Effect of pores on the stress field of high-frequency vibration of TC17 specimen manufactured by laser additive

Li, Yongkui; Chen, Zhenlin; Shi-chang, Liu; Wang, Haitao; Jun, Zhang

doi:10.1007/s10704-021-00613-z

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Furthermore, the cladding material can be altered during the deposition process, enabling different material properties in different areas of the component (Liu et al 2021;Hotz et al 2021). However, the LENS process is highly transient and localized, inevitably resulting in metallurgical defects such as porosity during the forming process (Sanaei et al 2021;Li et al 2022). Although process optimization and the adoption of high-quality powders can contribute to reducing defect quantities, complete defect elimination remains a challenging objective (Han et al 2024).…”

Section: Introductionmentioning

confidence: 99%

The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

Zhang,

Hu,

et al. 2024

Preprint

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Three-dimensional characterization of internal defects in Ti-6Al-4V alloy fabricated by Laser Engineered Net Shaping (LENS) was conducted by utilizing synchrotron X-ray imaging technology. Subsequently, the statistical analysis of defect size, quantity, and morphology characteristics was performed. Additionally, high cycle fatigue tests were conducted to analyze the high cycle fatigue performance of LENS Ti-6Al-4V alloy and elucidate the causes of its anisotropic behavior. Furthermore, based on the multi-stage crack growth model, the high cycle fatigue life of LENS Ti-6Al-4V alloy was predicted. The results showed that the quantity and size of internal defects were small, with defects predominantly spherical pores and no lack of fusion defects detected. Longitudinal specimens exhibited significantly higher fatigue life at high stress levels compared to transverse specimens. The anisotropic behavior of high cycle fatigue performance of LENS Ti-6Al-4V alloy at high stress levels was mainly attributed to the anisotropic distribution of its microstructure. As stress levels decreased, the fatigue life of both types of specimens approached each other, with fatigue strengths of 650 MPa and 656 MPa at 2×106 cycles for longitudinal and transverse specimens respectively, showing minimal difference. In addition, the predictions from the multi-stage crack growth model aligned well with experimental results, effectively predicting the high cycle fatigue life of LENS Ti-6Al-4V alloy.

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Section: Introductionmentioning

confidence: 99%

The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

Zhang,

Hu,

et al. 2024

Preprint

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show abstract

“…31 The size, position, and morphology of defects as well as loading, were usually selected as input parameters to predict the fatigue life of AM metals. 24,32,33 Despite the fact that the prediction accuracies of physical methods are optimized all the time, the machine learning approach can still achieve better predictions in some cases. [34][35][36] The common methods containing support vector regression (SVR), XGBoost models, and artificial neural networks (ANN) have been applied to the fatigue life prediction of AM metals in several publications, and several works reported that the machine learning methods have a better performance than conventional semi-empirical methods.…”

Section: Introductionmentioning

confidence: 99%

A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy

Li,

Sun,

Tian

et al. 2024

Fatigue Fract Eng Mat Struct

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The exploitation of fatigue life prediction methods based on fatigue indicator parameters revealed the influence of the defect size, position, and morphology on the fatigue life and fatigue behavior of additively manufactured metals. Meanwhile, Data‐driven life prediction methods are time‐efficient but inexplainable. Current machine learning‐based fatigue life prediction methods call for not only the accuracy but also the interpretability and stability of prediction results. Thus, the fusion of physical methods and machine learning methods has been a prevailing research topic in fatigue life prediction. In this study, a novel physics‐informed neural network framework is proposed by integrating a fatigue indicator parameter based on defects into the physical constraints term of a loss function. This method outperforms conventional machine learning methods in high‐cycle and very high‐cycle regimes, exhibiting superior prediction performance and generalization ability. Furthermore, the prediction results can be explained from a physical standpoint, correlating with the applicability range of the introduced physical equation describing defect positions.

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Void-induced mechanisms in tensile behavior of nickel-based single crystal superalloys

Zheng,

Bian,

et al. 2024

Phys. Scr.

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Void defects significantly impact the tensile properties of nickel-based single crystal superalloys. In this work, the dynamic response of void-included nickel-based single crystal superalloys under tensile loading was studied using molecular dynamics method. The effects of porosity and void size on the tensile behavior and the evolution of internal defects were explored from a microscopic perspective. The results indicate that the presence of voids promotes the development of internal dislocation defects and atomic phase transitions, especially in the initial stage of plastic deformation. The tensile strength decreases with increasing porosity. Plastic deformation and atomic phase transitions typically initiate between voids and continue until complete fracture, with shear strains and dislocation defects continuously concentrating around the voids. Notably, some HCP defect atoms distant from voids revert to FCC phase atoms during the tensile process, leading to a decrease in dislocation density. Additionally, the mode of fracture in the porous model is shear fracture, with shear strain and dislocation defects remaining at the fracture surface after complete fracture. The effects of void size on the tensile strength are relatively small. As the void size decreases, the shear strain bands in the models become more regular and the dislocation density decreases. However, the impact of small-sized voids on the material becomes increasingly evident with further stretching.

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Effect of pores on the stress field of high-frequency vibration of TC17 specimen manufactured by laser additive

Cited by 4 publications

References 26 publications

The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

The study on the high cycle fatigue performance and life prediction of Ti-6Al- 4V alloy fabricated by laser engineered net shaping

A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy

Void-induced mechanisms in tensile behavior of nickel-based single crystal superalloys

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