Defect Analysis for Additively Manufactured Materials in Fatigue from the Viewpoint of Quality Control and Statistics of Extremes

Murakami, Yukitaka; Masuo, Hiroshige; Tanaka, Yuzo; Nakatani, Masanori

doi:10.1016/j.prostr.2019.12.014

Cited by 45 publications

(28 citation statements)

References 9 publications

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“…This was also demonstrated for surface and subsurface pores in [16]. In [15] the authors clearly demonstrated, by the detection through computed tomography (CT) scan of all the defects present in specimens, that the critical defects were the ones with the largest stress intensity factor (SIF), thus supporting the adoption of "extreme value" concepts for estimating the maximum size of defects for fatigue assessment [12] and for comparing the "quality" of different treatments [17,18].…”

Section: Introductionmentioning

confidence: 74%

Killer notches: The effect of as-built surface roughness on fatigue failure in AlSi10Mg produced by laser powder bed fusion

Plessis

Beretta

2020

Additive Manufacturing

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Section: Introductionmentioning

confidence: 74%

Killer notches: The effect of as-built surface roughness on fatigue failure in AlSi10Mg produced by laser powder bed fusion

Plessis

Beretta

2020

Additive Manufacturing

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“…(4) must not be applied to the investigated AM material and probably this statement can be extended to metallic AM materials in general, at least if no post-fabrication treatment is carried out. Murakami et al [70], carrying out a comparable study, conclude that the relationship of Eqn. (4) and Fig.…”

Section: Monotonic Versus Cyclic Strengthmentioning

confidence: 90%

Damage tolerant design of additively manufactured metallic components subjected to cyclic loading: State of the art and challenges

Zerbst

Bruno

Buffière

et al. 2021

Progress in Materials Science

156

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“…Cumulative probability was estimated by Equation () with specimen number i and total number of specimens n . This is also important to consider as statistic of extremes can be used for quality control in the future [ 41 ] as fatigue strength distribution or failure probability can be estimated. [ 36 ]

P = 1 - exp [- exp (\frac{\sqrt{area} - λ}{δ})]

P = \frac{i - 0.5}{n}

…”

Section: Resultsmentioning

confidence: 99%

“…[40] Here, λ and δ are the location and scale parameters, respectively. Cumulative probability was estimated by Equation ( 3) with specimen number i and total number of specimens n. This is also important to consider as statistic of extremes can be used for quality control in the future [41] as fatigue strength distribution or failure probability can be estimated. [36] P Due to the aforementioned effect of defects and the number of specimens tested in this work, the resulting slopes of the Basquin equation in Figure 10 of 316L þ N compared to 316L cannot be used to describe the fatigue behavior of the material in a general way.…”

Section: Mechanical Propertiesmentioning

confidence: 99%

Improving the Defect Tolerance of PBF‐LB/M Processed 316L Steel by Increasing the Nitrogen Content

et al. 2022

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Nitrogen (N) in steels can improve their mechanical strength by solid solution strengthening. Processing N‐alloyed steels with additive manufacturing, here laser powder bed fusion (PBF‐LB), is challenging as the N‐solubility in the melt can be exceeded. This degassing of N counteracts its intended positive effects. Herein, the PBF‐LB processed 316L stainless steel with increased N‐content is investigated and compared to PBF‐LB 316L with conventional N‐content. The N is introduced into the steel by nitriding the powder and mixing it with the starting powder to achieve an N‐content of approximately 0.16 mass%. Thermodynamic calculations for maximum solubility to avoid N outgassing and pore formation under PBF‐LB conditions are performed beforehand. Based on the results, a higher defect tolerance under fatigue characterized by Murakami model can be achieved without negatively influencing the PBF‐LB processability of the 316L steel. The increased N‐content leads to higher hardness (+14%), yield strength (+16%), tensile strength (+9%), and higher failure stress in short time fatigue test (+16%).

show abstract

Defect Analysis for Additively Manufactured Materials in Fatigue from the Viewpoint of Quality Control and Statistics of Extremes

Cited by 45 publications

References 9 publications

Killer notches: The effect of as-built surface roughness on fatigue failure in AlSi10Mg produced by laser powder bed fusion

Killer notches: The effect of as-built surface roughness on fatigue failure in AlSi10Mg produced by laser powder bed fusion

Damage tolerant design of additively manufactured metallic components subjected to cyclic loading: State of the art and challenges

Improving the Defect Tolerance of PBF‐LB/M Processed 316L Steel by Increasing the Nitrogen Content

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