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Home All issues Volume 110 / No 3 (2022) Matériaux & Techniques, 110 3 (2022) 304 References
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Issue
Matériaux & Techniques
Volume 110, Number 3, 2022
Article Number 304
Number of page(s) 12
Section Mise en oeuvre des matériaux / Materials processing
DOI https://doi.org/10.1051/mattech/2022031
Published online 16 September 2022
  1. M.K. Thompson, G. Moroni, T. Vaneker, et al., Design for additive manufacturing: Trends, opportunities, considerations, and constraints, CIRP Ann. – Manuf. Technol. 65(2), 737–760 (2016) [CrossRef] [Google Scholar]
  2. Y. Al-Meslemi, N. Anwer, L. Mathieu, Modeling key characteristics in the value chain of additive manufacturing, Proc. CIRP 70 , 90–95 (2018) [CrossRef] [Google Scholar]
  3. I. Gibson, D.W. Rosen, B. Stucker, Additive manufacturing technologies – Rapid prototyping to direct digital manufacturing, Springer, 2009 [Google Scholar]
  4. Z. Baicheng, L. Xiaohua, B. Jiaming, et al., Study of selective laser melting (SLM) Inconel 718 part surface improvement by electrochemical polishing, Materials & Design 116 , 531–537 (2017) [CrossRef] [Google Scholar]
  5. F. Salvatore, F. Grange, R. Kaminski, et al., Experimental and numerical study of media action during tribofinishing in the case of SLM titanium parts, Proc. CIRP 58 , 451–456 (2017) [CrossRef] [Google Scholar]
  6. K.C. Yung, T.Y. Xiao, H.S. Choy, et al., Laser polishing of additive manufactured CoCr alloy components with complex surface geometry, J. Mater. Process. Tech. 262 , 53–64 (2018) [CrossRef] [Google Scholar]
  7. E. Brinksmeier, G. Levy, D. Meyer, A.B. Spierings, Surface integrity of selective-laser-melted components, CIRP Ann. 59(1), 601–606 (2010) [CrossRef] [Google Scholar]
  8. P. Stavropoulos, P. Foteinopoulos, A. Papacharalampopoulos, et al., Addressing the challenges for the industrial application of additive manufacturing: Towards a hybrid solution, Int. J. Light. Mater. Manuf. 1(3), 157–168 (2018) [Google Scholar]
  9. K. Sanjay, Fabrication strategy. Additive manufacturing solutions, Springer, Cham, pp. 111–143, 2022 [Google Scholar]
  10. F. Calignano, Design optimization of supports for overhanging structures in aluminum and titanium alloys by selective laser melting, J. Mater. 64 , 203–213 (2014) [CrossRef] [Google Scholar]
  11. A.T. Gaynor, J.K. Guest, Topology optimization considering overhang constraints: Eliminating sacrificial support material in additive manufacturing through design, Struct. Multidiscip. Optim. 54(5), 1157–1172 (2016) [CrossRef] [Google Scholar]
  12. Z. Chen, X. Wu, D. Tomus, et al., Davies, surface roughness of selective laser melted Ti–6Al–4V alloy components, Addit. Manuf. 21 , 91–103 (2018) [Google Scholar]
  13. T. Mishurova, S. Cabeza, T. Thiede, et al., The influence of the support structure on residual stress and distortion in SLM Inconel 718 parts, Metall. Mater. Trans. A 49(7), 3038–3046 (2018) [CrossRef] [Google Scholar]
  14. P. Mercelis, J.P. Kruth, Residual stresses in selective laser sintering and selective laser melting, Rapid Prototyp. J. 12(5), 254–265 (2006) [CrossRef] [Google Scholar]
  15. M.X. Gan, C.H. Wong, Practical support structures for selective laser melting, J. Mater. Process. Technol. 238 , 474–484 (2016) [CrossRef] [Google Scholar]
  16. A. Hussein, L. Hao, C. Yan, et al., Advanced lattice support structures for metal additive manufacturing, J. Mater. Process. Technol. 213(7), 1019–1026 (2013) [CrossRef] [Google Scholar]
  17. Y.H. Kuo, C.C. Cheng, Y.S. Lin, et al., Support structure design in additive manufacturing based on topology optimization, Struct. Multidiscip. Optim. 57(1), 183–195 (2019) [Google Scholar]
  18. L. Matthijs, Integrated component-support topology optimization for additive manufacturing with post-machining, Rapid Prototyp. J. 25(2), 255–265 (2019) [CrossRef] [Google Scholar]
  19. S. Seguy, G. Dessein, L. Arnaud, Surface roughness variation of thin wall milling, related to modal interactions, Int. J. Mach. Tools Manuf. 48(3-4), 261–274 (2008) [Google Scholar]
  20. P. Michalik, J. Zajac, J.M. Hatala, et al., Monitoring surface roughness of thin-walled components from steel C45 machining down and up milling, Measurement 58 , 416–428 (2014) [CrossRef] [Google Scholar]
  21. P. Didier, G. Coz, G. Robin, et al., Consideration of SLM additive manufacturing supports on the stability of flexible structures in finish milling, J. Manuf. Process. 62 , 213–220 (2021) [CrossRef] [Google Scholar]
  22. P. Didier, G. Coz, G. Robin, et al., Consideration of additive manufacturing supports for post-processing by end milling: a hybrid analytical-numerical model and experimental validation, Prog. Addit. Manuf. 7(1), 15–27 (2022) [CrossRef] [Google Scholar]
  23. S. Jayaram, S.G. Kapoor, R.E. Devor, Estimation of the specific cutting pressures for mechanistic cutting force models, Int. J. Mach. Tools Manuf. 41(2), 265–281 (2001) [CrossRef] [Google Scholar]
  24. E. Budak, Y. Altintas, E. Armarego, Prediction of milling force coefficients from orthogonal cutting data, J. Manuf. Sci. Eng. 118(2), (1996) [Google Scholar]
  25. A. Moufki, D. Dudzinski, A. Molinari, et al., Thermoviscoplastic modelling of oblique cutting: forces and chip flow predictions, Int. J. Mech. Sci. 42(6), 1205–1232 (2000) [CrossRef] [Google Scholar]
  26. A. Moufki, D. Dudzinski, G. Le Coz, Prediction of cutting forces from an analytical model of oblique cutting, application to peripheral milling of Ti–6Al–4V alloy, Int. J. Adv. Manuf. Technol. 81(1), 615–626 (2015) [CrossRef] [Google Scholar]
  27. J. Favre, P. Lohmuller, B. Piotrowski, et al., A continuous crystallographic approach to generate cubic lattices and its effect on relative stiffness of architectured materials, Addit. Manuf. 21 , 359–368 (2018) [Google Scholar]
  28. G.P. Steven, Homogenization of multicomponent composite orthotropic materials using FEA, Commun. Numer. Meth. Eng. 13(7), 517–531 (1997) [CrossRef] [Google Scholar]
  29. S. Xu, J. Shen, S. Zhou, et al., Design of lattice structures with controlled anisotropy, Materials & Design 93 , 443–447 (2016) [CrossRef] [Google Scholar]
  30. W.S.W. Harun, et al., A review of powdered additive manufacturing techniques for Ti–6Al–4V biomedical applications, Powder Technol. 331 , 74–97 (2018) [CrossRef] [Google Scholar]
  31. K. Moiduddin, A. Al-Ahmari, M.A. Kindi, et al., Customized porous implants by additive manufacturing for zygomatic reconstruction, Biocybern. Biomed. Eng. 36(4), 719–730 (2016) [CrossRef] [Google Scholar]
  32. C. Cosma, N. Balc, D. Leordean, et al. Customized medical applications of selective laser melting manufacturing, Acad. J. Manuf. Eng. 13(1), (2015) [Google Scholar]
  33. G. Odin, G. Scortecci, F. Levratto, et al., Techniques avancées en implantologie basale, 11(3), 1–10 (2011) [Google Scholar]
  34. D. Paul, Solutions matériaux et géométriques pour la réalisation de dispositifs médicaux implantables sur-mesure en alliages de titane : application aux implants endo-osseux et supra-osseux obtenus par fabrication additive, Thèse, Université de Lorraine, 2019 [Google Scholar]
  35. V.S. Deshpande, N.A. Fleck, M.F. Ashby, Effective properties of the octet-truss lattice material, 49 , 1747–1769 (2001) [Google Scholar]
  36. M. Arizmendi, F.J. Campa, J. Fernandez, et al., Model for surface topography prediction in peripheral milling considering tool vibration, CIRP Ann. – Manuf. Technol. 58(1), 93–96 (2009) [CrossRef] [Google Scholar]
  37. P.G. Benardos, G.C. Vosniakos, Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments, Robot. Comput. Integr. Manuf. 18(5-6), 343–354 (2002) [CrossRef] [Google Scholar]
  38. IMPLANT : Programme Opérationnel FEDER Lorraine et Massif des Vosges de l’Université de LORRAINE (2014–2020), LO0019216 [Google Scholar]

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