Additive manufacturing metal fatigue life prediction method and system

Abstract

The invention relates to the technical field of aeronautical materials, in particular to an additive manufacturing metal fatigue life prediction method and system. According to the method, a fatigue life analysis model considering the defect orientation angle, the stress level, the defect roundness and the defect size is established, then a corrected stress-fatigue life model based on the defect geometrical characteristics is established, and a data dimensionless processing mode and life analysis are adopted. According to the correction stress-fatigue life model based on the defect geometrical characteristics, the defect orientation parameters are considered, the formula model is improved, the index of each defect geometrical characteristic parameter is not fixed, the degree of freedom of the model is improved, and the goodness of fit of the model is higher.

Description

Technical Field

[0001] This disclosure relates to the field of aerospace materials technology, and in particular to a method and system for predicting the fatigue life of additive manufacturing metals. Background Technology

[0002] Due to manufacturing defects, additive manufacturing materials exhibit significant fatigue life dispersion. Traditional SN curve models describing the stress-life relationship, such as the Basquin formula, cannot account for the substantial fatigue dispersion caused by defects in additive manufacturing materials. Therefore, the influence of defect geometry must be considered in fatigue life models.

[0003] As research deepens and evaluation models continue to evolve, an increasing number of models are emerging. Simultaneously, multi-stage fatigue life models and crystal plasticity models are also used to predict the fatigue properties of different materials. However, due to the complexity of the factors influencing additive manufacturing fatigue and the difficulty in accurately describing the interaction mechanisms, machine learning-based fatigue prediction methods have received widespread attention in recent years. However, machine learning methods lack interpretability and rely heavily on extensive experimental data. Current additive manufacturing fatigue life models mainly suffer from the following shortcomings: reliance on large amounts of experimental data; insufficient representation of fatigue mechanisms in the models; and the inability to comprehensively characterize the effects of three-dimensional defects and defect orientation, considering only two-dimensional defect features and focusing solely on surface defects.

[0004] In summary, there are currently various methods for evaluating the fatigue performance of additive manufacturing materials. Traditional life prediction models are difficult to solve the fatigue dispersion problem of additive manufacturing, while machine learning methods lack interpretability and rely on a large amount of experimental data. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure provides a method and system for predicting the fatigue life of additive manufacturing metals. By analyzing the fatigue sources at the fatigue fracture surface, the defects of the crack initiation are characterized and their impact on fatigue performance is determined. A modified stress-fatigue life prediction model based on defect characteristics is proposed.

[0006] In a first aspect, a method for predicting the fatigue life of additively manufactured metals, the method comprising: The correlation between fatigue life of SLM formed TC4 alloy and stress level, defect orientation angle, defect size, and defect roundness was analyzed, and the correlation relationship was obtained. Based on the correlation, a modified stress is constructed using stress level, defect orientation angle, defect size, and defect roundness. The modified stress is used as a damage parameter for fatigue life prediction.

[0007] Further, related relationships include: Fatigue life is negatively correlated with stress level, defect orientation angle and defect size, and positively correlated with defect roundness.

[0008] Furthermore, modified stresses are constructed based on stress level, defect orientation angle, defect size, and defect roundness, including:

[0009] in, For maximum stress, To correct the stress, d For defect size, C For defect roundness, θ From the perspective of defect orientation, , , All of these are material constants.

[0010] Furthermore, defect roundness C Calculated using the following formula:

[0011] in, area The projected area of ​​the defect. M This is the maximum distance from the geometric center of the defect to the edge of the defect profile.

[0012] Furthermore, it also includes: For parameters d, C, θ The dimensionless transformation is performed, and the formula is as follows:

[0013] Where X represents d, C, θ Any parameter, X min , X max For the minimum and maximum values ​​of the corresponding parameter X, X * This is the value after dimensionless processing.

[0014] Furthermore, the modified stress is used as a damage parameter for fatigue life prediction, as shown in the following formula:

[0015] in, N f To predict lifespan, A and A are material constants.

[0016] Furthermore, material constants , , A and B are obtained by least squares fitting.

[0017] Secondly, an additive manufacturing metal fatigue life prediction system includes: Correlation analysis unit, modified stress construction unit, and lifetime prediction unit; The correlation analysis unit is used to analyze the correlation between the fatigue life of SLM formed TC4 alloy and stress level, defect orientation angle, defect size, and defect roundness, and obtain the correlation relationship. The modified stress building block, based on correlation, is used to construct modified stresses with stress level, defect orientation angle, defect size, and defect roundness. The fatigue life prediction unit is used to predict fatigue life by using the corrected stress as a damage parameter.

[0018] Further, related relationships include: Fatigue life is negatively correlated with stress level, defect orientation angle and defect size, and positively correlated with defect roundness.

[0019] Furthermore, modified stresses are constructed based on stress level, defect orientation angle, defect size, and defect roundness, including:

[0020] in, For maximum stress, To correct the stress, , , All of these are material constants.

[0021] Furthermore, defect roundness C Calculated using the following formula:

[0022] in, area The projected area of ​​the defect. M This is the maximum distance from the geometric center of the defect to the edge of the defect profile.

[0023] Furthermore, it also includes: For parameters d, C, θ The dimensionless transformation is performed, and the formula is as follows:

[0024] Where X represents d, C, θ Any parameter, X min , X max For the minimum and maximum values ​​of the corresponding parameter X, X * This is the value after dimensionless processing.

[0025] Furthermore, the modified stress is used as a damage parameter for fatigue life prediction, as shown in the following formula:

[0026] in, N f To predict lifespan, A and A are material constants.

[0027] Furthermore, material constants , , A and B are obtained by least squares fitting.

[0028] This disclosure includes at least the following beneficial effects: This disclosure establishes a fatigue life analysis model considering defect orientation angle, stress level, defect roundness, and defect size. Subsequently, a modified stress-fatigue life model based on defect geometric characteristics is established, employing dimensionless data processing and life analysis. The modified stress-fatigue life model based on defect geometric characteristics established in this disclosure considers defect orientation parameters and improves the formula model by not fixing the exponents of each defect geometric characteristic parameter, thus increasing the model's degrees of freedom and resulting in a higher model fit.

[0029] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the prediction method flow according to an embodiment of the present disclosure; Figure 2 This is a schematic diagram illustrating the characterization of defect geometric feature parameters according to an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the prediction system architecture according to an embodiment of the present disclosure; Figure 4 The present invention presents fatigue test results of SLM TC4 alloy under different stress levels in embodiments of this invention. Figure 5 This is a schematic diagram of the analysis results of the modified stress-fatigue life model in the embodiments of this disclosure. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0033] like Figure 1 As shown, a method for predicting the fatigue life of additively manufactured metals includes: S101, the correlation between alloy fatigue life and stress level, defect orientation angle, defect size and defect roundness was analyzed, and the correlation relationship was obtained; S102, based on the correlation, constructs the modified stress with stress level, defect orientation angle, defect size and defect roundness; S103 uses the modified stress as a damage parameter for fatigue life prediction.

[0034] The specific implementation details are as follows: Generally, large or irregularly shaped defects have higher stress concentration factors, and the orientation angle of the defects also affects fatigue performance. Quantitative characterization of defects at the fatigue fracture surface in additive manufacturing is required. For example... Figure 2 As shown, the defect size uses the Feret diameter parameter, which is defined as the straight-line distance between the two farthest points on the defect profile, denoted by the symbol... d The defect location is indicated by the distance from the geometric center of the defect to the sample surface. l parameter Defect orientation is defined as the angle between the extension of the Feret diameter and the normal to the material surface, expressed as... θ express.

[0035] Using defect roundness C To characterize the shape of defects:

[0036] in area It is the projected area of ​​the defect. M This is the maximum distance from the geometric center of the defect to the edge of the defect profile. When the defect shape is nearly circular... M The radius of a circle with approximately equal area, roundness C That is close to 1.

[0037] This study quantitatively investigates the influence mechanism of stress and defect geometry on fatigue life, using the Pearson Correlation Coefficient (PCC) to analyze the correlation between fatigue life and maximum stress σmax, defect size d, defect roundness C, defect location l, and orientation angle (θ). The analysis reveals that the orientation angle has the greatest impact on the fatigue life of the specimens, exhibiting a strong negative correlation; the larger the angle between the defect and the specimen surface normal, the more detrimental it is to fatigue performance. Stress level and defect size also show a significant negative correlation with fatigue life. Defect roundness is positively correlated with fatigue life, indicating that irregular defects are more detrimental to fatigue performance. Defect location is an important factor affecting fatigue; however, since the fatigue source for all specimens is a surface defect, under this premise, defect location has almost no effect on fatigue. Overall, the order of influence on the fatigue life of SLM-formed TC4 alloy from largest to smallest is: orientation angle > stress level > defect roundness > defect size > defect location.

[0038] Fatigue life exhibits significant dispersion under different stress levels, making it difficult to establish a stress-life relationship model using traditional Basquin formulas. Since the fatigue life of SLM-formed TC4 alloy is highly correlated with stress level, defect orientation angle, defect size, and defect roundness, a modified stress-fatigue life model based on defect geometry is established, as follows:

[0039]

[0040] in For maximum stress, For the corrected stress, , , A and B are both material constants, obtained through fitting. d, C, θ The parameters are dimensionless. To avoid parameter singularities, the processing method is as follows:

[0041] in X min , X max For the minimum and maximum values ​​of the corresponding parameter X, X * This is the value after dimensionless processing.

[0042] The modified stress was used as a damage parameter for lifetime prediction, and the material constants were obtained by least squares fitting.

[0043] like Figure 3As shown, an additive manufacturing metal fatigue life prediction system includes: Correlation analysis unit 301, modified stress construction unit 302, and lifetime prediction unit 303; The correlation analysis unit 301 is used to analyze the correlation between the fatigue life of SLM formed TC4 alloy and stress level, defect orientation angle, defect size, and defect roundness, and obtain the correlation relationship. The modified stress building unit 302, based on correlation, is used to build modified stress with stress level, defect orientation angle, defect size and defect roundness; The fatigue life prediction unit 303 is used to predict fatigue life by using the modified stress as a damage parameter.

[0044] To enable those skilled in the art to better understand this disclosure, the principles of this disclosure are explained below in conjunction with the accompanying drawings: Taking the tensile fatigue test of a batch of SLM-formed TC4 alloy as an example, fatigue testing was carried out using an MTS electro-hydraulic servo fatigue testing machine. An axial tension-tension loading method was adopted, with a constant amplitude sine wave load waveform, a stress ratio of R=0.1, and a test loading frequency of 20Hz. The results are as follows: Figure 4 As shown, fatigue life is generally negatively correlated with stress level, but it is not the decisive factor. Fatigue life under different stress levels exhibits a certain degree of dispersion, with the dispersion being most significant at lower maximum stress levels. This is mainly due to the presence of defects with large size spans and global distribution in the specimens. The fatigue initiation points for all specimens are surface defects, as these defects tend to generate higher stress concentrations, which become fatigue crack initiation points under alternating loads.

[0045] The fatigue life and fatigue source defect characteristics of each sample are shown in Table 1.

[0046] Table 1

[0047] The modified stress was used as a damage parameter for lifetime prediction. The material constants were obtained by least squares fitting. The results are shared as follows: Figure 5 As shown in the results, the goodness of fit of the lifetime analysis model is 0.72, and all data points fall within three times the error band of the fitted curve, verifying the effectiveness of the analysis model.

[0048] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for predicting the fatigue life of additively manufactured metals, characterized in that, The method includes: The correlation between alloy fatigue life and stress level, defect orientation angle, defect size, and defect roundness was analyzed, and the relevant relationships were obtained. Based on the correlation, a modified stress is constructed using stress level, defect orientation angle, defect size, and defect roundness. The modified stress is used as a damage parameter for fatigue life prediction.

2. The method for predicting the fatigue life of additive manufacturing metals according to claim 1, characterized in that, Relationships include: Fatigue life is negatively correlated with stress level, defect orientation angle and defect size, and positively correlated with defect roundness.

3. The method for predicting the fatigue life of additive manufacturing metals according to claim 1, characterized in that, Modified stresses are constructed using stress level, defect orientation angle, defect size, and defect roundness, including: in, For maximum stress, To correct the stress, d For defect size, C For defect roundness, θ From the perspective of defect orientation, , , All of these are material constants.

4. The method for predicting the fatigue life of additive manufacturing metals according to claim 3, characterized in that, Defect roundness C Calculated using the following formula: in, area The projected area of ​​the defect. M This is the maximum distance from the geometric center of the defect to the edge of the defect profile.

5. The method for predicting the fatigue life of additive manufacturing metals according to claim 3, characterized in that, Also includes: For parameters d, C, θ The dimensionless transformation is performed, and the formula is as follows: Where X represents d, C, θ Any parameter, X min , X max For the minimum and maximum values ​​of the corresponding parameter X, X * This is the value after dimensionless processing.

6. The method for predicting the fatigue life of additive manufacturing metals according to claim 3, characterized in that, The modified stress is used as a damage parameter for fatigue life prediction, as shown in the following formula: in, N f To predict lifespan, A and A are material constants.

7. The method for predicting the fatigue life of additive manufacturing metals according to claim 6, characterized in that, Material constants , , A and B are obtained by least squares fitting.

8. A fatigue life prediction system for additive manufacturing metals, characterized in that, include: Correlation analysis unit, modified stress construction unit, and lifetime prediction unit; The correlation analysis unit is used to analyze the correlation between alloy fatigue life and stress level, defect orientation angle, defect size, and defect roundness to obtain the correlation relationship; The modified stress building block, based on correlation, is used to construct modified stresses with stress level, defect orientation angle, defect size, and defect roundness. The fatigue life prediction unit is used to predict fatigue life by using the corrected stress as a damage parameter.

9. The additive manufacturing metal fatigue life prediction system according to claim 8, characterized in that, Relationships include: Fatigue life is negatively correlated with stress level, defect orientation angle and defect size, and positively correlated with defect roundness.

10. The additive manufacturing metal fatigue life prediction system according to claim 8, characterized in that, Modified stresses are constructed using stress level, defect orientation angle, defect size, and defect roundness, including: in, For maximum stress, To correct the stress, , , All of these are material constants.

11. The additive manufacturing metal fatigue life prediction system according to claim 10, characterized in that, Defect roundness C Calculated using the following formula: in, area The projected area of ​​the defect. M This is the maximum distance from the geometric center of the defect to the edge of the defect profile.

12. The additive manufacturing metal fatigue life prediction system according to claim 10, characterized in that, Also includes: For parameters d, C, θ The dimensionless transformation is performed, and the formula is as follows: Where X represents d, C, θ Any parameter, X min , X max For the minimum and maximum values ​​of the corresponding parameter X, X * This is the value after dimensionless processing.

13. The additive manufacturing metal fatigue life prediction system according to claim 10, characterized in that, The modified stress is used as a damage parameter for fatigue life prediction, as shown in the following formula: in, N f To predict lifespan, A and A are material constants.

14. The additive manufacturing metal fatigue life prediction system according to claim 13, characterized in that, Material constants , , A and B are obtained by least squares fitting.

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