Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models
Measurement Journal of the International Measurement Confederation · Aralık 2023
YÖKSİS Kayıtları
Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models
MEASUREMENT · 2023 SCI-Expanded
Doç. Dr. MUSTAFA KUNTOĞLU →
YÖKSİS Kayıtları — ISSN Eşleşmesi
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2012 ISSN: 02632241 SCI-Expanded
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Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis
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Optimization of tool geometry parameters for turning operations based on the response surface methodology
2011 ISSN: 02632241 SCI-Expanded
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A new process damping model for chatter vibration
2011 ISSN: 02632241 SCI-Expanded
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2021 ISSN: 0263-2241 SCI-Expanded Q1
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Makale Bilgileri
ISSN02632241
Yayın TarihiAralık 2023
Cilt / Sayfa223
Scopus ID2-s2.0-85176937284
Özet
Machine learning has numerous advantages, especially in the rapid digitization of the manufacturing industry that combines data from manufacturing processes and quality measures. Predictive quality allows manufacturers to make informed predictions about the quality of their products by analyzing data gathered during production. The quality of the machining, the total cost and the computation time need to be improved using contemporary production processes. With this concern, a series of experiments were carried out on Bohler steel both in dry, Minimum Quantity Lubrication (MQL) and nano-MQL conditions in varying quantities to explore the tool wear. In comparison to dry conditions, the utilization of MQL in machining processes demonstrates significantly enhanced efficacy in mitigating flank wear. The reduction in flank wear ranges from around 5% to 20% to 25%, contingent upon the application of MQL on the flank face, rake face, or both faces simultaneously. After that, the results of the tests were evaluated with the models of machine learning (ML) to determine which environment was optimal for cutting under both real and artificial circumstances.
Yazarlar (8)
1
Mehmet Erdi Korkmaz
2
Munish Kumar Gupta
ORCID: 0000-0002-0777-1559
3
Mustafa Kuntoğlu
ORCID: 0000-0002-7291-9468
4
Abhishek Dhananjay Patange
ORCID: 0000-0002-9130-694X
5
Nimel Sworna Ross
6
Hakan Yılmaz
7
Sumika Chauhan
8
Govind Vashishtha
ORCID: 0000-0002-5160-9647
Anahtar Kelimeler
Decision tree
Machine learning
Machining
Support vector regression
Tool condition monitoring
Kurumlar
Graphic Era Deemed to be University
Dehradun India
Karabük Üniversitesi
Karabuk Turkey
MKSSS’s Cummins College of Engineering for Women
Pune India
Opole University of Technology
Opole Poland
Politechnika Wrocławska
Wroclaw Poland
Selçuk Üniversitesi
Selçuklu Turkey
University of Johannesburg
Johannesburg South Africa
Scimago Dergi (ISSN Eşleşmesi)
Measurement: Journal of the International Measurement Confederation
Q1
SJR Skoru1,244
H-Index146
YayıncıElsevier B.V.
ÜlkeNetherlands
Applied Mathematics (Q1)
Condensed Matter Physics (Q1)
Education (Q1)
Electrical and Electronic Engineering (Q1)
Instrumentation (Q1)
Statistics and Probability (Q1)
Metrikler
71
Atıf
8
Yazar
5
Anahtar Kelime