Article detail · 2026
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
YÖKSİS
OpenAlex
SJR Q1
JCR Q2
Citations 6
Percentile 85.1%
FWCI 2.01
- Year
- 2026
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Arabian Journal for Science and Engineering
- Catalog match (ISSN) Arabian Journal for Science and Engineering
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
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Topics
Citations
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6 citations
OpenAlex cited_by_count (cache / database)
8 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Effect of Cooling/Lubrication Conditions on Machining Performance: An Experimental Investigation of 1040 Steel Under Dry, MQL, and Nano-MQL Environments 2025
- Effect of Cooling/Lubrication Conditions on Machining Performance: An Experimental Investigation of 1040 Steel Under Dry, MQL, and Nano-MQL Environments 2025
- Effect of Cooling/Lubrication Conditions on Machining Performance: An Experimental Investigation of 1040 Steel Under Dry, MQL, and Nano-MQL Environments 2025
- Built-up-edge Formation and its Effect on Surface Topography and Machinability Indicators in Sustainable Minimum Quantity Lubrication Turning of Al2024-T6 2026
- Built-up-edge Formation and its Effect on Surface Topography and Machinability Indicators in Sustainable Minimum Quantity Lubrication Turning of Al2024-T6 2025
- The Effects of Coating Materials on Machinability Characteristics of AA7075-T6 Alloys under Different Turning Conditions 2025
- The influences of tool geometry on machinability of hot tool work steel during application of various turning parameters 2025
- The Effects of Coating Materials on Machinability Characteristics of AA7075-T6 Alloys under Different Turning Conditions 2025