Fatigue crack growth rate prediction method and system for arc additive manufacturing across welding zones

Abstract

The present invention belongs to the technical field of computer-aided manufacturing, and relates to a method and system for predicting fatigue crack growth rate across weld zones in arc additive manufacturing, comprising: S1, preparing a specimen; S2, performing in-situ SEM fatigue testing on the specimen; S3, obtaining the correspondence between the crack growth rate of the specimen and the range of the stress intensity factor; S4, fitting a fatigue crack growth rate model for the specimen; S5, establishing a fatigue crack growth rate prediction model for a CZH specimen; and S6, predicting the fatigue crack growth rate of the specimen across the weld zone. The system also includes an electro-hydraulic fatigue testing system, a data acquisition module, a database, a fitting module, and a prediction module. The present invention uses a correction coefficient to predict the fatigue crack growth rate using the fatigue crack growth rate parameters of the base material zone.

Description

Technical Field

[0001] The present invention belongs to the field of computer-aided manufacturing technology, and in particular to the field of prediction technology for crack propagation in arc additive manufacturing processes, and more particularly to a method and system for predicting fatigue crack propagation rate across weld zones in arc additive manufacturing. Background Art

[0002] Over the past two decades, there has been an increasing demand for remanufacturing to extend the service life of critical infrastructure and reduce the waste of high-value-added components. The economic and environmental benefits of remanufacturing have been emphasized in various applications. WAAM offers an attractive remanufacturing method with the advantages of low tooling requirements, high deposition rates, flexible material selection, and computerized operations, which greatly improves the efficiency and accuracy of repairing large components. Mild steel is widely used in critical remanufacturing applications such as pipelines in the energy and chemical industries. For components to be directly put into service after in-situ remanufacturing repair, they are usually required to meet or exceed the performance of the original components. Fatigue crack growth is one of the main failure modes of such components. Therefore, predicting the fatigue crack growth rate of WAAM-remanufactured mild steel is of great significance to ensure the structural integrity of critical components and reduce the risk of fracture.

[0003] Existing research indicates that the heat input and thermal history of the build process in additive manufacturing (AM) technology directly impact the mechanical properties of the fabricated component. WAAM remanufacturing uses an arc as the heat source and a wire as the raw material to repair damaged components in a layer-by-layer manner. During the remanufacturing process, a transitional heat-affected zone (HAZ) forms between the deposited material (i.e., the fusion zone (FZ)) and the base material (BM) of the damaged component. The unique thermal history of WAAM produces a HAZ with a microstructure that differs from that formed by conventional welding. The HAZ is primarily influenced by one or two consecutively deposited WAAM layers. The temperature gradient and high heat input during the non-isothermal temperature cycle of the WAAM process can alter the grain size and generate hard phases in the HAZ during cooling. Research has shown that under high heat input and high cooling rates, the HAZ of a low-carbon dual-phase steel near the FZ fully austenitizes and subsequently transforms to martensite. Compared to the BM and FZ, the HAZ exhibits the greatest hardness, and tensile tests indicate that all specimens fractured at a considerable distance from the HAZ. Studies on the HAZ hardness of low-carbon bainitic steels have shown that when cooling rates exceed 5°C / s, hardness increases with increasing cooling rate. Phase transformations from bainite to austenite to martensite have also been observed in the HAZ of SA508, a low-carbon, low-alloy steel, repaired by WAAM. Furthermore, non-isothermal temperature cycling can introduce a degree of heterogeneity in the HAZ, such as carbon enrichment in austenite precipitates in low-carbon 9Ni steel due to the reheating history of multi-pass welding. The size and composition of the HAZ are significantly affected by the heat input and arc pulses used in WAAM. The low heat input and arc constriction caused by high-frequency pulses can reduce the root of the HAZ. Several studies have shown that HAZ materials in conventional welds generally exhibit lower fatigue resistance and hardness compared to the base material. Other studies have also demonstrated that fatigue resistance can be improved by precisely adjusting heat input parameters. For example, by reducing heat input and increasing cooling rate, a finer transformation microstructure can be achieved in WAAMed Ti-6Al-4V alloy, thereby increasing the fatigue limit. Studies have shown that by reducing the heat input, the harmful tensile stress on the surface of WAAM 316L stainless steel specimens can be reduced, thereby improving fatigue performance. However, for metallic materials, too low a heat input can lead to increased porosity, thereby reducing resistance to fatigue crack growth. In addition, the tensile deformation behavior of the HAZ, FZ, and BM was studied using cross-welded tensile specimens made of WAAM low-carbon steel S355. The results showed that the ultimate strength of the FZ and HAZ was greater than that of the BM, while the ductility of the former two was significantly lower than that of the BM.

[0004] The aforementioned studies demonstrate that the phase transformation and microstructural generation in the heat-affected zone (HAZ) of WAAM-treated mild steel can be quite complex, and the microstructure within the HAZ can vary significantly due to the WAAM thermal history. These phenomena are caused by the combination of high deposition rates, excessive energy input, and large temperature gradients during the WAAM process. Although numerous studies have focused on the effects of microstructure on mechanical properties (such as hardness and ultimate strength) within the HAZ of WAAM-treated mild steel, fatigue behavior (such as crack initiation and propagation) within the HAZ remains poorly understood, and the detailed mechanisms underlying macroscopic fatigue behavior remain inconclusive due to a lack of sufficient research. Recent studies have investigated fatigue crack growth behavior and microstructural mechanisms in other widely used additively manufactured materials. While valuable, the results and interpretations cannot be applied to mild steel due to material differences. In particular, the HAZ width of WAAM-remanufactured parts is typically several times greater than that of HAZs produced using non-wire-fed additive manufacturing methods using more focused heat sources (such as lasers). Crack propagation within the HAZ of WAAM-treated parts contributes to a greater proportion of the overall fatigue crack growth life, which is non-negligible from the perspective of damage tolerance analysis. A related study demonstrated that WAAM low-carbon steel outperformed conventional hot-rolled steel in fatigue crack growth resistance and exhibited very slight anisotropy. The study found that the interlocking morphology of the microstructure in WAAM low-carbon steel played a key role in enhancing fatigue crack growth resistance. A comparative study of WAAM low-carbon ER70S-6 demonstrated that the fatigue crack growth rate of WAAM was greater than that of the HAZ and BM of conventionally welded S355 steel. The results of these two studies indicate that fatigue crack growth rate is closely related to microstructure, and that extrapolating fatigue properties from the BM and FZ to the HAZ without understanding the microstructural mechanisms and their influence can lead to unreliable conclusions. While numerous studies have discussed the metallurgical characteristics of WAAM low-carbon steel, few studies have investigated the changes in fatigue crack growth behavior across the weld zone from the full WAAM to the base material. Furthermore, understanding the microstructural mechanisms underlying the differences between these locations is equally important for fabricating WAAM materials with desired fatigue properties. Summary of the Invention

[0005] The purpose of this method is to propose a method and system that can accurately predict the fatigue crack growth rate of the cross-weld zone based on the fatigue crack growth rate of the base material zone of the WAAM additively manufactured part.

[0006] This method involves first using arc additive manufacturing to fabricate a simulated surrogate of a remanufactured part. Fatigue specimens are sampled and fabricated from the base material (BM), heat-affected zone (HAZ), cross-weld zone (CZH), and fully additively materialized area (WAAM) of the simulated part, allowing crack initiation and propagation within the specified regions. Subsequently, fatigue crack growth tests are conducted on these specimens using an in-situ SEM fatigue testing machine, and fatigue crack growth rate data are recorded. Next, a crack growth rate model based on linear elastic fracture mechanics is used to fit and calibrate the growth rates of each of these regions to obtain crack growth rate parameters. Finally, a cross-weld zone correction parameter is proposed based on the experimental data. Using this correction parameter method, the cross-weld zone fatigue crack growth rate is corrected using the base material zone rate parameter to obtain a cross-weld zone rate correction coefficient. This approach enables the prediction of cross-weld zone fatigue crack growth rates using fatigue crack growth rate data for conventional materials or data from material handbooks.

[0007] The present invention discloses a method for predicting fatigue crack growth rate across welding zones in arc additive manufacturing, comprising the steps of:

[0008] S1, preparation of samples

[0009] A simulation part was manufactured using arc additive technology, and samples were taken from the BM and CZH areas of the simulation part, and notches were prefabricated as test specimens;

[0010] S2, in situ SEM fatigue test of the specimen

[0011] In-situ SEM fatigue tests were performed on BM and CZH specimens using an electrohydraulic fatigue testing system installed in a SEM vacuum chamber. The number of cycles N and SEM images during the fatigue test were obtained, and the observed crack length D corresponding to the number of cycles N was obtained based on the SEM images.

[0012] S3, obtain the corresponding relationship between the crack growth rate of the sample and the stress intensity factor range

[0013] Based on the number of cycles N and the corresponding crack size a recorded in S2, the corresponding relationship between the fatigue crack growth rate da / dN and the stress intensity factor range ΔK is obtained; the crack size a is obtained by adding the depth of the prefabricated notch and the observed crack length.

[0014] S4, fitting the fatigue crack growth rate model of the specimen

[0015] The fatigue crack growth rate model is

[0016]

[0017] Wherein, C and m are the first and second parameters related to the material;

[0018] Using the logarithm of both sides of the expression (3), according to the corresponding relationship between the fatigue crack growth rate da / dN and the stress intensity factor range △K of the BM specimen and the CZH specimen in step S3, linear regression is used to estimate the lnC and m fitting parameter values ​​of the BM specimen and the CZH specimen respectively; and then the fitting mean line of the fatigue crack growth rate model of the BM specimen and the CZH specimen in the logarithmic coordinate system is obtained;

[0019] S5, Establish a prediction model for fatigue crack growth rate of CZH specimens

[0020] According to the model fitting average lines of the BM specimen and the CZH specimen in the logarithmic coordinate system obtained in step S4, the intercept difference lnα of the two model fitting average lines is obtained, and the fatigue crack growth rate prediction model of the CZH specimen is determined to be:

[0021]

[0022] in, is the fatigue crack growth rate of the CZH specimen predicted based on the fitting parameters of the BM specimen, C BM and m BM are the first and second parameters of the BM specimen obtained in step S4, α is the correction parameter, and lnα is the intercept difference between the fitting mean lines of the fatigue crack growth rate model of the BM specimen and the CZH specimen in the logarithmic coordinate system;

[0023] S6, predicting the fatigue crack growth rate of specimens across the weld zone

[0024] When the experimental parameters of the in-situ SEM fatigue test in S2 are changed, it is only necessary to perform in-situ SEM fatigue test on the BM specimen and obtain the new C according to expression (3). BM and m BM , put into expression (4), the fatigue crack growth rate across the weld zone can be directly predicted.

[0025] Preferably, the specific steps of preparing the sample in S1 are:

[0026] S11, using a component with a full-length groove as the base material and welding wire as the filler material, arc additive technology is used to deposit the filler material in the groove in a stacked manner along the z-axis to obtain a simulated component;

[0027] S12, sampling from the BM area and the CZH area of ​​the simulation component to obtain BM samples and CZH samples;

[0028] S13, for BM and CZH samples, wire-cut electric discharge machining was used to create a notch on one side of the sample as a pre-crack:

[0029] S14, mechanical polishing and electrochemical polishing are performed on the BM sample and the CZH sample respectively to obtain a BM sample and a CZH sample.

[0030] Preferably, said S12 further includes sampling from the HAZ area and the WAAM area; and steps S13 and S14 are also performed on the HAZ sampling and WAAM sampling.

[0031] Preferably, the step S3 is to obtain the corresponding relationship between the crack growth rate and the stress intensity factor range of the sample, specifically:

[0032] According to the crack geometry, the stress intensity factor K affected by the tensile stress σ is calculated as follows

[0033]

[0034] Where a is the crack size, which is the sum of the depth of the prefabricated notch and the observed crack length; W is the width of the specimen cross section, and F(a / W) is the geometric correction factor function, expressed as:

[0035]

[0036] According to the number of cycles N and the corresponding crack size a recorded in S2, the fatigue crack growth rate da / dN is calculated by the adjacent two-point method, and the stress intensity factor range △K is obtained according to formula (1), △K = (1-R)K, where R is the load ratio. The corresponding relationship between the fatigue crack growth rate da / dN and the stress intensity factor range △K is thus obtained.

[0037] Preferably, in said S5, establishing a CZH specimen fatigue crack growth rate prediction model,

[0038] lnα=-1.3641

[0039] Therefore, the fatigue crack growth rate prediction model of CZH specimen is:

[0040]

[0041] in, is the fatigue crack growth rate of the CZH specimen predicted based on the fitting parameters of the BM specimen, C BM and m BM are the first parameter and the second parameter of the BM sample obtained in step S4.

[0042] The present invention also discloses a fatigue crack growth rate prediction system for arc additive manufacturing across weld zones, comprising the following components: an electro-hydraulic fatigue test system, a data acquisition module, a database, a fitting module, and a prediction module, specifically:

[0043] The electro-hydraulic fatigue testing system is equipped with a SEM vacuum chamber for performing fatigue testing on the specimen;

[0044] The data acquisition module collects the number of cycles N and SEM images during the fatigue test, obtains the observed crack length D corresponding to the number of cycles N according to the SEM images, and sends the collected data to a database.

[0045] The database is used to store experimental parameters in the electric hydraulic fatigue test system and data collected by the data acquisition module.

[0046] The fitting module obtains the first parameter and the second parameter in the fatigue crack growth rate model and the intercept difference of the fitting moving averages of the BM specimen and the CZH specimen based on the data stored in the database, and sends them to the prediction module. Specifically, the fitting module includes: obtaining the corresponding relationship between the crack growth rate and the stress intensity factor range of the specimen based on the recorded number of cycles N and the corresponding crack size a; obtaining the first parameter and the second parameter related to the material in the fatigue crack growth rate model based on the corresponding relationship and the fatigue crack growth rate model; and obtaining the intercept difference of the fitting moving averages of the BM specimen and the CZH specimen based on the fitted fatigue crack growth rate model.

[0047] The prediction module obtains a CZH sample fatigue crack growth rate prediction model based on the first parameter and the second parameter in the received BM sample fatigue crack growth rate model, as well as the intercept difference, and uses the CZH sample fatigue crack growth rate prediction model to predict the CZH sample fatigue crack growth rate.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This paper establishes a fatigue crack growth rate prediction model for CZH specimens. By using correction coefficients, it is possible to predict the fatigue crack growth rate using the fatigue crack growth rate parameters of the base material zone. Furthermore, this model can be used to predict the cross-weld zone crack growth rate using conventional material rates or data from material handbooks.

[0050] 2. The present invention correlates the fatigue crack growth behavior of the substrate and the cross-weld zone, which helps in the safety assessment of WAAM repaired parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1a is a schematic diagram of the remanufacturing process of the present invention;

[0052] Figure 1b These are the four sampling locations of the fatigue specimen of the present invention;

[0053] Figure 1cThe fatigue specimen size diagram and physical diagram of the present invention;

[0054] Figure 2 The FCGR test data of the present invention are represented by discrete marks, and the model mean fitting moving average is represented by a solid line;

[0055] Figure 3 This is a flow chart of the method for predicting fatigue crack growth rate across weld zones in arc additive manufacturing according to the present invention. DETAILED DESCRIPTION

[0056] The exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0057] like Figure 1a As shown, a hot-rolled Q235 mild steel block with a full-length groove (mild steel is used as an example here, but the following method can be used for metal materials) is used to represent the damaged part for WAAM remanufacturing. ER70S-3 wire with a diameter of 1.6 mm is used as the WAAM filler material. The WAAM process is based on tungsten inert gas (TIG) welding technology. The shielding gas (Ar) flows coaxially with the tungsten electrode at a constant flow rate of 15 L / min. The standard parameters used are an arc current of 160 A, a power of 3.15 kW, a welding gun travel speed of 160 mm / min, and a wire feed speed of 1.5 m / min. The preheat temperature is 125 °C, and the interlayer temperature is less than 250 °C. During the WAAM remanufacturing process, the filler material is deposited in the groove in a stacked manner along the z-axis, using an oscillating path strategy in the XY plane.

[0058] The sample is Figure 1b Samples were taken from four different locations as shown. Samples A, C, and D were taken entirely from the WAAM, BM, and HAZ, respectively. Sample B was taken from the cross weld zone (CZH), including the WAAM, HAZ, and BM materials. The shape and size of the dog bone specimens used for in-situ SEM (in-situ scanning electron microscopy) fatigue crack growth tests are shown in Figure 2. Figure 1c As shown in Figure 2. A notch with a depth of 0.625 mm and a width of 0.2 mm was made on one side of the specimen using wire electric discharge cutting (EDM) as a pre-crack. Figure 1c shown.

[0059] In situ SEM fatigue test specimens were mechanically polished using 400-, 1000-, 2000-, and 3000-mesh SiC waterproof sandpaper. To remove the surface deformation layer caused by mechanical polishing, the specimens were electrochemically polished. Electrochemical polishing was performed at -30°C using a polishing solution consisting of 7% perchloric acid and 93% ethanol. This process was performed at a voltage of 25 V and a current density of 450 mA / cm. 2 , lasting about 1 minute.

[0060] In-situ SEM fatigue tests were performed using an electrohydraulic fatigue testing system installed in the SEM vacuum chamber. The maximum load was determined by calculating the initial stress intensity factor range ΔK0 using Equation (1), the initial crack size, the specimen width, and the specimen thickness. In order to make ΔK as wide as possible within the linear range, the initial stress intensity factor range ΔK0 was designed to be approximately This is slightly above the experimentally reported threshold stress intensity factor range for the material. Given a load ratio R = 0.1, this initial ΔK value corresponds to a maximum load of approximately 800-1000 N. Therefore, a maximum load of 800 N was used for the first two specimens, BM and CZH. To expedite the testing process, a slightly larger value of 1000 N was used for the subsequent three specimens, BMT, HAZ, and WAAM. Fatigue testing was conducted at room temperature using a sinusoidal loading mode at a loading frequency of 10 Hz.

[0061] During fatigue testing, loading can be paused at any time to allow for SEM imaging. During these pauses, the cycle number and SEM image are recorded. The observed crack length (D) on the SEM image is measured using imaging software. The observed crack length is the distance between the crack tip and the notch root perpendicular to the load direction. The resulting observed crack length (D) versus cycle number (N) data is used for crack growth analysis.

[0062] Four specimens were manufactured for in-situ SEM fatigue testing. Table 1 lists some experimental parameters.

[0063] Table 1

[0064]

[0065]

[0066] For the crack geometry of Figure 1(c), the stress intensity factor K is calculated based on the maximum tensile stress σ as follows:

[0067]

[0068] Where a is the crack size, which is obtained by adding the depth of the prefabricated notch and the observed crack length. The initial crack size is the prefabricated notch depth; W is the width of the specimen cross section, and F(a / W) is the geometric correction factor function, expressed as:

[0069]

[0070] Using the obtained crack length (a) and number of cycles (N) data, FCGR is calculated using the adjacent two-point method. The FCGR data results da / dN vs.ΔK are as follows Figure 2 As shown in , the abscissa is ΔK and the ordinate is da / dN. Here, da is the crack size increment, dN is the cycle increment, da / dN is the crack size increment per cycle, and ΔK is the stress intensity factor range. ΔK = (1-R)K, where R is the load ratio, set to 0.1. This gives the corresponding relationship between the fatigue crack growth rate da / dN and the stress intensity factor range ΔK.

[0071] The fatigue crack growth rate model describes the relationship between the stable crack growth rate and the stress intensity factor, which is:

[0072]

[0073] Wherein, K and m are the first and second parameters related to the material;

[0074] The two parameters (lnC, m) are estimated using linear regression after taking the logarithm of both sides of expression (3). Table 2 shows the values ​​of the fatigue crack growth rate model fitting parameters lnC and m and the standard deviation of the model residual s 2 . Figure 2 The solid line in the figure shows the model fitting mean line, and the remaining marks are the original data of fatigue crack growth tests corresponding to different specimens.

[0075] Table 2

[0076]

[0077]

[0078] Figure 2 The fatigue crack growth rate (FCGR) of the cross-weld zone (CZH) specimen was the lowest among all the tested specimens, indicating that crack growth is inhibited by certain microstructural features in the CZH. Therefore, the crack growth rate of the repaired component cannot be directly predicted using the rates in the base material (BM) or the additive manufacturing (WAAM) region.

[0079] The cross-weld zone is usually a stress concentration area due to its non-uniform microstructural characteristics. The study of the fatigue growth behavior of the cross-weld zone helps to accurately assess the safety of components and prevent catastrophic failure. The fatigue crack growth rate study based on the damage tolerance design method is usually destructive and has limitations in predicting the life of service components. Therefore, this application introduces a correction factor into the classic Paris model to correlate the fatigue crack growth behavior of the substrate and the cross-weld zone, and predicts the fatigue crack growth rate of the cross-weld zone with the fatigue crack growth data of the substrate, which helps to assess the safety of WAAM repaired components.

[0080] Figure 2 The FCGR test data in

[15] show that the model fitting averages for the BM and CZH specimens have similar slopes in the logarithmic coordinate system (4.0968 and 4.1123, respectively), while the intercepts differ significantly (-22.1808 and -23.0167, respectively). Therefore, a correction factor α can be introduced into expression (3) to correlate the FCGR data of the two regions using the difference in the intercepts of the model fitting averages for the BM and CZH specimens. After using the correction parameters, the fatigue crack growth rate prediction model for the CZH specimen is obtained as:

[0081]

[0082] Among them C BM and m BM are the first and second parameters obtained after Paris mean fitting of the FCGR data of the BM specimen; is the fatigue crack growth rate of the CZH specimen predicted by the fitting parameters of the BM specimen. After taking the logarithm of both sides of expression (4), the expression of the model becomes:

[0083]

[0084] Similar to the expression (3) with logarithms on both sides, (lnα+lnC) in expression (5) is also the intercept of the Paris mean fitting line. Therefore, according to Figure 3 The difference in the intercepts of the Paris mean fitting lines of the CZH and BM samples can be directly obtained as lnα = -1.3641. Therefore, expression (4) can be expressed as:

[0085]

[0086] By applying the fatigue crack growth rate prediction model of the CZH specimen, the fatigue crack growth rate across the weld zone can be accurately predicted using the FCGR test data of the substrate, thereby achieving an accurate prediction of the fatigue crack growth rate of the WAAM repaired component.

[0087] The present invention also discloses a fatigue crack growth rate prediction system for arc additive manufacturing across weld zones, comprising the following components: an electro-hydraulic fatigue test system 1, a data acquisition module 2, a database 3, a fitting module 4, and a prediction module 5, specifically:

[0088] The electro-hydraulic fatigue testing system 1 is equipped with a SEM vacuum chamber for performing fatigue testing on the specimen;

[0089] The data acquisition module 2 acquires the cycle number N and SEM images during the fatigue test, obtains the observed crack length D corresponding to the cycle number N according to the SEM images, and sends the acquired data to a database.

[0090] The database 3 is used to store the experimental parameters in the electro-hydraulic fatigue test system and the data collected by the data acquisition module.

[0091] The fitting module 4 obtains the first parameter and the second parameter in the fatigue crack growth rate model, and the intercept difference of the fitting moving averages of the BM specimen and the CZH specimen based on the data stored in the database, and sends them to the prediction module. Specifically, it includes: obtaining the corresponding relationship between the crack growth rate and the stress intensity factor range of the specimen based on the recorded number of cycles N and the corresponding crack size a; obtaining the first parameter and the second parameter related to the material in the fatigue crack growth rate model based on the corresponding relationship and the fatigue crack growth rate model; and obtaining the intercept difference of the fitting moving averages of the BM specimen and the CZH specimen based on the fitted fatigue crack growth rate model.

[0092] The prediction module 5 obtains a CZH sample fatigue crack growth rate prediction model based on the first parameter and the second parameter in the received BM sample fatigue crack growth rate model, as well as the intercept difference, and uses the CZH sample fatigue crack growth rate prediction model to predict the CZH sample fatigue crack growth rate.

[0093] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for predicting fatigue crack growth rate across weld zones in arc additive manufacturing, characterized by: It includes the steps of: S1, preparation of samples A simulation part was manufactured using arc additive technology, and samples were taken from the BM and CZH areas of the simulation part, and notches were prefabricated as test specimens; S2, in-situ SEM fatigue testing of the specimen In-situ SEM fatigue tests were performed on BM and CZH specimens using an electrohydraulic fatigue testing system installed in a SEM vacuum chamber. The number of cycles N and SEM images during the fatigue test were obtained, and the observed crack length D corresponding to the number of cycles N was obtained based on the SEM images. S3, obtain the corresponding relationship between the crack growth rate of the sample and the stress intensity factor range Based on the number of cycles N and the corresponding crack size a recorded in S2, the corresponding relationship between the fatigue crack growth rate da / dN and the stress intensity factor range ΔK is obtained; the crack size a is obtained by adding the depth of the prefabricated notch and the observed crack length; The corresponding relationship between the crack growth rate and stress intensity factor range of the specimen is obtained, specifically: According to the crack geometry, the stress intensity factor ΔK affected by the tensile stress σ is calculated as follows Where a is the crack size, which is the sum of the depth of the prefabricated notch and the observed crack length; W is the width of the specimen cross section, and F(a / W) is the geometric correction factor function, expressed as: According to the number of cycles N and the corresponding crack size a recorded in S2, the fatigue crack growth rate da / dN is calculated by the adjacent two-point method, and the stress intensity factor range ΔK is obtained according to formula (2), ΔK = (1-R)K, where R is the load ratio. The corresponding relationship between the fatigue crack growth rate da / dN and the stress intensity factor range ΔK is thus obtained; S4, fitting the fatigue crack growth rate model of the specimen The fatigue crack growth rate model is Wherein, C and m are the first and second parameters related to the material; Using the logarithm of both sides of the expression (1), according to the corresponding relationship between the fatigue crack growth rate da / dN and the stress intensity factor range ΔK of the BM specimen and the CZH specimen in step S3, linear regression is used to estimate the lnC and m fitting parameter values ​​of the BM specimen and the CZH specimen respectively; and then the fatigue crack growth rate model fitting average line of the BM specimen and the CZH specimen in the logarithmic coordinate system is obtained; S5, Establish a prediction model for fatigue crack growth rate of CZH specimens According to the model fitting average lines of the BM specimen and the CZH specimen in the logarithmic coordinate system obtained in step S4, the intercept difference lnα of the two model fitting average lines is obtained, and the fatigue crack growth rate prediction model of the CZH specimen is determined to be: in, is the fatigue crack growth rate of the CZH specimen predicted based on the fitting parameters of the BM specimen, C BM and m BM are the first and second parameters of the BM specimen obtained in step S4, α is the correction parameter, and lnα is the intercept difference between the fitting mean lines of the fatigue crack growth rate model of the BM specimen and the CZH specimen in the logarithmic coordinate system; S6, predict the fatigue crack growth rate of the specimen across the weld zone When the experimental parameters of the in-situ SEM fatigue test in S2 are changed, it is only necessary to perform in-situ SEM fatigue test on the BM specimen and obtain the new C according to expression (1). BM and m BM , put into expression (4), the fatigue crack growth rate across the weld zone can be directly predicted.

2. The method for predicting fatigue crack growth rate across weld zones in arc additive manufacturing according to claim 1, characterized in that The specific steps of preparing the sample in S1 are as follows: S11, using a component with a full-length groove as the base material and welding wire as the filler material, arc additive technology is used to deposit the filler material in the groove in a stacked manner along the z-axis to obtain a simulated component; S12, sampling from the BM area and the CZH area of ​​the simulation component to obtain BM samples and CZH samples; S13, for BM and CZH samples, wire-cut electric discharge machining was used to create a notch on one side of the sample as a pre-crack; S14, mechanical polishing and electrochemical polishing are performed on the BM sample and the CZH sample respectively to obtain a BM sample and a CZH sample.

3. The method for predicting fatigue crack growth rate across weld zones in arc additive manufacturing according to claim 2, characterized in that The S12 also includes sampling from the HAZ area and the WAAM area; and steps S13 and S14 are also performed on the HAZ sampling and WAAM sampling.

4. The method for predicting fatigue crack growth rate across weld zones in arc additive manufacturing according to claim 1, characterized in that S5, establish the fatigue crack growth rate prediction model of CZH specimen, lnα＝-1.3641 Therefore, the fatigue crack growth rate prediction model of CZH specimen is: in, is the fatigue crack growth rate of the CZH specimen predicted based on the fitting parameters of the BM specimen, C BM and m BM are the first parameter and the second parameter of the BM sample obtained in step S4.

5. A system using the arc additive manufacturing cross-weld zone fatigue crack growth rate prediction method according to any one of claims 1 to 4, comprising the following components: an electro-hydraulic fatigue testing system, a data acquisition module, a database, a fitting module, and a prediction module, specifically: The electro-hydraulic fatigue testing system is equipped with a SEM vacuum chamber for performing fatigue testing on the specimen; The data acquisition module collects the number of cycles N and SEM images during the fatigue test, obtains the observed crack length D corresponding to the number of cycles N according to the SEM images, and sends the collected data to the database; The database is used to store the experimental parameters in the electric hydraulic fatigue test system and the data collected by the data acquisition module; The fitting module obtains the first parameter and the second parameter in the fatigue crack growth rate model and the intercept difference of the fitting moving averages of the BM sample and the CZH sample according to the data stored in the database, and sends them to the prediction module, specifically including: According to the recorded number of cycles N and the corresponding crack size a, the corresponding relationship between the crack growth rate and the stress intensity factor range of the specimen is obtained; According to the corresponding relationship and the fatigue crack growth rate model, the first parameter and the second parameter related to the material in the fatigue crack growth rate model are obtained; according to the fitted fatigue crack growth rate model, the intercept difference of the fitting average line of the BM specimen and the CZH specimen is obtained; The prediction module obtains a CZH sample fatigue crack growth rate prediction model based on the first parameter and the second parameter in the received BM sample fatigue crack growth rate model, as well as the intercept difference, and uses the CZH sample fatigue crack growth rate prediction model to predict the CZH sample fatigue crack growth rate.

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