The Effect of Internal and External MQL Methods Used for Environmentally Friendly Manufacturing on Machining Performance in Drilling AA2024 Alloys: A Comparison for ANN And Taguchi Analyzes
Öz
Anahtar Kelimeler
Destekleyen Kurum
Etik Beyan
Teşekkür
Kaynakça
- [1] Çakır, A., 2009. AA7075 and AA6013 Investigation of cutting parameter on aluminium alloys during drilling operations. Gazi University, Graduate School of Natural and Applied Sciences, Master Thesis, Ankara.
- [2] Çakır, A., 2015. Investigation of the effect of cooling conditions on cutting performance in drilling AA7075 and AA2024 aluminum materials. Gazi University, Graduate School of Natural and Applied Sciences, Ph.D. Thesis, Ankara.
- [3] Mills, B., Redford, A.H., 1983. Machinability of Engineering Materials. Applied Sci. Publishers Ltd.
- [4] Akkurt, M., 1998. Metal Cutting Methods and Machine Tools. Birsen Press, pp. 23-90.
- [5] Tonshoff, H.L., Spintig, W., Konig, W., Neises, A., 1994. Machining of holes developments in drilling technology. Annals of the CIRP, Vol. 43(2), pp. 551-561.
- [6] Ogawa, M., Inose, M., Arai, M., Saga, T., 1994. Micro drilling of 5056 wrought aluminum alloy. Journal of Japan Institute of Light Metals, Vol. 44(9), pp. 486-491. DOI: 10.2464/jilm.44.486.
- [7] Pirtini, M., Lazoglu, I., 2005. Forces and hole quality in drilling. International Journal of Machine Tools & Manufacture, Vol. 45(1), pp. 1271-1281. DOI: 10.1016/j.ijmachtools.2005.01.004.
- [8] Taşgetiren, S., Aslantaş, K., 2000. A new design of hard metal insert holder for cutting on turning. 3rd GAP Engineering Congress, pp. 150-157.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Makine Mühendisliğinde Optimizasyon Teknikleri, Triboloji, İmalat Süreçleri ve Teknolojileri
Bölüm
Araştırma Makalesi
Yazarlar
Abdullah Duran
0000-0001-6618-7275
Türkiye
Ulvi Şeker
0000-0001-6455-6858
Türkiye
Cevdet Şencan
0000-0002-7562-9896
Türkiye
Erken Görünüm Tarihi
15 Ocak 2025
Yayımlanma Tarihi
23 Ocak 2025
Gönderilme Tarihi
26 Ocak 2024
Kabul Tarihi
8 Mayıs 2024
Yayımlandığı Sayı
Yıl 2025 Cilt: 27 Sayı: 79
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Leveraging Advanced Machine Learning Algorithms for Energy Efficiency and Carbon Footprint Reduction in Diesel Engines
International Journal of Automotive Science And Technology
https://doi.org/10.30939/ijastech..1896122