Steel structure lap quality detection device and method
By designing node identification, force analysis and quality monitoring modules in steel structure overlap quality detection, identifying and managing abnormal nodes, the problems of low detection efficiency and poor accuracy in the existing technology are solved, and efficient and accurate welding quality is achieved Monitor and manage.
Patent Information
- Application Number
- CN202411428468.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2024-12-20
- Estimated Expiration
- 2044-10-14
AI Technical Summary
The prior art has low detection efficiency and poor accuracy in steel structure overlap quality detection, making it difficult to monitor welding quality in real time and identify abnormal nodes.
A steel structure overlap quality detection device and method is designed, including node identification module, stress analysis module, quality detection module, quality identification module, quality abnormality impact identification module and quality management module. By identifying overlap nodes, stress is performed. , strain and pressure distribution analysis, real-time monitoring of welding process, obtaining quality indicators, and identifying and managing abnormal nodes.
It improves the efficiency and accuracy of overlap quality detection, can monitor welding quality in real time, identify and repair abnormal nodes, and ensure the stability and consistency of the welding process.
Smart Images
Figure CN118937626B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of quality inspection, and in particular to a device and method for inspecting the quality of steel structure overlap. Background Art
[0002] With the rapid development of modern architecture and infrastructure, steel structures have been widely used in bridges, buildings, towers and other projects. Steel structures have become the preferred material in modern projects due to their high strength, light weight and fast construction speed. However, with the continuous expansion of project scale and the increasing complexity of construction technology, the monitoring and management of steel structure lap quality are also facing increasing challenges. Traditional steel structure lap quality inspection methods have the problems of low inspection efficiency and poor accuracy. Summary of the invention
[0003] The present application provides a steel structure lap joint quality detection device and method, which are used to solve the technical problems of low detection efficiency and poor accuracy in the prior art when performing steel structure lap joint quality detection.
[0004] In view of the above problems, the present application provides a steel structure lap quality detection device and method.
[0005] In a first aspect of the present application, a steel structure lap quality detection device is provided, the device comprising:
[0006] A node identification module, wherein the node identification module identifies multiple overlap nodes in the steel structure; a force analysis module, wherein the force analysis module performs force analysis on each overlap node in the multiple overlap nodes to generate an overlap node network, wherein the force analysis includes stress, strain and pressure distribution; a quality detection module, wherein the quality detection module is provided with a quality detection mechanism, wherein the quality detection mechanism is connected to the overlap node network, and when the overlap mechanism welds each overlap node, the quality detection mechanism is activated to perform quality detection on the overlap process to obtain multiple overlap quality indicators; a quality identification module, wherein the quality identification module identifies the overlap node network according to the multiple overlap quality indicators, and outputs an overlap node identification network after quality identification; a quality abnormality impact identification module, wherein the quality abnormality impact identification module identifies the quality abnormality impact on the overlap node identification network, and determines abnormal overlap nodes, wherein the abnormal overlap nodes are nodes whose quality abnormality impact is greater than a preset threshold; a quality management module, wherein the quality management module performs overlap quality management on the abnormal overlap nodes through the overlap mechanism.
[0007] A second aspect of the present application provides a method for detecting the quality of steel structure overlap, the method comprising:
[0008] Identify multiple lap joints in a steel structure; perform force analysis on each of the multiple lap joints to generate a lap joint network, wherein the force analysis includes stress, strain and pressure distribution; set a quality inspection mechanism, wherein the quality inspection mechanism is connected to the lap joint network, and when the lap joint mechanism welds each lap joint, the quality inspection mechanism is activated to perform quality inspection on the lap joint process to obtain multiple lap joint quality indicators; identify the lap joint network according to the multiple lap joint quality indicators, and output a lap joint node identification network after quality identification; identify the impact of quality abnormalities on the lap joint identification network, and determine abnormal lap joints, wherein the abnormal lap joints are nodes whose quality abnormality impact is greater than a preset threshold; perform lap joint quality management on the abnormal lap joints through the lap joint mechanism.
[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0010] The present application identifies multiple lap joints in a steel structure; performs a force analysis on each of the multiple lap joints to generate a lap joint network, wherein the force analysis includes stress, strain and pressure distribution; sets a quality inspection mechanism, wherein the quality inspection mechanism is connected to the lap joint network, and when the lap joint mechanism welds each lap joint, the quality inspection mechanism is activated to perform quality inspection on the lap joint process, and obtains multiple lap joint quality indicators; identifies the lap joint network according to multiple lap joint quality indicators, and outputs a lap joint node identification network after quality identification; identifies the impact of abnormal quality on the lap joint identification network, and determines abnormal lap joints, wherein the abnormal lap joint node is a node whose abnormal quality impact is greater than a preset threshold; and performs lap joint quality management on abnormal lap joints through the lap joint mechanism. The present invention solves the technical problems of low detection efficiency and poor precision in the existing technology when performing lap joint quality inspection of steel structures, and achieves the technical effect of improving the efficiency and precision of lap joint quality inspection by identifying the position of the lap joint node, performing a force analysis, monitoring the welding quality in real time, and identifying abnormal nodes. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0012] Figure 1 A schematic diagram of a steel structure lap quality detection device provided in an embodiment of the present application;
[0013] Figure 2A schematic flow chart of a steel structure lap quality inspection method provided in an embodiment of the present application.
[0014] Explanation of the reference numerals: node identification module 11 , force analysis module 12 , quality detection module 13 , quality identification module 14 , quality abnormality impact identification module 15 , quality management module 16 . DETAILED DESCRIPTION
[0015] The present application provides a steel structure lap joint quality detection device and method, aiming to solve the technical problems of low detection efficiency and poor accuracy in the prior art when performing steel structure lap joint quality detection. By identifying the lap joint node position, performing force analysis, real-time monitoring of welding quality, and identifying abnormal nodes, the technical effect of improving the efficiency and accuracy of lap joint quality detection is achieved.
[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.
[0018] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a steel structure overlap quality detection device, the device comprising:
[0019] The node identification module 11 identifies a plurality of overlapping nodes in the steel structure.
[0020] In an embodiment of the present application, a sensor such as a laser scanner or a camera is first used to comprehensively scan the steel structure to obtain its shape and surface feature data. The collected scan data is then processed, including noise filtering and data smoothing, to improve the quality and accuracy of the data. Then the edge detection stage is entered, and the edge detection is performed on the pre-processed data through an image processing algorithm, such as the Canny edge detection algorithm, to identify the edge features of the steel structure and extract the boundary and contour of the steel structure. Subsequently, the straight lines in the image are detected using methods such as the Hough transform, and the points in the image are converted into curves in the parameter space through the Hough transform, and the straight lines in the image are identified by finding the intersection of these curves. The geometric relationship between the detected straight lines is analyzed to determine the intersection of these straight lines, which are the intersection points of the steel structure, i.e., the overlap nodes.
[0021] Through the above process, multiple lap joints in the steel structure are identified.
[0022] A force analysis module 12 performs force analysis on each of the plurality of overlap nodes to generate an overlap node network, wherein the force analysis includes stress, strain and pressure distribution.
[0023] In an embodiment of the present application, when the force analysis module performs force analysis on each of the multiple lap joints, the finite element analysis software is used to divide the steel structure into multiple small units, and the force conditions under the actual working conditions are simulated through the discretization method, and the stress distribution data of each node under the action of external force is calculated. Next, in the strain analysis stage, based on the data obtained from the stress analysis, the deformation of each node after the force is calculated. Using the mechanical properties and basic formulas of the material, the strain distribution of each node is determined through the strain calculation function in the finite element software. In the pressure distribution analysis stage, the pressure field calculation function in the finite element analysis software is used to analyze the distribution of external forces at the nodes.
[0024] Finally, the stress, strain, and pressure distribution data of all nodes are integrated together, and a network analysis tool, such as NetworkX, is used to generate an overlapped node network.
[0025] The quality inspection module 13 is provided with a quality inspection mechanism, wherein the quality inspection mechanism is connected to the overlapping node network, and when the overlapping mechanism welds each overlapping node, the quality inspection mechanism is activated to perform quality inspection on the overlapping process to obtain multiple overlapping quality indicators.
[0026] In the embodiment of the present application, when the lap joint mechanism starts to weld each lap joint node, the quality detection mechanism uses a variety of sensors, temperature sensors, displacement sensors and speed sensors to collect various data generated during the welding process in real time. The temperature sensor monitors the temperature change of the welding area and records the temperature distribution from the center to the edge of the welding point. The displacement sensor monitors the displacement change of the welding material to ensure the correct position and movement trajectory of the welding material in the joint. At the same time, the speed sensor records the movement speed of the welding head during the welding process to ensure the stability and uniformity of the welding process.
[0027] During the entire welding process, the quality inspection agency uses these sensor data to calculate and analyze multiple overlap quality indicators in real time, including the uniformity of the weld, the penetration depth of the welding material, and the change in temperature gradient. These quality indicators can fully reflect the quality status of the welding process. The uniformity of the weld is an important manifestation of welding quality, the penetration depth determines the strength and durability of the weld, and the change in temperature gradient directly affects the reliability and consistency of the welding process.
[0028] Through these steps, the quality detection module obtains multiple overlap quality indicators.
[0029] The quality identification module 14 identifies the overlapping node network according to the multiple overlapping quality indicators and outputs the overlapping node identification network after quality identification.
[0030] In an embodiment of the present application, after receiving multiple overlap quality indicators, the quality identification module directly identifies these indicators on the corresponding overlap nodes, and finally outputs a quality-identified overlap node identification network, which displays the various quality indicators of each node.
[0031] The quality abnormality impact identification module 15 performs quality abnormality impact identification on the overlapping node identification network to determine abnormal overlapping nodes, wherein the abnormal overlapping nodes are nodes whose quality abnormality impact is greater than a preset threshold.
[0032] In the embodiment of the present application, when the quality anomaly impact identification module identifies the quality anomaly impact of the overlapped node identification network, the quality index of each node is analyzed, and the temperature gradient, infiltration uniformity and infiltration rate changes are compared with the threshold one by one. The quality threshold is pre-set by technical experts and is used to determine whether the node is abnormal. If a quality indicator exceeds the preset threshold, the node is marked as an abnormal overlap node. Through the above process, the abnormal overlap node is determined.
[0033] The quality management module 16 performs splicing quality management on the abnormal splicing nodes through the splicing mechanism.
[0034] In an embodiment of the present application, the quality management module obtains information about abnormal lap joints from the quality abnormality impact identification module, including specific quality indicators of each abnormal node and detailed data exceeding the threshold. Data processing software, such as MATLAB, is used to organize and prepare the data of abnormal nodes to ensure that all data are complete and available. Next, each abnormal node is analyzed to determine the root cause of the problem. For nodes with large temperature gradient changes, the data recorded by the temperature sensor is checked to analyze whether the temperature gradient changes during welding are in line with expectations. If the temperature gradient changes too much, it may be due to unstable temperature control. Compare the temperature data of the abnormal node with the temperature data of the normal node to determine whether there is a significant difference. If a problem is found, adjust the temperature control parameters of the welding equipment to ensure uniform temperature distribution. For nodes with low penetration uniformity, the weld image collected by the image sensor is used to analyze the penetration of the weld through the image processing software, check whether the material distribution inside the weld is uniform, and evaluate the penetration uniformity through edge detection and contour detection. If the penetration is uneven, it may be that the welding speed or welding material supply is unstable, and the welding speed or material supply needs to be adjusted. According to the analysis results, a repair plan is formulated. Specific operation steps include re-welding abnormal nodes, adjusting welding temperature, adjusting welding speed or improving material penetration.
[0035] Through the above process, the overlap quality management is completed.
[0036] Furthermore, in the device provided in the embodiment of the application, the quality detection mechanism is connected to the overlap mechanism, and further comprises:
[0037] The splicing mechanism sends a data request instruction to the quality detection mechanism, and locates the quality detection data of the abnormal splicing node in the quality detection mechanism; the quality detection mechanism returns the quality detection data to the splicing mechanism, and the splicing mechanism performs splicing repair on the abnormal splicing node according to the quality detection data.
[0038] In the embodiment of the present application, the quality detection mechanism is connected to the splicing mechanism, and when an abnormal splicing node is identified, the splicing mechanism sends a data request instruction to the quality detection mechanism. This instruction is transmitted via an industrial communication protocol or wireless communication, and the instruction content includes the identification information of the abnormal splicing node and the required quality detection data type.
[0039] After receiving the data request instruction, the quality inspection agency uses an embedded processor or industrial control computer to process and locate the quality inspection data of the abnormal overlap node in its internal database. Through database query technology, the quality inspection agency retrieves the quality inspection data such as temperature gradient change, infiltration uniformity change and infiltration rate change related to the abnormal overlap node.
[0040] After locating the required data, the quality inspection agency packages these quality inspection data and returns them to the overlap agency through the data transmission interface. The overlap agency analyzes the received quality inspection data to determine the root cause of the abnormal problem. For example, for nodes with large temperature gradient changes, analyze whether the temperature data shows unstable temperature control during welding; for nodes with low penetration uniformity, check whether there is uneven distribution of weld materials. Based on these analysis results, the overlap agency uses root cause analysis methods, such as fishbone diagrams or 5 Whys analysis methods, to analyze and determine specific quality problems and their root causes.
[0041] After the problem is identified, the jointing agency develops a repair plan for each abnormal node. The management plan includes specific repair methods and steps. For example, if the temperature gradient changes too much, the repair plan includes adjusting the temperature control parameters of the welding equipment; if the penetration uniformity is not high, the repair plan includes adjusting the welding speed or material supply to ensure a more uniform and stable welding process.
[0042] Once the repair plan is formulated, the splicing mechanism performs actual operations through the control system and actuators within it to ensure that the repair process proceeds as planned. Through the above process, the abnormal splicing nodes are spliced and repaired.
[0043] Furthermore, in the device provided in the application embodiment, the lap joint mechanism includes a lap joint fixing device, a lap joint execution device and a lap joint driving device, and the quality detection mechanism includes multiple sensors: wherein the lap joint fixing device is used to fix the steel structure of the lap joint node, the lap joint execution device is used to weld the weld of the lap joint node, the lap joint driving device is used to control the execution of the lap joint execution device, and the multiple sensors include temperature sensors, displacement sensors and speed sensors.
[0044] In the embodiment of the present application, the overlap mechanism includes an overlap fixing device, an overlap execution device and an overlap driving device. The overlap fixing device is used to fix the steel structure of the overlap node to ensure the stability of the node position during the welding process and prevent displacement during the welding process. The overlap execution device is used to weld the weld of the overlap node and ensure the welding quality by accurately controlling the welding operation. The overlap driving device is used to control the execution of the overlap execution device, provide power and control signals, and make the welding operation proceed according to predetermined parameters and steps.
[0045] Quality inspection mechanism temperature sensor, displacement sensor and speed sensor. The temperature sensor is used to monitor the temperature change of the welding area during the welding process. The displacement sensor is used to monitor the displacement of the welding material. The speed sensor is used to record the moving speed of the welding head.
[0046] Through the coordinated work of these devices and sensors, the lap joint mechanism and quality inspection mechanism can monitor and control the welding process in real time.
[0047] Furthermore, in the device provided in the embodiment of the application, when the overlapping mechanism welds each overlapping node, the quality detection mechanism is activated to perform quality detection on the overlapping process, and further includes:
[0048] Determine the welding surface of each overlapping node; when the overlapping mechanism welds the welding surface of each overlapping node, activate the quality detection mechanism to obtain the temperature gradient change, penetration uniformity change and penetration rate change from the edge of the welding surface to the center of the welding surface during the welding process; perform quality detection according to the temperature gradient change, penetration uniformity change and penetration rate change to obtain multiple overlapping quality indicators.
[0049] In the embodiment of the present application, the welding surface of each lap joint is first determined, and the steel structure is scanned using image recognition technology or a three-dimensional scanner to determine the specific position of each lap joint. The image recognition system captures the image of the steel structure and uses edge detection and morphological processing algorithms to identify the position and shape of each lap joint. Next, based on the node recognition results, a geometric model of the welding surface is generated. A three-dimensional model of the welding surface is generated by computer-aided design to ensure that the position and angle of the welding surface are accurate.
[0050] After determining the welding surface, use the overlap fixture to fix the steel structure of the overlap node to ensure the stability of the node position during the welding process. The overlap fixture includes a mechanical clamp or a magnetic fixture to ensure that the welding surface does not move during the welding process. After the welding surface is fixed, set the welding parameters, including welding temperature, welding speed and welding material. While the overlap mechanism welds the welding surface of each overlap node, the quality inspection mechanism is activated to monitor the welding process in real time. The quality inspection mechanism obtains the temperature gradient change, penetration uniformity change and penetration rate change from the edge of the welding surface to the center of the welding surface during the welding process through multiple sensors. The temperature gradient change is measured by measuring the center temperature of the contact surface during welding. and the contact surface edge temperature , calculate the temperature difference between the two and divide it by the distance from the edge to the center of the contact surface, and get it through the formula Calculation. The penetration uniformity is calculated by calculating the weld depth at each measuring point. Average weld depth The difference is evaluated by the formula , where n is the total number of measurement points. The change in penetration rate is calculated by the change in weld depth Changes with time The formula is .
[0051] Through the above calculations and real-time monitoring, the quality inspection agency conducts comprehensive quality inspections and obtains multiple overlap quality indicators, including changes in temperature gradient, changes in penetration uniformity, and changes in penetration rate.
[0052] Furthermore, in the device provided in the embodiment of the application, the force analysis of each overlapping node among the plurality of overlapping nodes is performed to generate an overlapping node network, and further includes:
[0053] Determine whether the steel structure is a double-layer steel structure. If the steel structure is a double-layer steel structure, layer the multiple overlap nodes and output a first overlap node network and a second overlap node network; analyze the relationship between the first overlap node network and the second overlap node network, and splice them to output a double-layer overlap node network.
[0054] In the embodiment of the present application, it is first determined whether the steel structure is a double-layer steel structure. The steel structure is scanned using image recognition technology or a 3D scanner to obtain its 3D image, and edge detection and morphological processing algorithms are used for analysis to identify the number of layers of the steel structure. By analyzing the structural features in the image, such as the height of the layer, the distance between the layers, and the node distribution, it is determined whether the steel structure is a double-layer structure.
[0055] If the steel structure is detected to be a double-layer structure, multiple overlap nodes are processed in layers. Use computer-aided design software to process overlap nodes in layers. Import 3D scanning data and generate a 3D model. Divide the nodes into two layers according to their geometric position and structural characteristics. In the CAD software, generate the first overlap node network and the second overlap node network. Each network represents a layer of overlap nodes, and construct the corresponding network model through the geometric position and connection relationship of the nodes.
[0056] Next, we analyze the relationship between the first overlapping node network and the second overlapping node network and splice them. We use network analysis tools to integrate the two networks to ensure that the connection and position relationships between the nodes remain consistent. By analyzing and calculating the relative position and connection relationship of each node in the two networks, we use graph theory algorithms to integrate the two networks into a complete double-layer overlapping node network.
[0057] Furthermore, in the device provided in the embodiment of the application, the abnormal quality impact of the splicing node identification network is identified to determine the abnormal splicing node, and further includes:
[0058] Identify the initial abnormal overlap node whose overlap quality index is less than the preset overlap quality index in the overlap node identification network; determine the stressed overlap adjacent node of the initial abnormal overlap node according to the overlap node identification network; analyze the abnormal quality resistance of the stressed overlap adjacent node and the initial abnormal overlap node to obtain the abnormal quality resistance index; if the abnormal quality resistance index does not meet the preset abnormal quality resistance threshold, output it as an abnormal overlap node.
[0059] In the embodiment of the present application, the quality data in the overlap node identification network is first analyzed by using a data analysis tool such as MATLAB. The overlap quality index of each node is compared with the preset overlap quality index, and the nodes below the preset standard are identified as the initial abnormal overlap nodes.
[0060] Next, use network analysis tools such as NetworkX to analyze the node connection relationship in the overlap node identification network, and use graph theory algorithms such as breadth-first search to identify the nodes directly connected to the initial abnormal overlap nodes, and determine these nodes as stress-bearing overlap neighbor nodes. Then use structural analysis software to perform mechanical simulation, build a model, apply loads and boundary conditions, run the simulation, extract stress and deformation results, and calculate the abnormal quality resistance index of the stress-bearing overlap neighbor nodes and the initial abnormal overlap nodes. Compare the calculated abnormal quality resistance index with the preset abnormal quality resistance threshold. If the resistance index is lower than the threshold, it is marked as an abnormal overlap node and the result is output.
[0061] Furthermore, in the device provided in the embodiment of the application, analyzing the abnormal mass resistance of the stressed overlapping adjacent node and the initial abnormal overlapping node also includes:
[0062] The stressed overlapping adjacent nodes are divided, and upper stressed overlapping adjacent nodes and lower stressed overlapping adjacent nodes are output; stress prediction is performed on the initial abnormal overlapping nodes according to the upper stressed overlapping adjacent nodes, and an upper stress prediction index is output; the self-stress prediction index of the initial abnormal overlapping nodes is obtained; the lower stress prediction index of the lower stressed overlapping adjacent nodes is obtained; the upper stress prediction index, the self-stress prediction index and the lower stress prediction index are fitted, and abnormal quality resistance analysis is performed on the stressed overlapping adjacent nodes to obtain an abnormal quality resistance index.
[0063] In an embodiment of the present application, when dividing the force-bearing overlapping adjacent nodes, the nodes are positionally classified using a three-dimensional coordinate system based on the spatial positions and mechanical properties of the nodes, and the force-bearing overlapping adjacent nodes are divided into upper force-bearing overlapping adjacent nodes and lower force-bearing overlapping adjacent nodes.
[0064] Next, use the structural analysis software to perform mechanical simulation and evaluate the force influence of the upper stress-bearing lap joint neighboring node on the initial abnormal lap joint node. A model including the initial abnormal lap joint node and the upper stress-bearing lap joint neighboring node is established in the structural analysis software, external loads and boundary conditions are applied, the simulation is run, and the upper force prediction index is extracted. At the same time, the force condition of the initial abnormal lap joint node is simulated separately, and its self-stress prediction index is calculated. This step is also carried out in the structural analysis software. A model of the initial abnormal lap joint node is established separately, external loads and boundary conditions are applied, the simulation is run, and the self-stress prediction index is extracted. Then evaluate the force influence of the lower stress-bearing lap joint neighboring node on the initial abnormal lap joint node, and obtain the lower force prediction index. Similar to the upper force prediction, a model including the initial abnormal lap joint node and the lower stress-bearing lap joint neighboring node is established in the structural analysis software, external loads and boundary conditions are applied, the simulation is run, and the lower force prediction index is extracted.
[0065] Next, the values of upper force prediction, self-force prediction and lower force prediction are organized into a unified format, such as array or matrix format. A model function is defined to represent the contribution of each prediction index to the comprehensive abnormal quality resistance index. The model function is defined as a linear combination, that is, , where W is the comprehensive abnormal quality resistance index, S is the upper force prediction index, M is the self-force prediction index, N is the lower force prediction index, and a, b, and c are the parameters that need to be fitted. Then use the statistical analysis tool to perform multivariate regression analysis and calculate the fitting parameters by fitting the model function. Specifically, prepare the input data matrix, take each prediction index as an independent variable, and prepare the target value (actually measured resistance index). Use the regression analysis tool to fit the model function and calculate the fitting parameters a, b, and c. The fitted parameters represent the contribution of each prediction index to the comprehensive resistance index. Finally, substitute the fitted parameters a, b, and c into the model function to calculate the comprehensive abnormal quality resistance index.
[0066] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:
[0067] The present application identifies multiple lap joints in a steel structure; performs a force analysis on each lap joint in the multiple lap joints to generate a lap joint network, wherein the force analysis includes stress, strain and pressure distribution; sets a quality inspection mechanism, wherein the quality inspection mechanism is connected to the lap joint network, and when the lap joint mechanism welds each lap joint, the quality inspection mechanism is activated to perform quality inspection on the lap joint process, and obtains multiple lap joint quality indicators; identifies the lap joint network according to multiple lap joint quality indicators, and outputs a lap joint node identification network after quality identification; identifies the impact of abnormal quality on the lap joint identification network, and determines abnormal lap joints, wherein the abnormal lap joint is a node whose abnormal quality impact is greater than a preset threshold; and performs lap joint quality management on abnormal lap joints through the lap joint mechanism. The present invention solves the technical problems of low detection efficiency and poor precision in the existing technology when performing lap joint quality inspection of steel structures, and achieves the technical effect of improving the efficiency and precision of lap joint quality inspection by identifying the position of the lap joint, performing force analysis, real-time monitoring of welding quality, and identifying abnormal nodes.
[0068] Embodiment 2, based on the same inventive concept as a steel structure overlap quality detection device in the above embodiment, Figure 2 As shown, an embodiment of the present application provides a method for detecting the quality of steel structure overlap, the method comprising:
[0069] Identify multiple lap joints in a steel structure; perform force analysis on each of the multiple lap joints to generate a lap joint network, wherein the force analysis includes stress, strain and pressure distribution; set a quality inspection mechanism, wherein the quality inspection mechanism is connected to the lap joint network, and when the lap joint mechanism welds each lap joint, the quality inspection mechanism is activated to perform quality inspection on the lap joint process to obtain multiple lap joint quality indicators; identify the lap joint network according to the multiple lap joint quality indicators, and output a lap joint node identification network after quality identification; identify the impact of quality abnormalities on the lap joint identification network, and determine abnormal lap joints, wherein the abnormal lap joints are nodes whose quality abnormality impact is greater than a preset threshold; perform lap joint quality management on the abnormal lap joints through the lap joint mechanism.
[0070] Furthermore, the quality detection mechanism is connected to the overlap mechanism, including:
[0071] The splicing mechanism sends a data request instruction to the quality detection mechanism, and locates the quality detection data of the abnormal splicing node in the quality detection mechanism; the quality detection mechanism returns the quality detection data to the splicing mechanism, and the splicing mechanism performs splicing repair on the abnormal splicing node according to the quality detection data.
[0072] Furthermore, the overlapping mechanism includes an overlapping fixing device, an overlapping execution device and an overlapping driving device, and the quality detection mechanism includes a plurality of sensors:
[0073] Among them, the overlap fixing device is used to fix the steel structure of the overlap node, the overlap execution device is used to weld the weld of the overlap node, the overlap driving device is used to control the execution of the overlap execution device, and the multiple sensors include temperature sensors, displacement sensors and speed sensors.
[0074] Furthermore, when the overlapping mechanism welds each overlapping node, the quality detection mechanism is activated to perform quality detection on the overlapping process, including:
[0075] Determine the welding surface of each overlapping node; when the overlapping mechanism welds the welding surface of each overlapping node, activate the quality detection mechanism to obtain the temperature gradient change, penetration uniformity change and penetration rate change from the edge of the welding surface to the center of the welding surface during the welding process; perform quality detection according to the temperature gradient change, penetration uniformity change and penetration rate change to obtain multiple overlapping quality indicators.
[0076] Furthermore, the stress analysis of each overlapping node among the plurality of overlapping nodes is performed to generate an overlapping node network, including:
[0077] Determine whether the steel structure is a double-layer steel structure. If the steel structure is a double-layer steel structure, layer the multiple overlap nodes and output a first overlap node network and a second overlap node network; analyze the relationship between the first overlap node network and the second overlap node network, and splice them to output a double-layer overlap node network.
[0078] Further, the quality abnormality impact identification is performed on the overlapping node identification network to determine the abnormal overlapping nodes, including:
[0079] Identify the initial abnormal overlap node whose overlap quality index is less than the preset overlap quality index in the overlap node identification network; determine the stressed overlap adjacent node of the initial abnormal overlap node according to the overlap node identification network; analyze the abnormal quality resistance of the stressed overlap adjacent node and the initial abnormal overlap node to obtain the abnormal quality resistance index; if the abnormal quality resistance index does not meet the preset abnormal quality resistance threshold, output it as an abnormal overlap node.
[0080] Further, analyzing the abnormal mass resistance of the stressed lap joint adjacent node and the initial abnormal lap joint node includes:
[0081] The stressed overlapping adjacent nodes are divided, and upper stressed overlapping adjacent nodes and lower stressed overlapping adjacent nodes are output; stress prediction is performed on the initial abnormal overlapping nodes according to the upper stressed overlapping adjacent nodes, and an upper stress prediction index is output; the self-stress prediction index of the initial abnormal overlapping nodes is obtained; the lower stress prediction index of the lower stressed overlapping adjacent nodes is obtained; the upper stress prediction index, the self-stress prediction index and the lower stress prediction index are fitted, and abnormal quality resistance analysis is performed on the stressed overlapping adjacent nodes to obtain an abnormal quality resistance index.
[0082] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0083] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.
[0084] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.
Claims
1. A steel structure lap quality detection device, characterized in that: The device comprises: A node identification module, wherein the node identification module identifies a plurality of overlapping nodes in the steel structure; A force analysis module, wherein the force analysis module performs force analysis on each of the plurality of overlap nodes to generate an overlap node network, wherein the force analysis includes stress, strain and pressure distribution; A quality inspection module, wherein the quality inspection module is provided with a quality inspection mechanism, wherein the quality inspection mechanism is connected to the overlap node network, and when the overlap mechanism welds each overlap node, the quality inspection mechanism is activated to perform quality inspection on the overlap process, and a plurality of overlap quality indicators are obtained; A quality identification module, wherein the quality identification module identifies the overlapping node network according to the multiple overlapping quality indicators and outputs the overlapping node identification network after quality identification; A quality abnormality impact identification module, wherein the quality abnormality impact identification module identifies the quality abnormality impact of the overlapping node identification network and determines abnormal overlapping nodes, wherein the abnormal overlapping nodes are nodes whose quality abnormality impact is greater than a preset threshold; A quality management module, wherein the quality management module performs splicing quality management on the abnormal splicing nodes through the splicing mechanism; Wherein, when the overlapping mechanism welds each overlapping node, the quality detection mechanism is activated to perform quality detection on the overlapping process, including: Determine the welding surface of each lap joint; When the overlapping mechanism welds the welding surface of each overlapping node, the quality detection mechanism is activated to obtain the temperature gradient change, the penetration uniformity change and the penetration rate change from the edge of the welding surface to the center of the welding surface during the welding process; Performing quality inspection according to the temperature gradient change, the infiltration uniformity change and the infiltration rate change to obtain a plurality of overlap quality indicators; Wherein, the temperature gradient change is obtained by measuring the center temperature and the edge temperature of the contact surface during welding, calculating the temperature difference between the two and dividing it by the distance from the edge to the center of the contact surface, the penetration uniformity change is evaluated by calculating the difference between the weld depth of each measuring point and the average weld depth, and the penetration rate change is determined by calculating the ratio of the change in weld depth to the change in time; The step of identifying the impact of abnormal quality on the overlapping node identification network and determining abnormal overlapping nodes includes: Identify an initial abnormal splicing node in the splicing node identification network whose splicing quality index is less than a preset splicing quality index; Determining the stress-bearing overlapping adjacent nodes of the initial abnormal overlapping nodes according to the overlapping node identification network; Analyze the abnormal quality resistance of the stressed overlapping adjacent node and the initial abnormal overlapping node to obtain an abnormal quality resistance index; If the abnormal quality resistance index does not meet the preset abnormal quality resistance threshold, the output is an abnormal overlap node; The abnormal mass resistance of the stressed overlapping adjacent node and the initial abnormal overlapping node is analyzed, including: Dividing the force-bearing overlapping adjacent nodes, and outputting upper force-bearing overlapping adjacent nodes and lower force-bearing overlapping adjacent nodes; Perform force prediction on the initial abnormal overlap node according to the upper force overlap adjacent node, and output the upper force prediction index; Obtaining a self-stress prediction index of the initial abnormal overlap node; Obtaining a lower force prediction index of the lower force lap adjacent node; The upper force prediction index, the self-force prediction index and the lower force prediction index are fitted, and abnormal quality resistance analysis is performed on the stress-bearing overlapping adjacent nodes to obtain abnormal quality resistance index.
2. A steel structure lap quality detection device as claimed in claim 1, characterized in that: The quality detection mechanism is connected to the overlapping mechanism, and includes: The splicing mechanism sends a data request instruction to the quality detection mechanism to locate the quality detection data of the abnormal splicing node in the quality detection mechanism; The quality detection mechanism returns the quality detection data to the splicing mechanism, and the splicing mechanism performs splicing repair on the abnormal splicing node according to the quality detection data.
3. A steel structure lap joint quality detection device as claimed in claim 1, characterized in that: The overlapping mechanism includes an overlapping fixing device, an overlapping execution device and an overlapping driving device, and the quality detection mechanism includes a plurality of sensors: Among them, the overlap fixing device is used to fix the steel structure of the overlap node, the overlap execution device is used to weld the weld of the overlap node, the overlap driving device is used to control the execution of the overlap execution device, and the multiple sensors include temperature sensors, displacement sensors and speed sensors.
4. A steel structure overlap quality detection device as claimed in claim 1, characterized in that: The stress analysis of each overlapping node among the plurality of overlapping nodes is performed to generate an overlapping node network, including: Determine whether the steel structure is a double-layer steel structure, and if the steel structure is a double-layer steel structure, layer the multiple overlapping nodes and output a first overlapping node network and a second overlapping node network; The relationship between the first overlapping node network and the second overlapping node network is analyzed and spliced, and a double-layer overlapping node network is output.
5. A method for detecting the quality of steel structure overlap, characterized in that: The method is performed by a steel structure overlap quality detection device according to any one of claims 1 to 4, comprising: Identify multiple lap joints in steel structures; Performing a force analysis on each of the plurality of overlapping nodes to generate an overlapping node network, wherein the force analysis includes stress, strain and pressure distribution; A quality inspection mechanism is provided, wherein the quality inspection mechanism is connected to the overlap node network, and when the overlap mechanism welds each overlap node, the quality inspection mechanism is activated to perform quality inspection on the overlap process, and a plurality of overlap quality indicators are obtained; Identify the overlap node network according to the multiple overlap quality indicators, and output the overlap node identification network after quality identification; Performing quality abnormality impact identification on the overlapping node identification network to determine abnormal overlapping nodes, wherein the abnormal overlapping nodes are nodes whose quality abnormality impact is greater than a preset threshold; The overlapping quality management of the abnormal overlapping nodes is performed through the overlapping mechanism.