Probabilistic-based random maximum defect estimation and defect-related fatigue life prediction for laser direct deposited 316L parts

Deng, Keke; Wei, Huifeng; Wen, Liu; Zhang, Min; Zhao, Penghui; Zhang, Yi

doi:10.1016/j.jmatprotec.2021.117389

Cited by 13 publications

(4 citation statements)

References 37 publications

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“…Deng et al 169 investigated fatigue life of laser direct deposition (LDD) 316Lparts. A lot of internal defects exist in parts, as shown in Figure 12, in which the defect of the maximum size is the key factor affecting the fatigue life, and crack is generated here.…”

Section: Non‐ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

“…Typical internal defects in LLD‐316L parts 169 . Reproduced from Deng et al 169 with permission from Elsevier.…”

Section: Non‐ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

“…Typical internal defects in LLD‐316L parts 169 . Reproduced from Deng et al 169 with permission from Elsevier. [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Non‐ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

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Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

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Additive manufacturing (AM) has emerged as a very promising technology for producing complex metallic components with enhanced design flexibility. However, the mechanical properties and fatigue behavior of AM metals differ significantly from conventionally manufactured materials, thereby presenting challenges in accurately predicting their fatigue life. This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of AM metals, with a particular emphasis on machine learning (ML) modeling techniques. This review recalls recent developments and achievements in fatigue characteristics of AM metals, ML‐based approaches for fatigue life prediction of AM metals, and non‐ML‐based methodologies for the same purpose. In particular, some commonly used regression and classification techniques for fatigue evaluation of AM metals are summarized and elaborated. The study intends to furnish researchers, engineers, and practitioners in the field of AM with a guidance for the accurate and efficient prediction of fatigue life in AM metal components.

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Section: Non‐ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

“…Typical internal defects in LLD‐316L parts 169 . Reproduced from Deng et al 169 with permission from Elsevier.…”

Section: Non‐ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

See 1 more Smart Citation

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…A lack of fusion (LOF) occurs due to the insufficient laser energy input between layers, un-melted particles and pores with different shapes due to the entrapped gas bubbles during solidification [10]. The effect of porosity on the fatigue life has been well established through statistical analysis methods in recent years [11][12][13][14][15][16][17], and a corresponding modified Kitagawa-Takahashi diagram has been proposed to predict the fatigue life. In addition, Romali Biswal studied the effect of the pore location on the fatigue performance of AM fabricated Ti6Al4V through finite element analysis [18], and established the relationship between the stress concentration factor and pore shape.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Process-Induced Porosity on Fatigue Properties of Ti6Al4V Alloy via High-Power Direct Energy Deposition

Zhang

et al. 2022

Coatings

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Titanium alloy is widely used in the aviation sector and has become the most important structural material in aircraft manufacturing. However, manufacturing a large-scale titanium component owns a high buy-to-fly ratio due to its poor machinability and expensive price. Over the last decade, the additive manufacturing (AM) technology has developed rapidly and has become a promising processing method for titanium alloys. In the future, in order to enhance processing efficiency and material utilization, a higher laser energy source is supposed to be applied in AM processes. Nevertheless, porosity within the AM fabricated part is the most important issue that restricts the application of AM technology. In the present work, two bulks with different porosities were fabricated using high-power direct energy deposition (HP-DED), and the high cycle fatigue (HCF) performance of the as-build part was tested and compared. The result shows that a lack of fusion (LOF), spherical pores and un-melted particles are the main porosity defects in the as-build part. The shape, size and location of the defect will have a synthetic effect on HCF performance. In addition, the unstable key-hole during the process will facilitate the formation of a pore, which consequently increases the porosity. Online monitoring and closed-loop feedback systems should be established for enhancing the process stability.

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Modeling and Prediction of Fatigue Properties of Additively Manufactured Metals

Tang

et al. 2023

Acta Mech. Solida Sin.

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Probabilistic-based random maximum defect estimation and defect-related fatigue life prediction for laser direct deposited 316L parts

Cited by 13 publications

References 37 publications

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

The Effect of Process-Induced Porosity on Fatigue Properties of Ti6Al4V Alloy via High-Power Direct Energy Deposition

Modeling and Prediction of Fatigue Properties of Additively Manufactured Metals

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