Method for improving soldering flux components based on thermodynamic activity model of soldering flux components

Abstract

The invention relates to a method for improving soldering flux components based on a thermodynamic activity model of the soldering flux components. The method comprises the steps that a target welding flux system and the total mole number of all components before reaction are determined; determining a structural unit of the soldering flux by referring to the related phase diagram and literature; setting component activity and related parameters; a welding slag activity calculation model is established according to the mass balance relation and the chemical reaction balance principle; scientific calculation software is used for programming calculation, and the final components of the welding flux during welding seam forming are controlled by adjusting the initial components. The method is suitable for various welding flux systems, welding appearance forming can be effectively improved, the internal defect rate of welding seams is reduced, and wide universality and practicability are achieved. By optimizing the components of the welding flux, a scientific and efficient improvement method is provided for the welding field, and the requirements of different welding processes and welding flux types are met.

Description

Technical Field

[0001] This invention relates to the field of molten welding flux, and more particularly to a method for improving flux formulations, specifically a method for improving flux composition based on a thermodynamic activity model of flux components. Background Technology

[0002] In the field of welding, the choice of flux plays a crucial role in weld quality. Traditionally, flux selection often relies on specifications or accumulated experience; however, this approach has several limitations. First, welding site conditions vary significantly across different regions, including factors such as air temperature, preheating temperature, and holding time, all of which affect welding results. Second, the composition of various steel grades differs, and even with a suitable flux, the welding effect cannot be guaranteed to be consistent across different regions or welding conditions. Furthermore, complex chemical reactions occur in the flux during welding. For example, components such as SiO2 in the flux are easily reduced by carbon monoxide generated from the incomplete combustion of propane or acetylene, leading to rapid fluctuations in flux composition and consequently affecting weld formation quality and internal defect rate.

[0003] Currently, while some methods exist for optimizing flux composition, most are limited to specific types of fluxes and lack versatility. For example, some methods optimize only for specific flux formulations or welding processes, making them difficult to apply to various flux systems. This makes it impossible to provide effective composition optimization solutions when faced with different types of fluxes and varying welding requirements.

[0004] With the development of welding technology, the need for a universal method for optimizing flux composition is becoming increasingly urgent. The existing Zhang Jianxiu molecular-ion coexistence theory provides an effective model for calculating slag activity, but its application scope needs further expansion.

[0005] How to use this theory to construct a thermodynamic activity model applicable to various flux systems, and how to optimize flux composition through scientific calculation methods, has become an urgent problem to be solved in the current welding field. Summary of the Invention

[0006] The purpose of this invention is to propose a universal algorithm for improving flux composition based on a thermodynamic activity model of flux components. This method analyzes the thermodynamic behavior of each component in the flux, establishes an activity calculation model, and uses scientific computing software for solving and optimization, thus providing an effective approach to improve the composition of various flux systems.

[0007] To achieve the above objectives, the present invention is implemented as follows: A method for improving flux composition based on a thermodynamic activity model of flux composition includes the following steps: S1. For the target flux system, determine the structural units of the flux within a specific temperature range by referring to relevant phase diagrams and / or literature, including simple ions, simple molecules and composite molecules, and number them; S2. Set the activity of the components, and determine the chemical reaction formula, Gibbs free energy change and complex molecular interaction concentration expression based on the law of mass action and the coexistence theory of weld slag structure. Establish a weld slag activity calculation model and form a series of equations. S3. Using scientific computing software, the model is programmed and calculated using a numerical iteration method. The final composition of the flux during weld formation is controlled by adjusting the initial composition.

[0008] The above-mentioned method for improving flux composition based on the thermodynamic activity model of flux composition includes, but is not limited to, the target flux system, the CaO-SiO2-Al2O3-MgO-MnO system, and also includes other flux systems containing multiple metal oxides.

[0009] The above-mentioned method for improving flux composition based on the thermodynamic activity model of flux composition, wherein the determination of the structural unit includes the analysis of the existence form and interaction of each component in the flux at a specific temperature, and the reference phase diagram includes, but is not limited to, ternary phase diagrams, and the literature includes thermodynamic databases and experimental research reports.

[0010] The above-mentioned method for improving flux composition based on the thermodynamic activity model of flux composition, wherein the setting of component activity includes determining the total number of moles of each major component in the flux before reaction and the effective concentration of each component in the slag, wherein the effective concentration is related to the mole fraction of a certain substance in the flux after reaction equilibrium.

[0011] The above-mentioned method for improving flux composition based on the thermodynamic activity model of flux composition includes a mass balance relationship and an expression for the concentration of each component. The specific form is constructed according to the characteristics of the target flux system and the determined structural units.

[0012] The above-mentioned method for improving flux composition based on the thermodynamic activity model of flux composition includes, but is not limited to, Matlab or other equivalent scientific computing software, and the numerical iteration method includes, but is not limited to, Newton's iteration method.

[0013] In the above method for improving flux composition based on the thermodynamic activity model of flux composition, the specific temperature range in step 1 is determined according to the welding process requirements and the characteristics of the target flux system.

[0014] The above-mentioned method for improving flux composition based on the thermodynamic activity model of flux composition, in step 3, adjusts the initial composition of the flux so that the final composition of the flux at the time of weld formation meets the welding quality requirements, including but not limited to improving the weld appearance and reducing the internal defect rate of the weld.

[0015] Furthermore, the present invention also proposes a flux composition improvement system based on the above-described improved method, comprising: The data input module is used to input the total number of moles of each major component in the flux before the reaction, as well as temperature parameters during welding, etc. The model building module is used to construct a welding slag activity calculation model based on the input data. The computation module uses scientific computing software and numerical iterative methods to perform programmed calculations on the constructed model; The composition adjustment module adjusts the initial composition of the flux based on the calculation results to control the final composition of the flux during weld formation.

[0016] Furthermore, the aforementioned flux composition improvement system is stored in a storage medium containing a computer program. When the computer program is executed by a processor, it implements the steps of the method for improving the flux composition based on the thermodynamic activity model of the flux composition.

[0017] This invention proposes an improved method for flux composition, which has broad applicability and can be applied to various flux types, such as the CaO-SiO2-Al2O3-MgO-MnO system and other flux systems containing multiple metal oxides. By optimizing the flux composition, this method effectively improves the weld appearance and reduces the internal defect rate of the weld, thereby significantly improving weld quality and reducing maintenance costs and safety hazards caused by welding defects.

[0018] Furthermore, this invention establishes a slag activity calculation model based on a thermodynamic activity model, combining the law of mass action and the theory of coexistence of slag structures to accurately describe the activity changes of each component in the flux, providing a scientific basis for flux composition optimization. In addition, numerical iterative methods (such as Newton's method) are used for solving the problem, and scientific computing software (such as Matlab, Python, etc.) is employed to achieve fast and efficient calculations, enabling timely adjustment of flux composition and improving production efficiency.

[0019] Based on the above general ideas, this method also proposes a general modeling approach based on the thermodynamic activity model of flux composition. By determining the structural units of the flux, setting the activity of components, and establishing an activity calculation model, the flux composition can be effectively predicted and optimized. The solution allows for flexible adjustment of model parameters and initial composition according to the characteristics of the target flux system and welding process requirements, making it applicable to various welding scenarios and flux types. Furthermore, in addition to providing improved algorithms, it also includes a related flux composition improvement system and storage medium, forming a complete solution that facilitates practical application and promotion. Detailed Implementation

[0020] The technical solution of the present invention will be described in detail below through specific embodiments, taking the CaO-SiO2-Al2O3-MgO-MnO system flux as an example. However, the present invention is not limited to this system and can also be applied to other flux systems containing multiple metal oxides.

[0021] Example 1 In a steel structure welding project, welding of thick high-strength steel plates is required. Based on the steel composition and welding process requirements, a CaO-SiO2-Al2O3-MgO-MnO flux system was selected. The flux selection process will follow these steps: first, determine the molar number of each component in the flux before the reaction; then, establish an activity calculation model and perform programmed calculations. By adjusting the initial composition of the flux, the optimized flux composition is finally obtained. The specific process is as follows: 1. Determine the flux system and its composition information. First, the target flux system is defined as CaO-SiO2-Al2O3-MgO-MnO. Based on the composition of the steel to be welded and the welding process requirements, the total molar numbers of each component (CaO, MgO, MnO, SiO2, Al2O3) in the flux before the reaction are obtained through lookup and calculation, denoted as b1, b2, b3, a1, and a2, respectively. For example, assuming that in a certain welding task, the calculated molar numbers of each component are b1 = 0.2 mol, b2 = 0.15 mol, b3 = 0.1 mol, a1 = 0.3 mol, and a2 = 0.25 mol.

[0022] 2. Determine the structural units of the flux. Referring to ternary phase diagrams such as CaO-SiO2-Al2O3, CaO-SiO2-MgO, and SiO2-MgO-MnO, and relevant literature, the structural units of this flux were determined within the temperature range of 1673K - 1773K. Specifically, they include the following categories: Simple ions: Ca²⁺, Mg²⁺, Mn²⁺, O²⁻.

[0023] Simple molecules: Al2O3, SiO2.

[0024] Composite molecules: see Table 1.

[0025] Table 1 CaOSiO2 2CaOSiO2 2MnOSiO2 12CaO7Al2O3 2CaOMgO2SiO2 MgO*SiO2 CaO Al203 3CaO-SiO2 CaO•MgO•SiO2 2MgO*2Al203*5Si02 CaO*6Al203 3CaO*2SiO2 MnO Al203 CaO Al2O3 SiO2 3CaO Al203 3SiO2 Al2O3■SiO2 3Al203*2Si02 3CaO-Al203 3CaO* 2MgO* 5SiO2 CaO*MgO*2SiO2 2MgO•SiO2 MgO Al203 2CaO Al203 3MgO Al203 3Si02 7MgO*9Al203*3Si02 CaO*2Al203 CaOMgO MnO*SiO2 2CaO Al203 Si02 CaO Al2O3 2SiO2 The molecular compounds present in the flux are numbered as shown in Table 2 below: Table 2 1 Al2O3 13 12CaO7Al2O3 25 CaOMgO2SiO2 2 SiO2 14 3CaO-Al203 26 2CaO*MgO*2SiO2 3 CaO 15 CaO*2Al203 27 MgO Al203 4 MgO 16 CaO*6Al203 28 2CaO Al203 5 MnO 17 2CaO*Al203*Si02 29 CaOMgO 6 CaO•SiO2 18 CaO Al2O3 2SiO2 30 7MgO*9Al203*3Si02 <!-- 3 -->]]> 7 2CaO*SiO2 19 CaO Al2O3 SiO2 31 2MgO*2Al203*5Si02 8 3CaO*2SiO2 20 3CaO Al203 3SiO2 32 3MgO Al203 3Si02 9 3CaO-SiO2 21 3Al203*2Si02 33 MnO*SiO2 10 2MgO•SiO2 22 Al2O3■SiO2 34 2MnO»SiO2 11 MgO*SiO2 23 CaO•MgO•SiO2 35 MnO Al203 12 CaO Al203 24 3CaO* 2MgO* 5SiO2 3. Set the component activities and related parameters and generate Table 3. The activity of each component in the flux is set, specifically including the total number of moles of CaO, MgO, MnO, SiO2, and Al2O3 in the flux before the reaction as b1, b2, b3, a1, and a2, respectively; the effective concentration of each component in the slag as Ni (i = 1, 2, ..., 35); the total mole fraction at reaction equilibrium as ∑X; and the mole fraction of a certain substance in the flux after reaction equilibrium as xi (i = 1, 2, ..., 35).

[0026] For example: ∑X CaO The corresponding variable b1 represents the total mole fraction of CaO; ∑X MgO The corresponding variable b2 represents the total mole fraction of MgO; ∑X MnO The corresponding variable b3 represents the total mole fraction of MnO; ∑X SiO2 The corresponding variable a1 represents the total mole fraction of SiO2; ∑X Al2O3 The corresponding variable a2 represents the total mole fraction of Al2O3.

[0027] For example, N1 corresponds to the effective concentration of Al2O3, N2 corresponds to the effective concentration of SiO2, and N3 corresponds to the effective concentration of CaO, etc.

[0028] Table 3 b1 ∑XCaO N10 N2M gOSiO2 N24 N3CaOMgO2SiO2 [b2] ∑X MgO ]] <![CDATA[N 11 ]]> <![CDATA[N MgOSiO2 ]]> <![CDATA[N 25 ]]> <![CDATA[N CaOMgO2SiO2 ]]> [b3] ∑X MnO ]]> <![CDATA[N 12 ]]> <![CDATA[N CaOAl2O3 ]]> <![CDATA[N 26 ]]> <![CDATA[N 2CaOMgO2SiO2 ]]> [a1] ∑X SiO2 ]] <![CDATA[N 13 ]]> <![CDATA[N 12CaO7Al2O3 ]]> <![CDATA[N 27 ]]> [0002N MgOAl2O3 ]]> [a2] ∑X Al2O3 ]]> <![CDATA[N 14 ]]> <![CDATA[N 3CaOAl2O3 ]]> <![CDATA[N 28 ]]> [0002S 2CaOAl2O3 ]]> [N1] <![CDATA[N Al2O3 ]]> <![CDATA[N 15 ]]> [0002N CaO2Al2O3 ]]> <![CDATA[N 29 ]]>

[0002] N

[0003] CaOMgO

[0004] ​ [N2] <![CDATA[N SiO2 ]]>

[0002] N

[0003] 16

[0004] ​ <![CDATA[N CaO6Al2O3 ]]>

[0002] N

[0003] 30

[0004] ​ <![CDATA[N 7MgO9Al2O33SiO2 ]]> <![CDATA[N3]]> <![CDATA[N CaO ]]> <![CDATA[N 17 ]]> <![CDATA[N 2CaOAl2O3SiO2 ]]> <![CDATA[N 31 ]]> <![CDATA[N 2MgO2Al2O35SiO2 ]]> <![CDATA[N4]]> <![CDATA[N MgO ]]> <![CDATA[N 18 ]]> <![CDATA[N CaOAl2O32SiO2 ]]> <![CDATA[N 32 ]]> <![CDATA[N 3MgOAl2O33SiO2 ]]> <![CDATA[N5]]> <![CDATA[N MnO ]]> <![CDATA[N 19 ]]> <![CDATA[N CaOAl2O3SiO2 ]]> <![CDATA[N 33 ]]> <![CDATA[N MnOSiO2 ]]> <![CDATA[N6]]> <![CDATA[N CaOSiO2 ]]> <![CDATA[N 20 ]]> <![CDATA[N 3CaOAl2O33SiO2 ]]> <![CDATA[N 34 ]]> <![CDATA[N 2MnOSiO2 ]]> <![CDATA[N7]]> <![CDATA[N 2CaOSiO2 ]]> <![CDATA[N 21 ]]> <![CDATA[N 3Al2O32SiO2 ]]> <![CDATA[N 35 ]]> <![CDATA[N MnOAl2O3 ]]> <![CDATA[N8]]> <![CDATA[N 3CaO2SiO2 ]]> <![CDATA[N 22 ]]> <![CDATA[N Al2O3SiO2 ]]> <![CDATA[N9]]> <![CDATA[N 3CaOSiO2 ]]> <![CDATA[N 23 ]]> <![CDATA[N CaOMgOSiO2 ]]> 4. Establish a calculation model for weld slag activity. Based on the above reactions and the mass balance relationship of various substances in the welding slag, an activity calculation model for the welding slag is established. According to the law of mass action and the coexistence theory of welding slag structure, the chemical reaction formulas, Gibbs free energy changes, and complex molecular interaction concentration expressions involved in this model are shown in Table 4. Table 4 Where T is the Kelvin temperature at which the flux and steel are fused together during welding, K. i (i=1,2,3,…29) are the equilibrium constants of each reaction, which can be obtained from… To obtain.

[0029] For example: The chemical reaction is: Ca²⁺ + O²⁻ = CaO, and the corresponding concentration expression is N³ = 2x³ / ∑X.

[0030] The chemical reaction is: 2(Ca²⁺ + O²⁻) + SiO₂ = 2CaO SiO₂. The corresponding concentration expression is N₇ = K₂N₂(N₃)², and the Gibbs free energy is -160431 + 4.106T.

[0031] Based on the above reaction and the mass balance relationship of various substances in the welding slag, a model for calculating the activity of welding slag can be established: , (1) b1=∑n(0.5N3+N6+2N7+3N8+3N9+N 12 +12N 13 +3N 14 +N 15 +N 16 +2N 17 +N 18 +N 19 +3N 20 +N 23 +3N 24 +N 25 +2N 26 +2N 28 +N 29 (2) b2=∑n(0.5N4+2N 10 +N 11 +N 23 +N 24 +N 25 +N 26 +N 27 +N 29 +7N 30 +2N 31 +3N 32 ), (3) b3=∑n(0.5N5+N 33 +2N 34 +N 35 ), (4) a1=n(N2+N6+N7+2N8+N9+N 10 +N 11 +N 17+2N 18 +N 19 +3N 20 +2N 21 +N 22 +N 23 +2N 24 +2N 25 +2N 26 +3N 30 +5N 31 +3N 32 +N 33 +N 34 ), (5) a2=n(N1+N 12 +7N 13 +N 14 +2N 15 +6N 16 +N 17 +N 18 +N 19 +N 20 +3N 21 +N 22 +N 27 +N 28 +9N 30 +2N 31 +N 32 +N 35 ), (6) By combining equations (2) and (3), (3) and (4), (4) and (5), and (5) and (6) respectively, we can obtain: b1(0.5N4+2N 10 +N 11 +N 23 +N 24 +N 25 +N 26 +N 27 +N 29 +7N 30 +2N 31 +3N 32 )=b2(0.5N3+N6+2N7+3N8+3N9+N 12 +12N 13 +3N 14 +N 15 +N 16 +2N 17 +N 18 +N 19 +3N 20 +N 23 +3N 24 +N 25 +2N 26 +2N28 +N 29 (7) b3(0.5N4+2N 10 +N 11 +N 23 +N 24 +N 25 +N 26 +N 27 +N 29 +7N 30 +2N 31 +3N 32 b²(0.5N⁵ + N) = b²(0.5N⁵ + N) 33 +2N 34 +N 35 (8) a1(0.5N5+N 33 +2N 34 +N 35 b3(N2+N6+N7+2N8+N9+N 10 +N 11 +N 17 +2N 18 +N 19 +3N 20 +2N 21 +N 22 +N 23 +2N 24 +2N 25 +2N 26 +3N 30 +5N 31 +3N 32 +N 33 +N 34 (9) a2(N2+N6+N7+2N8+N9+N 10 +N 11 +N 17 +2N 18 +N 19 +3N 20 +2N 21 +N 22 +N 23 +2N 24 +2N 25 +2N 26 +3N 30 +5N 31 +3N 32 +N 33 +N 34 a1(N1+N) = a1(N1+N) 12 +7N 13 +N 14 +2N 15+6N 16 +N 17 +N 18 +N 19 +N 20 +3N 21 +N 22 +N 27 +N 28 +9N 30 +2N 31 +N 32 +N 35 (10) Equations (7) to (10) and the concentration expression in Table 1 are the activity calculation model for flux CaO-MgO-MnO-SiO2-Al2O3.

[0032] 5. Programming calculations and component adjustment The parameters and equations from the above model are input into Matlab, and Newton's iteration method is used for programming calculations. When writing the code, the equilibrium constant Ki of each reaction is first calculated based on the known temperature T (for example, assuming the welding temperature is 1773K). Then, a nonlinear equation system is constructed based on the mass balance equation and the equilibrium equation, and the concentration Ni of each component in the weld slag is solved using Newton's iteration method.

[0033] Based on the calculated Ni value, analyze the changes in flux composition during the welding process and the final composition distribution. If the calculated weld formation quality does not meet the expected requirements (e.g., poor appearance or high internal defect rate), adjust the initial composition of the flux according to the analysis results, such as appropriately increasing or decreasing the content of certain components, and recalculate until the flux composition that meets the welding quality requirements is obtained.

[0034] By using the above activity calculation model and performing calculations, the initial composition of the flux was adjusted, and the optimized flux composition was finally obtained. After being applied to the actual welding of thick high-strength steel plates, the weld appearance was good, the internal defect rate of the weld was significantly reduced, and the welding quality was significantly improved.

[0035] As can be seen from the above embodiments: Traditional fusion welding relies on welding experience or standards to select the flux for the base material, which has limitations and may not guarantee good weld quality. However, this invention employs an improved algorithm based on the thermodynamic activity model of flux composition. By performing thermodynamic simulations before welding, the optimal flux composition in the initial state is selected, improving the final weld appearance and reducing the internal defect rate. This is an effective way to control weld quality.

[0036] Similarly, for other types of fluxes, such as six-element flux systems containing small amounts of other metal oxides, the method of this invention can be applied to optimize the composition to meet different welding requirements simply by redefining the structural units, establishing the corresponding activity calculation model, and adjusting the initial composition based on their specific composition and reaction characteristics.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for improving flux composition based on a thermodynamic activity model of flux composition, characterized in that, Includes the following steps: S1. For the target flux system, determine the structural units of the flux within a specific temperature range by referring to relevant phase diagrams and / or literature, including simple ions, simple molecules and composite molecules, and number them; S2. Set the activity of the components, and determine the chemical reaction formula, Gibbs free energy change and complex molecular interaction concentration expression based on the law of mass action and the coexistence theory of weld slag structure. Establish a weld slag activity calculation model and form a series of equations. S3. Using scientific computing software, the model is programmed and calculated using a numerical iteration method. The final composition of the flux during weld formation is controlled by adjusting the initial composition.

2. The method for improving flux composition based on the thermodynamic activity model of flux composition according to claim 1, characterized in that: The target flux system includes, but is not limited to, the CaO-SiO2-Al2O3-MgO-MnO system, and also includes other flux systems containing multiple metal oxides.

3. The method for improving flux composition based on the thermodynamic activity model of flux composition according to claim 1, characterized in that: The determination of the structural unit includes the analysis of the existence and interaction of each component in the flux at a specific temperature. The phase diagrams referenced include, but are not limited to, ternary phase diagrams, and the literature includes thermodynamic databases and experimental research reports.

4. The method for improving flux composition based on the thermodynamic activity model of flux composition according to claim 1, characterized in that: The set component activity includes determining the total number of moles of each major component in the flux before the reaction and the effective concentration of each component in the slag. The effective concentration is related to the mole fraction of a certain substance in the flux after the reaction reaches equilibrium.

5. The method for improving flux composition based on the thermodynamic activity model of flux composition according to claim 1, characterized in that: The slag activity calculation model includes a mass balance relationship and expressions for the concentration of each component. The specific form is constructed based on the characteristics of the target flux system and the determined structural units.

6. The method for improving flux composition based on the thermodynamic activity model of flux composition according to claim 1, characterized in that: The scientific computing software includes, but is not limited to, Matlab or other equivalent scientific computing software, and the numerical iteration method includes, but is not limited to, Newton's iteration method.

7. The method for improving flux composition based on the thermodynamic activity model of flux composition according to claim 1, characterized in that: In step 1, the specific temperature range is determined based on the welding process requirements and the characteristics of the target flux system.

8. The method for improving flux composition based on the thermodynamic activity model of flux composition according to claim 1, characterized in that: In step 3, by adjusting the initial composition of the flux, the final composition of the flux during weld formation is made to meet the welding quality requirements, including but not limited to improving the weld appearance and reducing the internal defect rate of the weld.

9. A flux composition improvement system based on the improved method according to any one of claims 1-8, characterized in that, include: The data input module is used to input the total number of moles of each major component in the flux before the reaction, as well as temperature parameters during welding, etc. The model building module is used to construct a welding slag activity calculation model based on the input data. The computation module uses scientific computing software and numerical iterative methods to perform programmed calculations on the constructed model; The composition adjustment module adjusts the initial composition of the flux based on the calculation results to control the final composition of the flux during weld formation.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for improving the flux composition based on the thermodynamic activity model of flux composition as described in any one of claims 1-8.

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