Method, medium and system for determining welding parameters of TP321H steel

Abstract

The invention provides a method, medium and system for determining welding parameters of TP321H steel, and belongs to the technical field of steel welding.The method comprises the steps that firstly, material characteristic parameters such as pitting corrosion resistance equivalent and tensile strength of the TP321H steel are collected, and small-scale orthogonal welding test data including welding parameters and welding quality data are obtained; based on this, a welding quality equation set considering material characteristics and welding parameters is established. After the lowest welding quality requirement of the construction technology is obtained, the reasonable range and step length of each parameter are determined according to parameter significance and experience, and a welding parameter optimization path problem is constructed. And after optimal welding parameters are obtained through optimization solution by adopting an artificial bee colony algorithm, actual welding verification is carried out. If the result does not meet the requirement, the weight of the fitness function of the algorithm is adjusted, optimization is repeated until the verification result meets the requirement, the optimal parameter is output, and the problems that the prior art usually depends on engineering experience, and welding parameters are difficult to determine are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of steel welding, and in particular, relates to a method, medium and system for determining welding parameters of TP321H steel. Background Art

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

[0003] The welding quality of TP321H stainless steel is crucial to ensure the safety and service life of welded structures. However, due to the large amount of alloying elements contained in TP321H steel, defects such as hot cracks, intergranular corrosion, and δ phase precipitation are prone to occur during welding, which seriously affects the welding quality. Existing welding processes usually rely on engineering experience, making it difficult to systematically optimize welding parameters and comprehensively evaluate welding quality. Therefore, it is urgent to establish a scientific and systematic method for optimizing TP321H steel welding parameters. 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 TP321H steel welding process usually relies on engineering experience and it is difficult to systematically determine the welding parameters.

[0005] The present invention is achieved in that:

[0006] A 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, linear expansion coefficient, austenitizing temperature range, carbon equivalent, nickel equivalent, and melting point;

[0008] S20, obtaining experimental data of 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, interlayer temperature, and shielding gas flow rate; the welding quality data includes weld strength, corrosion resistance, thermal crack sensitivity, intergranular corrosion sensitivity, ferrite content, and delta phase content;

[0009] S30, establishing a welding quality equation group that takes into account material characteristic parameters and welding parameters, including a strength prediction equation, a corrosion resistance prediction equation, a hot crack sensitivity prediction equation, an intergranular corrosion sensitivity prediction equation, a ferrite content prediction equation, and a δ phase content prediction equation; and fitting the welding quality equation group using the material characteristic parameters and experimental data;

[0010] S40, obtain the welding requirements of the construction process of TP321H steel as the minimum required welding quality;

[0011] S50. According to the physical meaning of welding parameters and engineering experience, determine the reasonable range and step size of each parameter, discretize the welding parameter space, and construct a multidimensional grid, where each grid point represents a set of welding parameter combinations; take the welding parameter combination corresponding to the minimum welding quality requirement as the starting point, and the theoretical optimal parameter combination corresponding to the ideal welding quality as the end point, and construct a welding parameter optimization path problem;

[0012] S60, establishing an artificial bee colony algorithm according to the welding parameter optimization path problem, wherein each bee represents a welding parameter combination to be selected; defining a fitness function as the difference between the weighted sum of the welding quality equation group and the minimum required welding quality;

[0013] S70, iteratively executing the artificial bee colony algorithm to solve the welding parameter optimization path problem to obtain the optimal welding parameters;

[0014] S80, performing actual welding verification on the optimal welding parameters; 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.

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

[0016] Wherein, the step S20 includes the following sub-steps: First, a series of small-scale orthogonal welding experiments are carried out to record welding parameters and welding quality data. Welding parameters include welding current, welding voltage, welding speed, heat input, interlayer temperature and shielding gas flow rate. Welding quality data include weld strength, corrosion resistance, hot crack sensitivity, intergranular corrosion sensitivity, ferrite content and δ phase content. These experimental data lay the foundation for the subsequent establishment of a welding quality prediction model.

[0017] Wherein, the step S30 includes the following sub-steps: first, establish a welding quality equation group considering material characteristic parameters and welding parameters, including six equations: 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. Then, fit these equations using the material characteristic parameters obtained in step S10 and the experimental data in step S20 to determine the specific values ​​of each undetermined coefficient.

[0018] Wherein, the step S40 includes the following sub-steps: obtaining the construction process requirements of 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 corrosion rate not exceeding 0.1mm / year, hot crack sensitivity index not greater than 1.5, intergranular corrosion sensitivity corrosion depth not exceeding 50μm, ferrite content controlled within the range of 5%-10%, δ phase content not exceeding 2%, etc. These requirements will serve as the target for subsequent optimization of welding parameters.

[0019] Wherein, the step S50 includes the following sub-steps: according to the physical meaning of welding parameters and engineering experience, determine the reasonable value range of parameters such as welding current, welding voltage, welding speed, heat input, interlayer temperature and shielding gas flow, and discretize each parameter according to a certain step length to construct a multidimensional grid. Each grid point represents a set of welding parameter combinations. The parameter combination corresponding to the minimum required welding quality in step S40 is used as the starting point, and the theoretical optimal parameter combination corresponding to the ideal welding quality is used as the end point to construct the welding parameter optimization path problem.

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

[0021] Wherein, the step S70 includes the following sub-steps: First, initialize the bee colony and randomly generate multiple groups of welding parameter combinations that meet the minimum requirements. Then, enter the 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. Next, enter the observation bee stage, according to the fitness function value, the observation bee selects a high-quality solution for further search. If a solution is not improved within a certain number of iterations, enter the scout bee stage, abandon the solution and randomly generate a new welding parameter combination. 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 do not meet the requirements, specifically referring to the weld strength being lower than 95% of the design requirements, or the corrosion resistance test results showing a corrosion rate exceeding 0.1 mm / year, or the thermal crack sensitivity index being greater than 1.5, or the intergranular corrosion sensitivity test results showing a corrosion depth exceeding 50 μm, or the ferrite content deviating from the ideal range of 5%-10%, or the δ phase content exceeding 2%.

[0023] Wherein, step S70 specifically 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 stage: Each hired bee searches for a new welding parameter combination near the current solution and evaluates the new solution according to the fitness function;

[0026] c) Observation bee stage: According to the fitness function value, the observation bee selects high-quality solutions for further search;

[0027] d) Scouting 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;

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

[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 default maximum number of iterations is 500 steps; the default convergence condition is that the vector similarity formed by the two welding parameter combinations is greater than 99.75%. Both the maximum number of iterations and the convergence condition can be adjusted according to experience or actual conditions.

[0031] The formula expression of each equation in the welding quality equation group is written below:

[0032] 1. Strength prediction equation:

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

[0034]

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

[0036] The parameter acquisition method is:

[0037] σ y and σ u Obtained from the material specification. I, v, T i and F are 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 is the predicted corrosion rate; PRE is the pitting corrosion resistance equivalent; H is the heat input; v is the welding speed; T i is the interlayer temperature; F is the protective gas flow rate; a1, a2, a3, a4, a5 are unknown coefficients; ε C is the second error term.

[0042] The parameter acquisition method is:

[0043] PRE is calculated by 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. Calculate, where U is the welding voltage, I is the welding current, and v is the welding speed.

[0044] 3. Thermal crack sensitivity prediction equation:

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

[0046]

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

[0048] The parameter acquisition method is:

[0049] C eq By formula calculate.

[0050] Ni eq By formula Ni eq = Calculated as %Ni + 30·%C + 0.5·%Mn.

[0051] The above element contents are obtained from the material composition analysis report. m Obtained from the material data sheet.

[0052] 4. Intergranular corrosion sensitivity prediction equation:

[0053] The intergranular corrosion susceptibility prediction equation is specifically expressed as follows:

[0054]

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

[0056] The parameter acquisition method is:

[0057] MARC is calculated by the formula MARC=%Cr+3.3·%Mo+16·%N-45·%C, and the element content is obtained from the material composition analysis report.

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

[0059] T s Obtained from the material data sheet.

[0060] 5. Ferrite content prediction equation:

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

[0062]

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

[0064] The parameter acquisition method is:

[0065] Cr eq By formula Cr eq = Calculated as %Cr + %Mo + 1.5·%Si + 0.5·%Nb.

[0066] Ni eq By formula Ni eq = Calculated as %Ni + 30·%C + 0.5·%Mn.

[0067] The above element contents are obtained from the material composition analysis report.

[0068] Represents the sum of temperature gradients in multiple directions, measured using a multi-point thermocouple array during the welding process.

[0069] 6.δ phase content prediction equation:

[0070] The δ phase content prediction equation is specifically expressed as follows:

[0071]

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

[0073] The parameter acquisition method is:

[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 the material data sheet or relevant literature.

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

[0077] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for determining welding parameters of TP321H steel.

[0078] A third aspect of the present invention provides a welding parameter determination system for TP321H steel, which includes the above-mentioned computer-readable storage medium.

[0079] Compared with the prior art, the method, medium and system for determining welding parameters of TP321H steel provided by the present invention have the following beneficial effects:

[0080] 1. Consider 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, thermophysical properties, etc., to lay the foundation for subsequent welding quality prediction and optimization. However, the existing process relies more on engineering experience and it is difficult to fully 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 including six equations, including weld strength, corrosion resistance, hot crack sensitivity, intergranular corrosion sensitivity, ferrite content and δ phase content, which can comprehensively evaluate the welding quality. However, existing processes usually only focus on a single performance indicator and it is difficult to take into account multiple factors.

[0082] 3. Use artificial bee colony algorithm for parameter optimization. This method uses artificial bee colony algorithm to search for the optimal parameter combination that meets quality requirements in the welding parameter space, and can systematically optimize welding parameters. However, most existing processes rely on trial and error methods, which makes 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 the synergy between the two and improving the optimization effect. However, it is usually difficult to establish a correlation model between materials and processes in existing processes.

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

[0085] In summary, the TP321H steel welding parameter determination method proposed in the present invention can comprehensively consider material properties and process parameters, establish a multi-performance comprehensive prediction model, and use an optimization algorithm for system optimization, thereby solving the problem that the existing TP321H steel welding process usually relies on engineering experience and is difficult to systematically determine welding parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0088] like Figure 1 FIG. 1 is a flow chart of a method for determining welding parameters of TP321H steel provided by the present invention. The method comprises 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, linear expansion coefficient, austenitizing temperature range, carbon equivalent, nickel equivalent, and melting point;

[0090] S20, obtaining experimental data of 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, interlayer temperature, and shielding gas flow rate; the welding quality data includes weld strength, corrosion resistance, thermal crack sensitivity, intergranular corrosion sensitivity, ferrite content, and δ phase content;

[0091] S30, establishing a welding quality equation group that takes into account material characteristic parameters and welding parameters, including a strength prediction equation, a corrosion resistance prediction equation, a hot crack sensitivity prediction equation, an intergranular corrosion sensitivity prediction equation, a ferrite content prediction equation, and a δ phase content prediction equation; and fitting the welding quality equation group using the material characteristic parameters and experimental data;

[0092] S40, obtain the welding requirements of the construction process of TP321H steel as the minimum required welding quality;

[0093] S50. According to the physical meaning of welding parameters and engineering experience, determine the reasonable range and step size of each parameter, discretize the welding parameter space, and construct a multidimensional grid, where each grid point represents a set of welding parameter combinations; take the welding parameter combination corresponding to the minimum welding quality requirement as the starting point, and the theoretical optimal parameter combination corresponding to the ideal welding quality as the end point, and construct the welding parameter optimization path problem;

[0094] S60, establishing an artificial bee colony algorithm according to the welding parameter optimization path problem, wherein each bee represents a welding parameter combination to be selected; defining a fitness function as the difference between the weighted sum of the welding quality equation group and the minimum required welding quality;

[0095] S70, iteratively executing the artificial bee colony algorithm to solve the welding parameter optimization path problem to obtain the optimal welding parameters;

[0096] S80, performing actual welding verification on the optimal welding parameters; 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.

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

[0098] The specific implementation method of step S10 is to first collect the material characteristic parameters of TP321H steel. These parameters include at least pitting resistance equivalent (PRE), intergranular corrosion resistance index (MARC), yield strength, tensile strength, elongation, thermal conductivity, linear expansion coefficient, austenitization temperature range, carbon equivalent (C_eq), nickel equivalent (Ni_eq) and melting point. These parameters can be obtained from the material specification or composition analysis report provided by the material supplier. Mastering these material characteristic parameters is crucial for subsequent welding quality prediction and optimization.

[0099] Step S20 is to obtain experimental data of small-scale orthogonal welding parameter experiments. In this step, a series of welding experiments need to be carried out to record welding parameters and welding quality data. Welding parameters include welding current, welding voltage, welding speed, heat input, interlayer temperature, and shielding gas flow rate. Welding quality data include weld strength, corrosion resistance, hot crack sensitivity, intergranular corrosion sensitivity, ferrite content, and δ phase content. These experimental data provide a basis for the subsequent establishment of a welding quality prediction model.

[0100] Step S30 is to establish a welding quality equation group that takes into account material characteristic parameters and welding parameters. This equation group includes 6 equations: strength prediction equation, corrosion resistance prediction equation, hot crack sensitivity prediction equation, intergranular corrosion sensitivity prediction equation, ferrite content prediction equation, and delta phase content prediction equation. The specific forms of these equations are as follows:

[0101] 1. Strength prediction equation:

[0102]

[0103] 2. Corrosion resistance prediction equation:

[0104]

[0105] 3. Thermal crack sensitivity prediction equation:

[0106]

[0107] 4. Intergranular corrosion susceptibility prediction equation:

[0108]

[0109] 5. Ferrite content prediction equation:

[0110]

[0111] 6.δ phase content prediction equation:

[0112]

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

[0114] S is the predicted weld strength, σ y is the yield strength of the parent material, σ u is the tensile strength of the base material, I is the welding current, v is the welding speed, T i is the interlayer temperature, F is the protective gas flow rate, k1 to k5 are unknown coefficients, ε S is the error term.

[0115] C R is the predicted corrosion rate, PRE is the pitting corrosion equivalent, H is the heat input, a1 to a5 are unknown coefficients, ε C is the error term.

[0116] HSC is the thermal crack sensitivity index, C eq is carbon equivalent, Ni eq is nickel equivalent, T m is the melting point of the material, b1 to b4 are unknown coefficients, ε H is the error term.

[0117] ICS is the intergranular corrosion sensitivity index, MARC is the intergranular corrosion resistance index, is the heat input change rate, T s is the sensitization temperature, c1 to c4 are unknown coefficients, ε I is the error term.

[0118] δF is the predicted ferrite content, Cr eq is chromium equivalent, is the temperature gradient, d1 to d5 are unknown coefficients, ε F is the error term.

[0119] δ p is the predicted δ phase content, E a is the activation energy of δ phase formation, R is the gas constant, T c is the critical temperature for the formation of δ phase, e1 to e4 are unknown coefficients, ε D is the error term.

[0120] These equations are established 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 unknown coefficient need to be determined by fitting small-scale experimental data.

[0121] Step S40 is to obtain the welding requirements of the construction process of TP321H steel as the minimum welding quality requirements. These requirements may include weld strength not less than 95% of the design requirements, corrosion resistance corrosion rate not exceeding 0.1mm / year, hot crack sensitivity index not greater than 1.5, intergranular corrosion sensitivity corrosion depth not exceeding 50μm, ferrite content controlled within the range of 5%-10%, δ phase content not exceeding 2%, etc. These requirements will serve as the target for subsequent optimization of welding parameters.

[0122] Step S50 is to determine the reasonable range and step size of each welding parameter, discretize the welding parameter space, and construct a multidimensional grid. Specifically, according to the physical meaning of the welding parameters and engineering experience, the reasonable value range of parameters such as welding current, welding voltage, welding speed, heat input, interlayer temperature and shielding gas flow rate is determined. Then, the value of each parameter is discretized according to a certain step size to construct a multidimensional grid. Each grid point represents a set of welding parameter combinations. The welding parameter combination corresponding to the minimum required welding quality determined in step S40 is used as the starting point, and the theoretical optimal parameter combination corresponding to the ideal welding quality is used as the end point to construct a welding parameter optimization path problem.

[0123] Step S60 is to establish an artificial bee colony algorithm (ABC) based on the welding parameter optimization path problem. In this algorithm, each bee represents a possible welding parameter combination. 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. In this way, the optimization goal can be achieved by finding the parameter combination with the minimum fitness function.

[0124] Step S70 is to iteratively execute 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 stage: Each hired bee searches for a new welding parameter combination near the current solution and evaluates the new solution according to the fitness function;

[0127] c) Observation bee stage: According to the fitness function value, the observation bee selects high-quality solutions for further search;

[0128] d) Reconnaissance 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;

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

[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 artificial bee colony algorithm can be used to search for the optimal welding parameter combination that meets the minimum requirements in the welding parameter space.

[0132] The last step S80 is to verify the optimal welding parameters. If the verification result does not meet the requirements, it is necessary to adjust the fitness function weight of the artificial bee colony algorithm and re-execute steps S70-S80 until the verification result meets the requirements.

[0133] In general, this invention proposes a systematic method to determine the optimal welding parameters of TP321H steel. It first collects material characteristic parameters, establishes a welding quality prediction model that considers material and process factors, and 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 through actual verification. This method can greatly improve the welding quality and reliability of TP321H steel.

[0134] In order 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 as follows:

[0135] The first step is step S10, which is to collect the material characteristic parameters of TP321H steel. These parameters mainly include:

[0136] Pitting Resistance Equivalent (PRE):

[0137] PRE=%Cr+3.3·%Mo+16·%N;

[0138] Among them, %Cr, %Mo and %N represent the mass fractions of chromium, molybdenum and nitrogen in the material respectively, and 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 judging corrosion resistance.

[0139] Intergranular Corrosion Susceptibility Index (MARC):

[0140] MARC=%Cr+3.3·%Mo+16·%N-45·%C;

[0141] Among them, %C represents the mass fraction of carbon in the material. The larger the MARC index, the less likely the material is to undergo intergranular corrosion.

[0142] Yield strength σ y , tensile strength σ u Mechanical properties parameters such as strength and elongation can be obtained directly from the material specification.

[0143] Thermal conductivity k, linear expansion coefficient α, austenitizing temperature range T β -T γ Thermophysical parameters such as temperature and humidity can also be obtained from the material data manual.

[0144] Carbon equivalent C eq and nickel equivalent Ni eq It can be calculated by 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 steel structure.

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

[0149] By collecting the above key material property parameters of TP321H steel, the foundation is laid for subsequent welding quality prediction and optimization.

[0150] Next is step S20, obtaining experimental data of small-scale orthogonal welding parameter experiments. In this step, a series of welding experiments need to be carried out to record welding parameters and welding quality data.

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

[0152] Welding quality data includes: weld strength S; corrosion resistance-corrosion rate C R ; Hot crack sensitivity index HSC; Intergranular corrosion sensitivity index ICS; Ferrite content δ F ;δ phase contentδ p .

[0153] These experimental data provide a basis for the subsequent establishment of welding quality prediction models.

[0154] Step S30 is to establish a welding quality equation group considering material characteristic parameters and welding parameters. This equation group includes 6 equations:

[0155] 1. Strength prediction equation:

[0156]

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

[0158] 2. Corrosion resistance prediction equation:

[0159]

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

[0161] 3. Thermal crack sensitivity prediction equation:

[0162]

[0163] Among them, b1 to b4 are unknown coefficients, ε H is the error term. This equation reflects the effects of carbon equivalent, nickel equivalent, heat input, interlayer temperature and shielding gas flow rate on thermal crack sensitivity.

[0164] 4. Intergranular corrosion susceptibility prediction equation:

[0165]

[0166] Among them, c1 to c4 are unknown coefficients, ε IThis equation describes the effect of MARC index, heat input change rate, interlayer temperature and sensitization temperature difference, and shielding gas flow rate on intergranular corrosion sensitivity.

[0167] 5. Ferrite content prediction equation:

[0168]

[0169] Among them, Cr eq is chromium equivalent, is the temperature gradient, d1 to d5 are unknown coefficients, ε F This equation reflects the influence of chromium equivalent, nickel equivalent, heat input, interlayer temperature, shielding gas flow rate and temperature gradient on ferrite content.

[0170] 6.δ phase content prediction equation:

[0171]

[0172] Among them, E a is the activation energy of δ phase formation, R is the gas constant, T c is the critical temperature for the formation of δ phase, e1 to e4 are unknown coefficients, ε D is the error term. This equation describes the effect 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 unknown coefficient in these prediction equations can be determined.

[0174] Step S40 is to obtain the construction process requirements of TP321H steel as the minimum requirements for welding quality. These requirements include at least: weld strength is not less than 95% of the design requirements; corrosion resistance corrosion rate does not exceed 0.1mm / year; hot crack sensitivity index is not greater than 1.5; intergranular corrosion sensitivity corrosion depth does not exceed 50μm; ferrite content is controlled within the range of 5%-10%; δ phase content does not exceed 2%; these minimum requirements will be used as the target for subsequent optimization of welding parameters.

[0175] Step S50 is to determine the reasonable range and step size of each welding parameter, discretize the welding parameter space, and construct a multidimensional grid. Specifically, according to 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] Shielding gas flow F: [F_min, F_max];

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

[0183] The welding parameter combination corresponding to the minimum required welding quality determined in step S40 is used as the starting point. s ,U s ,v s ,H s ,T i,s ,F s , and the theoretical optimal parameter combination corresponding to the ideal welding quality is taken as the end point, I t ,U t ,v t ,H t ,T i,t ,F t ,Construct a welding parameter optimization path problem.

[0184] Step S60 is to establish an artificial bee colony algorithm (ABC) based on the welding parameter optimization path problem. In this algorithm, each bee represents a possible welding parameter combination I, U, V, H, T i ,F. 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] Among them, S min , C R,min , HSC max ICS max , δ F,min and δ p,maxThey are the minimum weld strength requirement, the maximum corrosion rate of corrosion resistance, the maximum index of hot crack sensitivity, the maximum index of intergranular corrosion sensitivity, the minimum ferrite content requirement and the maximum δ phase content requirement specified in step S40. w1 to w6 are the weight coefficients of each quality index. By minimizing this fitness function, f→0, the optimal welding parameter combination that meets all quality requirements can be obtained.

[0187] Step S70 is to iteratively execute 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 groups 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 stage: For each set of initial solutions 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 , then the neighborhood solution is accepted as the new current solution with probability p.

[0190] c) Observation stage: According to the fitness function value f of each solution k ,The roulette wheel selection method is used to select m high-quality solutions for further search.

[0191] d) Scouting bee stage: If a solution is not improved in L iterations, it is abandoned and a new welding parameter combination is randomly generated as an alternative.

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

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

[0194] The last step S80 is to verify the optimal welding parameters in practice. If the verification result does not meet the requirements, it is necessary to adjust the fitness function weights w1 to w6 of the artificial bee colony algorithm and re-execute S70-S80 until the verification result meets the requirements.

[0195] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for determining welding parameters of TP321H steel.

[0196] A third aspect of the present invention provides a welding parameter determination system for TP321H steel, which includes the above-mentioned computer-readable storage medium.

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

[0198] First, the key material property parameters of TP321H steel are collected, including pitting corrosion resistance equivalent, intergranular corrosion resistance index, mechanical properties, thermophysical properties, etc. These parameters reflect the performance characteristics of the material itself and have an important impact on welding quality.

[0199] Secondly, a small-scale orthogonal welding experiment was conducted to record process parameters such as welding current, welding voltage, welding speed, heat input, interlayer temperature and shielding gas flow rate, as well as welding quality indicators such as weld strength, corrosion resistance, hot crack sensitivity, intergranular corrosion sensitivity, ferrite content and δ phase content. These experimental data provide a basis for the subsequent establishment of a welding quality prediction model.

[0200] Then, based on the collected material characteristic parameters and welding experimental data, a welding quality prediction model considering material and process factors was established. The model includes 6 equations, which predict weld strength, corrosion resistance, hot crack sensitivity, intergranular corrosion sensitivity, ferrite content and δ phase content respectively. These equations contain the complex coupling relationship between material parameters and process parameters, which can comprehensively evaluate the welding quality.

[0201] Next, the minimum requirements for welding quality are determined according to the welding process requirements, and the welding parameter optimization path problem is constructed. Specifically, the reasonable value range of each welding parameter is first determined, and it is discretized according to a certain step size to construct a multidimensional 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 end point, forming an optimization path problem.

[0202] Finally, the artificial bee colony algorithm is used to search for the optimal welding parameter combination in the parameter space. The algorithm simulates the foraging behavior of bees, and through the cooperative search of bees with different roles, the optimal parameters that can meet all quality index requirements are finally found. The algorithm parameters are continuously adjusted through actual welding verification to ensure that the optimization results meet the requirements of actual applications.

[0203] The key advantage of this method is that it fully integrates material characteristic parameters and welding process parameters, establishes a comprehensive welding quality prediction model, and uses intelligent optimization algorithms to quickly search for the optimal solution in the parameter space. Compared with the traditional method that relies on experience, this method is more systematic and scientific, and can provide a reliable parameter selection basis for TP321H steel welding, significantly improving welding quality and reliability.

[0204] In order to further better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: A chemical enterprise intends 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, it is necessary to strictly control the welding quality of TP321H steel to ensure the safety and reliability of the pipeline welding structure. To this end, the technical department of the enterprise uses the TP321H steel welding parameter determination method proposed in the present invention to optimize the welding process.

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

[0206] Table 1 TP321H steel performance parameters

[0207]

[0208]

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

[0210] Welding process parameters:

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

[0212] Welding voltage U: 20-24V, step size 1V;

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

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

[0215] Interlayer temperature T i :80-140℃, step length 10℃;

[0216] Welding quality index test:

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

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

[0219] Hot crack sensitivity index HSC: measured according to DH3 CTS test standard;

[0220] Intergranular corrosion sensitivity index ICS: measured according to ASTM A262 standard;

[0221] Ferrite content δ F :Through metallographic analysis;

[0222] δ phase content p :Analyzed by X-ray diffraction;

[0223] Table 2 Orthogonal experimental data

[0224]

[0225]

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

[0227] 1. Strength prediction equation:

[0228]

[0229] 2. Corrosion resistance prediction equation:

[0230]

[0231] 3. Thermal crack sensitivity prediction equation:

[0232]

[0233] 4. Intergranular corrosion susceptibility prediction equation:

[0234]

[0235] 5. Ferrite content prediction equation:

[0236]

[0237] 6.δ phase content prediction equation:

[0238]

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

[0240] Next, based on the requirements of chemical companies for the welding quality of TP321H steel pipelines, 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 Corrosion depth ICS max ≤40μm;

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

[0246] δ phase content p,max ≤2%;

[0247] Based on the above minimum quality requirements and the reasonable value 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 parameters of the artificial bee colony algorithm are set as follows:

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

[0252] The weights of each indicator in the fitness function: 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 welding parameter combination:

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

[0255] By bringing this parameter combination into each prediction model, the predicted values ​​of welding quality indicators can be obtained as follows:

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

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

[0258] Thermal crack sensitivity index HSC = 1.43 ≤ 1.5;

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

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

[0261] δ phase content p =1.9%≤2%;

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

[0263] In order to verify the reliability of the optimization results, the following actual welding tests were carried out:

[0264] Adoption I best =215A,U best =23V,v best =22cm / min,F best=15L / min,T i,best The TP321H steel plate was welded with the welding parameters of =110℃, and various performance tests were carried out after welding.

[0265] The test results show that the weld strength is 620MPa, the corrosion resistance corrosion rate is 0.052mm / a, the hot crack sensitivity index is 1.41, the intergranular corrosion sensitivity corrosion depth is 35μm, 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 the present invention.

[0267] According to the specific process of this embodiment 2, the TP321H steel welding parameter determination method proposed in the present 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. It can provide a reliable parameter selection basis for TP321H steel welding and significantly improve welding quality and reliability.

[0268] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for determining welding parameters of TP321H steel, characterized in that: The following steps are involved: 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, linear expansion coefficient, austenitizing temperature range, carbon equivalent, nickel equivalent, and melting point; S20, obtaining experimental data of 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, interlayer temperature, and shielding gas flow rate; the welding quality data includes weld strength, corrosion resistance, thermal crack sensitivity, intergranular corrosion sensitivity, ferrite content, and delta phase content; S30, establishing a welding quality equation group that takes into account material characteristic parameters and welding parameters, including a strength prediction equation, a corrosion resistance prediction equation, a hot crack sensitivity prediction equation, an intergranular corrosion sensitivity prediction equation, a ferrite content prediction equation, and a δ phase content prediction equation; and fitting the welding quality equation group using the material characteristic parameters and experimental data; S40, obtain the welding requirements of the construction process of TP321H steel as the minimum required welding quality; S50. According to the physical meaning of welding parameters and engineering experience, determine the reasonable range and step size of each parameter, discretize the welding parameter space, and construct a multidimensional grid, where each grid point represents a set of welding parameter combinations; take the welding parameter combination corresponding to the minimum welding quality requirement as the starting point, and the theoretical optimal parameter combination corresponding to the ideal welding quality as the end point, and 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 welding parameter combination to be selected; defining a fitness function as the difference between the weighted sum of the welding quality equation group 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 parameters; S80, performing actual welding verification on the optimal welding parameters; 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 for determining welding parameters of TP321H steel according to claim 1, characterized in that: The verification results do not meet the requirements, specifically referring to the weld strength being lower than 95% of the design requirements, or the corrosion resistance test results showing a corrosion rate exceeding 0.1 mm / year, or the hot crack sensitivity index being greater than 1.5, or the intergranular corrosion sensitivity test results showing a corrosion depth exceeding 50 μm, or the ferrite content deviating from the ideal range of 5%-10%, or the δ phase content exceeding 2%.

3. The method for determining welding parameters of TP321H steel according to claim 1, characterized in that: Step S70 specifically includes: a) Initialize the bee colony and randomly generate multiple sets of welding parameter combinations that meet the minimum requirements; b) Hired bee stage: Each hired bee searches for a new welding parameter combination near the current solution and evaluates the new solution according to the fitness function; c) Observation bee stage: According to the fitness function value, the observation bee selects high-quality solutions for further search; d) Scouting 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) Update the global optimal solution and record the current optimal welding parameter combination; 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.

4. The method for determining welding parameters of TP321H steel according to claim 3, characterized in that: The strength prediction equation is specifically expressed as follows: Where S is the predicted weld strength; σ y is the yield strength of the parent material; u is the tensile strength of the base material; I is the welding current; v is the welding speed; T i is the interlayer temperature; F is the protective gas flow rate; k1, k2, k3, k4, k5 are unknown coefficients; ε S is the first error term.

5. The method for determining welding parameters of TP321H steel according to claim 4, characterized in that: The corrosion resistance prediction equation is specifically expressed as follows: In the formula, C R is the predicted corrosion rate; PRE is the pitting corrosion resistance equivalent; H is the heat input; a1, a2, a3, a4, a5 are unknown coefficients; ε c is the second error term.

6. A method for determining welding parameters of TP321H steel according to claim 5, characterized in that: The thermal crack sensitivity prediction equation is specifically expressed as follows: Where HSC is the thermal crack sensitivity index; C eq is carbon equivalent; Ni eq is nickel equivalent; T m is the melting point of the material; b1, b2, b3, b4 are unknown coefficients; ε H is the third error term.

7. A method for determining welding parameters of TP321H steel according to claim 6, characterized in that: The intergranular corrosion susceptibility prediction equation is specifically expressed as follows: Where, ICS is the intergranular corrosion sensitivity index; MARC is the intergranular corrosion resistance index; is the rate of change of heat input; T s is the sensitization temperature; c1, c2, c3, c4 are unknown coefficients; ε I is the fourth error term.

8. The method for determining welding parameters of TP321H steel according to claim 7, characterized in that: The ferrite content prediction equation is specifically expressed as follows: In the formula, δ F is the predicted ferrite content; Cr eq is chromium equivalent; is the temperature gradient; d1, d2, d3, d4, d5 are unknown coefficients; ε F is the fifth error term; The δ phase content prediction equation is specifically expressed as follows: In the formula, δ p is the predicted δ phase content; E a is the activation energy of δ phase formation; R is the gas constant; T c is the critical temperature for the formation of δ phase; t is the welding time; e1, e2, e3, e4 are unknown 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 when the program instructions are executed in a computer, the method for determining welding parameters of a TP321H steel material according to any one of claims 1 to 8 is used to execute.

10. A welding parameter determination system for TP321H steel, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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