Additive fatigue life and residual strength prediction method based on random defects

Abstract

The invention relates to an additive fatigue life and residual strength prediction method based on random defects, and the method comprises the steps: obtaining the specific features and random distribution forms of the defects, and focuses on the calculation of three defects, i.e., corner cracks, surface cracks and embedded cracks, which are liable to cause structural failure in an actual working condition; compared with a traditional continuous medium mechanical calculation method, the obtained fatigue life is more consistent with the dispersity of an actual fatigue life experiment result, compared with a common equivalent area projection method, the method generates defects in a panel where a critical failure plane is located during life calculation, higher design precision and reliability are achieved, and the method is suitable for large-scale popularization and application. The defect model and the calculation process are simplified, the operation is more convenient under the condition that the precision is close, and particularly, the calculation is faster when the calculation target is a large-scale structural member.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to a method for predicting fatigue life and residual strength of additive manufacturing based on random defects. Background Art

[0002] Aviation fatigue and structural integrity assessment is an important guarantee for the safe service of aircraft. With the widespread application of advanced processes such as additive manufacturing, the fatigue crack growth life and residual strength analysis of additive manufacturing have become increasingly prominent. In the additive manufacturing process, due to factors such as inherent pores in metal powders, unstable laser power, incomplete overlap of scanning paths, severe heat exchange, and repeated phase changes of liquefaction and solidification, internal defects in additive structures are difficult to avoid.

[0003] In view of the technical problems of fatigue crack growth life and residual strength analysis of additive manufacturing, the existing methods for calculating fatigue crack growth life ignore the defect characteristics in the additive manufacturing process and the fatigue damage that may be caused, which limits the accurate prediction of fatigue crack growth life of additively manufactured aviation structures. Specifically, first, the fatigue performance and integrity assessment of traditional aircraft structures are mostly based on assumptions such as continuous medium mechanics and internal defects of materials. In the design of additive structures and fatigue life assessment, a large safety margin is often given to cover the internal heterogeneity of the material. Secondly, when considering the influence of defect factors on fatigue performance, the general method is to use stress concentration factors to correct the stress field at the crack tip according to the defect type and size, while the commonly used models are mostly based on the equivalent projected area method, which fails to fully reflect the spatial three-dimensional shape and position characteristics of defects inside the material, and the calculation margin for fatigue performance under harsh working conditions where multiple defects are in the same plane is insufficient.

[0004] In this regard, the prior art further proposes a more comprehensive fatigue crack growth life calculation method to perform fatigue crack growth life and residual strength analysis of additive manufacturing. However, this method has the technical defect of being cumbersome in calculation process, especially when the calculation target is a large structural part, it cannot quickly predict the structural life. Summary of the invention

[0005] The technical problem to be solved by the present invention is how to overcome the technical defects of low accuracy and complicated calculation process in the technical solutions for fatigue crack propagation life and residual strength analysis of additive materials in the prior art. In order to overcome the above defects of the prior art, the present invention provides a method for predicting fatigue life and residual strength of additive materials based on random defects.

[0006] The present invention provides a method for predicting fatigue life and residual strength of additive manufacturing based on random defects, comprising the following steps: S1: Use X-ray micro-tomography equipment to obtain defect information images of the additive printed parts of the tested wall material, and use a three-dimensional visualization module to obtain defect features using a feature extraction method based on grayscale transformation; S2: obtaining random distribution information of defect size by using the defect characteristics through parameter estimation; S3: obtaining a panel where a critical failure plane is located in the additive printed part, and generating a plurality of defects including corner cracks, surface cracks and buried cracks on the panel according to the random distribution information through an engineering simulation device; S4: Based on the multiple defects generated in step S3, given material properties and structural stress concentration factors, the number of cycles under specified working conditions is obtained through the engineering simulation device, and the number of cycles is used as the initial number of cycles; S5: loading the panel once, obtaining a crack increment according to a range of stress intensity factors of the panel during loading, and obtaining a current size of each defect on the panel using the crack increment; S6: calculating the distance between adjacent defects based on the current size of each defect, and judging whether crack fusion occurs in adjacent defects according to the distance between adjacent defects; If so, all adjacent defects where crack fusion occurs are merged into a new defect according to the defect connectivity criterion for defect updating, and then the next step is executed; If not, proceed to the next step; S7: obtaining a maximum stress intensity factor at a crack tip based on a current defect of the panel, and determining whether the maximum stress intensity factor is greater than the fracture toughness; If yes, the number of cycles at this time is taken as the fatigue crack growth life of the tested wall material; If not, the residual strength of the measured wall material is obtained using the residual strength calculation formula, and the number of cycles is updated, and then the process returns to execute step S5.

[0007] The method for predicting fatigue life and residual strength of additive materials based on random defects disclosed in the present invention aims at the technical problem of the present invention, obtains the specific characteristics and random distribution form of defects by executing steps S1-S3, and focuses on calculating the three defects (i.e., corner cracks, surface cracks, and embedded cracks) that are prone to structural failure in actual working conditions. The fatigue life obtained is more consistent with the dispersion of actual fatigue life experimental results than the results obtained by the traditional continuous medium mechanics calculation method. In addition, compared with the commonly used equivalent area projection method, since the method in this application generates defects based on the panel where the critical failure plane is located during life calculation, it has higher design accuracy and reliability; it simplifies the defect model and calculation process, and is more convenient to operate when the accuracy is close, especially when the calculation target is a large structural part, the calculation is also faster.

[0008] In a possible implementation, the defect characteristics include the volume, aspect ratio, angle and spatial distribution information of the defect; this solution can ensure more accurate extraction of defect characteristics and facilitate further analysis of the defect.

[0009] In a possible implementation, step S1 includes the following steps: S11: extracting the size and distribution characteristic information of defects in the additive printed part using the X-ray micro-tomography device to obtain the defect information image; S12: Acquire the defect feature from the defect information image by using the three-dimensional visualization module using a feature extraction method based on grayscale transformation.

[0010] In a possible implementation manner, step S2 includes the following steps: S21: considering the shape of the defect as the shape of an ellipsoid to obtain the maximum axis size of each defect, and using a log-normal distribution as a fitting random distribution of the maximum axis size; S22: using the spatial distribution information in the defect feature, solving all fitting parameters of the fitted random distribution by parameter estimation to obtain a probability distribution function of the maximum axis size; This scheme simplifies the defect shape into an ellipsoid shape and obtains a fitted random distribution of the maximum axis size based on the spatial distribution information in the defect characteristics, further ensuring the random regularity of defect generation on the panel where the critical failure plane is located.

[0011] In a possible implementation, the formula for the probability distribution function of the fitted random distribution is as follows: , In the formula, Represents the offset; Representative area; represents the logarithmic standard deviation; represents the central abscissa; represents the maximum shaft dimension; Represents the maximum shaft size The proportion of This scheme can calculate the range of defect slenderness ratios, further ensuring that defects are generated on the panel where the critical failure plane is located.

[0012] In a possible implementation manner, in the step S5, the formula for obtaining the crack increment according to the stress intensity factor range of the panel during loading is as follows; , In the formula, represents the crack increment; , and Represents the material constant of the measured wall material; represents the range of stress intensity factors of the panel during loading; represents the stress ratio; This scheme can ensure the feasibility of obtaining the current size of each defect on the panel through crack increment, which helps to realize defect updating, not only ensuring the accuracy of the calculation results, but also reducing the calculation process to a certain extent.

[0013] In a possible implementation manner, the residual strength calculation formula in step S7 is expressed as follows: , In the formula, Represents the residual strength of the measured wall material; The diameter of the hole representing the defect; represents the width of the panel; represents the thickness of the panel; represents the area of ​​the corner crack; Represents the area of ​​the surface crack; Represents the area of ​​buried cracks; represents the yield stress of the measured wall material; The above calculation formula can be used to calculate the residual strength more accurately and help determine whether the residual strength is insufficient.

[0014] In a possible implementation, in step S7, if it is determined that the maximum stress intensity factor is not greater than the fracture toughness, after obtaining the residual strength of the tested wall material, the following steps need to be performed before updating the number of cycles: Determining whether the residual strength is less than an allowable residual strength value; If yes, the number of cycles at this time is taken as the fatigue crack growth life of the tested wall material; If not, the number of cycles is updated directly. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of a method for predicting fatigue life and residual strength of additive manufacturing based on random defects disclosed in an embodiment of the present application; Figure 2 It is an appearance diagram of the additive printed part disclosed in the embodiment of the present application; Figure 3 A defect size distribution function curve diagram disclosed in the embodiments of the present application; Figure 4 A schematic diagram of a randomly generated single-hole plate with defects disclosed in an embodiment of the present application; Figure 5 A schematic diagram of the mesh division of the model and crack front disclosed in the embodiment of the present application; Figure 6 A schematic diagram of the adjacent defect connection method disclosed in the embodiment of the present application; Figure 7 The fatigue life prediction results and life distribution fitting curve diagram of the single-hole plate structure disclosed in the embodiments of the present application. DETAILED DESCRIPTION

[0016] First, those skilled in the art should understand that these implementations are only used to explain the technical principles of the embodiments of the present application, and are not intended to limit the protection scope of the embodiments of the present application. Those skilled in the art can make adjustments to them as needed to adapt to specific application scenarios.

[0017] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] See also Figure 1 to Figure 5 The present application discloses a method for predicting fatigue life and residual strength of additive manufacturing based on random defects. Figure 1 is a flow chart of the method, the method comprising the following steps: S1: Using an X-ray micro-tomography device to obtain defect information images of the additive printed part of the tested wall material, and using a three-dimensional visualization module to obtain defect features using a feature extraction method based on grayscale transformation. In this embodiment, the defect features include the volume, aspect ratio, angle and spatial distribution information of the defect.

[0019] Specifically, step S1 includes the following steps: S11: extracting the size and distribution feature information of defects in the additive printed part by using an X-ray micro-tomography device to obtain a defect information image. S12: obtaining defect features from the defect information image by using a feature extraction method based on grayscale transformation through a three-dimensional visualization module, and the feature extraction method based on grayscale transformation is a feature extraction method using binarization processing in this embodiment.

[0020] In this embodiment, the defect information image obtained by using an X-ray micro-tomography (CT scanning) device is as follows: Figure 2 As shown, Figure 2 The appearance characteristics and loading direction of the additively printed titanium alloy single-hole plate to be analyzed are shown. The size information of the titanium alloy single-hole plate is 80 mm long, 24 mm wide, and 4 mm thick. Then, a three-dimensional visualization module (in this embodiment, an Avizo processing module) is used for processing. Through this processing module, defect features can be obtained from the defect information image, and data such as defect volume, aspect ratio, angle, and spatial distribution information can be collected.

[0021] S2: Obtain the random distribution information of defect size by using defect characteristics through parameter estimation.

[0022] See also Figure 3 Specifically in this embodiment, step S2 includes the following steps: S21: The shape of the defect is regarded as the shape of an ellipsoid to obtain the maximum axis size of each defect, and the log-normal distribution is used as the fitted random distribution of the maximum axis size. The formula for the probability distribution function of the fitted random distribution is as follows: , In the formula, represents the offset, is the fitting parameter, Figure 3 In , it is written as y0; represents the area, which is the fitting parameter; represents the logarithmic standard deviation, which is the fitting parameter; Represents the central abscissa, is the fitting parameter, Figure 3 In the text box, it is written as xc; represents the maximum shaft size; The maximum shaft size is % of.

[0023] S22: Using the spatial distribution information in the defect characteristics, all fitting parameters of the fitted random distribution are solved by parameter estimation to obtain the probability distribution function of the maximum axis size.

[0024] The fitting function of defect size distribution is based on Figure 3 The statistical defect size distribution information is fitted to obtain: Figure 3 The CT scan result in the figure is the abbreviation of the spatial distribution information of the defect characteristics obtained by the X-ray micro-tomography equipment. The fitting parameters after heat treatment are selected for modeling. The proportion of ellipsoid defects , according to statistics, is evenly distributed in [1,4]. For the convenience of calculation, The value is 0, and after parameter estimation, we get is 9.99437, is 0.67627, It is 62.2079.

[0025] S3: Obtain the panel where the critical failure plane is located in the additive print, and generate multiple defects including corner cracks, surface cracks and buried cracks on the panel according to random distribution information through engineering simulation equipment.

[0026] See also Figure 2 In this embodiment, in order to obtain the panel where the critical failure plane is located, this step adopts the most severe working condition, that is, the total load P is set to 15KV on the titanium alloy single-hole plate, and a circular hole is set on the titanium alloy single-hole plate to apply a nail load. The diameter of the circular hole is 6mm, and the distance between the center of the circle and the left side and the upper and lower sides is 12mm. The nail load P1 is 9KV. Under this most severe working condition, the critical failure plane of the titanium alloy single-hole plate is Figure 2 The plane shown is perpendicular to the total load P and passes through the center of the hole ( Figure 2 The dashed box in the figure represents the critical failure plane).

[0027] On the panel containing the critical failure plane, a plurality of randomly distributed ellipsoidal defects are generated according to the law of the random distribution information obtained in step S22. According to the typical characteristics of aviation structures, the defects in this example include corner cracks, surface cracks, and embedded cracks. Figure 4 As shown, the crack size range of the generated cracks in the critical failure plane is 0.3mm-1mm, and the number of generated cracks is 4, which includes three types of defects: corner cracks, surface cracks, and embedded cracks.

[0028] S4: Based on the multiple defects generated in step S3, given the material properties and the structural stress concentration factor, the number of cycles under the specified working conditions is obtained through the engineering simulation equipment, and this number of cycles is used as the initial number of cycles.

[0029] Specifically, in this embodiment, given the material properties and structural stress concentration factor, the Young's modulus of the titanium alloy single-hole plate is 1150000 MPa, the Poisson's ratio is 0.33, the yield strength is 899 MPa, and the fatigue limit of the structure is 512.5 MPa. Subsequently, Abaqus is automatically modeled and calculated to calculate the number of cycles under the specified working conditions, that is, the initial number of cycles, which is 10000.

[0030] S5: Load the panel once, obtain the crack increment according to the stress intensity factor range of the panel during the loading process, and obtain the current size of each defect on the panel according to the crack increment.

[0031] Specifically, see Figure 5 , the crack increment is automatically calculated after loading by Python program. In the process of obtaining the stress intensity factor range of the panel during loading, the meshing method of the titanium alloy single-hole plate (see Figure 5 ) is: the grid size is set to 1mm, the grid size of the wedge unit in the center of the crack tip is 0.02mm, the grid size of the unit outside the crack tip increases by 0.02mm-0.05mm, and the total number of model grids is approximately 300,000.

[0032] In step S5, the formula for obtaining the crack increment according to the stress intensity factor range of the panel during loading is as follows; , In the formula, Represents the crack increment, that is, the increase in crack length per unit number of cycles; , and Represents the material constant of the wall material being tested, which needs to be determined through experiments. The material constant given in this example is is 5.217E-9, is 0.64, is 3.29; Represents the range of stress intensity factors of the panel during loading, indicating the change of stress intensity at the crack tip; Represents stress ratio, which is defined as the ratio of minimum stress to maximum stress and is set to 0.1 in this example.

[0033] S6: Calculate the distance between adjacent defects based on the current size of each defect, and determine whether there is crack fusion in any adjacent defects according to the distance between the adjacent defects; if so, merge all adjacent defects with crack fusion into a new defect according to the defect connectivity criterion for defect update, and then execute the next step; if not, execute the next step.

[0034] Specifically, the distance between adjacent defects is used to determine whether crack fusion occurs. If fusion occurs, the fused defect information is deleted and new defects are generated according to the given defect connectivity criterion. Then, the meshing can be re-performed to obtain the stress intensity factor range of the panel during loading in the next cycle. If fusion does not occur, the meshing is directly updated.

[0035] The defect connectivity criterion diagram used in this example is as follows: Figure 6 As shown, if it is case (1), that is, the defect has not developed into a through crack, a new defect is generated according to the envelope method; if it is case (2), that is, the defect that has developed into a through crack is merged with other defects, the equivalent area method is used; if it is case (3), that is, the corner crack expands, when the corner crack expansion area reaches 90% of the plane in which it is located, it develops into a through crack, and then it is merged in the manner of case (2), otherwise it is merged in the manner of case (1); if the defect fusion does not occur, it is case (4), and the crack expands normally according to the increment.

[0036] S7: obtaining a maximum stress intensity factor at the crack tip based on the current defects of the panel, and determining whether the maximum stress intensity factor is greater than the fracture toughness; If yes, the number of cycles at this time is taken as the fatigue crack growth life of the tested wall material; If not, the residual strength calculation formula is used to obtain the residual strength of the tested wall material, and then it is determined whether the residual strength is less than the allowable value of the residual strength; If yes, it is considered that the residual strength is insufficient, and the number of cycles at this time is taken as the fatigue crack growth life of the tested wall material; If not, the number of loops is updated (the original number of loops is increased by 1 to obtain a new number of loops), and then the process returns to step S5.

[0037] Specifically, the fracture toughness given in this example is 107.93 In step S7, the residual strength calculation formula is expressed as follows: , In the formula, Represents the residual strength of the wall material being tested; The diameter of the hole representing the defect; Represents the width of the panel; Represents the thickness of the panel; represents the area of ​​the corner crack; Represents the area of ​​the surface crack; Represents the area of ​​buried cracks; Represents the yield stress of the wall material being tested.

[0038] This example performs statistics on all results after running the calculation 20 times. The statistical results are as follows Figure 7 As shown, the life distribution function of the tested wall material was then fitted, which is consistent with the dispersion and estimated value of fatigue life in actual experiments. It can be seen from the figure that the fitting result still obeys the log-normal distribution.

[0039] It should be noted that traversing step S5-step S7 once is a cycle. If in step S7, the maximum stress intensity factor is greater than the fracture toughness, the number of cycles at this time is used as the fatigue crack growth life of the tested wall material. For example, if the number of cycles at this time is the initial number of cycles 10000, then 10000 is the fatigue crack growth life of the tested wall material. If the maximum stress intensity factor is not greater than the fracture toughness and it is necessary to enter the next cycle, that is, to return to step S5, the number of cycles at this time is the number of cycles of the previous time plus 1, that is, when the number of cycles is updated, if the number of cycles at this time is the initial number of cycles 10000, the update result is 10001.

[0040] The additive fatigue life and residual strength prediction method based on random defects disclosed in this embodiment obtains the specific characteristics and random distribution form of defects by executing steps S1-S3, and focuses on calculating the three defects that are easy to cause structural failure in actual working conditions, namely corner cracks, surface cracks, and embedded cracks. The fatigue life obtained is more consistent with the dispersion of actual fatigue life experimental results than the results obtained by the traditional continuous medium mechanics calculation method. In addition, compared with the commonly used equivalent area projection method, since the method in this embodiment generates defects based on the panel where the critical failure plane is located during life calculation, it has higher design accuracy and reliability; it simplifies the defect model and calculation process, and is more convenient to operate when the accuracy is close, especially when the calculation target is a large structural part, the calculation is also faster.

[0041] In the description of the embodiments of the present application, it should be noted that in the description of the present application, terms such as "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is only for the convenience of description, and does not indicate or imply that the device or component must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present application.

[0042] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "in the present embodiment", "specific example", or "some examples" etc. means that the specific features, mechanisms, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0043] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for predicting fatigue life and residual strength of additive manufacturing based on random defects, characterized in that: The steps include: S1: Use X-ray micro-tomography equipment to obtain defect information images of the additive printed parts of the tested wall material, and use a three-dimensional visualization module to obtain defect features using a feature extraction method based on grayscale transformation; S2: obtaining random distribution information of defect size by using the defect characteristics through parameter estimation; S3: obtaining a panel where a critical failure plane is located in the additive printed part, and generating a plurality of defects including corner cracks, surface cracks and buried cracks on the panel according to the random distribution information through an engineering simulation device; S4: Based on the multiple defects generated in step S3, given material properties and structural stress concentration factors, the number of cycles under specified working conditions is obtained through the engineering simulation device, and the number of cycles is used as the initial number of cycles; S5: loading the panel once, obtaining a crack increment according to a range of stress intensity factors of the panel during loading, and obtaining a current size of each defect on the panel using the crack increment; S6: calculating the distance between adjacent defects based on the current size of each defect, and judging whether crack fusion occurs in adjacent defects according to the distance between adjacent defects; If so, all adjacent defects where crack fusion occurs are merged into a new defect according to the defect connectivity criterion for defect updating, and then the next step is executed; If not, proceed to the next step; S7: obtaining a maximum stress intensity factor at a crack tip based on a current defect of the panel, and determining whether the maximum stress intensity factor is greater than the fracture toughness; If yes, the number of cycles at this time is taken as the fatigue crack growth life of the tested wall material; If not, the residual strength of the measured wall material is obtained using the residual strength calculation formula, and the number of cycles is updated, and then the process returns to execute step S5.

2. The method for predicting fatigue life and residual strength of additive manufacturing based on random defects according to claim 1, characterized in that: The defect characteristics include the volume, aspect ratio, angle and spatial distribution information of the defect.

3. The method for predicting fatigue life and residual strength of additive manufacturing based on random defects according to claim 2, characterized in that: The step S1 comprises the following steps: S11: extracting the size and distribution characteristic information of defects in the additive printed part using the X-ray micro-tomography device to obtain the defect information image; S12: Acquire the defect feature from the defect information image by using the three-dimensional visualization module using a feature extraction method based on grayscale transformation.

4. The method for predicting fatigue life and residual strength of additive manufacturing based on random defects according to claim 2 or 3, characterized in that: The step S2 comprises the following steps: S21: considering the shape of the defect as the shape of an ellipsoid to obtain the maximum axis size of each defect, and using a log-normal distribution as a fitting random distribution of the maximum axis size; S22: using the spatial distribution information in the defect feature, all fitting parameters of the fitted random distribution are solved by parameter estimation to obtain the probability distribution function of the maximum axis size.

5. The method for predicting fatigue life and residual strength of additive manufacturing based on random defects according to claim 4, characterized in that: The formula of the probability distribution function of the fitted random distribution is as follows: ， In the formula, Represents the offset; Representative area; represents the logarithmic standard deviation; represents the central abscissa; represents the maximum shaft dimension; Represents the maximum shaft size % of.

6. The method for predicting fatigue life and residual strength of additive manufacturing based on random defects according to claim 5, characterized in that: In the step S5, the formula for obtaining the crack increment according to the stress intensity factor range of the panel during loading is as follows; ， In the formula, represents the crack increment; , and Represents the material constant of the measured wall material; represents the range of stress intensity factors of the panel during loading; Represents the stress ratio.

7. The method for predicting fatigue life and residual strength of additive manufacturing based on random defects according to claim 5 or 6, characterized in that: The residual strength calculation formula in step S7 is expressed as follows: ， In the formula, Represents the residual strength of the measured wall material; The diameter of the hole representing the defect; represents the width of the panel; represents the thickness of the panel; represents the area of ​​the corner crack; Represents the area of ​​the surface crack; Represents the area of ​​buried cracks; Represents the yield stress of the tested wall material.

8. The method for predicting fatigue life and residual strength of additive manufacturing based on random defects according to claim 7, characterized in that: In step S7, if it is determined that the maximum stress intensity factor is not greater than the fracture toughness, after obtaining the residual strength of the measured wall material, the following steps need to be performed before updating the number of cycles: Determining whether the residual strength is less than an allowable residual strength value; If yes, the number of cycles at this time is taken as the fatigue crack growth life of the tested wall material; If not, the number of cycles is updated directly.

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