Wire electrical discharge machining (WEDM) of γ titanium aluminide is the subject of the current research. Due to the large number of process variables and sophisticated stochastic process mechanisms, selecting the best machining parameter combinations for increased cutting efficiency and accuracy is a difficult task in WEDM. In general, there is no perfect combination that can produce the fastest cutting speed and the finest surface finish quality at the same time. For this purpose, the data were selected from a literature study. This study describes an attempt to devise a suitable machining technique for achieving the highest possible process criteria yield. To model the machining process, a stochastic optimization method, differential evolution, has been performed. Cutting speed, surface roughness, and wire offset are the three most important criteria that have been used as indicators of process performance. The response characteristics can be predicted as a function of six different control parameters, namely pulse on time, pulse off time, peak current, wire tension, dielectric flow rate, and servo reference voltage. The limitations of the candidate models are checked after the R 2 training, R2 testing and R2 valiadtion values are calculated to reveal whether the model is realistic. Optimization results are 3.02 mm/min, 2.36 µm, and 0.13 mm for the maximum cutting speed, the minimum surface roughness, and minimum wire offset, respectively. It is shown that the machining model is suitable and that the optimization technique meets practical requirements.
Eser Adı (dc.title) | Modeling and optimum design for wire electrical discharge machining of γ titanium aluminide alloy |
Eser Sahibi (dc.contributor.author) | ÖMER FARUK BÜYÜKYAVUZ |
Yayın Tarihi (dc.date.issued) | 2021 |
Yayıncı (dc.publisher) | İzmir Katip Çelebi Üniversitesi |
Tür (dc.type) | Makale |
Özet (dc.description.abstract) | Wire electrical discharge machining (WEDM) of γ titanium aluminide is the subject of the current research. Due to the large number of process variables and sophisticated stochastic process mechanisms, selecting the best machining parameter combinations for increased cutting efficiency and accuracy is a difficult task in WEDM. In general, there is no perfect combination that can produce the fastest cutting speed and the finest surface finish quality at the same time. For this purpose, the data were selected from a literature study. This study describes an attempt to devise a suitable machining technique for achieving the highest possible process criteria yield. To model the machining process, a stochastic optimization method, differential evolution, has been performed. Cutting speed, surface roughness, and wire offset are the three most important criteria that have been used as indicators of process performance. The response characteristics can be predicted as a function of six different control parameters, namely pulse on time, pulse off time, peak current, wire tension, dielectric flow rate, and servo reference voltage. The limitations of the candidate models are checked after the R 2 training, R2 testing and R2 valiadtion values are calculated to reveal whether the model is realistic. Optimization results are 3.02 mm/min, 2.36 µm, and 0.13 mm for the maximum cutting speed, the minimum surface roughness, and minimum wire offset, respectively. It is shown that the machining model is suitable and that the optimization technique meets practical requirements. |
Kayıt Giriş Tarihi (dc.date.accessioned) | 06.06.2022 |
Açık Erişim Tarihi (dc.date.available) | 2022-06-06 |
Yayın Dili (dc.language.iso) | eng |
Konu Başlıkları (dc.subject) | γ titanium aluminide |
Konu Başlıkları (dc.subject) | Modeling |
Konu Başlıkları (dc.subject) | Optimization |
Konu Başlıkları (dc.subject) | Wire EDM |
Atıf için Künye (dc.identifier.citation) | Ö. F. Büyükyavuz , "Modeling and optimum design for wire electrical discharge machining of γ titanium aluminide alloy", Journal of Artificial Intelligence and Data Science, c. 1, sayı. 1, ss. 89-95, Ağu. 2021 |
ISSN (dc.identifier.issn) | 2791-8335 |
Yayının ilk sayfa sayısı (dc.identifier.startpage) | 89 |
Yayının son sayfa sayısı (dc.identifier.endpage) | 95 |
Dergi Adı (dc.relation.journal) | Journal of Artificial Intelligence and Data Science |
Dergi Sayısı (dc.identifier.issue) | 1 |
Dergi Cilt (dc.identifier.volume) | 1 |
Veritabanı (dc.source.database) | Hiçbiri |
Haklar (dc.rights) | Open access |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/11469/1934 |