Optimization and predictive modeling using S/N, RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel
Yazarlar (2)
Prof. Dr. Murat SARIKAYA Sinop Üniversitesi, Türkiye
Prof. Dr. Volkan Yılmaz Gazi Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Neural Computing and Applications (Q1) | ||
| Dergi ISSN | 0941-0643 Dergi Bilgileri (2018) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 05-2018 |
| Cilt / Sayı / Sayfa | 30 / 5 / 1503–1517 | DOI | 10.1007/s00521-016-2775-9 |
| Makale Linki | http://link.springer.com/10.1007/s00521-016-2775-9 | ||
| UAK Araştırma Alanları |
Üretim Teknolojileri
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| Özet |
| In present work, micro-deep holes on AISI 304 stainless steel were drilled via electrical discharge machining (EDM) method. In the first phase of this work, the effect of test parameters on the drilling performance and the profile of drilled holes were investigated experimentally. Test parameters including discharge current, dielectric spray pressure and electrode tool rotational speed were taken and then the machining rate (MR), electrode wear rate (EWR), average over-cut (AOC) and taper angle (TA) were measured in order to assess the drillability of EDM. After experimental study, an analysis of variance was performed to identify the effect of the importance of test parameters on experiment outputs. In the second phase of this study, optimum process parameters were determined using signal-to-noise analysis and response surface methodology (RSM) for mono-optimization and multi-response optimization … |
| Anahtar Kelimeler |
| Artificial neural networks | Electrical discharge machining | Micro-hole drilling | Optimization | Response surface methodology |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 46 |
| Scopus | 54 |
| Google Scholar | 60 |