Method for predicting mechanical property of marine high-strength steel welding rod

Abstract

The invention provides a method for predicting the mechanical property of a marine high-strength steel welding rod. The method comprises the steps that S1, uniform marine high-strength steel welding rod mechanical property data samples are obtained; s2, determining input characteristic variables of the model through correlation analysis and significance t test; s3, data standardization; s4, establishing a model, randomly selecting 80%-90% of the sample data as a training set, taking the rest 10%-20% of the sample data as a test set, modeling by adopting an XGBoost algorithm, and adjusting parameters; and calculating a determination coefficient R2, an average error and a root-mean-square error of the constructed model by using the test set, and when the determination coefficient R2 is greater than 0.92 and the average error percentage is less than or equal to 12%, representing that modeling succeeds. The mechanical property model of the high-strength steel welding rod for the ship is wide in application range and high in prediction precision, the experimental workload of research and development of the high-strength steel welding rod for the ship is greatly reduced, the experimental cost is saved, and the research and development period is shortened.

Description

Technical Field

[0001] The invention relates to the technical field of welding rod performance prediction, and in particular to a method for predicting the mechanical properties of a ship's high-strength steel welding rod. Background Art

[0002] The shipbuilding industry is a highly complex industry that involves the manufacture and maintenance of large structures that must withstand extreme environmental conditions such as corrosion from seawater, the impact of wind and waves, and mechanical stress. Welding rods play a vital role in the shipbuilding industry, and are a key factor in connecting metal structures and ensuring the integrity and safety of the hull. First of all, welding rods are the main material that constitutes the weld. By melting during the arc welding process, they are able to firmly connect different metal parts together. In shipbuilding, the use of welding rods runs through the entire manufacturing process, from the laying of the keel to the assembly of the hull structure to the construction of internal bulkheads and piping systems. Welding rods not only provide the necessary mechanical strength, but also form a sealed connection through fusion, which is essential to prevent seawater penetration and ensure the watertightness of the hull. Secondly, the chemical composition and properties of the welding rod have a direct impact on the quality of the weld. In the shipbuilding industry, special welding rods are often used that can resist the attack of salt and other corrosive chemicals in seawater. These welding rods usually contain special alloying elements such as chromium, nickel and molybdenum, which can form a dense oxide layer to protect the weld from corrosion. In addition, high-performance welding rods can withstand high temperatures and pressures, which is essential for welding in engine rooms, boilers and other high-temperature working areas. Welding rods also play an important role in ship repair and modification. As the age of ships increases, the hull structure may become fatigued, corroded or damaged. In these cases, welding is an effective means of repairing and reinforcing the hull. The use of welding rods can ensure the compatibility and integrity of the repaired parts with the original structure, extending the service life of the ship.

[0003] In the design and construction of modern ship structures, in order to reduce the weight of the structure, increase the carrying capacity and improve the maneuverability, a large amount of high-strength hull structural steel is used, and the strength of the corresponding welding rods is getting higher and higher. Due to the difference in the composition of the parent material and the welding material and the fact that the weld metal is a cast structure, the welded joint is often the weak part of the ship structure, and is prone to material failure problems such as fracture and fatigue cracks. For ship high-strength steel weld metal, strength, plasticity and toughness are the main mechanical performance indicators. Strength directly determines the carrying capacity of the hull structure, plasticity affects the difficulty of structural construction, and toughness directly affects the safety of the hull structure. Therefore, the mechanical properties of ship high-strength steel welding rods mainly include yield strength, tensile strength, elongation and impact toughness. There are many factors that affect the mechanical properties of ship high-strength steel welding rods, mainly including the chemical composition of the weld metal, welding process parameters, ambient temperature, etc.

[0004] The alloying elements contained in marine high-strength steel welding rods mainly include carbon (C), silicon (Si), manganese (Mn), chromium (Cr), nickel (Ni), molybdenum (Mo), titanium (Ti), vanadium (V), niobium (Nb), copper (Cu), aluminum (Al), boron (B), zirconium (Zr), etc. The existence form of alloying elements in marine high-strength steel welds is mainly to form solid solutions or compounds, and the compounds formed are mainly carbides, nitrides, oxides, sulfides or intermetallic compounds. In order to better control the microstructure of the weld metal and reduce the segregation degree of alloying elements, welding rods generally adopt a diversified design of alloying elements. With the increase of strength, the content of alloying elements also increases accordingly. The influence of alloying elements on the mechanical properties of welding rods not only depends on the properties and content of the elements themselves, but also is affected by the content of other elements due to the interaction between elements. Therefore, it is difficult to describe the influence of alloying elements on the mechanical properties of welding rods with a simple functional relationship.

[0005] Since ships are made by welding together steel of various specifications, gas shielded welding wire is the main welding material for ship structural steel welded joints. Therefore, welded joints are often the weakest parts of ship structures and are prone to material failures such as fracture and fatigue cracks. For ship high-strength steel weld metal, mechanical properties such as strength, plasticity and toughness are the most important performance indicators, which directly determine the overall performance and service safety of ship structures.

[0006] As an important material for ships and offshore structures, the performance level of welding materials will affect the safety of service. However, the current design method is still mainly based on trial and error, which leads to a large amount of experiments and a certain degree of randomness. To this end, the Chinese patent application number 200810236873.7 discloses a method for predicting the strength of 550-850MPa gas shielded welding welds, including (1) selecting welding materials and obtaining the chemical composition of the welds welded on the specified steel plate; (2) designing the welding process; (3) calculating. However, this model is only for gas shielded welding wires with ferrite weld structure and the calculation process is relatively complicated. Since the weld metal of high-strength steel for ships contains more bainite or martensite structures in addition to acicular ferrite structure, and is also required to have high low-temperature toughness and high strength, it is obvious that this model cannot be used to predict the strength of high-strength steel weld metal for ships.

[0007] In view of this, the present invention is proposed. Summary of the invention

[0008] The problem solved by the present invention is that the prior art cannot achieve rapid and accurate prediction of the mechanical properties of shipboard high-strength steel welding rods, resulting in a slow iteration rate in the development of shipboard high-strength welding rods.

[0009] In order to solve the above problems, the present invention provides a method for predicting the mechanical properties of a marine high-strength steel welding rod, comprising:

[0010] S1. Obtain uniform mechanical properties data samples of high-strength marine steel welding rods;

[0011] Based on historical test data, orthogonal test is used to design and prepare a series of welding rods with different compositions. Welding operations are carried out under different line energies. The alloy elements of the weld metal are analyzed and the yield strength, tensile strength, elongation and impact energy of the weld metal at different temperatures are measured. The above data are taken as data samples, and the maximum, minimum, average and standard deviation of the sample data are calculated.

[0012] S2. The input characteristic variables of the model are determined by correlation analysis and significance t-test, where the input characteristic variables of yield strength and tensile strength are line energy E, C, Si, Mn, Ni, Cr, Mo, V and Ti elements, the input characteristic variables of elongation are C, Si, Mn, Ni, Cr, Mo, V and Ti elements, and the input characteristic variables of impact energy are temperature T and C, Mn, Ni, Cr, Mo, V and Ti elements;

[0013] S3, data standardization;

[0014] S4. Model building

[0015] Randomly select 80%-90% of the sample data as the training set, and the remaining 10%-20% of the sample data as the test set, use the XGBoost algorithm to build the model and adjust the parameters; use the test set to calculate the determination coefficient R of the constructed model 2 , average error and root mean square error, when the determination coefficient R 2 When >0.92 and the average error percentage is ≤12%, it means that the modeling is successful;

[0016] S5. Input the preset “input characteristic variables” of the high-strength steel welding rod into the model, and output the yield strength, tensile strength, elongation and impact energy at different temperatures.

[0017] Preferably, step S2 includes analyzing the correlation coefficient and t-test value between alloy elements, line energy E and temperature and mechanical properties of welding rods, and determining the input characteristic variables of the model according to the significance level of the correlation between the two. Preferably, when the significance level α=0.05, when the t-test statistic of a factor is greater than 1.96, it indicates that there is a significant correlation between the factor and the mechanical properties.

[0018] Preferably, in step S3, the sample data is standardized according to the formula z=(x-μ) / σ, wherein μ and σ represent the mean value and standard deviation of the characteristic parameter respectively, and z represents the processed value.

[0019] Preferably, in step S4, the XGBoost model is adjusted by random grid search and five-fold cross validation method, the accuracy value is calculated for each division of cross validation, the average validation accuracy is calculated for each hyperparameter setting, and the hyperparameter with the highest average validation accuracy is selected as the model hyperparameter.

[0020] Preferably, the value range of the input characteristic variables in step S2 is: w(C): 0.03%~0.177%, w(Si): 0.071%~1.53%, w(Mn): 0.316%~2.6%, w(Ni): 0%~9.05%, w(Cr): 0%~1.23%, w(Mo): 0%~1.14%, w(V): 0%~0.075%, w(Ti): 0%~0.807%, w(Cu): 0%~0.879%, E: 6.6~22.0kJ / cm, T: -60~20℃.

[0021] Preferably, in step S1, a total of 276 sets of data samples related to yield strength, tensile strength and elongation, and 393 sets of data samples related to impact energy are obtained. Preferably, there are 108 sets of data samples for orthogonal test, accounting for no less than 10% of the total data samples, preferably 30% or 50%.

[0022] Compared with the prior art, the mechanical properties prediction method of the ship's high-strength steel welding rod described in the present invention has the following beneficial effects: 1) a wide range of applications, a large span of strength levels (300MPa to 1000MPa), and a wide temperature range (room temperature to -100°C); 2) all test data are derived from the measured data of ship structural steel welding rods, with high prediction accuracy, overcoming the shortcoming that empirical formulas cannot reflect interactions, and is suitable for the prediction of mechanical properties of ship steel welding rods, greatly reducing the experimental workload of developing ship high-strength steel welding rods. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The influence of temperature on the impact energy of weld metal in the prior art;

[0024] Figure 2 Correlation analysis between the mechanical properties and input characteristic variables described in the embodiment of the present invention;

[0025] Figure 3 This is the prediction result of the mechanical properties based on the constructed model according to the embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. The technical features of the embodiments of the present invention can be combined with each other without conflict.

[0027] In order to meet the needs of different fields, the types of new ships are also constantly expanding; since welded joints are the weakest parts of ship structures, simple physical tests cannot achieve rapid evaluation of the mechanical properties of weld metals, which seriously restricts the research and development of marine ship equipment. With the rapid development of computer technology, computing power has been greatly improved, and computer models and algorithms have gradually been used in the field of material performance prediction. Accurate prediction methods for the mechanical properties of marine high-strength steel weld metals can quickly evaluate the mechanical properties of marine steel welding rods, which is conducive to the design of auxiliary welding rod alloy composition, thereby effectively reducing the amount of testing and shortening the development cycle of welding rods. To this end, the applicant proposes the following technical solutions:

[0028] For shipbuilding high-strength steel weld metal, strength, plasticity and toughness are the main mechanical performance indicators, and the main factors affecting the mechanical properties of weld metal are the chemical composition of the weld, thermal cycle parameters and temperature. Among them, strength directly determines the bearing capacity of the hull structure, plasticity affects the difficulty of structural construction, and toughness directly affects the safety of the hull structure. With the large-scale use of high-strength hull structural steel in ship construction, weld metal is also required to have corresponding high strength, good plasticity, and a certain toughness.

[0029] The alloying elements contained in the weld metal of high-strength marine steel are mainly carbon (C), silicon (Si), manganese (Mn), chromium (Cr), nickel (Ni), molybdenum (Mo), titanium (Ti), vanadium (V), niobium (Nb), copper (Cu), aluminum (Al), boron (B), zirconium (Zr), etc. The alloying elements exist in the weld of high-strength marine steel mainly in the form of solid solution or compound. The formed compounds are mainly carbides, nitrides, oxides, sulfides or intermetallic compounds, such as Fe3O, Mn3C, VC, NbC, ZrC, TiC, VN, TiN, AlN, NbN, MnS, FeS, FeO, SiO2, etc. Among them, Ti, V, Nb, Mo are easy to form carbides, while Ti, Zr, V, Nb, Al, B are easy to form nitrides. Traditional high-strength steel welding materials are generally C-Mn-Ni-Cr-Mo system, which matches the alloy composition system of the parent material. Among them, carbon is the most important strengthening element. Due to its strong interstitial solid solution effect and influence on the structural transformation, it has a great influence on the strength, plasticity, toughness and crack resistance of the weld metal; the addition of other alloying elements such as Mn, Cr, Ni, Mo, etc., all affect the structural transformation of the weld metal to varying degrees, and at the same time play a certain role in solid solution strengthening. In order to better control the structural composition of the weld metal and reduce the segregation degree of the alloying elements, the weld metal generally adopts a diversified design of alloying elements. As the strength increases, the content of alloying elements also increases accordingly.

[0030] In addition to alloying elements affecting the mechanical properties of weld metal, temperature is also an important factor. The effect of temperature on the impact toughness of weld metal follows the following rule: as the service temperature gradually decreases, the impact energy of weld metal changes in three stages (e.g. Figure 1 As shown in the figure): When the service temperature is high, the fracture surfaces of the weld metal impact specimens are all ductile tearing fracture surfaces, which belong to ductile fracture. At this time, the impact energy of the weld metal is only affected by the inherent characteristics of the material and is little affected by temperature changes. Therefore, the impact energy remains on the upper platform with a very small variation range when the temperature is high; when the service temperature gradually drops to the ductile-brittle transition temperature range, brittle fractures begin to appear on the fracture surfaces of the impact specimens, and as the temperature drops, the area of ​​the brittle fractures increases rapidly, indicating that the impact specimens quickly change from ductile fractures to brittle fractures within this temperature range, and thus the impact energy decreases sharply as the temperature drops; when the service temperature is low, the fracture surfaces of the weld metal specimens are all transformed into brittle fractures, which belong to brittle fractures. Only a small amount of energy is needed to break the weld. At this time, the impact energy is at the lower platform position and does not change with temperature.

[0031] In other words: the main factors affecting the mechanical properties of shipbuilding high-strength steel weld metal include alloying elements (C, Si, Mn, Ni, Cr, Mo, V, Ti, Cu), welding line energy, and temperature. The corresponding mechanical performance parameters include weld metal yield strength, tensile strength, elongation, and impact energy.

[0032] The present invention provides a method for predicting the mechanical properties of a marine high-strength steel welding rod, comprising:

[0033] 1) Obtain uniform mechanical properties data samples of high-strength marine steel welding rods

[0034] Since the historical test data of welding rods matching marine structural steel are mainly concentrated on several fixed grades of alloy composition systems, the data volume is small and too concentrated to be conducive to subsequent modeling; 108 groups of orthogonal experiments were designed, and the pressure-coated welding rods were pressed according to the designed alloy composition. Based on the formula of a 600MPa-grade welding rod for a certain ship, various ferroalloys such as ferromolybdenum, ferrosilicon, metallic manganese, nickel powder and chromium powder were added to the coating, and the alloy element content in the weld was adjusted to make the alloy element content of the deposited metal as close to the design target as possible. A series of welding rods with different compositions were prepared, and welding operations were carried out under different line energies. The alloying elements (C, Si, Mn, Ni, Cr, Mo, V, Ti, Cu) of the weld metal were analyzed, and the yield strength, tensile strength, elongation and impact energy at different temperatures of the weld metal were determined. Combined with the above historical data, a total of 276 sets of data samples related to yield strength, tensile strength and elongation, and 393 sets of data samples related to impact energy were obtained. The maximum, minimum, average and standard deviation of the sample data were calculated. The results are shown in Table 1. The maximum and minimum values ​​of the alloying elements, line energy E and temperature in the table are the applicable ranges for the subsequent model establishment.

[0035] Table 1 Minimum, maximum, average and standard deviation of sample data

[0036]

[0037]

[0038] 2) Verify and determine input feature variables

[0039] SPSS and other software were used to analyze the correlation coefficients between alloy elements, line energy E and temperature and the mechanical properties of welding rods. The input characteristic variables of the model were determined according to the significance level of the correlation between the two. The results are shown in Figure 2 , Table 2.

[0040] Table 2 Correlation analysis between input characteristic variables and mechanical properties

[0041]

[0042] Note: The calculation formula of t-test statistic is: Where n is the total number of samples

[0043] According to the significance level α = 0.05 and the degree of freedom n-2, we can check the t distribution table and get: t α / 2=1.96; if the t-test value of an input characteristic variable is greater than 1.96, it indicates that there is a significant correlation with the mechanical properties. As shown in Table 2, the input characteristic variables that pass the t-test for yield strength and tensile strength are all line energy E, C, Si, Mn, Ni, Cr, Mo, V and Ti elements, the input characteristic variables that pass the correlation test in elongation are C, Si, Mn, Ni, Cr, Mo, V, Ti, and the input characteristic variables that pass the test in impact energy are C, Si, Mn, Ni, Cr, Mo, V, Ti, and temperature T. In summary, the input characteristic variables of each model are determined according to the correlation coefficient r and t-test results as shown in Table 3:

[0044] Table 3 Input characteristic variables and output variables of the model

[0045]

[0046] 3) Sample data preprocessing

[0047] The sample data is standardized according to the formula z = (x-μ) / σ, where μ and σ represent the mean and standard deviation of the characteristic parameters, respectively, and z represents the value after processing. Since data such as line energy, alloy element content, and test temperature have different physical meanings and different dimensions, the sample data should also be standardized before modeling. By transforming the input features into a standard normal distribution (i.e., the mean is 0 and the standard deviation is 1), it can ensure that all features have the same importance in subsequent modeling, and avoid the numerical values ​​of some characteristic variables being too large or too small to dominate the model learning process.

[0048] 4) Construct a prediction model for the mechanical properties of marine high-strength steel welding rods

[0049] Since the prediction of welding rod mechanical properties is a complex problem with multiple factors interacting, traditional regression fitting methods are difficult to solve this complex problem, and machine learning algorithms such as BP neural network, multi-layer perceptron (MLP), support vector machine (SVM), random forest (BF), gradient boosting machine (GBDT) and extreme gradient boosting machine (XGBOOST) can be used to deal with such problems. After research, it was found that XGBOOST is more effective than other algorithm modeling effects, including the following steps:

[0050] S41. Randomly select 80%-90% of the sample data as the training set, and the remaining 10%-20% of the sample data as the test set. Adjust the parameters of the XGBoost model through random grid search and five-fold cross validation. The specific range and step size of the hyperparameters are shown in Table 4. The accuracy value is calculated for each division of the cross validation. The average validation accuracy is calculated for each hyperparameter setting. The hyperparameter with the highest average validation accuracy is selected as the model hyperparameter to obtain the optimized hyperparameter combination, as shown in Table 5. The mechanical properties prediction model of high-strength steel welding rods is established using the optimized hyperparameter combination.

[0051] Table 4 Hyperparameter search range and step size

[0052]

[0053]

[0054] Table 5. Optimized XGBOOST model hyperparameters

[0055]

[0056] Since the prediction of welding rod mechanical properties is a complex problem with multiple factors interacting, traditional regression fitting methods are difficult to solve this complex problem, while machine learning algorithms such as BP neural network, multi-layer perceptron (MLP), support vector machine (SVM), random forest (BF), gradient boosting machine (GBDT) and extreme gradient boosting machine (XGBOOST) can be used to deal with such problems. After research, it was found that XGBOOST is more effective than other algorithms in modeling.

[0057] S42. Use the test set to calculate the coefficient of determination R of the constructed model 2 , average error and root mean square error, determine whether the coefficient R 2 >0.92 and the average error percentage is ≤12%. If so, it means that the model is successfully constructed; if not, the hyperparameter search range is expanded, including increasing the hyperparameter range and refining the step size, for example, the hyperparameter range is expanded to 120% of the original range and the step size is refined to 1 / 2-2 / 3 of the original step size, and then the process returns to step S41.

[0058] The coefficient of determination R 2 , average error and root mean square error and other indicators are used to analyze the prediction accuracy of the constructed model, and the prediction performance of the model is verified using the test set data. 2 >0.92 and the average error percentage ≤12%, indicating that the prediction accuracy of the constructed model is good; when the determination coefficient R 2When <0.92 or the average error percentage is >12%, it means that the prediction accuracy of the model is poor. Then the hyperparameter search range is expanded (including increasing the hyperparameter range and refining the step size), and the five-fold cross validation and grid search methods are continued to obtain the best hyperparameters until a prediction model with ideal prediction accuracy is obtained. The results are shown in Table 6. Figure 3 .

[0059] Table 6 Model prediction accuracy indicators

[0060]

[0061] From Table 5, Figure 3 It can be seen that the mechanical properties prediction method for ship high-strength steel welding rods established by the present invention has a wide range of application, a large span of strength levels (300MPa~1000MPa), and a wide temperature range (room temperature~-60℃); all test data are derived from the measured data of ship structural steel welding rods, with high prediction accuracy, overcoming the shortcoming that empirical formulas cannot reflect interactions, and is suitable for the prediction of mechanical properties of ship steel welding rods, greatly reducing the experimental workload of the development of ship high-strength steel welding rods, saving test costs and shortening the development cycle, greatly improving the research and development speed of marine ship equipment.

[0062] S5. Input the preset “input characteristic variables” of the high-strength steel welding rod into the model, and output the yield strength, tensile strength, elongation and impact energy at different temperatures.

[0063] The present invention supplements historical data through orthogonal experiments to obtain sample data with uniform and comprehensive feature coverage, which is beneficial to improving the prediction accuracy of model prediction; adopts a significant t-test to screen out corresponding input feature parameters for different mechanical properties of welding rods, which is beneficial to improving the accuracy of model prediction; adopts advanced XGBOOST algorithm modeling to overcome the shortcoming that empirical formulas cannot reflect the interaction between influencing factors, and greatly improves the accuracy of mechanical property prediction of ship high-strength steel welding rods; accurate mechanical property prediction model of ship high-strength steel welding rods can assist in the design of welding rod alloy composition, and significantly reduce the experimental workload of developing ship high-strength steel welding rods.

[0064] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A method for predicting the mechanical properties of a marine high-strength steel welding rod, characterized in that: The steps include: S1. Obtain uniform mechanical properties data samples of high-strength marine steel welding rods; Based on historical test data, orthogonal test is used to design and prepare a series of welding rods with different compositions. Welding operations are carried out under different line energies. The alloy elements of the weld metal are analyzed and the yield strength, tensile strength, elongation and impact energy of the weld metal at different temperatures are measured. The above data are taken as data samples, and the maximum, minimum, average and standard deviation of the sample data are calculated. S2. The input characteristic variables of the model are determined by correlation analysis and significance t-test, where the input characteristic variables of yield strength and tensile strength are line energy E, C, Si, Mn, Ni, Cr, Mo, V and Ti elements, the input characteristic variables of elongation are C, Si, Mn, Ni, Cr, Mo, V and Ti elements, and the input characteristic variables of impact energy are temperature T and C, Mn, Ni, Cr, Mo, V and Ti elements; S3, data standardization; S4. Model building 80%-90% of the sample data are randomly selected as the training set, and the remaining 10%-20% of the sample data are used as the test set. The XGBoost algorithm is used to build the model and adjust the parameters; the test set is used to calculate the determination coefficient R of the constructed model. 2 , average error and root mean square error, when the determination coefficient R 2 When >0.92 and the average error percentage is ≤12%, it means that the modeling is successful; S5. Input the preset "input characteristic variables" of the high-strength steel welding rod into the model, and output the yield strength, tensile strength, elongation and impact energy at different temperatures.

2. The method for predicting mechanical properties of a marine high-strength steel welding rod according to claim 1, characterized in that: Step S2 includes analyzing the correlation coefficients between alloy elements, line energy E and temperature and mechanical properties of welding rods, and determining the input characteristic variables of the model according to the significance level of the correlation between the two.

3. The method for predicting mechanical properties of a marine high-strength steel welding rod according to claim 1, characterized in that: In step S3, the sample data is standardized according to the formula z=(x-μ) / σ, where μ and σ represent the mean value and standard deviation of the characteristic parameter respectively, and z represents the processed value.

4. The method for predicting mechanical properties of a marine high-strength steel welding rod according to claim 1, characterized in that: In step S4, the XGBoost model is adjusted through random grid search and five-fold cross validation. The accuracy value is calculated for each division of cross validation, the average validation accuracy is calculated for each hyperparameter setting, and the hyperparameter with the highest average validation accuracy is selected as the model hyperparameter.

5. The method for predicting mechanical properties of a marine high-strength steel welding rod according to claim 1, characterized in that: The value range of the input characteristic variables in step S2 is: w(C): 0.03% to 0.177%, w(Si): 0.071% to 1.53%, w(Mn): 0.316% to 2.6%, w(Ni): 0% to 9.05%, w(Cr): 0% to 1.23%, w(Mo): 0% to 1.14%, w(V): 0% to 0.075%, w(Ti): 0%~0.807%, w(Cu): 0%~0.879%, E: 6.6~22.0kJ / cm, T: 0-60~20°C.

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