Active agent coating method, system and device based on A-TIG welding

Abstract

The invention relates to the technical field of welding, in particular to an active agent coating method, system and device based on A-TIG welding. The method comprises the steps that an active agent formula designed based on the nano composite material is obtained, material component analysis is conducted, and an active agent formula sequence suitable for the titanium alloy and the nickel-based alloy is obtained; carrying out a material adaptability test, and determining the optimal coating thickness and formula proportion of the active agent; the coating process is controlled through an automatic coating device, coating parameters are adjusted in real time through an intelligent monitoring system, and a self-adaptive coating process model is established. The welding quality is improved, and generation of air holes and cracks in the welding process is reduced.

Description

Technical Field

[0001] The present invention relates to the field of welding technology, and in particular to an activator coating method, system and device based on A-TIG welding. Background Art

[0002] Titanium alloys, nickel-based alloys and other difficult-to-weld materials are prone to defects such as pores and cracks in traditional welding processes due to their high melting points, strong oxidizing properties and special physical and chemical properties of the materials themselves, making it difficult to ensure welding quality. A-TIG welding technology optimizes the welding process by introducing an activator during the welding process, increasing the welding depth and the stability of the molten pool, thereby significantly improving the welding quality. However, when welding difficult-to-weld materials, the existing A-TIG technology still faces problems such as inappropriate activator formulas, uneven coating, and difficulty in dynamically adjusting process parameters, resulting in defects such as cracks and pores in the weld, and it is impossible to achieve the ideal welding effect.

[0003] In order to solve this technical problem, developing a process system that can adapt to different materials, optimize the activator formula and combine it with automated coating technology, and combine it with intelligent welding control has become an important direction for improving welding quality. Summary of the invention

[0004] The present invention provides an activator coating method, system and device based on A-TIG welding, so as to solve the problem of how to improve the welding quality of difficult-to-weld materials such as titanium alloys and nickel-based alloys and reduce the generation of cracks and pores during welding by optimizing the activator formula, automated coating technology and intelligent welding control.

[0005] In order to solve the above technical problems, the present invention provides an active agent coating method based on A-TIG welding, comprising:

[0006] Obtain active agent formulas based on nanocomposite materials, conduct material composition analysis, and obtain active agent formula sequences suitable for titanium alloys and nickel-based alloys;

[0007] Performing material adaptability testing from the active agent formula sequence to obtain active agent coating thickness and formula ratio of different materials;

[0008] Inputting the coating thickness and formulation ratio of the active agent into the control system of the automatic coating device to set the coating thickness, speed and material surface treatment parameters;

[0009] Use intelligent monitoring system to detect thickness change and uniformity in real time during coating process and adjust coating speed and pressure;

[0010] Based on the test data of different materials and coating control feedback, an adaptive coating process parameter model is established for the control of subsequent welding process.

[0011] Furthermore, in the step of obtaining the active agent formula designed based on the nanocomposite material, nanocomposite materials with high thermal stability and low melting point are screened as candidate components of the active agent.

[0012] Furthermore, in the step of conducting the material adaptability test, the active agent coating uniformity and adhesion of the titanium alloy and the nickel-based alloy are tested respectively, and the test data are recorded.

[0013] Furthermore, the automatic coating device controls the coating thickness by adjusting the pressure and nozzle distance of the coating nozzle.

[0014] Furthermore, the thickness variation during the coating process is detected by an optical sensor and an ultrasonic sensor.

[0015] Furthermore, in the intelligent monitoring system, the real-time data of the coating process is input into the automatic control device through a data feedback system for real-time adjustment.

[0016] Furthermore, in the step of establishing an adaptive coating process parameter model, a regression model between coating thickness and welding strength is formed through multiple experimental data.

[0017] Furthermore, the adaptive coating process parameter model adjusts welding current, welding speed and activator coating amount according to different materials.

[0018] Furthermore, an active agent coating system based on A-TIG welding comprises:

[0019] A module for active agent formulation design, which is used to design nanocomposite active agents and perform composition analysis;

[0020] Module for material adaptability testing, used to test coating uniformity and adhesion of different materials;

[0021] Module for automated coating, used to apply active agent to the material surface and control coating thickness and uniformity;

[0022] The module for intelligent monitoring and feedback is used to monitor the data during the coating and welding process in real time and adjust the coating and welding parameters.

[0023] Furthermore, an active agent coating device based on A-TIG welding comprises:

[0024] An active agent storage and proportioning unit, used for storing and preparing active agents;

[0025] An automated coating unit to control the coating thickness and speed of the active agent;

[0026] Sensor unit to monitor thickness variation and uniformity during coating process;

[0027] A welding control unit is used to automatically adjust the welding current and welding speed according to the coating process parameter model.

[0028] The key innovative features of the present invention include:

[0029] (1) Optimization of activator formula: The activator formula designed based on nanocomposite materials can meet the welding requirements of different materials and reduce defects such as cracks and pores.

[0030] (2) Automated coating and intelligent control: The coating thickness, speed and pressure are controlled in real time through the automated coating device to ensure uniform distribution of the active agent, and feedback adjustment is performed through the intelligent monitoring system to ensure the stability of the welding process.

[0031] (3) Adaptive coating process model: Based on the data from the coating and welding processes, an adaptive process parameter model is established to achieve optimized welding control for different materials, thereby improving the accuracy and consistency of welding.

[0032] The following are its main beneficial effects:

[0033] The present invention provides an activator coating method, system and device based on A-TIG welding, which improves the welding quality of difficult-to-weld materials such as titanium alloys and nickel-based alloys by optimizing the activator formula, automated coating technology and intelligent welding control. Compared with traditional welding methods, the present invention introduces an activator formula based on nanocomposite materials to ensure the stability of the activator in a high-temperature welding environment, thereby reducing the generation of pores and cracks during welding. In addition, through the automated coating device and the intelligent monitoring system, the coating thickness and uniformity can be detected in real time, and dynamic regulation can be performed, making the welding process more precise, effectively reducing manual adjustment errors, and improving welding efficiency and consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic flow chart of an active agent coating method based on A-TIG welding provided in an embodiment of the present application;

[0035] Figure 2 A structural block diagram of an active agent coating system based on A-TIG welding provided in an embodiment of the present application;

[0036] Figure 3 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0038] Reference to "embodiments" herein means that the specific features and structural characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0039] Embodiment 1:

[0040] Reference Figure 1 , is a schematic flow chart of an active agent coating method based on A-TIG welding provided by an embodiment of the present invention, and the process of the method may at least include steps S100-S400:

[0041] S100, obtaining an active agent formula designed based on nanocomposite materials, performing material composition analysis, and obtaining an active agent formula sequence suitable for titanium alloys and nickel-based alloys.

[0042] S200, performing material adaptability test from the active agent formula sequence to obtain optimal active agent coating thickness and formula ratio for different materials.

[0043] S300, performing intelligent control optimization of the automated coating device on the coating thickness and the formulation ratio of the active agent, and generating an adaptive coating and welding process parameter model.

[0044] S400, based on the coating process parameter model and the multi-material adaptability test results, the model is input into a welding control system to generate an intelligent and real-time controlled welding process optimization solution.

[0045] Step S100 at least includes steps S110-S130:

[0046] S110: Obtain composition information of different nanocomposites from the material database, and screen materials with high thermal stability and low melting points as candidate components of the active agent.

[0047] Specifically, first, an original data set containing nanocomposite materials is imported from a material database, wherein the data set contains the chemical composition, physical properties, and stability index under high temperature environment of each nanomaterial. Based on the input data, a screening algorithm is used to set screening conditions, and the melting point range T of the material is 1 <T<T 2 and high thermal stability requirements H≥H 0 , where T 1 and T 2 are the selected lower and upper limits of the melting point, respectively, H 0 is the critical value of thermal stability. Through this screening process, the candidate material set M is obtained. 1 , which contains a set of materials that satisfy the conditions {M 1a ,M 1b ,…,M 1n}.

[0048] Furthermore, the candidate material set M 1 Each material in the CAS No. 10000 has its electrical conductivity, oxidation sensitivity and compatibility with titanium alloys and nickel-based alloys in high temperature environments recorded. Based on the data, the material can be preliminarily evaluated as an active agent component and used as input data for subsequent combination design.

[0049] S120: Based on the chemical properties of candidate materials, conduct combination design, analyze the reactivity and melting point of each component, and establish a preliminary formula library of active agents.

[0050] From the candidate material set M 1 Among them, a subset M that meets the chemical compatibility criteria is further selected 2 , with particular attention paid to the chemical stability of the material during high temperature welding and the interface reaction behavior with titanium alloy and nickel-based alloy. 2i , analyze its chemical reaction equation with titanium alloy (Ti) and nickel-based alloy (Ni), for example:

[0051] M 2i +Ti>TiM 2i

[0052] M 2i +Ni>NiM 2i

[0053] Furthermore, the reaction activation energy E is defined as a,i and the reaction rate k i , using the Arrhenius equation to express its temperature dependence:

[0054]

[0055] Among them, A iFor material M 2i is the frequency factor, R is the gas constant, and T is the welding temperature.

[0056] Through this reactivity analysis process, the stability and reactivity of different material combinations on titanium alloys and nickel-based alloys can be determined. Based on the above analysis, the reaction rate k i Less than the critical value k 0 The material combination is classified into the preliminary formulation library P of active agents 1 .

[0057] S130: Conduct component analysis on each activator formula to verify its stability in high-temperature welding environment and its adaptability to the surface reaction of titanium alloy and nickel-based alloy, and form a preliminary activator formula sequence.

[0058] In the preliminary active agent formula library P 1 On this basis, the stability of each formula in a high-temperature welding environment was further analyzed. Specifically, each formula was thermodynamically simulated under simulated high-temperature welding conditions (e.g., 1500°C) to verify its stability on the surface of titanium alloy and nickel-based alloy. By analyzing the decomposition rate and oxidation rate of the activator at high temperature, the effective use time t in a high-temperature environment was calculated. eff The formula is as follows:

[0059]

[0060] Among them, W initial is the initial active agent weight, r decomp is the decomposition rate, in g / s.

[0061] Furthermore, the decomposition rate, oxidation reaction rate and material surface reactivity data were combined to screen out the formula with lower decomposition rate and oxidation rate. For each active agent formula, the interface adhesion after reaction with the titanium alloy and nickel-based alloy surface was calculated.

[0062] F adh =σ·A contact

[0063] Among them, σ is the interface bonding force constant of the material, A contact is the contact area between the active agent and the metal surface.

[0064] Through the composition analysis and interface reaction analysis, the active agent formula sequence P suitable for titanium alloy and nickel-based alloy is finally formed. 2 , which contains a series of optimized active agent formulations to be used for material suitability testing in subsequent steps.

[0065] Step S200 at least includes S210-S230:

[0066] S210: Select titanium alloy and nickel-based alloy as test materials, apply the active agent formula sequence respectively, and record the coating uniformity and adhesion of the active agent on different materials.

[0067] Specifically, the active agent formula sequence P obtained from S130 2 In the present invention, a plurality of formulas with high chemical compatibility and low decomposition rate are selected as test samples. Based on the formula, titanium alloy and nickel-based alloy are used as test substrates to carry out coating experiments of the active agent. The coating thickness of the active agent h 0 It is given by the preliminary reference value, which can be set to a range of 10-50μm according to laboratory conditions and can be further adjusted. Through special coating equipment, P is evenly coated on the surface of titanium alloy and nickel-based alloy. 2 Active agent in the formula.

[0068] During the coating process, real-time monitoring of the uniformity and adhesion of the active agent on different material surfaces adh The adhesion is calculated by the following formula:

[0069] F adh =σ·A contact

[0070] Among them, A contact is the actual contact area between the active agent and the material, and σ is the interfacial adhesion constant between the material and the active agent, in N / m 2 .

[0071] By combining the coating thickness and adhesion test data, the changing trend of the coating process is recorded and a preliminary coating result database is formed. 1 This database will serve as the basic data for subsequent molten pool depth and welding strength tests.

[0072] S220: Through high temperature welding tests, the molten pool depth, welding strength and porosity of each formula on different materials are tested to analyze its welding adaptability.

[0073] Furthermore, based on D 1 Based on the coating thickness and adhesion data recorded in the , high temperature welding tests were carried out on titanium alloys and nickel-based alloys coated with active agents. The welding process was carried out using A-TIG welding equipment at a set welding current I and welding speed v. The initial settings of I and v are based on the technical specifications of the welding equipment and can be adjusted according to the experiment. The core test indicator during the welding process is the molten pool depth d melt and welding strength S w .

[0074] The molten pool depth is expressed by the formula:

[0075]

[0076] Among them, α is a constant related to the thermal conductivity of the material, I is the welding current, v is the welding speed, h eff is the effective coating thickness of the active agent. melt and compare it with the set standard value.

[0077] At the same time, the mechanical strength S of the weld during welding is evaluated w and pore generation rate r porosity The tensile strength of the weld and the defect rate of the weld surface are measured using material mechanics analysis equipment. The data are correlated with the uniformity and thickness of the activator coating to form an adaptability analysis result.

[0078] S230: According to the test results, adjust the coating thickness and formula ratio of the active agent, optimize the matching of the material and the active agent, and obtain the optimal coating thickness and formula ratio of the active agent for different materials

[0079] According to the data of welding strength, molten pool depth and pore generation rate recorded in S220, the coating thickness of the activator h 0 Specifically, if the test results show that the molten pool depth is insufficient and the welding strength is low, the coating thickness of the activator needs to be appropriately increased. 0 , ensuring its effective combination with the material surface. At the same time, adjust the proportion of each ingredient in the surfactant formula β 1 ,β 2 ,…,β n To better adapt to the chemical reactivity of titanium alloy and nickel-based alloy, and optimize the welding strength S w and pore generation rate r porosity .

[0080] The adjustment of coating thickness and formulation ratio is optimized by the following formula:

[0081] h opt =f(d melt ,S w ,r porosityw )

[0082] Among them, h opt The function f is the model derived from experimental data and multivariate regression analysis for the optimal coating thickness after adjustment. 1 ,β 2 ,…,β n Also adjusted according to similar optimization methods.

[0083] Through this process, the optimal active agent coating thickness and formula ratio of titanium alloy and nickel-based alloy are obtained and stored as the optimized formula database D2 , for subsequent coating and welding steps.

[0084] S300 includes at least S310-S330:

[0085] S310: Input the optimal surfactant coating thickness and formula ratio into the control system of the automatic coating device to set the coating thickness, speed and material surface treatment parameters.

[0086] The optimal coating thickness h of the active agent obtained in S230 opt and optimized recipe ratio β 1 ,β 2 ,…,β n The parameters are used as input data and input into the control system of the automatic coating device. Specifically, the parameters are applied to the initial setting in the coating system, including the following steps:

[0087] Set coating thickness: according to the input h opt , the system automatically adjusts the distance d between the coating nozzle and the material surface nozzle To ensure consistent coating thickness on titanium alloy and nickel-based alloy surfaces, the calculation formula is as follows:

[0088] d nozzle =f(h opt ,p nozzle )

[0089] Among them, p nozzle is the coating pressure, and f is a function determined according to the geometric characteristics of the coating equipment.

[0090] Set coating speed: coating speed v coat Dynamically adjust according to the change of coating thickness to ensure the uniformity of coating of active agent on the surface of different materials. Coating speed and nozzle pressure p nozzle And material surface adhesion F adh Related, the formula is as follows:

[0091]

[0092] Among them, k 1 is an empirical constant that depends on the physical properties of the active agent and the equipment setting parameters.

[0093] Set material surface treatment parameters: In order to further optimize the adhesion of the active agent, the system will adjust the material surface roughness R a Adjust the surface preparation procedure to ensure surface cleanliness and microstructural suitability before coating. Surface preparation steps combined with surface roughness and material thermal conductivity λ material to ensure maximum adhesion.

[0094] S320: Use the intelligent monitoring system to detect the thickness change and uniformity during the coating process in real time, adjust the coating speed and pressure, and ensure the accurate distribution of the surfactant.

[0095] The automatic coating device is equipped with a real-time intelligent monitoring system to detect the thickness and uniformity of the active agent during the coating process. The system monitors the actual thickness of the coating through optical sensors and ultrasonic sensors. real , and with the setting h ropt If a deviation Δh=h is detected, real -h opt , the coating parameters are adjusted in real time. The specific process is as follows:

[0096] Adjust coating speed v coat :According to the sensor feedback, if Δh>0, it indicates that the coating is too thick, and the system automatically reduces the coating speed v coat To reduce the coating thickness. If Δh<0, increase the coating speed.

[0097] v′ coat =v coat -k 2 ·Δh

[0098] Among them, k 2 To adjust the gain factor.

[0099] Adjust coating pressure p nozzle : When the coating thickness is too thick, the system will also reduce the coating pressure p nozzle , to reduce the amount of surfactant sprayed; when the coating is too thin, increase the spray pressure to ensure that the coating thickness returns to the set value.

[0100] p′ nozzle =p nozzle +k 3 ·Δh

[0101] Among them, k 3 It is the adjustment coefficient, which is used to control the dynamic change of nozzle pressure.

[0102] Through this closed-loop control mechanism, the coating system is able to dynamically adjust parameters throughout the coating process to ensure uniform distribution of the active agent on the surface of titanium alloy and nickel-based alloy, and to ensure that the coating thickness meets the expected standards.

[0103] S330: Based on the test data of different materials and coating control feedback, an adaptive coating process parameter model is established for precise control of the subsequent welding process.

[0104] The feedback data of various parameters in the coating process obtained in S320, including the coating thickness h real , coating speed v′ coat, nozzle pressure p′ nozzle And the adhesion after surface treatment F adh , was further analyzed to generate an adaptive coating process parameter model. Specifically, by collecting data from multiple coating experiments, the following regression model was established:

[0105] h model =f(v coat ,p nozzle ,F adh ,R a )

[0106] Among them, h model It represents the final coating thickness prediction value, and function f is a multivariate model fitted based on the previous experimental data.

[0107] In addition, based on the changes in surface conditions of different materials, the system generates independent coating parameter models M for titanium alloys and nickel-based alloys. Ti and M Ni , respectively used for coating process control of different materials:

[0108] Through the model, the system can automatically select appropriate coating process parameters according to the input material type and surface conditions to ensure the uniformity of the coating layer and the welding quality in the subsequent welding process.

[0109] S400 includes at least S410-S430:

[0110] S410: Import the coating process parameter model and the material adaptability test data into the welding control system, and initialize the welding parameters in combination with the welding process requirements of different materials.

[0111] Specifically, firstly, the coating process parameter model M obtained from the S330 module Ti and M Ni is imported into the welding control system. The model includes the active agent coating thickness h opt , nozzle pressure p nozzle , coating speed v coat and material surface treatment parameters, etc. The parameters are consistent with the material adaptability test results S obtained in S230 w (welding strength), d melt (melt pool depth) and r porosity (porosity) combination as the initial input data for the welding process.

[0112] The initialization parameters of welding are obtained by calculation, and the welding current I 0 and welding speed v 0 The following formula is used to preliminarily set it:

[0113] I 0 =f1 (S w ,d melt )

[0114] v 0 =f 2 (r porosity ,h opt )

[0115] Among them, f 1 and f 2 It is an empirical formula matched according to different materials and coating thickness. The different characteristics of titanium alloy and nickel-based alloy determine the initial setting values ​​of welding current and speed, which are stored in the welding control system as I Ti and I Ni , and the corresponding welding speed v Ti and v Ni .

[0116] S420: Through real-time monitoring during welding, the welding current, welding speed and activator coating amount are automatically adjusted to optimize the parameter configuration of the welding process

[0117] Then, the system monitors the welding parameters in real time during the welding process. The molten pool temperature T is obtained in real time using infrared temperature sensors and laser range finders. melt and the molten pool depth d melt and the initial setting value d in S410 melt,0 If T melt d melt If it exceeds the allowable range, the system will automatically adjust the welding current and welding speed to ensure a stable welding process.

[0118] Specifically, if the molten pool temperature T melt If it is too high, the welding current I needs to be reduced accordingly:

[0119] I'=I 0 k 1 ·(T melt -T melt,0 )

[0120] Among them, k 1 is the adjustment coefficient, which is used to control the amplitude of current change. On the contrary, if the temperature is too low, the current is increased. Similarly, the welding speed v will be adjusted according to the actual molten pool depth. If the depth is too shallow, the welding speed is reduced:

[0121] v'=v 0 k 2 ·(d melt -d melt,0 )

[0122] The coating amount of the active agent V coatIt will also be based on the porosity r during welding. porosity If the porosity is detected to increase, the amount of activator sprayed is increased to improve the surface stability of the weld. The amount of activator applied is related to the nozzle pressure p nozzle Distance d from nozzle nozzle The formula is as follows:

[0123] v′ coat =f 3 (p nozzle ,d nozzle )

[0124] The feedback from the real-time monitoring system can ensure that the welding parameters are always in a dynamic adjustment state, ensuring the stability of welding quality.

[0125] S430: Generate an optimization plan for the welding process based on real-time welding data feedback and process parameter models to ensure the consistency and quality stability of welding effects for different materials.

[0126] During the welding process, all monitoring data, such as welding current I', welding speed v', molten pool temperature T melt , coating amount v' coat etc., will be recorded in the data set D w In the process, the system uses the data and the process parameter model M in S410. Ti and M Ni Constantly adjust the parameter configuration of the welding process.

[0127] Using machine learning algorithms, the system is based on real-time feedback data w Update the model and generate the optimal welding solution through regression analysis:

[0128] M opt =ML(M Ti ,M Ni ,D w )

[0129] Among them, M opt It is the final welding process optimization model. According to the characteristics of different materials, the system generates independent welding optimization solutions for titanium alloys and nickel-based alloys to ensure that the welding consistency and welding quality of the materials are maintained in the subsequent welding process.

[0130] The key innovative features of the present invention include:

[0131] (1) Optimization of activator formula: The activator formula designed based on nanocomposite materials can meet the welding requirements of different materials and reduce defects such as cracks and pores.

[0132] (2) Automated coating and intelligent control: The coating thickness, speed and pressure are controlled in real time through the automated coating device to ensure uniform distribution of the active agent, and feedback adjustment is performed through the intelligent monitoring system to ensure the stability of the welding process.

[0133] (3) Adaptive coating process model: Based on the data from the coating and welding processes, an adaptive process parameter model is established to achieve optimized welding control for different materials, thereby improving the accuracy and consistency of welding.

[0134] The following are its main beneficial effects:

[0135] The present invention provides an activator coating method, system and device based on A-TIG welding, which improves the welding quality of difficult-to-weld materials such as titanium alloys and nickel-based alloys by optimizing the activator formula, automated coating technology and intelligent welding control. Compared with traditional welding methods, the present invention introduces an activator formula based on nanocomposite materials to ensure the stability of the activator in a high-temperature welding environment, thereby reducing the generation of pores and cracks during welding. In addition, through the automated coating device and the intelligent monitoring system, the coating thickness and uniformity can be detected in real time, and dynamic regulation can be performed, making the welding process more precise, effectively reducing manual adjustment errors, and improving welding efficiency and consistency.

[0136] Embodiment 2:

[0137] Figure 2 FIG. 2 shows a system structure block diagram of an active agent coating method based on A-TIG welding according to an embodiment of the present invention. Figure 2 As shown, the system may include:

[0138] Active agent formula design and material adaptability test module 10, which is responsible for designing and optimizing active agent formula for A-TIG welding and performing adaptability tests on different materials (such as titanium alloy and nickel-based alloy). Its specific functions include:

[0139] Data input and material screening: Import the composition information of different nanocomposites from the material database and screen based on parameters such as melting point and thermal stability of the material.

[0140] Formula combination and reaction analysis: Chemical compatibility analysis is performed on the screened materials, the interface reaction rate and reaction energy with titanium alloy and nickel-based alloy are calculated, and the active agent formula is optimized based on the Arrhenius equation.

[0141] Adaptability testing and coating thickness optimization: Based on the experimental results, test the coating uniformity and adhesion of each material, adjust the coating thickness and formula ratio, and obtain the best surfactant formula suitable for different materials.

[0142] The automatic coating and intelligent control module 20 is responsible for controlling the coating process of the active agent, ensuring uniform distribution of the coating and optimizing control parameters. Its functions include:

[0143] Coating thickness and coating speed setting: According to the test data, set the coating thickness, coating speed and material surface treatment parameters, and automatically adjust the operation of the coating device.

[0144] Real-time monitoring and feedback control: The coating thickness and uniformity during the coating process are detected through the intelligent monitoring system, and the coating speed, nozzle pressure and nozzle distance are adjusted in real time to ensure the consistency of coating.

[0145] Adaptive coating process model generation: Based on multiple experimental data, an adaptive coating process parameter model is established, and the coating control scheme is dynamically adjusted to adapt to the characteristics of different material surfaces.

[0146] The welding control and optimization module 30 mainly controls the process parameters during welding and automatically adjusts the welding current, welding speed, etc. in combination with real-time feedback data to ensure welding quality. Its functions include:

[0147] Initial parameter setting: According to the coating process model of the automatic coating module and the material adaptability test results, set the initial parameters such as welding current and welding speed.

[0148] Real-time monitoring and parameter adjustment of welding: The temperature and depth of the welding pool are monitored in real time through temperature sensors and laser monitoring equipment, and the welding current and welding speed are automatically adjusted according to actual conditions to ensure the stability of the welding process.

[0149] Optimize welding plan generation: Utilize real-time feedback data and process parameter models to continuously optimize welding process parameters and generate welding plans suitable for different materials.

[0150] Feedback and learning module 40, which uses machine learning algorithms to learn and analyze data during the welding process and continuously optimize the control strategy of the system. Its functions include:

[0151] Real-time data collection and analysis: Collect various data during the welding process (such as molten pool depth, welding strength, coating thickness, etc.) and input the data into the learning algorithm for analysis.

[0152] Model update and optimization: Dynamically update the welding process model based on feedback data, and use regression analysis methods to generate the optimal welding plan to ensure the system's adaptability in subsequent welding processes.

[0153] Adaptive optimization of welding process: The system adjusts welding process parameters according to the characteristics of different materials to ensure the consistency of welding effect.

[0154] The embodiment of the present invention can significantly improve the welding quality of difficult-to-weld materials such as titanium alloys and nickel-based alloys by adopting an active agent coating technology based on A-TIG welding. By combining an automated coating device with an intelligent control system, precise coating of the active agent is achieved, the generation of pores and cracks during welding is reduced, and the strength and consistency of the weld are improved. The real-time monitoring and feedback mechanism of the system ensures dynamic optimization of the welding process, improves overall welding efficiency, reduces production costs, and has significant industrial application prospects.

[0155] Embodiment three:

[0156] Figure 3 FIG. 2 shows a block diagram of a computer device according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0157] The active agent storage and proportioning module 310 is responsible for storing the active agent raw materials for A-TI G welding and proportioning them. Through the storage tanks of various active agent components and the proportioning controller, the raw materials are mixed according to the set proportion to generate an active agent formula suitable for titanium alloy and nickel-based alloy.

[0158] Composition: surfactant storage tank, ratio controller, mixing unit.

[0159] Function: By precisely controlling the proportion of the active agent, the chemical composition of the active agent is ensured to be stable, providing the best formula for the subsequent coating process.

[0160] The automatic coating module 320 is used to evenly coat the proportioned active agent on the surface of the welding material, and to adjust the coating thickness, speed and pressure through the control system to ensure the uniform distribution of the active agent on the surface of the material.

[0161] Composition: coating nozzle, coating control system, thickness sensor, pressure regulating unit.

[0162] Function: Combined with the precise control of the automated coating system, it ensures the consistency of the activator coating and improves the surface treatment effect of the material during welding.

[0163] The real-time monitoring and feedback module 330 is used to monitor the active agent coating process and welding parameters in real time. The data such as coating thickness, welding temperature, molten pool depth, etc. are collected in real time through sensors and fed back to the control system for parameter adjustment.

[0164] Composition: optical sensor, infrared temperature sensor, laser rangefinder, data acquisition unit.

[0165] Function: Real-time detection of changes in coating and welding processes, timely adjustment of process parameters, and ensuring the stability and high quality of the welding process.

[0166] Welding control system 340, the welding control system automatically adjusts the welding current, welding speed, etc. according to the monitoring data and preset process parameters to ensure efficient and stable welding. The system is linked with the coating module to optimize the welding parameters according to the material type and surface conditions.

[0167] Composition: main control unit, welding current regulator, welding speed controller, data processing module.

[0168] Function: Based on real-time feedback data, dynamically adjust welding parameters to ensure welding quality consistency, and optimize welding effects in combination with coating process.

[0169] Feedback and optimization module 350, which is used to analyze the data during the welding process and continuously optimize the welding parameters and the activator coating process through machine learning algorithms. By collecting and analyzing the real-time data during welding, the process model is updated to generate the optimal welding plan.

[0170] Composition: data storage unit, learning algorithm processor, model optimization module.

[0171] Function: Through machine learning and feedback mechanism, the welding process is continuously improved, the welding process is adaptively optimized, and the working efficiency and welding quality of the equipment are improved.

[0172] The device of the embodiment of the present invention can effectively improve the welding quality of materials such as titanium alloys and nickel-based alloys by integrating an automated coating system, a real-time monitoring and feedback control system, and a welding process control module. The device not only improves welding efficiency and reduces defects in the welding process through intelligent control and automated operation, but also optimizes the welding process in real time through a feedback mechanism, and has high efficiency and stability for industrial applications.

[0173] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing the embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to replace some of the technical features therein with equivalents. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is also within the scope of patent protection of this application.

Claims

1. A method for coating an active agent based on A-TIG welding, characterized in that: The steps include: Obtain active agent formulas based on nanocomposite materials, conduct material composition analysis, and obtain active agent formula sequences suitable for titanium alloys and nickel-based alloys; Performing material adaptability testing from the active agent formula sequence to obtain active agent coating thickness and formula ratio of different materials; Inputting the coating thickness and formulation ratio of the active agent into the control system of the automatic coating device to set the coating thickness, speed and material surface treatment parameters; Use intelligent monitoring system to detect thickness change and uniformity in real time during coating process and adjust coating speed and pressure; Based on the test data of different materials and coating control feedback, an adaptive coating process parameter model is established for the control of subsequent welding process.

2. The active agent coating method based on A-TIG welding according to claim 1, characterized in that: In the step of obtaining an active agent formulation based on nanocomposite material design, nanocomposite materials with high thermal stability and low melting point are screened as candidate components of the active agent.

3. The activator coating method based on A-TIG welding according to claim 1, characterized in that: In the step of conducting material adaptability testing, the active agent coating uniformity and adhesion of titanium alloy and nickel-based alloy are tested respectively, and the test data are recorded.

4. The activator coating method based on A-TIG welding according to claim 1, characterized in that: The automatic coating device controls the coating thickness by adjusting the pressure and nozzle distance of the coating nozzle.

5. The active agent coating method based on A-TIG welding according to claim 4, characterized in that: The thickness variation during the coating process is detected by optical sensors and ultrasonic sensors.

6. The active agent coating method based on A-TIG welding according to claim 1, characterized in that: In the intelligent monitoring system, the real-time data of the coating process is input into the automatic control device through the data feedback system for real-time adjustment.

7. The active agent coating method based on A-TIG welding according to claim 1, characterized in that: In the step of establishing an adaptive coating process parameter model, a regression model between coating thickness and welding strength is formed through multiple experimental data.

8. The active agent coating method based on A-TIG welding according to claim 1, characterized in that: The adaptive coating process parameter model adjusts welding current, welding speed and activator coating amount according to different materials.

9. An active agent coating system based on A-TIG welding, characterized in that: include: A module for active agent formulation design, which is used to design nanocomposite active agents and perform composition analysis; Module for material adaptability testing, used to test coating uniformity and adhesion of different materials; Module for automated coating, used to apply active agent to the material surface and control coating thickness and uniformity; The module for intelligent monitoring and feedback is used to monitor the data during the coating and welding process in real time and adjust the coating and welding parameters.

10. An active agent coating device based on A-TIG welding, characterized in that: include: An active agent storage and proportioning unit, used for storing and preparing active agents; An automated coating unit to control the coating thickness and speed of the active agent; Sensor unit to monitor thickness variation and uniformity during coating process; A welding control unit is used to automatically adjust the welding current and welding speed according to the coating process parameter model.

Images