Heteroscedasticity in fatigue life of additively manufactured maraging steel: Implications for design curve reliability
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OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Erosion and Abrasive Machining
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References (41)
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1. A holistic review on fatigue properties of additively manufactured metals10.1016/j.jmatprotec.2024.118425 ↗
2. Review on Fatigue of Additive Manufactured Metallic Alloys: Microstructure, Performance, Enhancement, and Assessment Methods
3. Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review10.1016/j.pmatsci.2020.100724 ↗
4. Mechanical behavior of additive manufactured, powder-bed laser-fused materials10.1016/j.msea.2015.10.068 ↗
5. Fatigue crack initiation in additively manufactured maraging steel: A statistical insight10.1016/j.ijfatigue.2025.108957 ↗
6. Extreme variation in fatigue: Fatigue life prediction and dependence on build volume location in laser powder bed fusion of 17-4 stainless steel10.1016/j.ijfatigue.2022.106737 ↗
7. Probabilistic modeling of the size effect and scatter in High Cycle Fatigue using a Monte-Carlo approach: Role of the defect population in cast aluminum alloys10.1016/j.ijfatigue.2021.106177 ↗
8. Transferability of anomaly data to fatigue properties of PBF-LB AlSi10Mg parts with different volumes10.1016/j.ijfatigue.2025.108852 ↗
9. A comparison of fatigue strength sensitivity to defects for materials manufactured by AM or traditional processes10.1016/j.ijfatigue.2016.06.020 ↗
10. Probabilistic prediction of the size effect on the fatigue strength for variable length specimens of selective laser melted 316L stainless steel10.1016/j.ijfatigue.2024.108225 ↗
11. A microstructure sensitive machine learning-based approach for predicting fatigue life of additively manufactured parts10.1016/j.ijfatigue.2024.108724 ↗
12. A machine-learning fatigue life prediction approach of additively manufactured metals10.1016/j.engfracmech.2020.107508 ↗
13. Numerical framework for predicting fatigue scatter in additively manufactured parts10.1016/j.ijmecsci.2024.109562 ↗
14. A comprehensive study on the effects of surface post-processing on fatigue performance of additively manufactured AlSi10Mg: An augmented machine learning perspective on experimental observations
15. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach10.1016/j.ijfatigue.2022.107483 ↗
16. Multivariate interpolation and machine learning models for extreme defects-based fatigue life prediction of Ti6Al4V specimens fabricated by SLM10.1016/j.engfracmech.2024.110756 ↗
17. Additive manufacturing and non-destructive testing of topology-optimised aluminium components10.1080/10589759.2020.1774582 ↗
18. Fatigue behaviour of additively manufactured Ti-6Al-4V alloy: The role of defects on scatter and statistical size effect10.1016/j.ijfatigue.2020.105811 ↗
19. High and low-cycle-fatigue properties of 17–4 PH manufactured via selective laser melting in as-built, machined and hipped conditions10.1007/s40964-021-00217-y ↗
20. High-cycle and very-high-cycle fatigue of an additively manufactured aluminium alloy under axial cycling at ultrasonic and conventional frequencies10.1016/j.ijfatigue.2024.108363 ↗
21. Probabilistic stress and strain-life fatigue crack initiation models with mean stress effects and life-dependent scatter considering runouts10.1016/j.ijfatigue.2022.107315 ↗
22. Heterogeneous effect of aging temperature on the fatigue life of additively manufactured thin-walled 18Ni300 maraging steel tubular specimen10.1016/j.matdes.2023.112561 ↗
23. Machine learning insight into the mean stress impact on fatigue life of additively manufactured 18Ni300 maraging steel under various multiaxial stress paths10.1016/j.ijfatigue.2025.109023 ↗
24. A concept for application of B-spline algorithm to density estimation of fatigue failures in 18Ni300 steel10.12913/22998624/200780 ↗
25. Bayes Factors10.1080/01621459.1995.10476572 ↗
26. On the safe use of prior densities for Bayesian model selection10.1002/wics.1595 ↗
27. Statistical estimation of fatigue design curves from datasets involving failures from defects10.1016/j.ijfatigue.2023.107882 ↗
28. Prediction of material fatigue parameters for low alloy forged steels considering error circle10.1016/j.ijfatigue.2018.12.002 ↗
29. A study on modified P-S-N curve of notched specimens based on small sample under multiaxial loading10.1111/ffe.13833 ↗
30. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
31. Johnson · An Introduction to the Bootstrap · 2001 · 23 · 49
32. Effect of Process Parameters on Powder Bed Fusion Maraging Steel 300: A Review10.1007/s40516-022-00182-6 ↗
33. Influence mechanism of parameters process and mechanical properties evolution mechanism of maraging steel 300 by selective laser melting10.1016/j.msea.2017.06.033 ↗
34. Murakami Y. Effects of Small Defects and Nonmetallic Inclusions on the Fatigue Strength of Metals. JSME International Journal Ser 1, Solid Mechanics, Strength of Materials 1989;32:167–80. https://doi.org/10.1299/jsmea1988.32.2_167.10.1299/jsmea1988.32.2_167 ↗
35. Takahashi · 1999
36. Surface roughness effect on multiaxial fatigue behavior of additive manufactured metals and its modeling10.1016/j.tafmec.2019.102260 ↗
37. Modelling of the surface morphology and size effects on fatigue strength of L-PBF Inconel 718 by comparing different testing specimens10.1016/j.ijfatigue.2023.108120 ↗
38. High cycle fatigue behavior and life prediction for additively manufactured 17-4 PH stainless steel: Effect of sub-surface porosity and surface roughness10.1016/j.tafmec.2020.102477 ↗
39. Influence of defects, surface roughness and HIP on the fatigue strength of Ti-6Al-4V manufactured by additive manufacturing10.1016/j.ijfatigue.2018.07.020 ↗
40. Explainable machine learning-based fatigue assessment of 316L stainless steel fabricated by laser-powder bed fusion10.1016/j.ijfatigue.2024.108588 ↗
41. Tests of equality between sets of coefficients in two linear regressions10.2307/1910133 ↗