A method, medium and system for determining welding parameters of TP321H steel material

Abstract

The application provides a kind of TP321H steel material welding parameter determination method, medium and system, belong to steel material welding technical field, including: first, collect the material characteristic parameters of TP321H steel material, such as corrosion resistance equivalent, tensile strength and the like, and obtain small-scale orthogonal welding test data, including welding parameter and welding quality data.Based on this, the welding quality equation set considering material characteristics and welding parameters is established.After obtaining the minimum welding quality requirement of construction process, the reasonable range and step length of each parameter are determined according to the parameter meaning and experience, and the welding parameter optimization path problem is constructed.After obtaining the optimal welding parameters by using artificial bee colony algorithm optimization solution, actual welding verification is carried out.If the result does not meet the requirements, adjust the fitness function weight of the algorithm and repeat the optimization until the verification result meets the requirements, output the optimal parameters, solve the problem that the existing technology usually depends on engineering experience and is difficult to determine the welding parameters.

Description

Technical Field

[0001] This invention belongs to the field of steel welding technology, and specifically relates to a method, medium and system for determining welding parameters of TP321H steel. Background Technology

[0002] Stainless steel is an important metallic material widely used in chemical, petroleum, marine, and nuclear power industries. Its excellent corrosion resistance, good mechanical properties, and wide operating temperature range make it the preferred material in these high-end fields. Among them, TP321H (18Cr-10Ni-Ti) austenitic stainless steel, with its excellent heat and corrosion resistance, is widely used in components such as chemical equipment and nuclear power pipelines under high temperature and high pressure environments.

[0003] The welding quality of TP321H stainless steel is crucial for ensuring the safety and service life of welded structures. However, due to its high alloy content, TP321H steel is prone to defects such as hot cracking, intergranular corrosion, and δ-phase precipitation during welding, severely impacting weld quality. Existing welding processes typically rely on engineering experience, making it difficult to systematically optimize welding parameters and comprehensively assess weld quality. Therefore, there is an urgent need to establish a scientific and systematic method for optimizing welding parameters of TP321H steel. Summary of the Invention

[0004] In view of this, the present invention provides a method, medium and system for determining welding parameters of TP321H steel, which can solve the problem that the existing welding process of TP321H steel usually relies on engineering experience and is difficult to systematically determine welding parameters.

[0005] This invention is implemented as follows:

[0006] The first aspect of the present invention provides a method for determining welding parameters of TP321H steel, comprising the following steps:

[0007] S10. Collect material property parameters of TP321H steel, including at least pitting corrosion resistance equivalent, intergranular corrosion resistance index, yield strength, tensile strength, elongation, thermal conductivity, coefficient of linear expansion, austenitizing temperature range, carbon equivalent, nickel equivalent, and melting point.

[0008] S20. Obtain experimental data from a small-scale orthogonal welding parameter experiment, including welding parameters and welding quality data; wherein, the welding parameters include welding current, voltage, speed, heat input, interpass temperature, and shielding gas flow rate; the welding quality data includes weld strength, corrosion resistance, hot cracking susceptibility, intergranular corrosion susceptibility, ferrite content, and δ phase content.

[0009] S30. Establish a set of welding quality equations that consider material property parameters and welding parameters, including strength prediction equations, corrosion resistance prediction equations, hot cracking sensitivity prediction equations, intergranular corrosion sensitivity prediction equations, ferrite content prediction equations, and δ phase content prediction equations; and fit the set of welding quality equations using the material property parameters and experimental data.

[0010] S40. Obtain the welding requirements for the construction process of TP321H steel as the minimum required welding quality.

[0011] S50. Based on the physical meaning and engineering experience of the welding parameters, determine the reasonable range and step size of each parameter, discretize the welding parameter space, construct a multi-dimensional grid, and each grid point represents a set of welding parameter combinations; take the welding parameter combination corresponding to the minimum required welding quality as the starting point and the theoretical optimal parameter combination corresponding to the ideal welding quality as the ending point, and construct the welding parameter optimization path problem.

[0012] S60. Based on the welding parameter optimization path problem, establish an artificial bee colony algorithm, where each bee represents a candidate welding parameter combination; define the fitness function as the difference between the weighted sum of the welding quality equations and the minimum required welding quality.

[0013] S70. Iteratively execute the artificial bee colony algorithm to solve the welding parameter optimization path problem and obtain the optimal welding parameters;

[0014] S80. Perform actual welding verification on the optimal welding parameters; if the verification result does not meet the requirements, adjust the fitness function weight of the artificial bee colony algorithm and re-execute S70-S80 until the verification result meets the requirements.

[0015] Specifically, step S10 includes the following sub-steps: First, collect the material property parameters of TP321H steel. These parameters include at least the pitting corrosion resistance equivalent, intergranular corrosion resistance index, yield strength, tensile strength, elongation, thermal conductivity, coefficient of linear expansion, austenitizing temperature range, carbon equivalent, nickel equivalent, and melting point. These parameters can be obtained from the material specifications or composition analysis reports provided by the material supplier. Mastering these material property parameters is of great significance for subsequent welding quality prediction and optimization.

[0016] Step S20 includes the following sub-steps: First, a series of small-scale orthogonal welding experiments are conducted, and welding parameters and welding quality data are recorded. Welding parameters include welding current, welding voltage, welding speed, heat input, interpass temperature, and shielding gas flow rate. Welding quality data includes weld strength, corrosion resistance, hot cracking susceptibility, intergranular corrosion susceptibility, ferrite content, and δ-phase content. These experimental data lay the foundation for establishing a subsequent welding quality prediction model.

[0017] Step S30 includes the following sub-steps: First, a set of welding quality equations considering material property parameters and welding parameters is established, including six equations: strength prediction equation, corrosion resistance prediction equation, hot cracking sensitivity prediction equation, intergranular corrosion sensitivity prediction equation, ferrite content prediction equation, and δ phase content prediction equation. Then, these equations are fitted using the material property parameters obtained in step S10 and the experimental data from step S20 to determine the specific values ​​of each undetermined coefficient.

[0018] Step S40 includes the following sub-steps: obtaining the construction process requirements for TP321H steel as the minimum requirements for welding quality. These requirements may include weld strength not less than 95% of the design requirements, corrosion resistance with a corrosion rate not exceeding 0.1 mm / year, hot cracking sensitivity index not greater than 1.5, intergranular corrosion sensitivity with a corrosion depth not exceeding 50 μm, ferrite content controlled within the range of 5%-10%, and δ phase content not exceeding 2%, etc. These requirements will serve as targets for subsequent optimization of welding parameters.

[0019] Step S50 includes the following sub-steps: Based on the physical meaning and engineering experience of the welding parameters, determine the reasonable value ranges for parameters such as welding current, welding voltage, welding speed, heat input, interpass temperature, and shielding gas flow rate, and discretize each parameter according to a certain step size to construct a multi-dimensional grid. Each grid point represents a set of welding parameter combinations. Taking the parameter combination corresponding to the minimum required welding quality in step S40 as the starting point and the theoretically optimal parameter combination corresponding to the ideal welding quality as the ending point, construct a welding parameter optimization path problem.

[0020] Step S60 includes the following sub-steps: Based on the welding parameter optimization path problem constructed in step S50, an artificial bee colony algorithm is established. In this algorithm, each bee represents a possible combination of welding parameters. The fitness function of the algorithm is defined as the difference between the weighted sum of the welding quality equations and the minimum required welding quality. The optimization objective is achieved by finding the parameter combination with the minimum fitness function.

[0021] Step S70 includes the following sub-steps: First, initialize the bee colony and randomly generate multiple sets of welding parameter combinations that meet the minimum requirements. Then, enter the hired bee phase, where each hired bee searches for new welding parameter combinations near the current solution and evaluates the new solution based on the fitness function. Next, enter the observer bee phase, where observer bees select high-quality solutions for further searching based on the fitness function value. If a solution is not improved within a certain number of iterations, enter the scout bee phase, abandon the solution, and randomly generate new welding parameter combinations. Finally, update the global optimal solution, record the current optimal welding parameter combination, and repeat the above steps until the convergence condition is met.

[0022] Among them, the verification results that do not meet the requirements specifically refer to weld strength being less than 95% of the design requirements, or corrosion resistance test results showing a corrosion rate exceeding 0.1 mm / year, or hot cracking sensitivity index being greater than 1.5, or intergranular corrosion sensitivity test results showing a corrosion depth exceeding 50 μm, or ferrite content deviating from the ideal range of 5%-10%, or δ phase content exceeding 2%.

[0023] Specifically, step S70 includes:

[0024] a) Initialize the bee colony and randomly generate multiple sets of welding parameter combinations that meet the minimum requirements;

[0025] b) Hired Bee Phase: Each hired bee searches for new combinations of welding parameters near the current solution and evaluates the new solution based on the fitness function;

[0026] c) Observation bee phase: Based on the fitness function value, the observation bee selects a high-quality solution for further search;

[0027] d) Scout Bee Phase: If a solution is not improved within a certain number of iterations, then the solution is abandoned and a new combination of welding parameters is randomly generated;

[0028] e) Update the global optimal solution and record the current optimal combination of welding parameters;

[0029] f) Repeat steps b)-e) until the maximum number of iterations is reached or the convergence condition is met to obtain the optimal welding parameters.

[0030] The maximum number of iterations is set to 500 by default; the default convergence condition is that the similarity of the vectors formed by the combinations of welding parameters in two consecutive iterations is greater than 99.75%. Both the maximum number of iterations and the convergence condition can be adjusted based on experience or actual circumstances.

[0031] The formula for each equation in the welding quality equation set is written below:

[0032] 1. Intensity prediction equation:

[0033] The intensity prediction equation is specifically expressed as follows:

[0034]

[0035] In the formula, S is the predicted weld strength; σ y σ is the yield strength of the base material; u I is the tensile strength of the base material; I is the welding current; v is the welding speed; T i Interlayer temperature; F is the protective gas flow rate; k1, k2, k3, k4, k5 are undetermined coefficients; ε S This is the first error term.

[0036] The parameter acquisition method is as follows:

[0037] σ y and σ u Obtain from the material specifications. I, V, T i F represents welding process parameters, which are set and recorded by the welding operator.

[0038] 2. Corrosion resistance prediction equation:

[0039] The corrosion resistance prediction equation is specifically expressed as follows:

[0040]

[0041] In the formula, C R Predicted corrosion rate; PRE is pitting corrosion resistance equivalent; H is heat input; v is welding speed; T i Interlayer temperature; F is the protective gas flow rate; a1, a2, a3, a4, a5 are undetermined coefficients; ε C This is the second error term.

[0042] The parameter acquisition method is as follows:

[0043] PRE is calculated using the following formula: PRE = %Cr + 3.3%Mo + 16%N, where %Cr, %Mo, and %N are the mass fractions of the corresponding elements in the material, obtained from the material composition analysis report. H is calculated using the formula... Calculate, where U is the welding voltage, I is the welding current, and v is the welding speed.

[0044] 3. Hot crack susceptibility prediction equation:

[0045] The hot crack sensitivity prediction equation is specifically expressed as follows:

[0046]

[0047] In the formula, HSC is the hot cracking sensitivity index; C eq It is the carbon equivalent; Ni eq H is the nickel equivalent; H is the heat input; v is the welding speed; T is the welding speed. i T represents the interlayer temperature. m ε is the melting point of the material; F is the flow rate of the protective gas; b1, b2, b3, b4 are coefficients to be determined; ε H This is the third error term.

[0048] The parameter acquisition method is as follows:

[0049] C eq Through formula calculate.

[0050] Ni eq Through the formula Ni eq =%Ni + 30%C + 0.5%Mn.

[0051] The elemental contents mentioned above were obtained from the material composition analysis report. m Obtained from the materials data handbook.

[0052] 4. Equation for predicting intergranular corrosion susceptibility:

[0053] The specific equation for predicting intergranular corrosion susceptibility is expressed as follows:

[0054]

[0055] In the formula, ICS is the intergranular corrosion susceptibility index; MARC is the intergranular corrosion resistance index. T is the rate of change of heat input; i T represents the interlayer temperature. s ε is the sensitization temperature; F is the protective gas flow rate; c1, c2, c3, c4 are undetermined coefficients; ε I This is the fourth error term.

[0056] The parameter acquisition method is as follows:

[0057] MARC is calculated using the formula MARC = %Cr + 3.3%Mo + 16%N - 45%C, with the elemental contents obtained from the material composition analysis report.

[0058] It is obtained by continuously measuring the heat input and calculating its rate of change, and can be monitored in real time using thermocouples and data acquisition systems.

[0059] T s Obtained from the materials data handbook.

[0060] 5. Ferrite content prediction equation:

[0061] The ferrite content prediction equation is specifically expressed as follows:

[0062]

[0063] In the formula, δ F For the predicted ferrite content; Cr eq For chromium equivalent; Ni eq H is the nickel equivalent; H is the heat input; v is the welding speed; T is the welding speed. i Interlayer temperature; F is the protective gas flow rate; d1, d2, d3, d4, d5 are undetermined coefficients; ε F This is the fifth error term.

[0064] The parameter acquisition method is as follows:

[0065] Cr eq Through the formula Cr eq =%Cr +%Mo + 1.5%Si + 0.5%Nb.

[0066] Ni eq Through the formula Ni eq =%Ni + 30%C + 0.5%Mn.

[0067] The above elemental contents were obtained from the material composition analysis report.

[0068] It represents the sum of temperature gradients in multiple directions, obtained by measuring using a multi-point thermocouple array during the welding process.

[0069] 6. Equation for predicting δ phase content:

[0070] The specific equation for predicting the δ-phase content is expressed as follows:

[0071]

[0072] In the formula, δ p The predicted δ phase content; Cr eq For chromium equivalent; Ni eq H is the nickel equivalent; H is the heat input; v is the welding speed; E is the welding speed. a The activation energy for the formation of the δ phase is T; R is the gas constant; T is the activation energy for the formation of the δ phase. i Interpass temperature; F is the shielding gas flow rate; T is the welding temperature; T c ε is the critical temperature for the formation of the δ phase; t is the welding time; e1, e2, e3, e4 are undetermined coefficients; ε D This is the sixth error term.

[0073] The parameter acquisition method is as follows:

[0074] Cr eq and Ni eq The calculation method is the same as the ferrite content prediction equation.

[0075] E a and T c Obtain from materials data handbooks or relevant literature.

[0076] This indicates the cumulative time during the welding process when the temperature exceeds the critical temperature, obtained through a real-time temperature monitoring system.

[0077] A second aspect of the present invention provides a computer-readable storage medium storing program instructions that, when executed in a computer, perform the above-described method for determining welding parameters for TP321H steel.

[0078] A third aspect of the present invention provides a system for determining welding parameters of TP321H steel, wherein the system includes the aforementioned computer-readable storage medium.

[0079] Compared with existing technologies, the beneficial effects of the welding parameter determination method, medium, and system for TP321H steel provided by this invention are:

[0080] 1. Considering material properties. This method first collects key material property parameters of TP321H steel, such as pitting corrosion resistance equivalent, intergranular corrosion resistance index, mechanical properties, and thermophysical properties, laying the foundation for subsequent welding quality prediction and optimization. Existing processes rely more on engineering experience and struggle to comprehensively consider the impact of material properties on welding quality.

[0081] 2. Establish a comprehensive prediction model for multiple performance indicators. This method establishes a welding quality prediction model that includes six equations: weld strength, corrosion resistance, hot cracking susceptibility, intergranular corrosion susceptibility, ferrite content, and δ-phase content. This model can comprehensively evaluate welding quality. In contrast, existing processes typically focus on only a single performance indicator, making it difficult to consider multiple factors simultaneously.

[0082] 3. Parameter optimization is performed using the artificial bee colony algorithm. This method utilizes the artificial bee colony algorithm to search for the optimal combination of parameters that meets the quality requirements in the welding parameter space, enabling systematic optimization of welding parameters. In contrast, most existing processes rely on trial-and-error methods, making it difficult to quickly find the optimal parameters.

[0083] 4. Integration of Material Properties and Process Parameters. This method organically integrates material property parameters and welding process parameters into the welding quality prediction model and optimization algorithm, giving full play to their synergistic effect and improving the optimization effect. Existing processes typically struggle to establish a correlation model between materials and processes.

[0084] 5. Continuous optimization through practical verification. This method also includes a step of verifying the optimization results through actual welding. If the verification results do not meet the requirements, the algorithm parameters can be adjusted and optimization repeated to ensure that the optimal welding parameters that meet the requirements of practical applications are finally obtained. This iterative optimization based on experimental verification helps to improve the applicability and reliability of the method.

[0085] In summary, the method for determining welding parameters of TP321H steel proposed in this invention can comprehensively consider material properties and process parameters, establish a multi-performance comprehensive prediction model, and use optimization algorithms for system optimization. This solves the problem that existing TP321H steel welding processes usually rely on engineering experience and are difficult to systematically determine welding parameters. Attached Figure Description

[0086] Figure 1 A flowchart of the method provided by the present invention. Detailed Implementation

[0087] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0088] like Figure 1 The diagram shown is a flowchart of a method for determining welding parameters of TP321H steel provided by this invention. This method includes the following steps:

[0089] S10. Collect material property parameters of TP321H steel, including at least pitting corrosion resistance equivalent, intergranular corrosion resistance index, yield strength, tensile strength, elongation, thermal conductivity, coefficient of linear expansion, austenitizing temperature range, carbon equivalent, nickel equivalent, and melting point.

[0090] S20. Obtain experimental data from small-scale orthogonal welding parameter experiments, including welding parameters and welding quality data; wherein, welding parameters include welding current, voltage, speed, heat input, interpass temperature, and shielding gas flow rate; welding quality data include weld strength, corrosion resistance, hot cracking susceptibility, intergranular corrosion susceptibility, ferrite content, and δ phase content;

[0091] S30. Establish a set of welding quality equations that consider material property parameters and welding parameters, including strength prediction equations, corrosion resistance prediction equations, hot cracking sensitivity prediction equations, intergranular corrosion sensitivity prediction equations, ferrite content prediction equations, and δ phase content prediction equations; and fit the set of welding quality equations using material property parameters and experimental data.

[0092] S40. Obtain the welding requirements for the construction process of TP321H steel as the minimum required welding quality.

[0093] S50. Based on the physical meaning and engineering experience of the welding parameters, determine the reasonable range and step size of each parameter, discretize the welding parameter space, construct a multi-dimensional grid, and each grid point represents a set of welding parameter combinations; take the welding parameter combination corresponding to the minimum required welding quality as the starting point and the theoretically optimal parameter combination corresponding to the ideal welding quality as the ending point, and construct the welding parameter optimization path problem.

[0094] S60. Establish an artificial bee colony algorithm based on the welding parameter optimization path problem, where each bee represents a candidate welding parameter combination; define the fitness function as the difference between the weighted sum of the welding quality equations and the minimum required welding quality.

[0095] S70. Iteratively execute the artificial bee colony algorithm to solve the welding parameter optimization path problem and obtain the optimal welding parameters;

[0096] S80. Perform actual welding verification on the optimal welding parameters; if the verification results do not meet the requirements, adjust the fitness function weights of the artificial bee colony algorithm and re-execute S70-S80 until the verification results meet the requirements.

[0097] The specific implementation methods of the above steps are described in detail below:

[0098] The specific implementation of step S10 involves first collecting the material property parameters of TP321H steel. These parameters include at least the Pitting Resistance Equivalent (PRE), Intergranular Corrosion Susceptibility Index (MARC), Yield Strength, Tensile Strength, Elongation, Thermal Conductivity, Coefficient of Linear Expansion, Austenitizing Temperature Range, Carbon Equivalent (C_eq), Nickel Equivalent (Ni_eq), and Melting Point. These parameters can be obtained from the material specifications or composition analysis reports provided by the material supplier. Understanding these material property parameters is crucial for subsequent weld quality prediction and optimization.

[0099] Step S20 involves obtaining experimental data from a small-scale orthogonal welding parameter experiment. This step requires conducting a series of welding experiments and recording welding parameters and quality data. Welding parameters include welding current, welding voltage, welding speed, heat input, interpass temperature, and shielding gas flow rate. Welding quality data includes weld strength, corrosion resistance, hot cracking susceptibility, intergranular corrosion susceptibility, ferrite content, and δ-phase content. This experimental data provides the foundation for subsequently establishing a welding quality prediction model.

[0100] Step S30 involves establishing a set of welding quality equations that consider material properties and welding parameters. This set of equations includes six equations: strength prediction equation, corrosion resistance prediction equation, hot cracking susceptibility prediction equation, intergranular corrosion susceptibility prediction equation, ferrite content prediction equation, and δ-phase content prediction equation. The specific forms of these equations are as follows:

[0101] 1. Intensity prediction equation:

[0102]

[0103] 2. Corrosion resistance prediction equation:

[0104]

[0105] 3. Hot crack susceptibility prediction equation:

[0106]

[0107] 4. Equation for predicting intergranular corrosion susceptibility:

[0108]

[0109] 5. Ferrite content prediction equation:

[0110]

[0111] 6. Equation for predicting δ phase content:

[0112]

[0113] The meanings of the parameters and variables in these equations are as follows:

[0114] S is the predicted weld strength, σ y σ is the yield strength of the base material. u Where I is the tensile strength of the base material, I is the welding current, v is the welding speed, and T is the welding speed. i The interlayer temperature is given by F, the protective gas flow rate is given by k1 to k5, and ε is a coefficient to be determined. S This is the error term.

[0115] C R The predicted corrosion rate is given by PRE, the pitting corrosion resistance equivalent is given by H, and a1 to a5 are undetermined coefficients. ε C This is the error term.

[0116] HSC is the hot crack susceptibility index, C eq Ni is the carbon equivalent. eq T is the nickel equivalent. m ε represents the melting point of the material, b1 to b4 are undetermined coefficients, and ε H This is the error term.

[0117] ICS is the intergranular corrosion susceptibility index, and MARC is the intergranular corrosion resistance index. T is the rate of change of heat input. s The sensitization temperature is given, and c1 to c4 are coefficients to be determined. ε I This is the error term.

[0118] δF For the predicted ferrite content, Cr eq In chromium equivalent, The temperature gradient is represented by d1 to d5, where ε is an undetermined coefficient. F This is the error term.

[0119] δ p For the predicted δ phase content, E a The activation energy for the formation of the δ phase is given by R, where R is the gas constant and T is the activation energy. c The critical temperature for the formation of the δ phase is given, e1 to e4 are undetermined coefficients, and ε D This is the error term.

[0120] These equations are all based on the material properties and welding parameters of TP321H steel, and can be used to predict the welding quality of TP321H steel under different welding processes. The specific values ​​of each undetermined coefficient need to be determined by fitting small-scale experimental data.

[0121] Step S40 involves obtaining the welding requirements for the construction process of TP321H steel, serving as the minimum required weld quality. These requirements may include weld strength not less than 95% of the design requirement, corrosion resistance with a corrosion rate not exceeding 0.1 mm / year, hot cracking susceptibility index not greater than 1.5, intergranular corrosion susceptibility with a corrosion depth not exceeding 50 μm, ferrite content controlled within the range of 5%-10%, and δ phase content not exceeding 2%, etc. These requirements will serve as targets for subsequent optimization of welding parameters.

[0122] Step S50 involves determining the reasonable range and step size for each welding parameter, discretizing the welding parameter space, and constructing a multidimensional grid. Specifically, based on the physical meaning and engineering experience of the welding parameters, reasonable value ranges for parameters such as welding current, welding voltage, welding speed, heat input, interpass temperature, and shielding gas flow rate are determined. Then, the values ​​of each parameter are discretized according to a certain step size, constructing a multidimensional grid. Each grid point represents a set of welding parameter combinations. Taking the welding parameter combination corresponding to the minimum required welding quality determined in step S40 as the starting point, and the theoretically optimal parameter combination corresponding to the ideal welding quality as the ending point, a welding parameter optimization path problem is constructed.

[0123] Step S60 involves establishing an Artificial Bee Colony (ABC) algorithm based on the welding parameter optimization path problem. In this algorithm, each bee represents a possible combination of welding parameters. The fitness function of the algorithm is defined as the difference between the weighted sum of the welding quality equations and the minimum required welding quality. Thus, the optimization objective can be achieved by finding the parameter combination that minimizes the fitness function.

[0124] Step S70 involves iteratively executing the artificial bee colony algorithm to solve the welding parameter optimization path problem and obtain the optimal welding parameters. The specific implementation steps of the algorithm include:

[0125] a) Initialize the bee colony and randomly generate multiple sets of welding parameter combinations that meet the minimum requirements;

[0126] b) Hired Bee Phase: Each hired bee searches for new combinations of welding parameters near the current solution and evaluates the new solution based on the fitness function;

[0127] c) Observation bee phase: Based on the fitness function value, the observation bee selects a high-quality solution for further search;

[0128] d) Scout Bee Phase: If a solution is not improved within a certain number of iterations, then the solution is abandoned and a new combination of welding parameters is randomly generated;

[0129] e) Update the global optimal solution and record the current optimal combination of welding parameters;

[0130] f) Repeat steps b)-e) until the maximum number of iterations is reached or the convergence condition is met.

[0131] In this way, the optimal combination of welding parameters that meets the minimum requirements can be searched in the welding parameter space using the artificial bee colony algorithm.

[0132] The final step, S80, involves verifying the obtained optimal welding parameters through actual welding. If the verification results do not meet the requirements, the fitness function weights of the artificial bee colony algorithm need to be adjusted, and steps S70-S80 need to be re-executed until the verification results meet the requirements.

[0133] In summary, this invention proposes a systematic method for determining the optimal welding parameters for TP321H steel. It first collects material property parameters, establishes a welding quality prediction model considering both material and process factors, then uses an artificial bee colony algorithm to search for the optimal welding parameters that meet the requirements in the parameter space, and continuously optimizes them through practical verification. This method can significantly improve the welding quality and reliability of TP321H steel.

[0134] To better understand and implement the present invention, a specific embodiment 1 of the method of the first aspect of the present invention is provided below. The relevant steps of this embodiment 1 are described in detail below:

[0135] The first step is S10, which involves collecting the material property parameters of TP321H steel. These parameters mainly include:

[0136] Pitting Resistance Equivalent (PRE):

[0137] PRE=%Cr+3.3·%Mo+16·%N;

[0138] Here, %Cr, %Mo, and %N represent the mass fractions of chromium, molybdenum, and nitrogen in the material, respectively. These data can be obtained from the material composition analysis report. The PRE index reflects the material's resistance to pitting corrosion and is an important parameter for evaluating corrosion resistance.

[0139] Intergranular Corrosion Susceptibility Index (MARC):

[0140] MARC=%Cr+3.3·%Mo+16·%N-45·%C;

[0141] Here, %C represents the mass fraction of carbon in the material. The higher the MARC index, the less susceptible the material is to intergranular corrosion.

[0142] Yield strength σ y Tensile strength σ u Mechanical properties such as elongation can be obtained directly from the material specification sheet.

[0143] Thermal conductivity k, coefficient of linear expansion α, austenitizing temperature range T β -T γ Isothermal physical parameters can also be obtained from material data handbooks.

[0144] Carbon equivalent C eq and nickel equivalent Ni eq It can then be calculated using the following formula:

[0145]

[0146] Ni eq =%Ni+30·%C+0.5·%Mn;

[0147] The content of each element is still obtained from the material composition analysis report. Carbon equivalent and nickel equivalent are important parameters reflecting the stability of the steel structure.

[0148] Finally, the melting point T of the material m It also needs to be found in the material data handbook.

[0149] By collecting the key material property parameters of TP321H steel, we can lay the foundation for subsequent welding quality prediction and optimization.

[0150] The next step is S20, which involves obtaining experimental data from a small-scale orthogonal welding parameter experiment. This step requires conducting a series of welding experiments and recording the welding parameters and welding quality data.

[0151] Welding parameters include: welding current I; welding voltage U; welding speed v; and heat input H. Interlayer temperature T i Protective gas flow rate F.

[0152] Welding quality data includes: weld strength S; corrosion resistance - corrosion rate C. R Hot crack susceptibility index (HSC); intergranular corrosion susceptibility index (ICS); ferrite content (δ) F δ phase content δ p .

[0153] These experimental data provide a foundation for the subsequent development of a welding quality prediction model.

[0154] Step S30 involves establishing a set of welding quality equations that consider material properties and welding parameters. This set of equations consists of six equations:

[0155] 1. Intensity prediction equation:

[0156]

[0157] Where k1 to k5 are undetermined coefficients, ε S This represents the error term. This equation reflects the influence of welding parameters such as current, speed, interpass temperature, and shielding gas flow rate on weld strength.

[0158] 2. Corrosion resistance prediction equation:

[0159]

[0160] Where a1 to a5 are undetermined coefficients, ε C This is the error term. This equation describes the effects of PRE, heat input, interpass temperature, and protective gas flow rate on the corrosion resistance of the material.

[0161] 3. Hot crack susceptibility prediction equation:

[0162]

[0163] Where b1 to b4 are undetermined coefficients, ε H This represents the error term. This equation reflects the effects of carbon equivalent, nickel equivalent, heat input, interpass temperature, and shielding gas flow rate on hot crack susceptibility.

[0164] 4. Equation for predicting intergranular corrosion susceptibility:

[0165]

[0166] Where c1 to c4 are undetermined coefficients, ε IThe error term is represented by . This equation describes the effects of the MARC exponent, the rate of change of heat input, the difference between interlayer temperature and sensitization temperature, and the protective gas flow rate on the susceptibility to intergranular corrosion.

[0167] 5. Ferrite content prediction equation:

[0168]

[0169] Among them, Cr eq In chromium equivalent, The temperature gradient is represented by d1 to d5, where ε is an undetermined coefficient. F This represents the error term. This equation reflects the effects of chromium equivalent, nickel equivalent, heat input, interpass temperature, protective gas flow rate, and temperature gradient on ferrite content.

[0170] 6. Equation for predicting δ phase content:

[0171]

[0172] Among them, E a The activation energy for the formation of the δ phase is given by R, where R is the gas constant and T is the activation energy. c The critical temperature for the formation of the δ phase is given, e1 to e4 are undetermined coefficients, and ε D The error term is represented by . This equation describes the effects of chromium equivalent, nickel equivalent, heat input, interpass temperature, shielding gas flow rate, and welding temperature history on the δ phase content.

[0173] By fitting small-scale experimental data, the specific values ​​of each undetermined coefficient in these prediction equations can be determined.

[0174] Step S40 involves obtaining the construction process requirements for TP321H steel, serving as the minimum requirements for welding quality. These requirements include at least: weld strength not less than 95% of the design requirement; corrosion resistance with a corrosion rate not exceeding 0.1 mm / year; hot cracking susceptibility index not greater than 1.5; intergranular corrosion susceptibility with a corrosion depth not exceeding 50 μm; ferrite content controlled within the range of 5%-10%; and δ phase content not exceeding 2%. These minimum requirements will serve as targets for subsequent optimization of welding parameters.

[0175] Step S50 involves determining the reasonable range and step size for each welding parameter, discretizing the welding parameter space, and constructing a multidimensional mesh. Specifically, based on the physical meaning and engineering experience of the following parameters, their reasonable value ranges are determined:

[0176] Welding current I: [I_min, I_max];

[0177] Welding voltage U:[U_min,U_max];

[0178] Welding speed v:[v_min,v_max];

[0179] Heat input H: [H_min, H_max];

[0180] Interlayer temperature T i :[T i _min,T i _max];

[0181] Protective gas flow rate F: [F_min, F_max];

[0182] _min and _max are used to represent the minimum or maximum value of the corresponding parameter, determining the range of the corresponding parameter, and then according to a certain step size ΔI, ΔU, Δv, ΔH, ΔT i Each parameter is discretized using ΔF, forming a multidimensional grid with a step size of one percent of the parameter range. Each grid point represents a set of welding parameter combinations.

[0183] Taking the welding parameter combination corresponding to the minimum required welding quality determined in step S40 as the starting point, I s U s ,v s H s ,T i,s ,F s And taking the theoretically optimal parameter combination corresponding to the ideal welding quality as the endpoint, I t U t ,v t H t ,T i,t ,F t Construct a welding parameter optimization path problem.

[0184] Step S60 involves establishing an Artificial Bee Colony (ABC) algorithm based on the welding parameter optimization path problem. In this algorithm, each bee represents a possible combination of welding parameters I, U, v, H, T. i The fitness function f of the algorithm is defined as the difference between the weighted sum of the welding quality equations and the minimum required welding quality:

[0185]

[0186] Where, S min C R,min HSC max ICS max δ F,min and δ p,maxThese are the minimum weld strength requirement, maximum corrosion rate for corrosion resistance, maximum hot cracking susceptibility index, maximum intergranular corrosion susceptibility index, minimum ferrite content requirement, and maximum δ phase content requirement specified in step S40. w1 to w6 are the weighting coefficients for each quality index. By minimizing this fitness function, f→0, the optimal combination of welding parameters that satisfies all quality requirements can be obtained.

[0187] Step S70 involves iteratively executing the artificial bee colony algorithm to solve the welding parameter optimization path problem and obtain the optimal welding parameters. The specific implementation steps of the algorithm include:

[0188] a) Initialize the bee colony and randomly generate N sets of initial welding parameter combinations that meet the minimum requirements of step S40, I1, U1, v1, H1, T i,1 ,F1;I2,U2,v2,H2,T i,2 F2; ...; I N U N ,v N U N ,T i,N ,F N .

[0189] b) Hired Bee Phase: For each initial solution I k U k ,v k H k ,T i,k ,F k Randomly generate a neighborhood solution I k ′,U k ′,v k ′,H k ′,T i,k ′,F k ', calculate its fitness function f k If f k ′ <f k If the probability is p, then the solution in that neighborhood is accepted as the new current solution.

[0190] c) Observation phase: Based on the fitness function value f of each solution k We use a roulette wheel selection method to select m high-quality solutions for further searching.

[0191] d) Scout Bee Phase: If a solution is not improved in L iterations, then the solution is abandoned and a new combination of welding parameters is randomly generated as a replacement.

[0192] e) Update the global optimal solution I best U best ,v best H best ,T i,best,F best Record the current optimal combination of welding parameters.

[0193] f) Repeat steps b)-e) until the maximum number of iterations T is reached. max Or it may satisfy the convergence condition.

[0194] The final step, S80, involves performing actual welding verification on the obtained optimal welding parameters. If the verification results do not meet the requirements, the fitness function weights w1 to w6 of the artificial bee colony algorithm need to be adjusted, and S70-S80 need to be re-executed until the verification results meet the requirements.

[0195] A second aspect of the present invention provides a computer-readable storage medium storing program instructions that, when executed in a computer, perform the above-described method for determining welding parameters for TP321H steel.

[0196] A third aspect of the present invention provides a system for determining welding parameters of TP321H steel, wherein the system includes the aforementioned computer-readable storage medium.

[0197] Specifically, the principle of this invention is to establish a welding quality prediction model that considers material properties and welding process parameters, and to use the artificial bee colony algorithm to search for the optimal combination of welding parameters that meets the quality requirements in the parameter space.

[0198] First, key material property parameters of TP321H steel were collected, including pitting corrosion resistance equivalent, intergranular corrosion resistance index, mechanical properties, and thermophysical properties. These parameters reflect the inherent performance characteristics of the material and have a significant impact on welding quality.

[0199] Secondly, small-scale orthogonal welding experiments were conducted to record process parameters such as welding current, welding voltage, welding speed, heat input, interpass temperature, and shielding gas flow rate, as well as welding quality indicators such as weld strength, corrosion resistance, hot cracking susceptibility, intergranular corrosion susceptibility, ferrite content, and δ-phase content. These experimental data provide a foundation for establishing a welding quality prediction model.

[0200] Then, based on the collected material property parameters and welding experimental data, a welding quality prediction model considering both material and process factors was established. This model includes six equations, which predict weld strength, corrosion resistance, hot cracking susceptibility, intergranular corrosion susceptibility, ferrite content, and δ-phase content, respectively. These equations incorporate complex coupling relationships between material and process parameters, enabling a comprehensive assessment of welding quality.

[0201] Next, based on the welding process requirements, the minimum welding quality requirements are determined, and a welding parameter optimization path problem is constructed. Specifically, the reasonable value range of each welding parameter is first determined, and then discretized according to a certain step size to construct a multi-dimensional parameter space grid. The parameter combination that meets the minimum quality requirements is taken as the starting point, and the parameter combination corresponding to the ideal welding quality is taken as the ending point, forming an optimization path problem.

[0202] Finally, an artificial bee colony algorithm was employed to search for the optimal combination of welding parameters in the parameter space. This algorithm simulates the foraging behavior of bees, and through the cooperative search of bees in different roles, it ultimately finds the optimal parameters that meet all quality requirements. The algorithm parameters were continuously adjusted through actual welding verification to ensure that the optimization results meet the requirements of practical applications.

[0203] The key advantage of this method lies in its comprehensive integration of material property parameters and welding process parameters, establishing a complete welding quality prediction model and employing intelligent optimization algorithms to quickly search for the optimal solution in the parameter space. Compared to traditional methods that rely on experience, this method is more systematic and scientific, providing a reliable basis for parameter selection for TP321H steel welding, and significantly improving welding quality and reliability.

[0204] To further understand and implement this invention, a specific application scenario is provided in Example 2: A chemical company plans to use TP321H stainless steel as the main pipeline material in its newly built ethylene cracking unit. Due to the harsh working environment of the cracking unit, strict control over the welding quality of the TP321H steel is required to ensure the safety and reliability of the pipeline welding structure. Therefore, the company's technical department optimized the welding process using the TP321H steel welding parameter determination method proposed in this invention.

[0205] First, the material property parameters of TP321H steel were collected. The specifications for TP321H steel obtained from the material supplier are shown in Table 1.

[0206] Table 1 Performance parameters of TP321H steel

[0207]

[0208]

[0209] Secondly, a small-scale orthogonal welding experiment was conducted to obtain welding parameters and welding quality data. The orthogonal experimental data are shown in Table 2. The specific experimental design is as follows:

[0210] Welding process parameters:

[0211] Welding current I: 180-220A, step size 10A;

[0212] Welding voltage U: 20-24V, in 1V increments;

[0213] Welding speed v: 20-30cm / min, step size 2cm / min;

[0214] Protective gas (Ar) flow rate F: 12-18 L / min, step size 2 L / min;

[0215] Interlayer temperature T i :80-140℃, in 10℃ increments;

[0216] Welding quality index testing:

[0217] Weld strength S: tested by a universal testing machine;

[0218] Corrosion resistance - corrosion rate C R Tested according to ASTM G48 standard;

[0219] Hot crack susceptibility index (HSC): determined according to the DH3 CTS test standard;

[0220] Intergranular corrosion susceptibility index (ICS): determined according to ASTM A262 standard;

[0221] Ferrite content δ F Metallographic analysis;

[0222] δ phase content δ p X-ray diffraction analysis was used.

[0223] Table 2 Orthogonal Experiment Data

[0224]

[0225]

[0226] With the experimental data above, the next step is to establish a welding quality prediction model that considers material property parameters and welding process parameters.

[0227] 1. Intensity prediction equation:

[0228]

[0229] 2. Corrosion resistance prediction equation:

[0230]

[0231] 3. Hot crack susceptibility prediction equation:

[0232]

[0233] 4. Equation for predicting intergranular corrosion susceptibility:

[0234]

[0235] 5. Ferrite content prediction equation:

[0236]

[0237] 6. Equation for predicting δ phase content:

[0238]

[0239] in, and Dynamic parameters such as these need to be obtained through real-time measurement.

[0240] Next, based on the welding quality requirements of chemical enterprises for TP321H steel pipes, the following minimum standards are determined:

[0241] Weld strength S min ≥585MPa;

[0242] Corrosion resistance - corrosion rate C R,min ≤0.06mm / a;

[0243] Hot Crack Sensitivity Index (HSC) max ≤1.5;

[0244] Intergranular corrosion sensitivity and corrosion depth (ICS) max ≤40μm;

[0245] Ferrite content δ F,min =5-10%;

[0246] δ phase content δ p,max ≤2%;

[0247] Based on the aforementioned minimum quality requirements and considering the reasonable range of welding parameters, the following welding parameter optimization path problem is established:

[0248] Starting point: I s =190A,U s =22V,v s =24cm / min,F s =14L / min,T i,s =100℃;

[0249] End point: I t =220A,U t =24V,v t =20cm / min,F t =16L / min,T i,t=120℃;

[0250] The parameter settings for the artificial bee colony algorithm are as follows:

[0251] Population size N = 100, number of observation bees m = 20, maximum number of iterations T max =500, abandon threshold L=50;

[0252] Weights of each fitness function metric: w1 = 0.25, w2 = 0.2, w3 = 0.15, w4 = 0.15, w5 = 0.15, w6 = 0.1;

[0253] After 500 iterations of optimization, the artificial bee colony algorithm finally found the following optimal combination of welding parameters:

[0254] I best =215A,U best =23V,v best =22cm / min,F best =15L / min,T i,best =110℃;

[0255] By combining these parameters and inputting them into each prediction model, we can obtain the following predicted values ​​for welding quality indicators:

[0256] Weld strength S = 615 MPa ≥ 585 MPa;

[0257] Corrosion resistance - corrosion rate C R =0.054mm / a≤0.06mm / a;

[0258] Hot crack susceptibility index HSC = 1.43 ≤ 1.5;

[0259] Intergranular corrosion sensitivity: corrosion depth ICS = 37 μm ≤ 40 μm;

[0260] Ferrite content σ F =6.8% ∈ [5,10]%;

[0261] δ phase content δ p =1.9% ≤ 2%;

[0262] All predicted indicators meet the minimum requirements, indicating that this parameter combination can achieve the optimal quality of TP321H steel pipe welding.

[0263] To verify the reliability of the optimization results, the following actual welding tests were conducted:

[0264] Using I best =215A,U best =23V,v best =22cm / min,F best=15L / min,T i,best The TP321H steel plate was welded at a welding temperature of 110℃, and various performance tests were performed after welding.

[0265] Test results show that the weld strength is 620 MPa, the corrosion rate is 0.052 mm / a, the hot cracking sensitivity index is 1.41, the intergranular corrosion sensitivity corrosion depth is 35 μm, the ferrite content is 7.2%, and the δ phase content is 1.8%.

[0266] All test indicators met the preset quality requirements, verifying the reliability of the optimal welding parameters determined by the method of this invention.

[0267] As can be seen from the specific process of this embodiment 2, the method for determining welding parameters of TP321H steel proposed in this invention, by systematically considering material properties and process parameters, establishes a comprehensive welding quality prediction model, and uses an intelligent optimization algorithm to search for the optimal solution in the parameter space, can provide a reliable basis for parameter selection for TP321H steel welding, and significantly improve welding quality and reliability.

[0268] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining welding parameters of a TP321H steel material, characterized by, The method comprises the following steps: S10, collecting material characteristic parameters of TP321H steel material, at least including point corrosion resistance equivalent, intergranular corrosion resistance index, yield strength, tensile strength, elongation, thermal conductivity, linear expansion coefficient, austenitizing temperature range, carbon equivalent, nickel equivalent, melting point; S20, obtaining experimental data of small-scale orthogonal welding parameter experiment, including welding parameters and welding quality data; wherein, the welding parameters include welding current, voltage, speed, heat input, interlayer temperature, protective gas flow; the welding quality data includes weld strength, corrosion resistance, hot crack sensitivity, intergranular corrosion sensitivity, ferrite content, and δ phase content; S30, establishing a welding quality equation set considering material characteristic parameters and welding parameters, including strength prediction equation, corrosion resistance prediction equation, hot crack sensitivity prediction equation, intergranular corrosion sensitivity prediction equation, ferrite content prediction equation, and δ phase content prediction equation; and fitting the welding quality equation set by using the material characteristic parameters and the experimental data; S40, obtaining welding requirements of the construction process of the TP321H steel material as the minimum required welding quality; S50, determining a reasonable range and step length of each parameter according to the physical meaning of the welding parameters and engineering experience, discretizing the welding parameter space, constructing a multi-dimensional grid, and each grid point representing a set of welding parameter combination; taking the welding parameter combination corresponding to the minimum required welding quality as the starting point, and taking the theoretical optimal parameter combination corresponding to the ideal welding quality as the terminal point, to construct a welding parameter optimization path problem; S60, establishing an artificial bee colony algorithm according to the welding parameter optimization path problem, wherein each bee represents a selected welding parameter combination; and defining the fitness function as the difference between the weighted sum of the welding quality equation set and the minimum required welding quality; S70, iteratively executing the artificial bee colony algorithm to solve the welding parameter optimization path problem to obtain the optimal welding parameter; S80, verifying the optimal welding parameter in actual welding; if the verification result does not meet the requirements, adjusting the fitness function weight of the artificial bee colony algorithm, and re-executing S70-S80 until the verification result meets the requirements.

2. The method of claim 1, wherein the TP321H steel material is a TP321H steel pipe. The verification result does not meet the requirements, specifically referring to that the weld strength is lower than 95% of the design requirement, or the corrosion resistance test result shows that the corrosion rate exceeds 0.1 mm / year, or the hot crack sensitivity index is greater than 1.5, or the intergranular corrosion sensitivity test result shows that the corrosion depth exceeds 50 μm, or the ferrite content deviates from the ideal range of 5%-10%, or the δ phase content exceeds 2%.

3. The method of claim 1, wherein the TP321H steel material is a TP321H steel pipe. Step S70 specifically comprises: a) initializing the bee colony, and randomly generating multiple sets of welding parameter combinations meeting the minimum requirements; b) employed bee stage: each employed bee searches for a new welding parameter combination near the current solution, and evaluates the new solution according to the fitness function; c) onlooker bee stage: according to the fitness function value, the onlooker bee selects a high-quality solution for further search; d) scout bee stage: if a solution is not improved within a certain number of iterations, the solution is abandoned, and a new welding parameter combination is randomly generated; e) updating the global optimal solution, and recording the current optimal welding parameter combination; f) repeating steps b) - e) until a maximum number of iterations is reached or a convergence condition is met, to obtain optimal welding parameters.

4. The method of claim 3, wherein the TP321H steel material is a TP321H steel pipe. The strength prediction equation is specifically expressed as follows: where S is the predicted weld strength; σ y is the base metal yield strength; σ u is the base metal tensile strength; I is the welding current; v is the welding speed; T i is the interpass temperature; F is the shielding gas flow rate; k1, k2, k3, k4, k5 are undetermined coefficients; ε S is the first error term.

5. The method of claim 4, wherein the TP321H steel material is a TP321H steel pipe. The corrosion resistance prediction equation is specifically expressed as follows: In the formula, C R is the predicted corrosion rate; PRE is the pitting resistance equivalent; H is the heat input; a1, a2, a3, a4, a5 are undetermined coefficients; ε c is the second error term.

6. The method of claim 5, wherein the TP321H steel material is a TP321H steel pipe. The hot crack sensitivity prediction equation is specifically expressed as follows: where HSC is a hot crack sensitivity index; C eq is a carbon equivalent; Ni eq is a nickel equivalent; T m is a material melting point; b1, b2, b3, b4 are undetermined coefficients; ε H is a third error term.

7. The method of claim 6, wherein the TP321H steel material is a TP321H steel pipe. The intergranular corrosion sensitivity prediction equation is specifically expressed as follows: In the formula, ICS is an intergranular corrosion sensitivity index; MARC is an intergranular corrosion resistance index; is a heat input rate; T s is a sensitization temperature; c1, c2, c3, c4 are undetermined coefficients; ε I is a fourth error term.

8. The method of claim 7, wherein the TP321H steel material is a TP321H steel pipe. The ferrite content prediction equation is specifically expressed as follows: where δ F is the predicted ferrite content; Cr eq is the chromium equivalent; is the temperature gradient; d1, d2, d3, d4, d5 are undetermined coefficients; ε F is the fifth error term; The δ phase content prediction equation is specifically expressed as follows: where δ p is the predicted δ phase content; E a is the activation energy for δ phase formation; R is the gas constant; T c is the critical temperature for δ phase formation; t is the welding time; e1, e2, e3, e4 are undetermined coefficients; ε D is the sixth error term.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores program instructions, and the program instructions are used to execute the welding parameter determination method of the TP321H steel material in any one of claims 1-8 when running in the computer.

10. A system for determining welding parameters of a TP321H steel material, characterized by, The computer readable storage medium of claim 9.

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