Machine Learning of Impact Behavior in Cold Spray of Similar and Dissimilar Metals
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: High-Temperature Coating Behaviors · Nuclear Materials and Properties · Intermetallics and Advanced Alloy Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (75)
Show source-provided references
1. Yin S, Cavaliere P, Aldwell B, Jenkins R, Liao H, Li W, Lupoi R (2018) Cold spray additive manufacturing and repair: fundamentals and applications. Addit Manuf 21:628–650. https://doi.org/10.1016/j.addma.2018.04.01710.1016/j.addma.2018.04.017 ↗
2. Prashar G, Vasudev H (2021) A comprehensive review on sustainable cold spray additive manufacturing: state of the art, challenges and future challenges. J Clean Prod. https://doi.org/10.1016/j.jclepro.2021.12760610.1016/j.jclepro.2021.127606 ↗
3. Raoelison RN, Verdy C, Liao H (2017) Cold gas dynamic spray additive manufacturing today: deposit possibilities, technological solutions and viable applications. Mater Des 133:266–287. https://doi.org/10.1016/j.matdes.2017.07.06710.1016/j.matdes.2017.07.067 ↗
4. Fardan A, Berndt CC, Ahmed R (2021) Numerical modelling of particle impact and residual stresses in cold sprayed coatings: a review. Surf Coat Technol. https://doi.org/10.1016/j.surfcoat.2021.12683510.1016/j.surfcoat.2021.126835 ↗
5. Assadi H, Kreye H, Gärtner F, Klassen T (2016) Cold spraying – a materials perspective. Acta Mater 116:382–407. https://doi.org/10.1016/j.actamat.2016.06.03410.1016/j.actamat.2016.06.034 ↗
6. Bae G, Kumar S, Yoon S, Kang K, Na H, Kim HJ, Lee C (2009) Bonding features and associated mechanisms in kinetic sprayed titanium coatings. Acta Mater 57:5654–5666. https://doi.org/10.1016/j.actamat.2009.07.06110.1016/j.actamat.2009.07.061 ↗
7. Li WY, Liao H, Li CJ, Li G, Coddet C, Wang X (2006) On high velocity impact of micro-sized metallic particles in cold spraying. Appl Surf Sci 253:2852–2862. https://doi.org/10.1016/j.apsusc.2006.05.12610.1016/j.apsusc.2006.05.126 ↗
8. Li WY, Liao H, Li CJ, Bang HS, Coddet C (2007) Numerical simulation of deformation behavior of Al particles impacting on Al substrate and effect of surface oxide films on interfacial bonding in cold spraying. Appl Surf Sci 253:5084–5091. https://doi.org/10.1016/j.apsusc.2006.11.02010.1016/j.apsusc.2006.11.020 ↗
9. Rahmati S, Zúñiga A, Jodoin B, Veiga RGA (2020) Deformation of copper particles upon impact: a molecular dynamics study of cold spray. Comput Mater Sci. https://doi.org/10.1016/j.commatsci.2019.10921910.1016/j.commatsci.2019.109219 ↗
10. Hassani-Gangaraj M, Veysset D, Nelson KA, Schuh CA (2018) In-situ observations of single micro-particle impact bonding. Scr Mater 145:9–13. https://doi.org/10.1016/j.scriptamat.2017.09.04210.1016/j.scriptamat.2017.09.042 ↗
11. Hassani-Gangaraj M, Veysset D, Champagne VK, Nelson KA, Schuh CA (2018) Adiabatic shear instability is not necessary for adhesion in cold spray. Acta Mater 158:430–439. https://doi.org/10.1016/j.actamat.2018.07.06510.1016/j.actamat.2018.07.065 ↗
12. Nikravesh Y, Frantziskonis G, Latypov MI, Muralidharan K (2023) Atomistic characterization of impact bonding in cold spray deposition of copper. Materialia 28:101736. https://doi.org/10.1016/j.mtla.2023.10173610.1016/j.mtla.2023.101736 ↗
13. Hassani M, Veysset D, Sun Y, Nelson KA, Schuh CA (2020) Microparticle impact-bonding modes for mismatched metals: from co-deformation to splatting and penetration. Acta Mater 199:480–494. https://doi.org/10.1016/j.actamat.2020.08.03810.1016/j.actamat.2020.08.038 ↗
14. Tiamiyu AA, Schuh CA (2020) Particle flattening during cold spray: mechanistic regimes revealed by single particle impact tests. Surf Coat Technol. https://doi.org/10.1016/j.surfcoat.2020.12638610.1016/j.surfcoat.2020.126386 ↗
15. Tiamiyu AA, Sun Y, Nelson KA, Schuh CA (2021) Site-specific study of jetting, bonding, and local deformation during high-velocity metallic microparticle impact. Acta Mater 202:159–169. https://doi.org/10.1016/j.actamat.2020.10.05710.1016/j.actamat.2020.10.057 ↗
16. Hussain T, McCartney DG, Shipway PH, Zhang D (2009) Bonding mechanisms in cold spraying: the contributions of metallurgical and mechanical components. J Therm Spray Technol 18:364–379. https://doi.org/10.1007/s11666-009-9298-110.1007/s11666-009-9298-1 ↗
17. Song X, Jin XZ, Zhai W, Tan AWY, Sun W, Li F, Marinescu I, Liu E (2021) Correlation between the macroscopic adhesion strength of cold spray coating and the microscopic single-particle bonding behaviour: simulation, experiment and prediction. Appl Surf Sci. https://doi.org/10.1016/j.apsusc.2021.14916510.1016/j.apsusc.2021.149165 ↗
18. Assadi H, Gärtner F, Stoltenhoff T, Kreye H (2003) Bonding mechanism in cold gas spraying. Acta Mater 51:4379–4394. https://doi.org/10.1016/S1359-6454(03)00274-X10.1016/S1359-6454(03)00274-X ↗
19. Schmidt T, Gärtner F, Assadi H, Kreye H (2006) Development of a generalized parameter window for cold spray deposition. Acta Mater 54:729–742. https://doi.org/10.1016/j.actamat.2005.10.00510.1016/j.actamat.2005.10.005 ↗
20. Leach AR, Leach AR (2001) Molecular modelling: principles and applications. Pearson education, New York
21. Joshi A, James S (2018) Molecular dynamics simulation study of cold spray process. J Manuf Process 33:136–143. https://doi.org/10.1016/j.jmapro.2018.05.00510.1016/j.jmapro.2018.05.005 ↗
22. Zhao P, Zhang Q, Guo Y, Liu H, Deng Z (2020) Atomic simulation of crystal orientation effect on coating surface generation mechanisms in cold spray. Comput Mater Sci. https://doi.org/10.1016/j.commatsci.2020.10985910.1016/j.commatsci.2020.109859 ↗
23. Rahmati S, Veiga RGA, Jodoin B, Zúñiga A (2021) Crystal orientation and grain boundary effects on plastic deformation of FCC particles under high velocity impacts. Materialia. https://doi.org/10.1016/j.mtla.2021.10100410.1016/j.mtla.2021.101004 ↗
24. Grujicic M, Zhao CL, DeRosset WS, Helfritch D (2004) Adiabatic shear instability based mechanism for particles/substrate bonding in the cold-gas dynamic-spray process. Mater Des 25:681–688. https://doi.org/10.1016/j.matdes.2004.03.00810.1016/j.matdes.2004.03.008 ↗
25. Li CJ, Li WY, Liao H (2006) Examination of the critical velocity for deposition of particles in cold spraying. J Therm Spray Technol 15:212–222. https://doi.org/10.1361/105996306X10809310.1361/105996306X108093 ↗
26. Yildirim B, Fukanuma H, Ando T, Gouldstone A, Müftü S (2014) A numerical investigation into cold spray bonding processes. J Tribol. https://doi.org/10.1115/1.402847110.1115/1.4028471 ↗
27. Yu M, Li WY, Wang FF, Liao HL (2012) Finite element simulation of impacting behavior of particles in cold spraying by Eulerian approach. J Therm Spray Technol. https://doi.org/10.1007/s11666-011-9717-y10.1007/s11666-011-9717-y ↗
28. Li WY, Yu M, Wang FF, Yin S, Liao HL (2014) A generalized critical velocity window based on material property for cold spraying by eulerian method. J Therm Spray Technol 23:557–566. https://doi.org/10.1007/s11666-013-0023-810.1007/s11666-013-0023-8 ↗
29. Li W, Yang K, Zhang D, Zhou X, Guo X (2016) Interface behavior of particles upon impacting during cold spraying of Cu/Ni/Al mixture. Mater Des 95:237–246. https://doi.org/10.1016/j.matdes.2016.01.12210.1016/j.matdes.2016.01.122 ↗
30. Wang FF, Li WY, Yu M, Liao HL (2014) Prediction of critical velocity during cold spraying based on a coupled thermomechanical eulerian model. J Therm Spray Technol. https://doi.org/10.1007/s11666-013-0009-610.1007/s11666-013-0009-6 ↗
31. Li WY, Gao W (2009) Some aspects on 3D numerical modeling of high velocity impact of particles in cold spraying by explicit finite element analysis. Appl Surf Sci 255:7878–7892. https://doi.org/10.1016/j.apsusc.2009.04.13510.1016/j.apsusc.2009.04.135 ↗
32. Xie J, Nélias D, Le Berre HW, Ogawa K, Ichikawa Y (2015) Simulation of the cold spray particle deposition process. J Tribol. https://doi.org/10.1115/1.403025710.1115/1.4030257 ↗
33. Song X, Ng KL, Chea JMK, Sun W, Tan AWY, Zhai W, Li F, Marinescu I, Liu E (2020) Coupled Eulerian-Lagrangian (CEL) simulation of multiple particle impact during metal cold spray process for coating porosity prediction. Surf Coat Technol. https://doi.org/10.1016/j.surfcoat.2020.12543310.1016/j.surfcoat.2020.125433 ↗
34. Li WY, Yin S, Wang XF (2010) Numerical investigations of the effect of oblique impact on particle deformation in cold spraying by the SPH method. Appl Surf Sci 256:3725–3734. https://doi.org/10.1016/j.apsusc.2010.01.01410.1016/j.apsusc.2010.01.014 ↗
35. Manap A, Okabe T, Ogawa K (2011) Computer simulation of cold sprayed deposition using smoothed particle hydrodynamics. Proc Eng. https://doi.org/10.1016/j.proeng.2011.04.19010.1016/j.proeng.2011.04.190 ↗
36. Manap A, Ogawa K, Okabe T (2012) Numerical analysis of interfacial bonding of Al-Si particle and mild steel substrate by cold spray technique using the SPH method. J Solid Mech Mater Eng 6:241–250. https://doi.org/10.1299/jmmp.6.24110.1299/jmmp.6.241 ↗
37. Gnanasekaran B, Liu GR, Fu Y, Wang G, Niu W, Lin T (2019) A smoothed particle hydrodynamics (SPH) procedure for simulating cold spray process - a study using particles. Surf Coat Technol. https://doi.org/10.1016/j.surfcoat.2019.07.03610.1016/j.surfcoat.2019.07.036 ↗
38. Stoltenhoff T, Kreye H, Richter HJ (2002) An analysis of the cold spray process and its coatings. J Therm Spray Technol 11:542–550. https://doi.org/10.1361/10599630277034868210.1361/105996302770348682 ↗
39. Bae G, Xiong Y, Kumar S, Kang K, Lee C (2008) General aspects of interface bonding in kinetic sprayed coatings. Acta Mater 56:4858–4868. https://doi.org/10.1016/j.actamat.2008.06.00310.1016/j.actamat.2008.06.003 ↗
40. Hussain T, McCartney DG, Shipway PH (2012) Bonding between aluminium and copper in cold spraying: story of asymmetry. Mater Sci Technol 28:1371–1378. https://doi.org/10.1179/1743284712Y.000000005110.1179/1743284712Y.0000000051 ↗
41. Meng F, Hu D, Gao Y, Yue S, Song J (2016) Cold-spray bonding mechanisms and deposition efficiency prediction for particle/substrate with distinct deformability. Mater Des 109:503–510. https://doi.org/10.1016/j.matdes.2016.07.10310.1016/j.matdes.2016.07.103 ↗
42. Nikbakht R, Seyedein SH, Kheirandish S, Assadi H, Jodoin B (2018) Asymmetrical bonding in cold spraying of dissimilar materials. Appl Surf Sci 444:621–632. https://doi.org/10.1016/j.apsusc.2018.03.10310.1016/j.apsusc.2018.03.103 ↗
43. Nikbakht R, Seyedein SH, Kheirandish S, Assadi H, Jodoin B (2019) The role of deposition sequence in cold spraying of dissimilar materials. Surf Coat Technol 367:75–85. https://doi.org/10.1016/j.surfcoat.2019.03.06510.1016/j.surfcoat.2019.03.065 ↗
44. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C (2017) Machine learning in materials informatics: recent applications and prospects. Npj Comput Mater. https://doi.org/10.1038/s41524-017-0056-510.1038/s41524-017-0056-5 ↗
45. Mueller T, Kusne AG, Ramprasad R (2016) Machine learning in materials science: recent progress and emerging applications. Rev Comput Chem. https://doi.org/10.1002/9781119148739.ch410.1002/9781119148739.ch4 ↗
46. Xue D, Xue D, Yuan R, Zhou Y, Balachandran PV, Ding X, Sun J, Lookman T (2017) An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta Mater 125:532–541. https://doi.org/10.1016/j.actamat.2016.12.00910.1016/j.actamat.2016.12.009 ↗
47. He J, Li J, Liu C, Wang C, Zhang Y, Wen C, Xue D, Cao J, Su Y, Qiao L, Bai Y (2021) Machine learning identified materials descriptors for ferroelectricity. Acta Mater. https://doi.org/10.1016/j.actamat.2021.11681510.1016/j.actamat.2021.116815 ↗
48. Revi V, Kasodariya S, Talapatra A, Pilania G, Alankar A (2021) Machine learning elastic constants of multi-component alloys. Comput Mater Sci. https://doi.org/10.1016/j.commatsci.2021.11067110.1016/j.commatsci.2021.110671 ↗
49. Wang Z, Cai S, Chen W, Ali RA, Jin K (2021) Analysis of critical velocity of cold spray based on machine learning method with feature selection. J Therm Spray Technol 30:1213–1225. https://doi.org/10.1007/s11666-021-01198-810.1007/s11666-021-01198-8 ↗
50. Yang K, Xu X, Yang B, Cook B, Ramos H, Krishnan NMA, Smedskjaer MM, Hoover C, Bauchy M (2019) Predicting the Young’s modulus of silicate glasses using high-throughput molecular dynamics simulations and machine learning. Sci Rep. https://doi.org/10.1038/s41598-019-45344-310.1038/s41598-019-45344-3 ↗
51. Wan J, Jiang JW, Park HS (2020) Machine learning-based design of porous graphene with low thermal conductivity. Carbon N Y 157:262–269. https://doi.org/10.1016/j.carbon.2019.10.03710.1016/j.carbon.2019.10.037 ↗
52. Li J, Xie B, Fang Q, Liu B, Liu Y, Liaw PK (2021) High-throughput simulation combined machine learning search for optimum elemental composition in medium entropy alloy. J Mater Sci Technol 68:70–75. https://doi.org/10.1016/j.jmst.2020.08.00810.1016/j.jmst.2020.08.008 ↗
53. Xie B, Fang Q, Li J (2022) Investigation of nanomachining-induced plastic behavior using machine learning-assisted high-throughput molecular dynamics simulations. Int J Adv Manuf Technol 119:8057–8068. https://doi.org/10.1007/s00170-022-08802-310.1007/s00170-022-08802-3 ↗
54. Wei A, Ye H, Guo Z, Xiong J (2022) SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array. Nanoscale Adv 4:1455–1463. https://doi.org/10.1039/d1na00457c10.1039/d1na00457c ↗
55. Zhou XW, Johnson RA, Wadley HNG (2004) Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers. Phys Rev B. https://doi.org/10.1103/PhysRevB.69.14411310.1103/PhysRevB.69.144113 ↗
56. Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117:1–19. https://doi.org/10.1006/jcph.1995.103910.1006/jcph.1995.1039 ↗
57. Stukowski A (2010) Visualization and analysis of atomistic simulation data with OVITO-the open visualization tool. Model Simul Mater Sci Eng. https://doi.org/10.1088/0965-0393/18/1/01501210.1088/0965-0393/18/1/015012 ↗
58. Ward L, Dunn A, Faghaninia A, Zimmermann NER, Bajaj S, Wang Q, Montoya J, Chen J, Bystrom K, Dylla M, Chard K, Asta M, Persson KA, Snyder GJ, Foster I, Jain A (2018) Matminer: an open source toolkit for materials data mining. Comput Mater Sci 152:60–69. https://doi.org/10.1016/j.commatsci.2018.05.01810.1016/j.commatsci.2018.05.018 ↗
59. Foiles SM, Baskes MI, Daw MS (1986) Embedded-atom-method functions for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys. Phys Rev B 33:7983–7991. https://doi.org/10.1103/PhysRevB.33.798310.1103/PhysRevB.33.7983 ↗
60. Foiles SM, Adams JB (1989) Thermodynamic properties of fcc transition metals as calculated with the embedded-atom method. Phys Rev B 40:5909–5915. https://doi.org/10.1103/PhysRevB.40.590910.1103/PhysRevB.40.5909 ↗
61. Daw MS, Foiles SM, Baskes MI (1993) The embedded-atom method: a review of theory and applications. Mater Sci Rep 9:251–310. https://doi.org/10.1016/0920-2307(93)90001-U10.1016/0920-2307(93)90001-U ↗
62. J.A. Dean, Lange’s Handbook Of Chemistry, 15th ed., 1999.
63. Ong SP, Richards WD, Jain A, Hautier G, Kocher M, Cholia S, Gunter D, Chevrier VL, Persson KA, Ceder G (2013) Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis. Comput Mater Sci 68:314–319. https://doi.org/10.1016/j.commatsci.2012.10.02810.1016/j.commatsci.2012.10.028 ↗
64. Material Properties | Website about Elements and Materials, Mater. Prop. (2022). https://material-properties.org/ (accessed August 7, 2022)
65. Deml AM, O’Hayre R, Wolverton C, Stevanović V (2016) Predicting density functional theory total energies and enthalpies of formation of metal-nonmetal compounds by linear regression. Phys Rev B. https://doi.org/10.1103/PhysRevB.93.08514210.1103/PhysRevB.93.085142 ↗
66. Cohesive Energy | The Elements Handbook at KnowledgeDoor Cohesive Energy | The Elements Handbook at KnowledgeDoor, (2016) 2–5. http://www.knowledgedoor.com/2/elements_handbook/cohesive_energy.html (accessed August 7, 2022)
67. Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson KA (2013) Commentary: The materials project: a materials genome approach to accelerating materials innovation. APL Mater. https://doi.org/10.1063/1.481232310.1063/1.4812323 ↗
68. Material Science | News | Materials Engineering | News, (n.d.). https://www.azom.com/ (accessed October 8, 2023)
69. Sendecor GW, Cochran WG (1989) Statistical methods. Blackwell, New York
70. Raschka S (2018) MLxtend: providing machine learning and data science utilities and extensions to Python’s scientific computing stack. J Open Source Softw 3:638. https://doi.org/10.21105/joss.0063810.21105/joss.00638 ↗
71. Sobol IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55:271–280. https://doi.org/10.1016/S0378-4754(00)00270-610.1016/S0378-4754(00)00270-6 ↗
72. Paleyes A, Pullin M, Mahsereci M, McCollum C, Lawrence ND, Gonzalez J (2021) Emulation of physical processes with Emukit, ArXiv Prepr. ArXiv2110.13293. http://arxiv.org/abs/2110.13293
73. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:101093340432410.1023/A:1010933404324 ↗
74. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: Machine learning in python. J Mach Learn Res 12:2825–2830
75. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press10.7551/mitpress/3206.001.0001 ↗