EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 11 (2024) Manufacturing Rev., 11 (2024) 4 References
Open Access
Issue
Manufacturing Rev.
Volume 11, 2024
Article Number 4
Number of page(s) 16
DOI https://doi.org/10.1051/mfreview/2024002
Published online 01 March 2024
  1. J. Mohd Jani, M. Leary, A. Subic, M.A. Gibson, A review of shape memory alloy research, applications and opportunities, Mater. Des. 56 (2014) 1078–113 [CrossRef] [Google Scholar]
  2. C. Dimitris, Lagoudas. Shape memory alloys: modeling and engineering applications. 2008 th edn. Springer, 2008 [Google Scholar]
  3. K. Otsuka, C.M. Wayman, Shape memory materials, Cambridge University Press, 1999 [Google Scholar]
  4. A. Günen, F. Ceritbinmez, K. Patel, M.A. Akhtar, S. Mukherjee, E. Kanca et al., WEDM machining of MoNbTaTiZr refractory high entropy alloy, CIRP J. Manuf. Sci. Technol. 38 (2022) 547–559 [CrossRef] [Google Scholar]
  5. R. Sabban, K. Dash, S. Suwas, B.S. Murty, Strength-ductility synergy in high entropy alloys by tuning the thermo-mechanical process parameters: a comprehensive review, J. Indian Inst. Sci. 102 (2022) 91–116 [CrossRef] [Google Scholar]
  6. T. Richter, D. Schröpfer, M. Rhode, A. Börner, Influence of modern machining processes on the surface integrity of high-entropy alloys, IOP Conf. Ser. Mater. Sci. Eng. 882 (2020) 012016 [CrossRef] [Google Scholar]
  7. G.S. Firstov, T.A. Kosorukova, Yu.N. Koval, V.V. Odnosum, High entropy shape memory alloys, Mater. Today Proc. 2 (2015) S499–S503 [CrossRef] [Google Scholar]
  8. G.S. Firstov, T.A. Kosorukova, Y.N. Koval, P.A. Verhovlyuk, Directions for high-temperature shape memory alloys' improvement: straight way to high-entropy materials? Shape Mem. Superelasticity 1 (2015) 400–407 [CrossRef] [Google Scholar]
  9. D. Piorunek, J. Frenzel, N. Jöns, C. Somsen, G. Eggeler, Chemical complexity, microstructure and martensitic transformation in high entropy shape memory alloys, Intermetallics (Barking) 122 (2020)106792 [CrossRef] [Google Scholar]
  10. S. Li, D. Cong, X. Sun, Y. Zhang, Z. Chen, Z. Nie et al., Wide-temperature-range perfect superelasticity and giant elastocaloric effect in a high entropy alloy, Mater. Res. Lett. 7 (2019) 482–489 [CrossRef] [Google Scholar]
  11. G. Zhao, D. Li, G. Xu, D. Fang, Y. Ye, C. Huang et al., As-cast high entropy shape memory alloys of (TiHfX)50(NiCu)50 with large recoverable strain and good mechanical properties, J. Mater. Eng. Perform. 31 (2022) 10089–10098 [CrossRef] [Google Scholar]
  12. S.-Y. Kuo, W.-P. Kao, S.-H. Chang, T.-E. Shen, J.-W. Yeh, C.-W. Tsai, Effect of homogenization on the transformation temperatures and mechanical properties of Cu15Ni35Hf12. 5Ti25Zr12.5 and Cu15Ni35Hf15Ti20Zr15 high-entropy shape memory alloys, Materials 16 (2023) 3212 [CrossRef] [Google Scholar]
  13. E.J. Pickering, N.G. Jones, High-entropy alloys: a critical assessment of their founding principles and future prospects, Int. Mater. Rev. 61 (2016) 183–202 [CrossRef] [Google Scholar]
  14. Y. Zhang, T.T. Zuo, Z. Tang, M.C. Gao, K.A. Dahmen, P.K. Liaw et al., Microstructures and properties of high-entropy alloys, Prog. Mater. Sci. 61 (2014) 1–93 [CrossRef] [Google Scholar]
  15. P. Sharma, V.K. Dwivedi, S.P. Dwivedi, Development of high entropy alloys: a review, Mater. Today Proc. 43 (2021) 502–509 [CrossRef] [Google Scholar]
  16. E.P. George, D. Raabe, R.O. Ritchie, High-entropy alloys, Nat. Rev. Mater. 4 (2019) 515–534 [CrossRef] [Google Scholar]
  17. J.-W. Yeh, Alloy design strategies and future trends in high-entropy alloys, JOM 65 (2013) 1759–1771 [CrossRef] [Google Scholar]
  18. B.S. Murty, J.-W. Yeh, S. Ranganathan, P.P. Bhattacharjee, High-entropy alloys, 2nd edn. Elsevier, Amsterdam, 2019 [Google Scholar]
  19. D.O. Svensson, High entropy alloys: breakthrough materials for aero engine applications? Diploma work in the Master programme, Appl. Phys., nd, [Google Scholar]
  20. M.-H. Tsai, J.-W. Yeh, High-entropy alloys: a critical review, Mater. Res. Lett. 2 (2014) 107–123 [CrossRef] [Google Scholar]
  21. K. Weinert, V. Petzoldt, Machining of NiTi based shape memory alloys, Mater. Sci. Eng. A 378 (2004) 180–184 [CrossRef] [Google Scholar]
  22. F. Ceritbinmez, A. Günen, M.A. Akhtar, K. Patel, S. Mukherjee, L. Yünlü et al., Surface integrity characteristics in wire-EDM of HfTaTiVZr refractory high entropy alloy, Adv. Mater. Process. Technol. (2022) 1–18. [Google Scholar]
  23. J. Kumar, S. Sharma, J. Singh, S. Singh, G. Singh, Optimization of wire-EDM process parameters for Al-Mg-0. 6Si-0.35Fe/15%RHA/5%Cu hybrid metal matrix composite using TOPSIS: processing and characterizations, J. Manuf. Mater. Process. 6 (2022) 150 [Google Scholar]
  24. M. Umar Farooq, M. Pervez Mughal, N. Ahmed, N. Ahmad Mufti, A.M. Al-Ahmari, Y. He, On the Investigation of surface integrity of Ti6Al4V ELI using si-mixed electric discharge machining, Materials 13 (2020) 1549 [CrossRef] [Google Scholar]
  25. M.U. Farooq, S. Anwar, M.S. Kumar, A. AlFaify, M.A. Ali, R. Kumar et al., A novel flushing mechanism to minimize roughness and dimensional errors during wire electric discharge machining of complex profiles on Inconel 718, Materials 15 (2022) 7330 [CrossRef] [Google Scholar]
  26. J. Guo, M. Goh, Z. Zhu, X. Lee, M.L.S. Nai, J. Wei, On the machining of selective laser melting CoCrFeMnNi high-entropy alloy, Mater. Des. 153 (2018) 211–20 [CrossRef] [Google Scholar]
  27. A. Polishetty, M.M.R. Barla, G. Littlefair, D. Fabijanic, Machinability assessment of multi component high entropy alloys.METMG 2015: Proceedings of the Manufacturing Engineering and Technology for Manufacturing Growth 2015 International Conference 2015, 2015 [Google Scholar]
  28. M. Kunieda, B. Lauwers, K.P. Rajurkar, B.M. Schumacher, Advancing EDM through fundamental insight into the process, CIRP Ann. 54 (2005) 64–87 [CrossRef] [Google Scholar]
  29. H.F. Mahdy, M.A. Al-Kinani, S.H. Al-Shafaie, R.M. Jailawi, Mathematical modeling for performance measure on the electrical discharge machining of inconel 718 by response surface methodology, J. Mech. Eng. Res. Dev. (2021) 44 [Google Scholar]
  30. C. Douglas, Montgomery. Design and analysis of experiments, 10th ed. Wiley, 2019 [Google Scholar]
  31. S.H.A.-S. Saad Hameed Al-Shafaie, Optimization of tool wear for turning operation based the response surface methodology, Int. J. Mech. Prod. Eng. Res. Dev. 8 (2018) 391–396 [Google Scholar]
  32. E.C. Jameson, Electrical discharge machining, Society of Manufacturing Engineers, 2001 [Google Scholar]
  33. S.H. Al-Shafaie, Prediction of MAF parameters for AISI 316 SS using RBFNN and ANN based Box-Behnken design, J. Eng. Appl. Sci. 17 (2017) 7951–7958 [Google Scholar]
  34. S. Assarzadeh, M. Ghoreishi, Neural-network-based modeling and optimization of the electro-discharge machining process, Int. J. Adv. Manuf. Technol. 39 (2008) 488–500 [CrossRef] [Google Scholar]
  35. E. Iuras, Experimental contributions regarding numerical modeling of the relative wear of the tool-electrode at EDM process, Int. J. Mater. Forming 1 (2008) 1351–1354 [CrossRef] [Google Scholar]
  36. A. Ozgedik, C. Cogun, An experimental investigation of tool wear in electric discharge machining, Int. J. Adv. Manuf. Technol. 27 (2006) 488–500 [CrossRef] [Google Scholar]
  37. H.T. Sánchez, M. Estrems, F. Faura, Development of an inversion model for establishing EDM input parameters to satisfy material removal rate, electrode wear ratio and surface roughness, Int. J. Adv. Manuf. Technol. 57 (2011) 189–201 [CrossRef] [Google Scholar]
  38. V. Prakash, P. Kumar, P. Singh, M. Hussain, A. Das, S. Chattopadhyaya, Micro-electrical discharge machining of difficult-to-machine materials: a review, Proc. Inst. Mech. Eng. B J. Eng. Manuf. 233 (2019) 339–370 [CrossRef] [Google Scholar]
  39. H.T. Lee, T.Y. Tai, Relationship between EDM parameters and surface crack formation, J. Mater. Process. Technol. 142 (2003) 676–683 [CrossRef] [Google Scholar]
  40. S.L. Chen, S.F. Hsieh, H.C. Lin, M.H. Lin, J.S. Huang, Electrical discharge machining of a NiAlFe ternary shape memory alloy, J. Alloys Compd. 464 (2008) 446–451 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.