Sustainable Machining of Nimonic 80A: Machine Learning-Driven Optimization of Energy Consumption Under Environmentally Conscious Cooling Strategies
Yazarlar (2)
Mohd Danish
University Of Jeddah, Suudi Arabistan
University Of Jeddah, Suudi Arabistan
Prof. Dr. Murat SARIKAYA Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Arabian Journal for Science and Engineering (Q2) | ||
| Dergi ISSN | 2193-567X Dergi Bilgileri (2026) | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2026 |
| Cilt / Sayı / Sayfa | 51 / 14 / 16865–16883 | DOI | 10.1007/s13369-025-10947-8 |
| Makale Linki | https://link.springer.com/content/pdf/10.1007/s13369-025-10947-8.pdf | ||
| UAK Araştırma Alanları |
Mühendislik
|
||
| Özet |
| Machining advanced materials like Nimonic 80A in a way that is both environmentally friendly and energy efficient is becoming more important around the world. However, achieving an acceptable level of machining performance without harming the environment remains challenging. The present study is focused on resolving this issue. Machine Learning (ML) was employed to predict and optimize cutting power and specific cutting energy (SCE). Four novel sustainable cooling and lubrication (C/L) environments were studied which are dry, minimum quantity lubrication (MQL), cryogenic CO 2, and hybrid CO 2+ MQL configuration. The main contribution of the study is the systematic comparison of ML techniques such as support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) for getting the prediction and optimization of the cutting power and SCE among different C/L environments … |
| Anahtar Kelimeler |
| Advanced cooling strategies | Cryogenic CO2 | Energy efficiency | Machine learning | Nimonic 80A | Specific cutting energy | Sustainable machining |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 2 |
| Google Scholar | 3 |