Scopus
🔓 Açık Erişim
Machine learning models for online detection of wear and friction behaviour of biomedical graded stainless steel 316L under lubricating conditions
International Journal of Advanced Manufacturing Technology · Eylül 2023
Makale Bilgileri
DergiInternational Journal of Advanced Manufacturing Technology
Yayın TarihiEylül 2023
Cilt / Sayfa128 · 2671-2688
Scopus ID2-s2.0-85167355865
Erişim🔓 Açık Erişim
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 wear situations. In addition, the graphic showed the accuracy values for several models. The J48 model is the most precise amongst all others, with a value of 100% (minimum wear) and an average of 98.92% (higher wear). Amongst all the models tested under varying machining conditions, the J48’s 98.92% (low wear) and 98.92% (high wear) recall scores stand out as very impressive (higher wear). In terms of F1-score, J48 performs better than any competing model at 99.45% (low wear) and 98.92% (higher wear). As a result, the J48 improves the model’s overall performance.
Yazarlar (8)
1
Mehmet Erdi Korkmaz
2
Munish Kumar Gupta
ORCID: 0000-0002-0777-1559
3
Gurminder Singh
4
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
5
Abhishek Dhananjay Patange
ORCID: 0000-0002-9130-694X
6
Recep Demirsoz
7
Nimel Sworna Ross
8
Brijesh Prasad
Anahtar Kelimeler
Biomedical material
Friction
Machine learning
Stainless steel 316L
Tribology
Wear
Kurumlar
College of Engineering, Pune
Pune India
Graphic Era Deemed to be University
Dehradun India
Indian Institute of Technology Bombay
Mumbai India
Karabük Üniversitesi
Karabuk Turkey
MKSSS’s Cummins College of Engineering for Women
Pune India
Opole University of Technology
Opole Poland
Selçuk Üniversitesi
Selçuklu Turkey
University of Johannesburg
Johannesburg South Africa
Metrikler
10
Atıf
8
Yazar
6
Anahtar Kelime