Physics-constrained dimensionless Gaussian process modelling of laser surface polishing in inconel 718
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: Advanced Surface Polishing Techniques · Laser Material Processing Techniques · Advanced Measurement and Metrology Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
Show source-provided references
1. L. Beránek et al., Laser Polishing of Nickel Superalloy Haynes 230 Manufactured by Laser Powder Bed Fusion Process. Available at SSRN 6139850
2. M.A.R. Bures, N. T. I., L. A. V. Zetek. Application of laser surface polishing on additive manufactured parts of Inconel 718 nickel-based superalloy. MM SJ. 2020, 3873–3877 (2020)10.17973/MMSJ.2020_03_2019141 ↗
3. J.A. Estrada-Díaz et al., A mathematical dimensional model for predicting bulk density of Inconel 718 parts produced by selective laser melting. Materials 14.3 (2021): 51210.3390/ma14030512 ↗
4. A.M. Hafiz, E.V. Khalid, Bordatchev, O. Remus, Tutunea-Fatan, Experimental analysis of applicability of a picosecond laser for micro-polishing of micromilled Inconel 718 superalloy. Int. J. Adv. Manuf. Technol. 70.9 (2014): 1963–197810.1007/s00170-013-5408-9 ↗
5. F. Zhihao et al., Laser polishing of additive manufactured superalloy. Procedia CIRP. 71, 150–154 (2018)10.1016/j.procir.2018.05.088 ↗
6. N. Ahmed et al., Controlling the material removal and roughness of Inconel 718 in laser machining. Mater. Manuf. Processes. 34, 1169–1181 (2019)10.1080/10426914.2019.1615082 ↗
7. Y. Kaynak, Finish machining-induced surface roughness, microhardness and XRD analysis of selective laser melted Inconel 718 alloy. Procedia CIRP. 71, 500–504 (2018)10.1016/j.procir.2018.05.013 ↗
8. B.K. Khandai et al., Laser Surface Polishing of Additively Manufactured Components via Beam Shaping Influence on Surface Topography, Microstructure, and Mechanical Properties
9. Z. Baicheng et al., Study of selective laser melting (SLM) Inconel 718 part surface improvement by electrochemical polishing. Mater. Design. 116, 531–537 (2017)10.1016/j.matdes.2016.11.103 ↗
10. A. Shrivastava et al., Electropolishing of Inconel 718 manufactured by laser powder bed fusion: Effect of heat treatment on hardness, 3D surface topography and material ratio curve. Opt. Laser Technol. 144, 107448 (2021)10.1016/j.optlastec.2021.107448 ↗
11. D. Lesyk et al., Surface polishing of laser powder bed fused superalloy components by magnetic post-treatment. 2020 IEEE 10th International Conference Nanomaterials: Applications & Properties (NAP). IEEE, 202010.1109/NAP51477.2020.9309600 ↗
12. Y. Li, Z. Zhang, Y. Guan, Thermodynamics analysis and rapid solidification of laser polished Inconel 718 by selective laser melting. Appl. Surf. Sci. 511, 145423 (2020)10.1016/j.apsusc.2020.145423 ↗
13. Y. Liu et al., Improving surface quality and superficial microstructure of LDED Inconel 718 superalloy processed by hybrid laser polishing. J. Mater. Process. Technol. 300, 117428 (2022)10.1016/j.jmatprotec.2021.117428 ↗
14. F. Lv et al., On the role of laser in situ re-melting into pore elimination of Ti–6Al–4V components fabricated by selective laser melting. J. Alloys Compd. 854, 156866 (2021)10.1016/j.jallcom.2020.156866 ↗
15. M. Nguyen-Tuong, Duy, Seeger, J. Peters, Model learning with local Gaussian process regression. Adv. Robot. 23.15 (2009): 2015–203410.1163/016918609X12529286896877 ↗
16. J. Park et al., Gaussian process regression (GPR) representation in predictive model markup language (PMML). Smart Sustainable Manuf. Syst. 1(1), 121–141 (2017)10.1520/SSMS20160008 ↗
17. A. Temmler et al., Experimental investigation on surface structuring by laser remelting (WaveShape) on Inconel 718 using varying laser beam diameters and scan speeds. Appl. Surf. Sci. 541, 147814 (2021)10.1016/j.apsusc.2020.147814 ↗
18. Z. Yang et al., Surface morphological characteristics and mechanisms of laser-assisted electrochemical polishing on Inconel 718 alloy. Opt. Laser Technol. 181, 111591 (2025)10.1016/j.optlastec.2024.111591 ↗
19. A. Kumar, Effect of three-dimensional melt pool convection on process characteristics during laser cladding. Comput. Mater. Sci. 46(2), 495–506 (2009)10.1016/j.commatsci.2009.04.002 ↗
20. G.-W.R.B.E. Roemer, J. Meijer, Analytical model describing the relationship between laser power, beam velocity, and melt pool depth in the case of laser remelting, realloying, and redispersing. Lasers in Material Processing. Vol. 3097. SPIE, 199710.1117/12.281110 ↗
21. S.A. Sharma, Shashank, Ramakrishna, J. Ramkumar, Numerical simulation of melt hydrodynamics in laser micro processing of metals. Procedia CIRP. 95, 944–949 (2020)10.1016/j.procir.2020.01.186 ↗
22. S. Tiwari et al., Laser Melted Inconel 718 Superalloy. Advancement in Combustion Technologies Towards Net Zero Carbon Emission: Select Proceedings of ICICEPC 2024 (2026): 5910.1007/978-981-95-2239-2_5 ↗
23. G. Yu et al., Influence of processing parameters on laser penetration depth and melting/re-melting densification during selective laser melting of aluminum alloy. Appl. Phys. A 122(10), 891 (2016)10.1007/s00339-016-0428-6 ↗
24. M. Zavala-Arredondo et al., Investigating the melt pool properties and thermal effects of multi-laser diode area melting. Int. J. Adv. Manuf. Technol. 97(1), 1383–1396 (2018)10.1007/s00170-018-2038-2 ↗
25. T.I. Zohdi, Modeling and simulation of laser processing of particulate-functionalized materials. Arch. Comput. Methods Eng. 24(1), 89–113 (2017)10.1007/s11831-015-9160-1 ↗
26. S. Jin et al., A Gaussian process model-guided surface polishing process in additive manufacturing. J. Manuf. Sci. Eng. 142(1), 011003 (2020)10.1115/1.4045334 ↗
27. G. Tapia, A.H. Elwany, H. Sang, Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Additive Manuf. 12, 282–290 (2016)10.1016/j.addma.2016.05.009 ↗
28. M. Balbaa, S. Mekhiel, M. Elbestawi, J. McIsaac, On selective laser melting of Inconel 718: Densification, surface roughness, and residual stresses. Mater. Design. 193, 108818 (2020)10.1016/j.matdes.2020.108818 ↗
29. M.O. Vasylyev, B.M. Mordyuk, S.M. Voloshko, Surface Post-Processing of Inconel 718 Alloy Fabricated by Additive Manufacturing: Selective Laser Melting. Progress Phys. Met. 25(3), 614–642 (2024)
30. S. Dadbakhsh, L. Hao, C.Y. Kong, Surface finish improvement of LMD samples using laser polishing. Virtual Phys. Prototyp. 5(4), 215–221 (2010)10.1080/17452759.2010.528180 ↗
31. S. Canacoo, E. Contreras Lopez, O. Coronel, F. Ahmed, J. Li, A.K. Srivastava, Ultrafast laser ablation of Inconel 718 for surface improvement (Manufacturing Letters, 2022)10.1016/j.mfglet.2022.07.054 ↗
32. S.K. Sharma, K. Biswas, A.K. Nath, I. Manna, J.D. Majumdar, Microstructural characterization of laser surface-melted Inconel 718. J. Opt. 49(4), 494–509 (2020)10.1007/s12596-020-00649-9 ↗
33. Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V. M., Guo, H., Hamdia, K.,… Rabczuk, T. (2020). 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, 362, 112790.10.1016/j.cma.2019.112790 ↗
34. P. Ravindranatha Reddy, G.J. Reddy, G. Prasanthi, Mathematical Modeling of Material Removal Rate Using Buckingham Pi Theorem in Electrical Discharge Machining of Hastelloy C276. Advances in Applied Mechanical Engineering: Select Proceedings of ICAMER 2019. Springer Singapore, 2020. 843–85210.1007/978-981-15-1201-8_90 ↗
35. J. Singh, R. Singh, H. Singh, Surface roughness prediction using Buckingham’s Pi-theorem for SS-316L hip implant prepared as rapid investment casting. Materials Today: Proceedings 5.9 (2018): 18080–1808810.1016/j.matpr.2018.06.142 ↗
36. R. Singh, S. Singh, Modelling of dimensional accuracy in precision investment casting using Buckingham’s Pi approach. Materials Today: Proceedings 4.2 (2017): 1598–160510.1016/j.matpr.2017.01.183 ↗
37. Y. Wang, Y. Li, Y. Guan, Surface modification and mechanical properties of laser powder bed fusion Inconel 718 after magnetic-assisted laser polishing. Opt. Laser Technol. 162, 109291 (2023)10.1016/j.optlastec.2023.109291 ↗
38. Y. Liu et al., Tribological behaviors of LDED Inconel 718 samples polished with a hybrid laser polishing technique. J. Mater. Res. Technol. 25, 633–646 (2023)10.1016/j.jmrt.2023.05.230 ↗
39. Z. Xu et al., Enhancing high-temperature performance of Inconel 718 LDED samples through hybrid laser polishing post-treatment. Virtual Phys. Prototyp. 19(1), e2345391 (2024)10.1080/17452759.2024.2345391 ↗
40. M. Ćwikła, R. Dziedzic, J. Reiner, Influence of overlap on surface quality in the laser polishing of 3D printed Inconel 718 under the effect of air and argon. Materials. 14(6), 1479 (2021)10.3390/ma14061479 ↗
41. C. Du, Y. Zhao, Z. Xu, Y. Sun, Influence of in situ laser polishing on pore defects and mechanical properties of IN718 alloy fabricated by selective laser melting. J. Mater. Eng. Perform. 34(8), 7202–7217 (2025)10.1007/s11665-024-09678-y ↗
42. I. Gholaminezhad et al., Uncertainty quantification and robust modeling of selective laser melting process using stochastic multi-objective approach. Int. J. Adv. Manuf. Technol. 86(5), 1425–1441 (2016)10.1007/s00170-015-8238-0 ↗
43. F. Hermann et al., Data-driven prediction and uncertainty quantification of process parameters for directed energy deposition. Materials. 16, 7308 (2023)10.3390/ma16237308 ↗
44. Y. Hu et al., Predictive modeling and uncertainty quantification of laser shock processing by Bayesian Gaussian processes with multiple outputs. J. Manuf. Sci. Eng. 136(4), 041014 (2014)10.1115/1.4027539 ↗
45. N. Menon, S. Mondal, and Amrita Basak,. Multi-fidelity surrogate-based process mapping with uncertainty quantification in laser directed energy deposition. Materials 15.8 (2022): 290210.3390/ma15082902 ↗
46. U. Thakre, G. Rakesh, Mote. Uncertainty quantification and statistical modeling of selective laser sintering process using polynomial chaos based response surface method. J. Manuf. Process. 81, 893–906 (2022)10.1016/j.jmapro.2022.07.022 ↗