Machine learning models for online detection of wear and friction behaviour of biomedical graded stainless steel 316L under lubricating conditions
Yazarlar (8)
Doç. Dr. Mehmet Erdi Korkmaz Karabük Üniversitesi, Türkiye
Munish Kumar Gupta
Opole University Of Technology, Polonya
Opole University Of Technology, Polonya
Gurminder Singh Indian Institute Of Technology Bombay, Hindistan
Doç. Dr. Mustafa Kuntoğlu Selçuk Üniversitesi, Türkiye
Abhishek Patange Coep Technological University, Pune, Hindistan
Doç. Dr. Recep DEMİRSÖZ Karabük Üniversitesi, Türkiye
K Nimel Sworna Ross University Of Johannesburg, Güney Afrika Cumhuriyeti
· Brijesh Prasad Graphic Era Deemed To Be University, Hindistan
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Advanced Manufacturing Technology (Q2) | ||
| Dergi ISSN | 0268-3768 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 08-2023 |
| Cilt / Sayı / Sayfa | 128 / 5 / 2671–2688 | DOI | 10.1007/s00170-023-12108-3 |
| Makale Linki | https://link.springer.com/article/10.1007/s00170-023-12108-3 | ||
| UAK Araştırma Alanları |
Malzeme Tasarım ve Davranışları
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| Özet |
| Particularly in sectors where mechanisation is increasing, there has been persistent effort to maximise the use of existing assets. Since maintenance management is accountable for the accessibility of assets, it stands to acquire prominence in this setting. One of the most common methods for keeping equipment in good working order is predictive maintenance with machine learning methods. Failures can be spotted before they cause any downtime or extra expenses, and with this aim, the present work deals with the online detection of wear and friction characteristics of stainless steel 316L under lubricating conditions with machine learning models. Wear rate and friction forces were taken into account as reaction parameters, and biomedical-graded stainless steel 316L was chosen as the work material. With more testing, the J48 method’s accuracy improves to 100% in low wear conditions and 99.27% in heavy … |
| Anahtar Kelimeler |
| Biomedical material | Friction | Machine learning | Stainless steel 316L | Tribology | Wear |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 25 |
| Google Scholar | 30 |