Role of defects in fatigue performance of IN100

Raghavendra, Arjun Kalkur Matpadi; Armanni, Thibaut; Charles, Sandrine; Marcin, Lionel

doi:10.1016/j.engfracmech.2021.108224

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…2 b. Defect size is defined by where Area is the projected area of defect on a plane perpendicular to loading direction 6 while sphericity is a morphological parameter which measures how spherical a defect is: a value of 1 indicates a perfectly spherical defect 15 . Sphericity, is given by where is the volume of defect and is the surface area.…”

Section: Resultsmentioning

confidence: 99%

“…Morphology of the defects varies with respect to its size in an exponential pattern 15 , 44 . It is difficult to train GANs to reproduce defects that can respect this relationship since all defects will be initially resized to a fixed size for training.…”

Section: Resultsmentioning

confidence: 99%

“…K-functions were analysed at different values ranging from 1 to 0.1 mm. From our previous work 15 , defect population was classified into three groups: a) Shrinkages, b) broken shrinkage pores and c) gaseous pores. Statistically, all defects larger than approximately 0.3–0.4 mm are shrinkages due to their tortuous morphology as seen in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…1 ) are known to produce very complex mechanical response under fatigue loading. Although image based finite element (FE) models can simulate this response and aid in locating the crack-initiation site, it is still very difficult to simplify the process and predict its fatigue life with respect to defect characteristics 15 . For an isolated defect, a Kitagawa– Takahashi diagram can be used but the same Linear Elastic Fracture Mechanical (LEFM) approach cannot be applied to clustered defects.…”

Section: Introductionmentioning

confidence: 99%

“…Four as-cast Inconel 100 (IN100) cylindrical samples machined from cast ingot bars are used to generate synthetic microstructures. The samples are 40 mm long with a gauge diameter of 3.7 mm and contain clustered defects whose characteristics are assessed via X-ray computed tomographic images (XCT) as seen in our previous work 15 . The XCT volumes can be used to build image-based FE models of the same.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Generation of synthetic microstructures containing casting defects: a machine learning approach

Raghavendra¹,

Lacourt²,

Marcin³

et al. 2023

Sci Rep

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This paper presents a new strategy to generate synthetic samples containing casting defects. Four samples of Inconel 100 containing casting defects such as shrinkages and pores have been characterized using X-ray tomography and are used as reference for this application. Shrinkages are known to be tortuous in shape and more detrimental for the mechanical properties of materials, especially metal fatigue, whereas pores can be of two types: broken shrinkage pores with arbitrary shape and gaseous pores of spherical shape. For the generation of synthetic samples, an integrated module of Spatial Point Pattern (SPP) analysis and deep learning techniques such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) are used. The SPP analysis describes the spatial distributions of casting defects in material space, whereas GANs and CNNs generate a defect of arbitrary morphology very close to real defects. SPP analysis reveals the existence of two different void nucleation mechanisms during metal solidification associated to shrinkages and pores. Our deep learning model successfully generates casting defects with defect size ranging from 100 µm to 1.5 mm and of very realistic shapes. The entire synthetic microstructure generation process respects the global defect statistics of reference samples and the generated samples are validated by statistically comparing with real samples.

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