design requirements after closure.

[0125] like Figure 3 As shown, the second aspect of the present invention provides a steel truss node welding deformation control system based on the feedback of the first piece deformation law, which is used to implement the above simulation method, including a first piece node selection and test preparation module, a first piece node welding and data acquisition module, a welding deformation law analysis and process optimization module, and an optimized process parameter verification and batch application module.

[0126] The first test node selection and test preparation module is used to select representative nodes as the first test objects based on the structural type of the main truss nodes of the steel truss bridge; to build a special welding jig and to install displacement sensors and temperature sensors at key positions of the first test objects;

[0127] The first piece node welding and data acquisition module is used to perform welding operations on the first piece test object on the special welding jig according to the initially preset welding process parameters; and to collect deformation data and temperature field data in real time during the welding process through the displacement sensor and temperature sensor to form a first piece welding deformation database.

[0128] The welding deformation law analysis and process optimization module is used to process the welding deformation database data of the first piece, analyze the dominant factors of welding deformation of the first piece test object, and summarize the welding deformation laws of the main truss nodes that are consistent with or highly similar to the first piece test object in terms of structural composition, connection form, steel plate specifications and stress characteristics; based on the welding deformation laws, the initially preset welding process parameters are adjusted and optimized to obtain optimized welding process parameters.

[0129] The optimized process parameter verification and batch application module is used to select a verification node of the same type as the first test piece, conduct welding tests using the optimized welding process parameters, collect verification deformation data and compare it with the data in the first piece welding deformation database to verify the effectiveness of the optimized welding process parameters; if the verification is qualified, the optimized welding process parameters are applied to the batch production of the main truss node.

[0130] It should be noted that the steel truss node welding deformation control system based on the first piece deformation law feedback provided in this embodiment can be a computer program (including program code) running on a computer device. For example, the steel truss node welding deformation control system based on the first piece deformation law feedback is an application software. The steel truss node welding deformation control system based on the first piece deformation law feedback can be used to execute the corresponding steps in the above-mentioned method provided in the embodiments of this application.

[0131] In some feasible implementations, the steel truss node welding deformation control system based on the first piece deformation law feedback provided in this embodiment can be implemented in a combination of hardware and software. As an example, the steel truss node welding deformation control system based on the first piece deformation law feedback provided in this application embodiment can be a processor in the form of a hardware decoding processor, which is programmed to execute the steel truss node welding deformation control method based on the first piece deformation law feedback provided in this application embodiment. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0132] In some feasible implementations, the steel truss node welding deformation control system based on the first piece deformation law feedback provided in this embodiment can be implemented in software. It can be software in the form of programs and plug-ins, and includes a series of modules to implement the steel truss node welding deformation control method based on the first piece deformation law feedback provided in this embodiment of the invention.

[0133] A third aspect of the present invention also provides an electronic device, Figure 4 This is a schematic diagram of the electronic device in this embodiment, as shown below. Figure 4 As shown, the electronic device 1000 in this embodiment may include: a processor 1001, a network interface 1004, and a memory 1005. Furthermore, the electronic device 1000 may also include: a user interface 1003, and at least one communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 4 As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a device control application.

[0134] like Figure 4 In the electronic device 1000 shown, the network interface 1004 provides network communication functions; the user interface 1003 is mainly used to provide an input interface for users; and the processor 1001 can be used to call the device control application stored in the memory 1005 to implement the various steps of the steel truss node welding deformation control method based on the feedback of the first piece deformation law.

[0135] It should be understood that in some feasible implementations, the processor 1001 described above may be a central processing unit (CPU), which may also be other general-purpose processors, DSPs, ASICs, FPGAs, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0136] In specific implementation, the aforementioned electronic device 1000 can perform the above-described actions through its built-in functional modules. Figure 1 The implementation methods provided for each step are detailed in the above-mentioned implementation methods, and will not be repeated here.

[0137] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement... Figure 1 The methods provided in each step are detailed in the implementation methods provided in the above steps, and will not be repeated here.

[0138] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0139] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece, characterized in that, Includes the following steps: S1. Based on the structural type of the main truss node of the steel truss bridge, select a representative node as the first test object; build a welding jig and install displacement sensors and temperature sensors at key positions of the first test object; the welding jig includes a base platform (1), an independent grid frame body (2) set on the base platform (1), an adjustable elevation column (3) set on the independent grid frame body (2), a limiting stop (4) and a lateral stiffening support (5). S2. Welding of the first test piece is carried out on the welding jig according to the initially preset welding process parameters and the determined welding sequence; deformation data and temperature field data during the welding process are collected in real time through displacement sensors and temperature sensors to form a first piece welding deformation database. S3. Process the welding deformation database data of the first piece, analyze the dominant factors of welding deformation of the first piece test object, and summarize the welding deformation law of the main truss node that is consistent with the first piece test object in terms of structural composition, connection form, steel plate specifications and stress characteristics; based on the welding deformation law, adjust and optimize the initially preset welding process parameters to obtain optimized welding process parameters. S4. Select a verification node of the same type as the first test piece, conduct a welding test using the optimized welding process parameters, collect verification deformation data and compare it with the data in the first piece welding deformation database to verify the effectiveness of the optimized welding process parameters; If the verification is successful, the optimized welding process parameters will be applied to the mass production of the main truss nodes; Step S3 includes: The first piece welding deformation database constructed in step S2 is filtered and classified and labeled according to "weld type", "welding stage" and "structural part"; at the same time, the corresponding temperature field data and welding process parameters are associated to form a structured data matrix. The core influencing factors of the welding deformation of the first piece node were analyzed by combining actual data comparison with finite element simulation verification in the first piece welding deformation database. Based on the analysis results, the welding deformation patterns of the main truss nodes, which are consistent with the first test object in terms of structural composition, connection form, steel plate specifications, and stress characteristics, were extracted. The patterns were summarized as follows: "The deformation increases when the weld is far from the support point of the jig" and "In multi-layer welding, the weld of the later weld is prone to secondary deformation of the previous weld." For the arch foot embedded section node, the pattern was summarized as "When welding the upper chord using the inverted method, the weld connecting the web plate and the bottom plate is prone to excessive angular deformation." At the same time, combined with the requirements of anti-corrosion coating and geometric accuracy, the allowable deformation threshold of welds in different parts was clarified. Based on the deformation pattern and allowable deformation threshold, the initial welding process is adjusted and optimized accordingly. The adjusted process parameters, operating requirements, and quality inspection standards were compiled into standardized process documents.

2. The method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece as described in claim 1, characterized in that: The structural types of the main truss nodes of the steel truss bridge described in step S1 include arch foot embedded section nodes, chord nodes, web member nodes, connecting strut nodes, horizontal bracing nodes, closure section nodes, column nodes, and cap beam nodes; chord nodes include upper chord nodes and lower chord nodes; web member nodes include box-type web member nodes and I-type web member nodes; closure section nodes include upper chord closure nodes, lower chord closure nodes, and web member closure nodes; column nodes include standard column nodes and auxiliary tower column nodes on the arch. The welding jig is an independent grid frame made of structural steel, and the independent grid frame is equipped with adjustable-elevation columns and limit stops.

3. The method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece as described in claim 2, characterized in that: The key locations mentioned in step S1 include the connection weld between the node plate and the chord, the joint of the longitudinal ribs, and the weld between the partition plate and the top and bottom plates.

4. The method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece according to claim 3, characterized in that, Step S2 includes: S21. Determine the initial preset welding process parameters: Select welding wire and flux that are suitable for the main truss steel material, set the welding current to 180~220A, control the arc voltage at 24~28V, maintain the welding speed at 15~20cm / min, and preheat the welding area with induction heating equipment before welding, with the preheating temperature set at 80~120℃. S22. The first piece node welding operation shall be carried out in a symmetrical and balanced welding sequence; the principle of "multi-layer and multi-pass welding" shall be strictly followed during the welding process, and the welding slag shall be cleaned in time after each weld is completed; at the same time, the interpass temperature shall be ensured not to be lower than the lower limit of the preheating temperature. S23. After the welding operation begins, the sensor data acquisition system is activated to simultaneously collect deformation data and temperature field data; at the same time, the welding sequence, the welding time of each weld, and the actual operating parameters of the welding equipment are recorded to form a complete first-piece welding process record document; the deformation data includes the transverse shrinkage, longitudinal elongation, and angular deformation during the welding process of each weld segment; the temperature field data includes the real-time temperature change curves of the preheating stage, welding stage, and post-heating stage, with key annotations on the preheating temperature reaching the standard time, the interpass temperature fluctuation range, and the post-weld slow cooling temperature decrease rate; S24. During the welding process, the weld quality is monitored by a combination of real-time visual inspection and intermittent non-destructive testing. S25. After all welding operations of the first piece are completed, stop the acquisition of sensor data, classify and organize the collected deformation data and temperature field data according to "welding stage" and "weld location", and remove invalid data; at the same time, associate the parameters in the welding process record document with the sensor data to form a first piece welding deformation database containing "process parameters - process data - quality record".

5. The method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece according to any one of claims 1-4, characterized in that, The core influencing factors of welding deformation mentioned in step S3 include welding heat input, jig constraint strength, welding sequence, and steel plate thickness.

6. The method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece, as described in claim 5, is characterized in that... The adjustment and optimization measures in step S3 include: adjusting the welding sequence to symmetrical welding in the same direction; using multi-layer and multi-pass welding for thick steel plate welds, adjusting the welding current to 160~190A and using induction heating equipment; raising the preheating temperature to 100~150℃, keeping the weld warm and cooling slowly for ≥2h; and adding H-beam temporary supports at easily deformable parts of the joints.

7. The method for controlling welding deformation of steel truss nodes based on feedback of the deformation law of the first piece according to any one of claims 1-4, 5, and 6, characterized in that, Step S4 also includes verifying the effect of the optimized process from two aspects: deformation comparison and quality compliance verification. If both the deformation comparison and quality compliance verification indicators meet the standards, the optimized welding process parameters are deemed to be qualified. If they do not meet the standards, return to step S3 to re-analyze the deformation pattern and further adjust the process parameters. The deformation comparison includes: checking whether the deformation of each key part of the verification node is significantly reduced compared with the first piece test, and whether it meets the geometric accuracy requirements of the main truss node; the geometric accuracy requirements of the main truss node are: inter-node length deviation ≤1mm / m, diagonal misalignment ≤0.5mm; The quality compliance verification includes: conducting visual inspection and non-destructive testing on the welds at the verification nodes according to the weld quality inspection standards to ensure that the weld quality meets the acceptance standards.

8. The method for controlling welding deformation of steel truss nodes based on the feedback of the deformation law of the first piece according to claim 7, characterized in that, Step S4 also includes periodically sampling 5% to 10% of batch nodes for deformation detection during the batch production process, continuously collecting batch deformation data, and fine-tuning the optimized welding process parameters based on the batch deformation data.

9. A steel truss node welding deformation control system based on the feedback of the deformation law of the first piece, characterized in that, The method for controlling welding deformation of steel truss nodes based on the first piece deformation law feedback as described in any one of claims 1-8 includes a first piece node selection and test preparation module, a first piece node welding and data acquisition module, a welding deformation law analysis and process optimization module, and an optimized process parameter verification and batch application module. The first-piece node selection and test preparation module is used to select representative nodes as the first-piece test objects based on the structural type of the main truss nodes of the steel truss bridge. A welding jig was erected, and displacement and temperature sensors were installed at key locations on the first test piece. The first piece node welding and data acquisition module is used to perform welding operations on the first piece test object on the welding jig according to the initially preset welding process parameters. The displacement sensor and temperature sensor are used to collect deformation data and temperature field data in real time during the welding process, forming a first-piece welding deformation database. The welding deformation law analysis and process optimization module is used to process the welding deformation database data of the first piece, analyze the dominant factors of welding deformation of the first piece test object, summarize the welding deformation law of the main truss node that is consistent with the first piece test object in terms of structural composition, connection form, steel plate specifications and stress characteristics; and adjust and optimize the initially preset welding process parameters based on the welding deformation law to obtain optimized welding process parameters. The optimized process parameter verification and batch application module is used to select a verification node of the same type as the first test piece, conduct welding tests using the optimized welding process parameters, collect verification deformation data and compare it with the data in the first piece welding deformation database to verify the effectiveness of the optimized welding process parameters; if the verification is qualified, the optimized welding process parameters are applied to the batch production of the main truss node.

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