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Warianty tytułu
Języki publikacji
Abstrakty
Shape memory alloys are important biomaterials but difficult-to-machine (DTM). Their machining needs to be done using intelligent techniques to obtain a better machinability. Hybrid optimization is one of such techniques which can do modelling and optimization of machining parameters for the best values of machinability indicators. Wire electric discharge machining (WEDM) of shape memory alloy has been found as a prominent alternate to the conventional machining techniques, however it needs the assistance of intelligent techniques to machine such materials to get the optimum values of machinability indicators. In this paper, WEDM of shape memory alloy Ni55.8Ti is reported. WEDM has been done by varying four process parameters i.e. servo voltage SV, pulse-on time Pon, pulse-off time Poff, and wire feed rate WF using Taguchi L16 robust design of experiment technique. A hybrid optimization technique TOPSIS-Fuzzy-PSO has been successfully used to optimize these parameters (SV-50V; Pon-1µs; Poff-17 µs; WF-4 m/min) for the best possible values of material removal rate (MRR)- 0.049 g/min, maximum roughness- 11.45 µm, and recast layer- 22.10 µm simultaneously.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
43--53
Opis fizyczny
Bibliogr. 16 poz., fig., tab.
Twórcy
autor
- Dept. of Mechanical and Industrial Engineering Technology, University of Johannesburg, Doornfontein-2028, Johannesburg, South Africa
Bibliografia
- 1. Jani J.M.. Leary M., Subic A., Gibson M.A. A review of shape memory alloy research, applications and opportunities. Materials and Design. 2014;56:1078–1113.
- 2. Petrini L., Migliavacca F. Biomedical Applications of Shape Memory Alloys. Journal of Metallurgy 2011;501483:1–15 DOI: 10.1155/2011/501483
- 3. Elahinia M.H., Hashemi M., Tabesh M., Bhaduri S.B. Manufacturing and processing of NiTi implants: A review. Progress in Material Science. 2012;57:911–946.
- 4. Fabrication and Processing of Shape Memory Alloys, Springer; 2018, ISBN 978-3-319-99306-5.
- 5. Non-traditional Manufacturing Processes, Marcel Dekker Inc.; 1987, ISBN 0824773527.
- 6. Khanna K., Singh H. Comparison of optimized settings for cryogenic-treated and normal D-3 steel on WEDM using grey relational theory. Proceedings of IMechE Part L: Journal of Materials Design and Applications 2016;230:219–232.
- 7. Zolpakar N.A., Yasak M.F., Pathak S. A review: use of evolutionary algorithm for optimisation of machining parameters. Int J Adv Manuf Technol; 2021.
- 8. Spavieri G., Ferreira R.T., Fernandes R.A., Lage G.G., Barbosa D., Oleskovicz M. Particle swarm optimization-based approach for parameterization of power capacitor models fed by harmonic voltages. Applied Soft Computing. 2017; 56:55– 64.
- 9. Choudhuri B., Sen R., Ghosh S.K., Saha S.C. Modelling and multi-response optimization of wire electric discharge machining parameters using response surface methodology and grey–fuzzy algorithm. Proceedings of IMechE. Part B Journal of Engineering Manufacture 2017;231:1760–1774.
- 10. Caydas U., Hascalik A., Ekici S. An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Systems with Applications. 2009;36:6135–6139.
- 11. Singh R., Hussain S.A.L., Dash A., Rai R.N. Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2020;42:344.
- 12. Majumder H., Maity K. Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM. Measurement. 2018;118:1–13.
- 13. Mukherjee R., Chakraborty S., Samanta S. Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms. Applied Soft Computing. 2012;12:2506–2516.
- 14. Magabe R., Sharma N., Gupta K., Davim J.P. Modeling and Optimization of Wire-EDM Parameters for Machining of Ni55.8-Ti Shape Memory Alloy using hybrid approach of Taguchi and NSGA-II. The International Journal of Advanced Manufacturing Technology. 2019;102(5): 1703–1717.
- 15. EI-Bahloul S.A. Optimization of wire electrical discharge machining using statistical methods coupled with artificial intelligence techniques and soft computing. SN Applied Science. 2020;2, Article No. 49.
- 16. Tzeng C.Z., Yang Y.K., Hsieh M.H., Jeng M.C. Optimization of wire electrical discharge machining of pure tungsten using neural network and response surface methodology. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2011;225(6):841–852.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f0fb5f69-4eea-4426-bce1-eacccfc5f133