Temperature change self-adaptive electric fusion welding control method and system

Abstract

The invention discloses a temperature change self-adaptive electric fusion welding control method and system, and relates to the technical field of intelligent welding. Comprising the following steps: S1, installing a high-precision temperature sensor, and checking and preprocessing each structure; s2, the resistance value of the welding equipment is fitted based on a self-adaptive least square method, the relation that the temperature changes along with the resistance value is analyzed, and the welding temperature of the welding equipment is adjusted based on the analysis result; s3, a to-be-welded material is selected and checked, and the to-be-welded material is preheated before electric fusion welding; s4, the data are analyzed and processed in real time based on the improved double-flow graph convolutional network, the optimal welding temperature of a welding joint is predicted, and self-adaptive adjustment is carried out; and S5, after the electric fusion welding is finished, the welding material is subjected to post-heat treatment. The temperature change in the welding process can be accurately controlled, the device is suitable for different welding materials, the welding quality is improved, and meanwhile personal errors can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent welding technology, and in particular to a temperature-dependent adaptive electric fusion welding control method and system. Background Art

[0002] In the traditional electrofusion welding process, the welding parameters are usually fixed and cannot be automatically adjusted according to the changes in ambient temperature. This results in large fluctuations in welding quality under different temperature conditions.

[0003] For example, in a low temperature environment, the melting speed of the welding material will slow down, and higher welding energy is required to achieve a good welding effect. However, if the welding parameters at room temperature are still used, it may lead to insufficient welding and reduced weld strength. On the contrary, in a high temperature environment, the melting speed of the welding material is accelerated. If the welding parameters are not adjusted in time, overheating may occur, resulting in coarse weld structure and reduced welding quality.

[0004] The change of ambient temperature affects electrofusion welding in many ways. First of all, temperature will affect the physical properties of welding materials, such as thermal conductivity, specific heat capacity, etc. The change of these properties will directly affect the heat transfer and melting state during welding.

[0005] Secondly, the ambient temperature will also affect the performance of the welding equipment. For example, in a low temperature environment, the output power of the welding power supply may be affected, resulting in unstable welding current. At the same time, temperature changes will also affect the life and performance of the welding electrode.

[0006] In addition, different welding materials have different sensitivities to temperature. Some materials are prone to welding defects such as cracks and pores when the temperature changes greatly. Therefore, a control method that can automatically adjust the welding parameters according to the changes in ambient temperature is needed to ensure the stability of the welding quality.

[0007] With the development of modern industry, the requirements for welding quality are getting higher and higher. Especially in some key fields such as aerospace, automobile manufacturing, petrochemical industry, etc., the quality of welding is directly related to the safety and reliability of the product.

[0008] In these fields, welded joints often need to withstand harsh environments such as high temperature, high pressure, and corrosion. Therefore, the stability and reliability of welding quality must be guaranteed. Traditional electric fusion welding methods can no longer meet these high requirements, and a more advanced welding control method is needed.

[0009] In order to solve the above problems, the present invention proposes a temperature-adaptive electric fusion welding control method and system. Summary of the invention

[0010] The main purpose of the present invention is to provide a temperature-adaptive electric fusion welding control method and system, which can effectively solve the problems in the background technology.

[0011] In order to achieve the above object, the present invention adopts the following technical solutions:

[0012] A temperature-dependent adaptive electric fusion welding control method comprises the following steps:

[0013] S1: Install high-precision temperature sensors and connect them to the welding control system; then perform parameter setting, inspection and maintenance of the welding equipment, as well as signal transmission debugging of the welding equipment;

[0014] S2: Detect the temperature change of the welding equipment under constant voltage conditions, collect the voltage, current and temperature change data of the welding equipment respectively, fit the resistance value of the welding equipment based on the adaptive least squares method, and analyze the relationship between the temperature of the welding equipment and the resistance value change, and adjust the welding temperature of the welding equipment based on the analysis results;

[0015] S3: Select and inspect the materials to be welded, and pre-treat the materials to be welded before electrofusion welding;

[0016] S4: During the electrofusion welding process, the temperature sensor continuously monitors the temperature data of the welding joint and the surrounding welding environment, and transmits it to the welding control system in real time. The welding control system analyzes and processes the collected data in real time based on the improved dual-flow graph convolutional network, predicts the optimal welding temperature of the welding joint, and performs adaptive adjustment of the temperature of the welding equipment;

[0017] S5: After the electric fusion welding is completed, the welding materials are subjected to post-heat treatment.

[0018] Preferably, the adaptive least squares method in S2 is specifically as follows:

[0019] According to the internal circuit design rules of welding equipment, combined with Kirchhoff's voltage law and current law, a circuit equivalent model is established, and the model is discretized. The collected voltage and current data of the welding equipment are preprocessed, and the resistance value is fitted based on the following least squares method:

[0020]

[0021] Among them, s is the time; β is the identification parameter matrix; K s is the gain value; y s is the system output; δ s is the input and output data vector; Q s is the covariance matrix; α s is the forgetting factor; T is the transpose of the vector;

[0022]

[0023]

[0024] Among them, γ is a random number between (0.9, 1); ξ is an adjustable parameter; ε s is the residual at time s;

[0025] The adaptive dynamic adjustment of the forgetting factor is performed based on an adaptive genetic algorithm, and the adaptive genetic algorithm is specifically as follows:

[0026] The initial population is randomly generated according to the range of the forgetting factor. The individual represents the value of the forgetting factor, and the inverse of the fitting error is used as the fitness function.

[0027] The method of selecting individuals is based on the mixed selection of roulette and tournament. Specifically, the roulette selection is performed twice, a pair of individuals is selected, and the fitness of the two individuals is compared. The individual with higher fitness is selected, and this process is repeated until the number of individuals is full.

[0028] By the crossover probability p a and mutation probability p b Control the frequency of chromosome crossover and mutation, where the crossover probability p a and mutation probability p b The details are as follows:

[0029]

[0030] Among them, f max is the maximum fitness value in the adaptive genetic algorithm population; f bmax is the one with the larger fitness value among the two chromosomes; is the average fitness value of the population; c 1 、c 2 、c 3 and c 4 is a random number between (0,1];

[0031] The maximum number of evolutionary generations and the maximum number of consecutive generations of the best individuals in the population are set, and a new generation of population is generated based on the above selection method, crossover probability and mutation probability until the termination condition is met.

[0032] Preferably, the pretreatment in S3 is preheating the material to be welded, and the preheating includes cleaning the surface of the welding material, preheating the surface and the whole of the material to be welded, and keeping the welding material warm.

[0033] Preferably, in S3, corresponding welding auxiliary materials are prepared according to the welding process requirements, and the welding auxiliary materials are inspected and pretreated. The inspection of the welding auxiliary materials includes the model selection of the welding auxiliary materials, the cleanliness of the welding auxiliary materials, the shelf life of the welding auxiliary materials and the state of the welding auxiliary materials. The pretreatment of the welding auxiliary materials includes the cleaning of the welding auxiliary materials, the state change of the welding auxiliary materials and the preheating treatment of the welding auxiliary materials. The state of the welding auxiliary materials is the liquid, solid and gaseous state of the welding materials.

[0034] Preferably, the welding auxiliary materials include welding shielding gas, welding solvent, welding filler material, anti-spatter agent and slag cleaning agent.

[0035] Preferably, in S4, the welding control system collects welding state data of the welding material and performs corresponding analysis based on the improved dual flow graph convolutional network:

[0036] The graph structure is constructed according to the entities and relationships in the welding process. Different types of welding materials, various types of equipment in the welding process, and environmental factors are used as nodes. The nodes of different states of the same type of welding materials and different components of complex welding equipment are also subdivided. Each node contains corresponding physical information; then the edge between the welding material node and the welding equipment node is established to represent the interactive relationship between the material and the equipment; the edge from the wire feeder node to the welding material node is established to represent the wire feeder node to the welding gun to provide welding wire; the edge between different welding equipment nodes is established to represent the collaborative relationship between different welding equipment; the edge between the automation control equipment and other equipment nodes in the welding system is established to represent the transmission relationship of the control signal; the edge between the welding material node and the environment node is established to reflect the impact of the environment on the material; the edge between the environmental humidity node and the welding material node is established to represent the corrosive effect of humidity on the welding material;

[0037] Based on the relationship between nodes and edges established above, a topological structure described as G = (V, F) is constructed, where V is the set of nodes; F is the set of edges connecting nodes; the adjacency matrix R is used to represent the relationship between nodes. If there is an edge between node a and node b, then R ab =1, otherwise R ab =0; the degree matrix D is used to represent the number of edges directly connected to the node, and its calculation method is expressed by the following equation:

[0038]

[0039] Combine the adjacency matrix R, degree matrix D and node feature matrix X to get the enhanced node feature matrix The details are as follows:

[0040]

[0041] Calculate the aggregated neighbor features of node a

[0042]

[0043] in, The feature representation vector representing the i-1th layer aggregated neighbor features of node a; N(a) represents the set of neighbor nodes of the node; RELU(·) represents the activation function;

[0044] The time dependency between data is captured based on the gated recursive unit model, and welding state features and welding data features are output. The gated recursive unit model is specifically as follows:

[0045] cz t =σ(W cz X t +W czh h t-1 +b cz );

[0046] gz t =σ(W gz X t +W gzh h t-1 +b gz );

[0047]

[0048] Among them, cz t is the reset gate at time t; gz t is the update gate at time t; is the memory cell at time t; h t is the hidden state at time t; σ(·) and tanh(·) are activation functions; X t is the input at time t; W cz , W gz and W h Input X to reset gate, update gate and memory cell respectively t The weight matrix W czh , W gzh and W hh They are the reset gate, update gate and hidden state h in the memory cell respectively. t-1 The weight matrix of cz 、b gz and b h They are the reset gate, update gate and offset of memory cells respectively.

[0049] Preferably, a dual-stream graph convolutional network is used to input the welding state features and welding data features output by the gated recursive unit model into two identical dual-stream convolutional networks respectively. First, the data is input into the convolution module for convolution operation:

[0050]

[0051] Among them, F is the output of the convolution module; x in is the input of the convolution module; J is the number of partition strategies; ω j is the weight matrix; ω 1j and ω 2j They are all weight matrices of normalized embedded Gaussian functions;

[0052] Then input the output of the convolution module into the modeling module:

[0053]

[0054] Among them, F' is the output of the modeling module; F AMP is the attention map;

[0055] F AMP =G×f SE (f avg (G));

[0056] Among them, f SE is the attention score learned by the SE module; f avg is average pooling;

[0057] Finally, the output features of the two-stream convolutional network are concatenated and input into the prediction module.

[0058] The prediction module sets 16 hidden layers, each hidden layer has 128 neurons, and for the lth (l=1,…,16) hidden layer, there are;

[0059]

[0060] Among them, Y l is the output of the lth layer; σ' is the activation function of the lth layer; W l is the weight of the lth layer; Y l is the output of the l-1th layer; B l is the bias of the lth layer;

[0061] Then conduct adversarial training. During the training process, the parameters of the target task prediction module are updated based on the adaptive genetic algorithm by maximizing the prediction accuracy and minimizing the prediction error, and the mean square error is used as the target loss function.

[0062] Preferably, in S4, the voltage of the welding equipment is adjusted to achieve the predicted optimal welding temperature of the welded joint, and the welded joint is subjected to multi-layer welding.

[0063] A temperature-dependent adaptive electric fusion welding control system, comprising:

[0064] Equipment detection module: used to install and connect high-precision temperature sensors; then perform parameter setting, inspection and maintenance of welding equipment, as well as signal transmission debugging of welding equipment;

[0065] Equipment analysis module: used to detect temperature changes of welding equipment under constant voltage conditions, collect voltage, current and temperature change data of welding equipment respectively, fit the resistance value of welding equipment based on adaptive least squares method, and analyze the relationship between temperature and resistance value of welding equipment, and adjust the welding temperature of welding equipment based on the analysis results;

[0066] Pretreatment module: used to select and check the materials to be welded, and to pretreat the materials to be welded before electrofusion welding;

[0067] Adaptive adjustment module: It is used to receive temperature data of the welding joint and the surrounding welding environment from the temperature sensor in real time during the electric fusion welding process, analyze and process the collected data in real time based on the improved dual-flow graph convolution network, predict the optimal welding temperature of the welding joint, and perform adaptive adjustment of the temperature of the welding equipment;

[0068] Post-processing module: used for post-heat treatment of welding materials after electrofusion welding.

[0069] Preferably, the device detection module includes a sensor detection unit and a device detection processing unit;

[0070] The sensor detection unit is used to install and connect a high-precision temperature sensor;

[0071] The equipment detection processing unit is used for parameter setting, inspection and maintenance of welding equipment, as well as signal transmission debugging of welding equipment;

[0072] The device analysis module includes a resistance fitting unit and a temperature analysis unit;

[0073] The resistance fitting unit is used to detect the temperature change of the welding equipment under constant voltage conditions, collect the voltage, current and temperature change data of the welding equipment respectively, and fit the resistance value of the welding equipment based on the adaptive least squares method;

[0074] The temperature analysis unit is used to analyze the relationship between the temperature of the welding equipment and the change of the resistance value based on the fitting result, and adjust the welding temperature of the welding equipment based on the analysis result;

[0075] The adaptive adjustment module includes a temperature prediction unit and a temperature adjustment unit;

[0076] The temperature prediction unit is used for continuously monitoring the temperature data of the welding joint and the surrounding welding environment by the temperature sensor during the electric fusion welding process, and receiving the temperature data of the welding joint and the surrounding welding environment continuously monitored by the temperature sensor in real time, analyzing and processing the collected data in real time based on the improved dual-flow graph convolution network, and predicting the optimal welding temperature of the welding joint;

[0077] The temperature adjustment unit is used to adaptively adjust the temperature of the welding equipment according to the optimal welding temperature predicted by the temperature prediction unit.

[0078] Compared with the prior art, the present invention provides a temperature-dependent adaptive electric fusion welding control method and system, which has the following beneficial effects:

[0079] The present invention fits the resistance value of the welding equipment based on the adaptive least squares method, and analyzes the relationship between the temperature of the welding equipment and the change of the resistance value, so as to realize the precise control of the temperature of the welding equipment; constructs a graph structure according to the entities and relationships in the welding process, predicts the optimal welding temperature of the welding joint in the welding process based on the improved dual-stream graph convolutional network, and controls the welding equipment to perform multi-layer precise welding; also improves the quality of the welding joint based on preprocessing and post-processing operations; the present invention can accurately control the temperature change in the welding process, is suitable for different welding materials, improves the welding quality and can reduce human errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a flow chart of the method mentioned in Example 1 of the present invention;

[0081] Figure 2 This is a flow chart of the adaptive genetic algorithm mentioned in Example 1 of the present invention;

[0082] Figure 3 This is a schematic diagram of the improved dual-stream graph convolutional network architecture mentioned in Example 1 of the present invention;

[0083] Figure 4 This is a system block diagram mentioned in Embodiment 2 of the present invention;

[0084] Figure 5 This is a flow chart of welding temperature control of the present invention.

[0085] In the figure:

[0086] 100, device detection module; 110, sensor detection unit; 120, device detection processing unit; 200, device analysis module; 210, resistance fitting unit; 220, temperature analysis unit; 300, pre-processing module; 400, adaptive adjustment module; 410, temperature prediction unit; 420, temperature adjustment unit; 500, post-processing module. DETAILED DESCRIPTION

[0087] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0088] Embodiment 1:

[0089] See also Figure 1-Figure 3 The present invention provides a temperature-dependent adaptive electric fusion welding control method, comprising the following steps:

[0090] S1: Install a high-precision temperature sensor and connect the temperature sensor to the welding control system; then perform parameter setting, inspection and maintenance of the welding equipment, as well as signal transmission debugging of the welding equipment; the details are as follows:

[0091] Install high-precision temperature sensors to ensure that they can accurately monitor the temperature of the welding area and the surrounding environment. Connect the sensors to the welding control system and debug them to ensure accurate transmission and processing of temperature data.

[0092] Check the parameter settings of the welding equipment to ensure that it is in normal working condition. Check and maintain the welding power supply, electrodes, fixtures, etc. to ensure the stability of the welding process.

[0093] S2: Detect the temperature change of the welding equipment under constant voltage conditions, collect the voltage, current and temperature change data of the welding equipment respectively, fit the resistance value of the welding equipment based on the adaptive least squares method, and analyze the relationship between the temperature of the welding equipment and the resistance value change, and adjust the welding temperature of the welding equipment based on the analysis results; the details are as follows:

[0094] During the electrofusion welding process, the welding quality of the welding materials may fluctuate greatly under different temperature conditions, so it is necessary to accurately control the temperature of the welding equipment.

[0095] According to the internal circuit design rules of welding equipment, combined with Kirchhoff's voltage law and current law, a circuit equivalent model is established, and the model is discretized. The collected voltage and current data of the welding equipment are preprocessed, and the resistance value is fitted based on the following least squares method:

[0096]

[0097] Among them, s is the time; β is the identification parameter matrix; K s is the gain value; y s is the system output; δ s is the input and output data vector; Q s is the covariance matrix; α is the forgetting factor; T is the transpose of the vector;

[0098]

[0099]

[0100] Among them, γ is a random number between (0.9, 1); ξ is an adjustable parameter; ε s is the residual at time s;

[0101] The adaptive dynamic adjustment of the forgetting factor is performed based on the adaptive genetic algorithm. The adaptive genetic algorithm is as follows:

[0102] Reference Figure 2 , the initial population is randomly generated according to the range of the forgetting factor, the individual represents the value of the forgetting factor, and the inverse of the fitting error is used as the fitness function.

[0103] The method of selecting individuals is based on the mixed selection of roulette and tournament. Specifically, the roulette selection is performed twice, a pair of individuals is selected, and the fitness of the two individuals is compared. The individual with higher fitness is selected, and this process is repeated until the number of individuals is full.

[0104] By the crossover probability p a and mutation probability p b Control the frequency of chromosome crossover and mutation, where the crossover probability p a and mutation probability p b The details are as follows:

[0105]

[0106] Among them, f max is the maximum fitness value in the adaptive genetic algorithm population; f bmax is the larger fitness value of the two chromosomes; f is the average fitness value of the population; c 1 、c 2 、c 3 and c 4 is a random number between (0,1];

[0107] The maximum number of evolutionary generations and the maximum number of consecutive generations of the best individuals in the population are set, and a new generation of population is generated based on the above selection method, crossover probability and mutation probability until the termination condition is met.

[0108] In the adaptive least squares method, the value of the forgetting factor has a key impact on the performance of the model. The forgetting factor is used to weigh the contribution of old data and new data to the estimation of model parameters. A suitable forgetting factor can allow the model to better track the dynamic changes of the system. The adaptive ant colony algorithm has a strong global search capability. It can find the optimal forgetting factor in a more complex parameter space. Through continuous iterative search, it can find the forgetting factor value that makes the least squares model based on the forgetting factor fit the actual data more accurately. The adaptive adjustment of the forgetting factor can make the model more sensitive to new data. By optimizing the forgetting factor, the adaptive ant colony algorithm can make the least squares model based on the forgetting factor update parameters faster to adapt to new data changes. The ant colony algorithm simulates the foraging process of ants and guides the search direction through the transmission and update of pheromones. In the process of optimizing the forgetting factor, it can gradually narrow the search range according to the feedback information in the search process and find the optimal forgetting factor. Compared with traditional methods such as manual adjustment or simple enumeration, this can greatly reduce the time and workload of parameter selection.

[0109] Temperature sensors are also used to continuously monitor the temperature of the welding equipment. The temperature-resistance relationship curve is analyzed by combining Joule's law and Ohm's law. Based on the results of temperature monitoring and analysis, a temperature model of the welding area is established. This model can describe the relationship between temperature and time, welding parameters and other factors, and the temperature regulation of the welding equipment can be controlled according to the temperature model.

[0110] S3: Select and check the materials to be welded. Before electrofusion welding, pre-treat the materials to be welded; the details are as follows:

[0111] Before welding, the welding materials are preheated. Preheating can reduce the cold brittleness of the welding materials and improve the quality and reliability of the welded joints.

[0112] Select welding materials suitable for temperature-adaptive electrofusion welding to ensure good welding performance at different temperatures. Inspect and pre-treat welding materials, such as cleaning and drying, to improve welding quality.

[0113] According to the welding process requirements, prepare the corresponding auxiliary materials, including welding shielding gas, welding solvent, welding filler material, anti-spatter agent and slag cleaning agent; among them, for example, the welding shielding gas can be selected from inert gases such as argon and helium, which can prevent the material from being oxidized, while increasing the welding speed and achieving better penetration control; for example, welding solvents generally include molten flux and sintered flux, which can be selected according to actual needs; carbon dioxide gas protection can also be used to prevent spatter.

[0114] S4: During the electrofusion welding process, the temperature sensor continuously monitors the temperature data of the welding joint and the surrounding welding environment, and transmits it to the welding control system in real time. The welding control system analyzes and processes the collected data in real time based on the improved dual-flow graph convolutional network, predicts the optimal welding temperature of the welding joint, and performs adaptive adjustment of the temperature of the welding equipment; the details are as follows:

[0115] The improved dual-stream graph convolutional network constructs a graph structure based on the entities and relationships in the welding process, and treats different types of welding materials as independent nodes. For example, if there are different welding materials such as steel and aluminum, a node can be created for each material. These nodes can contain information about the physical properties of the material, such as melting point, thermal conductivity, specific heat capacity, etc. For the same type of welding material, the nodes can also be subdivided according to their different states. For example, the unwelded material, the material being welded, and the welded material are treated as different nodes, and each node can record the temperature, hardness, microstructure and other characteristics of the material in that state.

[0116] The various equipment used in the welding process, such as electric welders, welding guns, wire feeders, etc., are taken as nodes. These nodes can contain parameter information of the equipment, such as power, current, voltage, etc. For complex welding systems, different parts of the equipment can also be taken as child nodes. For example, the electrode, nozzle, handle, etc. of the welding gun are taken as child nodes of the welding gun node, and each child node can record the specific attributes of the component.

[0117] Considering the welding environment factors, the ambient temperature, humidity, air pressure, etc. are used as environmental nodes. These nodes can record the environmental conditions of the welding site in real time, which has an important impact on the prediction of temperature changes. The geographical location of the welding site, wind direction and other factors can also be used as extended environmental nodes to further improve the accuracy of the prediction.

[0118] Then, an edge is established between the welding material node and the welding equipment node to represent the interaction between the material and the equipment. For example, an edge can be established from the welding material node to the welding gun node to indicate that the material is being welded by the welding gun. This edge can be given a weight to represent parameters such as welding strength and speed. For the relationship between the wire feeder and the welding material, an edge can be established from the wire feeder node to the welding material node to indicate that the wire feeder provides welding wire for the welding material. The weight of the edge can be determined based on factors such as wire feeding speed and wire diameter.

[0119] Connect different welding equipment nodes to represent the collaborative relationship between them. For example, an edge can be established between the welding machine node and the welding gun node to indicate that the welding machine provides power to the welding gun. The weight of the edge can be determined based on parameters such as current size and voltage stability. If there is an automated control device in the welding system, such as a robot controller, it can be connected to other device nodes to indicate the transmission relationship of the control signal.

[0120] Establish an edge between the welding material node and the environment node to reflect the impact of the environment on the material. For example, an edge can be established from the welding material node to the ambient temperature node to indicate that the temperature of the material is affected by the ambient temperature. The weight of the edge can be determined based on factors such as the heat transfer coefficient and the amplitude of the ambient temperature change. The edge between the ambient humidity node and the welding material node can represent the corrosive effect of humidity on the welding material, and the weight of the edge can be determined based on factors such as the humidity level and the corrosion resistance of the material.

[0121] Then assign state features and data features to nodes and edges; for welding material nodes, data features may include the chemical composition, physical properties, current temperature, hardness, etc. of the material. This information can be obtained through experimental measurements or material specifications. Data features of welding equipment nodes may include equipment model, parameter settings, operating status, etc. For example, the features of electric welding machine nodes may include output current, voltage, power factor, etc.; the features of welding gun nodes may include nozzle diameter, gas flow, electrode wear, etc. The features of environmental nodes may be real-time measured environmental parameters, such as temperature, humidity, air pressure, etc. State features are generally qualitative states, such as indoor or outdoor environment, presence or absence of ventilation equipment, etc.

[0122] The data features of the edge can be determined based on the relationship represented by the edge. For example, the features of the material and equipment edge can include welding speed, welding current, welding pressure, etc.; the features of the equipment and equipment edge can include signal transmission delay, priority of control instructions, etc.; the features of the material and environment edge can include heat transfer coefficient, the degree of influence of humidity on the material, etc. The state feature is generally whether the edge exists.

[0123] Reference Figure 3 , according to the entities and relationships in the welding process, a graph structure is constructed. Based on the relationship between nodes and edges established above, a topological structure described as G = (V, F) is constructed, where V is a set of nodes; F is a set of edges connecting nodes; the adjacency matrix R is used to represent the relationship between nodes. If there is an edge between node a and node b, then R ab =1, otherwise R ab =0; the degree matrix D is used to represent the number of edges directly connected to the node, and its calculation method is expressed by the following equation:

[0124]

[0125] Combine the adjacency matrix R, degree matrix D and node feature matrix X to get the enhanced node feature matrix The details are as follows:

[0126]

[0127] Calculate the aggregated neighbor features of node a

[0128]

[0129] in, The feature representation vector representing the i-1th layer aggregated neighbor features of node a; N(a) represents the set of neighbor nodes of the node; RELU(·) represents the activation function;

[0130] The time dependency between data is captured based on the gated recursive unit model, and welding state features and welding data features are output. The gated recursive unit model is specifically as follows:

[0131] cz t =σ(W cz X t +W czh h t-1 +b cz );

[0132] gz t =σ(W gz X t +W gzh h t-1 +b gz );

[0133]

[0134] Among them, cz t is the reset gate at time t; gz t is the update gate at time t; h~ t is the memory cell at time t; h t is the hidden state at time t; σ(·) and tanh(·) are activation functions; X t is the input at time t; W cz , W gz and W h Input X to reset gate, update gate and memory cell respectively t The weight matrix W czh , W gzh and W hh They are the reset gate, update gate and hidden state h in the memory cell respectively. t-1 The weight matrix of cz 、b gz and b h They are the reset gate, update gate and offset of memory cells respectively.

[0135] The welding status and welding data output by the gated recurrent unit model are respectively input into two identical two-stream convolutional networks. First, the data is input into the convolution module for convolution operation:

[0136]

[0137] Among them, F is the output of the convolution module; x in is the input of the convolution module; J is the number of partition strategies; ω j is the weight matrix; ω 1j and ω 2j They are all weight matrices of normalized embedded Gaussian functions;

[0138] Then input the output of the convolution module into the modeling module:

[0139]

[0140] Among them, F' is the output of the modeling module; F AMP is the attention map;

[0141] F AMP =G×f SE (f avg (G));

[0142] Among them, f SE is the attention score learned by the SE module; f avg is average pooling;

[0143] Finally, the output features of the two-stream convolutional network are concatenated and input into the prediction module.

[0144] The prediction module sets 16 hidden layers, each hidden layer has 128 neurons, and for the lth (l=1,…,16) hidden layer, there are;

[0145]

[0146] Among them, Y l is the output of the lth layer; σ' is the activation function of the lth layer; W l is the weight of the lth layer; Y l is the output of the l-1th layer; B l is the bias of the lth layer;

[0147] The graph convolutional network model is trained using the collected welding data, and then adversarial training is performed. During the training process, the parameters of the target task prediction module are updated based on an adaptive genetic algorithm by maximizing the prediction accuracy and minimizing the prediction error, and the mean square error is used as the target loss function.

[0148] Based on the trained model, new welding state data and welding data are input, and the temperature change is predicted by improving the dual-flow graph convolutional network. The predicted temperature change is applied to the control and optimization of the welding process, such as adjusting welding parameters and taking cooling measures, to ensure welding quality and safety.

[0149] The graph convolution network is used as model 1, the dual-stream graph convolution network is used as model 2, and the improved dual-stream graph convolution network of this embodiment is used as model 3. The temperature of the same welding material is predicted based on the three models, and the prediction results are statistically analyzed. The specific prediction results can be referred to Table 1:

[0150] Table 1 Temperature prediction results of three models for the same welding material

[0151]

[0152] It can be seen from the table that the RMSE of the prediction results driven by the model in this embodiment for the next 15 minutes, 30 minutes and 60 minutes are 0.0421, 0.0428 and 0.0461 respectively; the MAE are 0.0316, 0.0321 and 0.0334 respectively; the MAPE are 5.9029, 6.0391 and 7.1054 respectively. Compared with Model 1 and Model 2, the deviation between the predicted value and the true value of the model is smaller, and the prediction result is more accurate.

[0153] S5: After the electric fusion welding is completed, the welding materials are post-heat treated. The details are as follows:

[0154] After welding, the welding materials are post-heat treated to eliminate welding residual stress and increase the service life of the welded joint.

[0155] Embodiment 2:

[0156] See also Figure 4 The present invention provides a temperature-dependent adaptive electric fusion welding control system, comprising:

[0157] The equipment detection module 100 includes a sensor detection unit 110 and an equipment detection processing unit 120; the sensor detection unit 110 is used to install and connect a high-precision temperature sensor; the equipment detection processing unit 120 is used to perform parameter setting, inspection and maintenance of welding equipment, and signal transmission debugging of welding equipment; select materials to be welded, and inspect and pre-treat the materials to be welded;

[0158] The equipment analysis module 200 includes a resistance fitting unit 210 and a temperature analysis unit 220; the resistance fitting unit 210 is used to detect the temperature change of the welding equipment under constant voltage conditions, collect the voltage, current and temperature change data of the welding equipment respectively, and fit the resistance value of the welding equipment based on the adaptive least squares method; the temperature analysis unit 220 is used to analyze the relationship between the temperature of the welding equipment and the resistance value change, and adjust the welding temperature of the welding equipment based on the analysis result;

[0159] Pretreatment module 300: used for pretreatment of welding materials before electrofusion welding;

[0160] The adaptive adjustment module 400 includes a temperature prediction unit 410 and a temperature adjustment unit 420; the temperature prediction unit 410 is used to receive the temperature data of the welding joint and the surrounding welding environment continuously monitored by the temperature sensor in real time during the electric fusion welding process, and to analyze and process the collected data in real time based on the improved dual-flow graph convolution network, and predict the optimal welding temperature of the welding joint; the temperature adjustment unit 420 is used to adaptively adjust the temperature of the welding equipment according to the optimal welding temperature predicted by the temperature prediction unit 410.

[0161] Post-processing module 500: used for post-heat treatment of welding materials after electrofusion welding.

[0162] Embodiment three:

[0163] See also Figure 5 , Figure 5 The figure is a flow chart of electrical signal control of the temperature sensor and welding equipment in the present invention. This control can generally be controlled by a PLC controller. The temperature sensor monitors the ambient temperature in real time and feeds back temperature control information to the temperature control unit based on its own structural design. When the ambient temperature changes, the temperature value of the temperature sensor will change. When it exceeds the set temperature value, it will send a temperature control signal to the controller. After receiving the temperature change signal, the controller performs temperature control on the temperature control system, such as temperature increase control and temperature decrease control. The temperature control here is generally mainly performed by increasing or decreasing the current. Because the ambient temperature will not change in a jumpy manner, there is no need to stop the welding action during temperature control. When the temperature adjustment meets the standard, the existing welding temperature is maintained to continue welding. If it does not meet the standard, the temperature adjustment system continues to adjust until the standard is met and welding continues. The time during the temperature adjustment period will not be too long, so there is no need to stop the welding action, thereby ensuring the stability and efficiency of welding.

[0164] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A temperature-dependent adaptive electric fusion welding control method, characterized in that: The steps include: S1: Install high-precision temperature sensors and connect them to the welding control system; then perform parameter setting, inspection and maintenance of the welding equipment, as well as signal transmission debugging of the welding equipment; S2: Detect the temperature change of the welding equipment under constant voltage conditions, collect the voltage, current and temperature change data of the welding equipment respectively, fit the resistance value of the welding equipment based on the adaptive least squares method, and analyze the relationship between the temperature of the welding equipment and the resistance value change, and adjust the welding temperature of the welding equipment based on the analysis results; S3: Select and inspect the materials to be welded, and pre-treat the materials to be welded before electrofusion welding; S4: During the electrofusion welding process, the temperature sensor continuously monitors the temperature data of the welding joint and the surrounding welding environment, and transmits it to the welding control system in real time. The welding control system analyzes and processes the collected data in real time based on the improved dual-flow graph convolutional network, predicts the optimal welding temperature of the welding joint, and performs adaptive adjustment of the temperature of the welding equipment; S5: After the electric fusion welding is completed, the welding materials are subjected to post-heat treatment.

2. A temperature-dependent adaptive electric fusion welding control method according to claim 1, characterized in that: The adaptive least squares method in S2 is specifically as follows: According to the internal circuit design rules of welding equipment, combined with Kirchhoff's voltage law and current law, a circuit equivalent model is established, and the model is discretized. The collected voltage and current data of the welding equipment are preprocessed, and the resistance value is fitted based on the following least squares method: Among them, s is the time; β is the identification parameter matrix; K s is the gain value; y s is the system output; δ s is the input and output data vector; Q s is the covariance matrix; α s is the forgetting factor; T is the transpose of the vector; Among them, γ is a random number between (0.9, 1); ξ is an adjustable parameter; ε s is the residual at time s; The adaptive dynamic adjustment of the forgetting factor is performed based on an adaptive genetic algorithm, and the adaptive genetic algorithm is specifically as follows: The initial population is randomly generated according to the range of the forgetting factor. The individual represents the value of the forgetting factor, and the inverse of the fitting error is used as the fitness function. The method of selecting individuals is based on the mixed selection of roulette and tournament. Specifically, the roulette selection is performed twice, a pair of individuals is selected, and the fitness of the two individuals is compared. The individual with higher fitness is selected, and this process is repeated until the number of individuals is full. By the crossover probability p a and mutation probability p b Control the frequency of chromosome crossover and mutation, where the crossover probability p a and mutation probability p b The details are as follows: Among them, f max is the maximum fitness value in the adaptive genetic algorithm population; f bmax is the one with the larger fitness value among the two chromosomes; is the average fitness value of the population; c1, c2, c3 and c4 are random numbers between (0,1]; The maximum number of evolutionary generations and the maximum number of consecutive generations of the best individuals in the population are set, and a new generation of population is generated based on the above selection method, crossover probability and mutation probability until the termination condition is met.

3. A temperature-dependent adaptive electric fusion welding control method according to claim 1, characterized in that: The pretreatment in S3 is preheating the material to be welded, and the preheating includes cleaning the surface of the welding material, preheating the surface and the whole of the material to be welded, and keeping the welding material warm.

4. A temperature-dependent adaptive electric fusion welding control method according to claim 3, characterized in that: In S3, corresponding welding auxiliary materials are prepared according to the welding process requirements, and the welding auxiliary materials are inspected and pretreated. The inspection of the welding auxiliary materials includes the model selection of the welding auxiliary materials, the cleanliness of the welding auxiliary materials, the shelf life of the welding auxiliary materials and the state of the welding auxiliary materials. The pretreatment of the welding auxiliary materials includes the cleaning of the welding auxiliary materials, the state change of the welding auxiliary materials and the preheating treatment of the welding auxiliary materials. The state of the welding auxiliary materials includes the liquid, solid and gaseous states of the welding materials.

5. A temperature-dependent adaptive electric fusion welding control method according to claim 4, characterized in that: The welding auxiliary materials include welding shielding gas, welding solvent, welding filling material, anti-spatter agent and slag cleaning agent.

6. A temperature-dependent adaptive electric fusion welding control method according to claim 1, characterized in that: In S4, the welding control system collects welding status data of the welding material and performs corresponding analysis based on the improved dual flow graph convolutional network: The graph structure is constructed according to the entities and relationships in the welding process. Different types of welding materials, various types of equipment in the welding process, and environmental factors are used as nodes. The nodes of different states of the same type of welding materials and different components of complex welding equipment are also subdivided. Each node contains corresponding physical information; then the edge between the welding material node and the welding equipment node is established to represent the interactive relationship between the material and the equipment; the edge from the wire feeder node to the welding material node is established to represent the wire feeder node to the welding gun to provide welding wire; the edge between different welding equipment nodes is established to represent the collaborative relationship between different welding equipment; the edge between the automation control equipment and other equipment nodes in the welding system is established to represent the transmission relationship of the control signal; the edge between the welding material node and the environment node is established to reflect the impact of the environment on the material; the edge between the environmental humidity node and the welding material node is established to represent the corrosive effect of humidity on the welding material; Based on the relationship between nodes and edges established above, a topological structure described as G = (V, F) is constructed, where V is the set of nodes; F is the set of edges connecting nodes; the adjacency matrix R is used to represent the relationship between nodes. If there is an edge between node a and node b, then R ab =1, otherwise R ab =0; the degree matrix D is used to represent the number of edges directly connected to the node, and its calculation method is expressed by the following equation: Combine the adjacency matrix R, degree matrix D and node feature matrix X to get the enhanced node feature matrix The details are as follows: Calculate the aggregated neighbor features of node a in, The feature representation vector representing the i-1th layer aggregated neighbor features of node a; N(a) represents the set of neighbor nodes of the node; RELU(·) represents the activation function; The time dependency between data is captured based on the gated recursive unit model, and welding state features and welding data features are output. The gated recursive unit model is specifically as follows: cz t =σ(W cz X t +W czh h t-1 +b cz ); gz t =σ(W gz X t +W gzh h t-1 +b gz ); Among them, cz t is the reset gate at time t; gz t is the update gate at time t; is the memory cell at time t; h t is the hidden state at time t; σ(·) and tanh(·) are activation functions; X t is the input at time t; W cz , W gz and W h Input X to reset gate, update gate and memory cell respectively t The weight matrix W czh , W gzh and W hh They are the reset gate, update gate and hidden state h in the memory cell respectively. t-1 The weight matrix of cz , b gz and b h They are the reset gate, update gate and offset of memory cells respectively.

7. A temperature-dependent adaptive electric fusion welding control method according to claim 6, characterized in that: The welding state features and welding data features output by the gated recurrent unit model are respectively input into two identical dual-stream convolutional networks using a dual-stream graph convolutional network. First, the data is input into the convolution module for convolution operation: Among them, F is the output of the convolution module; x in is the input of the convolution module; J is the number of partition strategies; ω j is the weight matrix; ω 1j and ω 2j They are all weight matrices of normalized embedded Gaussian functions; Then input the output of the convolution module into the modeling module: Among them, F' is the output of the modeling module; F AMP is the attention map; F AMP =G×f SE (f avg (G)); Among them, f SE is the attention score learned by the SE module; f avg is average pooling; Finally, the output features of the two-stream convolutional network are concatenated and input into the prediction module. The prediction module sets 16 hidden layers, each hidden layer has 128 neurons, and for the lth (l=1,…,16) hidden layer, there are; Among them, Y l is the output of the lth layer; σ' is the activation function of the lth layer; W l is the weight of the lth layer; Y l is the output of the l-1th layer; B l is the bias of the lth layer; Then conduct adversarial training. During the training process, the parameters of the target task prediction module are updated based on the adaptive genetic algorithm by maximizing the prediction accuracy and minimizing the prediction error, and the mean square error is used as the target loss function.

8. A temperature-dependent adaptive electric fusion welding control method according to claim 7, characterized in that: In S4, the voltage of the welding equipment is adjusted to reach the predicted optimal welding temperature of the welding joint, and the welding joint is subjected to multi-layer welding.

9. A temperature-adaptive electric fusion welding control system according to any one of claims 1 to 8, characterized in that: include: Equipment detection module (100): used to install and connect a high-precision temperature sensor; Then perform parameter setting, inspection and maintenance of welding equipment, as well as signal transmission debugging of welding equipment; Equipment analysis module (200): used to detect temperature changes of welding equipment under constant voltage conditions, respectively collect voltage, current and temperature change data of the welding equipment, fit the resistance value of the welding equipment based on the adaptive least squares method, and analyze the relationship between the temperature of the welding equipment and the resistance value change, and adjust the welding temperature of the welding equipment based on the analysis result; Pre-processing module (300): used to select and check the materials to be welded, and to pre-process the materials to be welded before electrofusion welding; An adaptive adjustment module (400): used to receive temperature data of the welding joint and the surrounding welding environment continuously monitored by the temperature sensor in real time during the electric fusion welding process, analyze and process the collected data in real time based on the improved dual-flow graph convolution network, predict the optimal welding temperature of the welding joint, and perform adaptive adjustment of the temperature of the welding equipment; Post-processing module (500): used for performing post-heat treatment on welding materials after the electrofusion welding is completed.

10. A temperature-dependent adaptive electric fusion welding control system according to claim 9, characterized in that: The device detection module (100) comprises a sensor detection unit (110) and a device detection processing unit (120); The sensor detection unit (110) is used to install and connect a high-precision temperature sensor; The equipment detection processing unit (120) is used for parameter setting, inspection and maintenance of welding equipment, as well as signal transmission debugging of welding equipment; The device analysis module (200) comprises a resistance fitting unit (210) and a temperature analysis unit (220); The resistance fitting unit (210) is used to detect temperature changes of the welding equipment under constant voltage conditions, collect voltage, current and temperature change data of the welding equipment respectively, and fit the resistance value of the welding equipment based on an adaptive least squares method; The temperature analysis unit (220) is used to analyze the relationship between the temperature of the welding equipment and the change in the resistance value based on the fitting result, and adjust the welding temperature of the welding equipment based on the analysis result; The adaptive adjustment module (400) comprises a temperature prediction unit (410) and a temperature adjustment unit (420); The temperature prediction unit (410) is used for continuously monitoring the temperature data of the welding joint and the surrounding welding environment by the temperature sensor during the electric fusion welding process, and receiving the temperature data of the welding joint and the surrounding welding environment continuously monitored by the temperature sensor in real time, analyzing and processing the collected data in real time based on the improved dual-flow graph convolution network, and predicting the optimal welding temperature of the welding joint; The temperature adjustment unit (420) is used to adaptively adjust the temperature of the welding equipment according to the optimal welding temperature predicted by the temperature prediction unit (410).

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