Effect on Machinability Characteristics of Cryogenic Process and Performance Assessment by Using Machine Learning Approach with Scaled Conjugate Gradient Algorithm
Yazarlar (2)
Doç. Dr. Mehmet Akkaş Kastamonu Üniversitesi, Türkiye
Doç. Dr. Mehmet GÜRDAL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Iranian Journal of Science and Technology Transactions of Mechanical Engineering (Q3) | ||
| Dergi ISSN | 2228-6187 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2025 |
| Cilt / Sayı / Sayfa | 49 / 1 / 181–200 | DOI | 10.1007/s40997-025-00837-7 |
| Makale Linki | https://link.springer.com/journal/40997 | ||
| UAK Araştırma Alanları |
Kompozit Malzemeler
Üretim Teknolojileri
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| Özet |
| The present investigation is focused on the machinability and characterisation of cryogenised AISI 4140 steels, which are renowned for exhibiting superior hardness, durability, wear resistance, dimensional and chemical stability, and fatigue strength when compared to conventional steels. Hard turning experiments were conducted on cryogenised steel specimens employing dry cutting conditions with a carbide insert. As the cutting tip, the TT5100 quality TIN coated type with code WNMG 080408 MT produced by TaeguTec company was used. The study examines the impact of various cutting parameters, including three distinct cutting speeds (160, 200, 240 m/min), three feed rates (0.04, 0.08, 0.12 mm/rev), and three depths of cut (0.1, 0.15, 0.2 mm), on power consumption and surface roughness values. The utilisation of the artificial neural network (ANN) approach, a machine learning methodology, for the analysis … |
| Anahtar Kelimeler |
| AISI 4140 cryogenized | CBN tool | Characterization | Hard turning | Machinability | Surface roughness |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 2 |
| Google Scholar | 3 |