Fatigue prediction and life assessment method for metal laser powder bed fusion parts

Wits, Wessel W.; Amsterdam, Emiel

doi:10.1016/j.cirp.2023.03.011

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Luo et al 142 compared SLM Inconel 718 fatigue life prediction model based on linear regression algorithm with other ML algorithms and found that linear regression algorithm performed well, as indicated in Figure 5A; however, its generalization performance was slightly inadequate. Wits et al 144 also used linear regression algorithm to study the influence of initial defect size on fatigue life of SLM AlSi10Mg specimens and achieved considerable results. Elangeswaran et al 143 developed fatigue life prediction models for SLM metal structures with varying manufacturing directions, heat treatment, and surface treatment parameters using linear regression and Gaussian process regression algorithms.…”

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

confidence: 99%

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: Ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

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

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Eutectic high-entropy alloys and their applications in materials processing engineering: A review

Liu,

Li,

Lin

et al. 2024

Journal of Materials Science & Technology

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Investigation on the fatigue strength of AlSi10Mg fabricated by PBF-LB/M and subjected to low temperature heat treatments

Roveda,

Serrano-Munoz,

Haubrich

et al. 2024

Materials & Design

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Fatigue prediction and life assessment method for metal laser powder bed fusion parts

Cited by 4 publications

References 15 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

Eutectic high-entropy alloys and their applications in materials processing engineering: A review

Investigation on the fatigue strength of AlSi10Mg fabricated by PBF-LB/M and subjected to low temperature heat treatments

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