Machine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components

Centola, Alessio; Ciampaglia, Alberto; Tridello, Andrea; Paolino, Davide Salvatore

doi:10.1111/ffe.14125

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…In view of this point, additively manufactured (AM) metallic materials have drawn significant attention from both academia and industry [7]. For AM metallic materials, fatigue performance, especially in VHCF regime, has become a key factor restricting their engineering applications [6,[8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Microstructure Study on Very High Cycle Fatigue of an Additively Manufactured Aluminium Alloy via Advanced Characterization Methods

Liu,

Wang,

et al. 2024

Applied Sciences

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The engineering application of additively manufactured (AM) metallic materials is quite limited by their fatigue behaviors, which are very inconsistent with that of conventionally wrought or cast ones. Here, based on advanced material characterization techniques, such as focused ion beam (FIB), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), the microstructures underneath fracture surfaces were thoroughly investigated in an AM aluminum (AlSi10Mg) alloy with horizontal and vertical building orientation enduring very high cycle fatigue (VHCF) loading under the stress ratios R = −1, 0, and 0.5. Two VHCF failure specimens A and B were representatively selected to further examine SEM and TEM sample preparation via FIB milling. Specimen A was horizontally printed and failed at R = −1; specimen B was vertically printed and failed at R = 0. TEM samples A1 and B1 were lifted from locations near the crack initiation sites on the fracture surfaces of specimens A and B; The locations of TEM samples A2 and B2 kept away from the crack origin sites but still within the “fish-eye” region of crack steady growth. TEM observations show that there was no characteristic microstructure induced by VHCF in different oriented specimens and under various R values.

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Section: Introductionmentioning

confidence: 99%

Microstructure Study on Very High Cycle Fatigue of an Additively Manufactured Aluminium Alloy via Advanced Characterization Methods

Liu,

Wang,

et al. 2024

Applied Sciences

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Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Wu,

Xu,

Fang

et al. 2024

Arch Computat Methods Eng

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In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.

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Probabilistic Machine Learning for preventing fatigue failures in Additively Manufactured SS316L

Centola,

Ciampaglia,

Paolino

et al. 2025

Engineering Failure Analysis

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Machine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components

Cited by 8 publications

References 39 publications

Microstructure Study on Very High Cycle Fatigue of an Additively Manufactured Aluminium Alloy via Advanced Characterization Methods

Microstructure Study on Very High Cycle Fatigue of an Additively Manufactured Aluminium Alloy via Advanced Characterization Methods

Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Probabilistic Machine Learning for preventing fatigue failures in Additively Manufactured SS316L

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