Fatigue crack growth characterization of Inconel 718 after additive manufacturing by laser powder bed fusion and heat treatment

Gruber, Konrad; Szymczyk‐Ziółkowska, Patrycja; Dziuba, Szymon; Duda, Szymon; Zielonka, Paweł; Seitl, Stanislav; Lesiuk, Grzegorz

doi:10.1016/j.ijfatigue.2022.107287

Cited by 19 publications

(7 citation statements)

References 38 publications

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“…Gruber et al. [ 196 ] tested the FCGR of the IN718 superalloy process by SLM and compared the results between the samples with and without HIP. They found that the FCGRs were similar in the stable growth region.…”

Section: Fatigue Performance Of Amed Metalsmentioning

confidence: 99%

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Review on Fatigue of Additive Manufactured Metallic Alloys: Microstructure, Performance, Enhancement, and Assessment Methods

Liu,

Yu,

Guo

et al. 2023

Advanced Materials

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Additive manufacturing (AM), which is a process of building objects in a layer‐upon‐layer fashion from designed models, has received unprecedented attention from research and industry because it offers outstanding merits of flexibility, customization, reduced buy‐to‐fly ratio, and cost‐effectiveness. However, the fatigue performance of safety‐critical industrial components fabricated by AM is still far below that obtained from conventional methods. This review discusses the microstructural heterogeneities, randomly dispersed defects, poor surface quality, and complex residual stress generated during the AM process that can negatively impact the fatigue performance of as‐printed parts. The difference in microstructural origin of fatigue failure between conventionally manufactured and printed metals is reviewed with particular attention to the effects of the trans‐scale microstructures on AM fatigue failure mechanisms. Various methods for mitigating the fatigue issue, including pre‐process, inter‐process and post‐process treatments, are illustrated. Empirical, semi‐empirical and microstructure‐sensitive models are presented to predict fatigue strength and lifetime. Summary and outlooks for future development of the fatigue performance of AM materials are provided.This article is protected by copyright. All rights reserved

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Section: Fatigue Performance Of Amed Metalsmentioning

confidence: 99%

“…Thus, the methods that can eliminate defects like HIP and process control improve FCG performance. [195] Gruber et al [196] tested the FCGR of the IN718 superalloy process by SLM and compared the results between the samples with and without HIP. They found that the FCGRs were similar in the stable growth region.…”

Section: Fatigue Performancementioning

confidence: 99%

Review on Fatigue of Additive Manufactured Metallic Alloys: Microstructure, Performance, Enhancement, and Assessment Methods

Liu,

Yu,

Guo

et al. 2023

Advanced Materials

View full text Add to dashboard Cite

show abstract

“…Recently, it has been found that similar heat treatments conducted with or without HIP showed similar fatigue crack growth rates (FCGR) and crack growth path using compact tension (CT) specimens to investigate the long crack regime at room temperature [10]. CT specimens are straightforward to analyze this regime since they limit the plasticity levels in mode I.…”

Section: Introductionmentioning

confidence: 99%

Anisotropy in cyclic behavior and fatigue crack growth of IN718 processed by laser powder bed fusion

prost

Köster

Missoum-Benziane

et al. 2023

Additive Manufacturing

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“…In718 alloy is a precipitation-strengthened nickel alloy that possesses excellent properties, such as mechanical strength, strong oxidation resistance, and high corrosion resistance at temperatures up to 650 °C [ 1 , 2 , 3 ]. In718 alloy has been widely used in applications in which mechanical performance at high temperatures is important [ 2 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures

et al. 2023

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Artificial neural networks (ANNs) have been an important approach for predicting the value of flow stress, which is dependent on temperature, strain, and strain rate. However, there is still a lack of sufficient knowledge regarding what structure of ANN should be used for predicting metal flow stress. In this paper, we train an ANN for predicting flow stress of In718 alloys at high temperatures using our experimental data, and the structure of the ANN is optimized by comparing the performance of four ANNs in predicting the flow stress of In718 alloy. It is found that, as the size of the ANN increases, the ability of the ANN to retrieve the flow stress results from a training dataset is significantly enhanced; however, the ability to predict the flow stress results absent from the training does not monotonically increase with the size of the ANN. It is concluded that the ANN with one hidden layer and four nodes possesses optimized performance for predicting the flow stress of In718 alloys in this study. The reason why there exists an optimized ANN size is discussed. When the ANN size is less than the optimized size, the prediction, especially the strain dependency, falls into underfitting and fails to predict the curve. When the ANN size is less than the optimized size, the predicted flow stress curves with the temperature, strain, and strain rate will contain non-physical fluctuations, thus reducing their prediction accuracy of extrapolation. For metals similar to the In718 alloy, ANNs with very few nodes in the hidden layer are preferred rather than the large ANNs with tens or hundreds of nodes in the hidden layers.

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Fatigue crack growth characterization of Inconel 718 after additive manufacturing by laser powder bed fusion and heat treatment

Cited by 19 publications

References 38 publications

Review on Fatigue of Additive Manufactured Metallic Alloys: Microstructure, Performance, Enhancement, and Assessment Methods

Review on Fatigue of Additive Manufactured Metallic Alloys: Microstructure, Performance, Enhancement, and Assessment Methods

Anisotropy in cyclic behavior and fatigue crack growth of IN718 processed by laser powder bed fusion

Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures

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