Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach
Source abstract
Abstract
In this work, a framework based on the machine learning (ML) approach and Spearman’s rank correlation analysis is introduced as an effective instrument to solve the influence of defects detected by micro-computed tomography (μCT) method, and stress amplitude on the fatigue life performance of AM Ti-6Al-4V. Artificial neural network (ANN), random forest regressor (RFR) and support vector regressor (SVR) models are implemented and optimized. The optimization is performed on training set by tuning the hyperparameters and parameters using the leave-one-out cross validation (LOOCV) technique. The results present comparison between predicted and experimental results and validate the proposed framework.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (51)
Show source-provided references
1. Metal Additive Manufacturing: A Review10.1007/s11665-014-0958-z ↗
2. A selective laser melting and solution heat treatment refined Al–12Si alloy with a controllable ultrafine eutectic microstructure and 25% tensile ductility10.1016/j.actamat.2015.05.017 ↗
3. Evaluation of Titanium Alloys Fabricated Using Rapid Prototyping Technologies—Electron Beam Melting and Laser Beam Melting10.3390/ma4101776 ↗
4. Selective Laser Melting Technique of Co-Cr Dental Alloys: A Review of Structure and Properties and Comparative Analysis with Other Available Techniques10.1111/jopr.12268 ↗
5. In-situ investigation on fatigue behaviors of Ti-6Al-4V manufactured by selective laser melting10.1016/j.ijfatigue.2019.105424 ↗
6. Very-high-cycle fatigue behavior of Ti-6Al-4V manufactured by selective laser melting: Effect of build orientation10.1016/j.ijfatigue.2020.105628 ↗
7. Fatigue behaviour of additive manufactured materials: An overview of some recent experimental studies on Ti-6Al-4V considering various processing and loading direction effects10.1111/ffe.13000 ↗
8. The effect of manufacturing defects on the fatigue life of selective laser melted Ti-6Al-4V structures10.1016/j.matdes.2020.108708 ↗
9. Revisiting fundamental welding concepts to improve additive manufacturing: From theory to practice10.1016/j.pmatsci.2019.100590 ↗
10. Keyhole-induced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation
11. Corner fatigue cracking behavior of hybrid laser AA7020 welds by synchrotron X-ray computed microtomography10.1016/j.msea.2015.11.011 ↗
12. A new approach to correlate the defect population with the fatigue life of selective laser melted Ti-6Al-4V alloy10.1016/j.ijfatigue.2020.105584 ↗
13. Investigation of new volumetric non-destructive techniques to characterise additive manufacturing parts10.1007/s40194-018-0593-7 ↗
14. Additive manufacturing and non-destructive testing of topology-optimised aluminium components10.1080/10589759.2020.1774582 ↗
15. Senck S, Happl M, Scheerer M, Glinz J, Reiter T, Kastner J. Quantification of surface-near porosity in additively manufactured aluminum brackets using X-ray microcomputed tomography. AIAA Scitech 2021 Forum, Reston, Virginia: American Institute of Aeronautics and Astronautics; 2021, pp. -. https://doi.org/10.2514/6.2021-0536.10.2514/6.2021-0536 ↗
16. Material defects as the basis of fatigue design10.1016/j.ijfatigue.2011.12.001 ↗
17. Fatigue life evaluation of Ti–6Al–4V welded joints manufactured by electron beam melting10.1111/ffe.13490 ↗
18. Extended finite element simulation on Tensile, fracture toughness and fatigue crack growth behaviour of additively manufactured Ti6Al4V alloy10.1016/j.tafmec.2021.103163 ↗
19. Three-dimensional finite element modelling for additive manufacturing of Ti-6Al-4V components: Effect of scanning strategies on temperature history and residual stress10.1016/j.jajp.2022.100106 ↗
20. Micropore-propagation-based model of fatigue life analysis of SLM manufactured Ti-6Al-4V
21. Multi-scale damage mechanics method for fatigue life prediction of additive manufacture structures of Ti-6Al-4V10.1016/j.msea.2016.05.073 ↗
22. A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function10.1177/1475921716639587 ↗
23. A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity10.1007/s11831-016-9197-9 ↗
24. Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering 2020;vol. 362. https://doi.org/10.1016/j.cma.2019.112790.10.1016/j.cma.2019.112790 ↗
25. Optimization of selective laser melting process parameters for Ti-6Al-4V alloy manufacturing using deep learning10.1016/j.jmapro.2020.04.014 ↗
26. Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images10.1007/s10845-018-1412-0 ↗
27. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
28. Prediction of greenhouse gas emissions reductions via machine learning algorithms: Toward an artificial intelligence-based life cycle assessment for automotive lightweighting. Sustainable
29. Fatigue life prediction of bending polymer films using random forest10.1016/j.ijfatigue.2022.107230 ↗
30. Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting10.1016/j.ijfatigue.2022.106764 ↗
31. Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V
32. A deep learning-based fatigue crack growth rate measurement method using mobile phones
33. A machine-learning fatigue life prediction approach of additively manufactured metals10.1016/j.engfracmech.2020.107508 ↗
34. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L10.1016/j.ijfatigue.2020.105941 ↗
35. A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing10.1016/j.ijfatigue.2020.106089 ↗
36. Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features10.1016/j.ijfatigue.2022.107147 ↗
37. Comparison of the microstructures and mechanical properties of Ti–6Al–4V fabricated by selective laser melting and electron beam melting10.1016/j.matdes.2015.12.135 ↗
38. Fatigue properties of Ti6Al4V cellular specimens fabricated via SLM: CAD vs real geometry10.1016/j.prostr.2017.11.068 ↗
39. Fatigue performance evaluation of selective laser melted Ti–6Al–4V10.1016/j.msea.2014.01.041 ↗
40. The potency of defects on fatigue of additively manufactured metals10.1016/j.ijmecsci.2022.107185 ↗
41. Application of artificial neural network for predicting crack growth direction in multiple cracks geometry10.1016/j.asoc.2015.02.003 ↗
42. Remaining fatigue life assessment of in-service road bridge decks based upon artificial neural networks10.1016/j.engstruct.2018.05.122 ↗
43. Approximating support vector machine with artificial neural network for fast prediction10.1016/j.eswa.2014.02.025 ↗
44. Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine10.1016/j.ijfatigue.2022.106761 ↗
45. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines10.1016/j.oregeorev.2015.01.001 ↗
46. Awad M, Khanna R. Efficient learning machines: theories, concepts, and applications for engineers and system designers. [Berkley?]: Apress Open; 2015.10.1007/978-1-4302-5990-9 ↗
47. Vapnik V. Statistical learning theory. New York: Wiley; 1998.
48. Lei C, Deng J, Cao K, Xiao Y, Ma L, Wang W, Ma T, Shu C. A comparison of random forest and support vector machine approaches to predict coal spontaneous combustion in gob. Fuel 2019;vol. 239:297-311. https://doi.org/10.1016/j.fuel.2018.11.006.10.1016/j.fuel.2018.11.006 ↗
49. French M. Fundamentals of Optimization. Cham: Springer International Publishing; 2018. https://doi.org/10.1007/978-3-319-76192-3.10.1007/978-3-319-76192-3 ↗
50. Optimal control by least squares support vector machines10.1016/S0893-6080(00)00077-0 ↗
51. Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness10.1016/j.apm.2010.07.048 ↗