Fatigue properties of AlSi10Mg obtained by additive manufacturing: Defect-based modelling and prediction of fatigue strength
By S. Romano, A. Brückner-foit, A. Brandão, J. Gumpinger, T. Ghidini, Stefano Beretta. 2017
467 citations recorded by the source corpus.
Subfields: Automotive Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/897000872eed27315a07c1097aa92de10db0e0cb
How researchers see this paper
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In Automotive Engineering
- A. Fatemi
- N. Sanaei
- N. Phan
- Nima Shamsaei
- R. Molaei
- Jonathan W. Pegues
- Jialuo Ding
- Stewart W. Williams
In Mechanical Engineering
- A. Fatemi
- N. Sanaei
- Nima Shamsaei
- N. Phan
- R. Molaei
- Jialuo Ding
- Jonathan W. Pegues
- Stewart W. Williams
Citations in the corpus
- Criticality of porosity defects on the fatigue performance of wire + arc additive manufactured titanium alloy (2019, background): “These approaches [29,30,36,39] have been proven to be accurate for the lack of fusion defects [9,32,40], which are similar to a planar crack geometry.”
- Fatigue of additive manufactured Ti-6Al-4V, Part II: The relationship between microstructure, material cyclic properties, and component performance (2020, methodology): “This parameter has been implemented to represent defect size in several AM studies since the geometrical parameter √ area reportedly allows calculating the maximum Stress Intensity Factor (SIF) along the front of an internal or surface crack with irregular shape [8,27].”
- Fatigue of additively manufactured 316L stainless steel: The influence of porosity and surface roughness (2019, unknown): “15,20,29 A surface roughness Ra of 10.1 was obtained from measurements using a confocal microscope.”
- High cycle fatigue behavior and life prediction for additively manufactured 17-4 PH stainless steel: Effect of sub-surface porosity and surface roughness (2020, unknown): “, AlSi10Mg [7,11], Ti-6Al-4V [7,57], and In718 [8]).”
- X-Ray Microcomputed Tomography in Additive Manufacturing: A Review of the Current Technology and Applications (2018, unknown): “Some recent studies using witness specimens have identified methods to predict fatigue life of the part based on statistical analysis of pores detected in witness specimens.(60,96) Currently there are standards being developed for nondestructive testing of AM parts(99) and specifically for seeding o”