Article detail · 2025 · article
Improving the Prediction Accuracy of Surface Roughness in the Boring Process using Integrated Machine Learning Methods
ISSN2234-7593
YÖKSİS
OpenAlex
Year2025
Citations3OpenAlex
Citations2Semantic Scholar · 1 influential
Percentile%70.5
FWCI0.91.00 = world average
Scopus (SJR)Q2
WoS (JCR)Q1
Data source split
- YÖKSİSYÖKSİS article record
- YÖKSİS venueInternational Journal of Precision Engineering and Manufacturing
- Catalog match (ISSN)International Journal of Precision Engineering and Manufacturing
- OpenAlexOpenAlex enrichment (abstract, citations, topics)
- Semantic Scholarcitation count (not merged with OpenAlex)
Abstract
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Topics
Citations
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3citationsOpenAlex · cited_by_count (cache / database)
4 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- 2026 Prediction of surface roughness in boring of 1.2311 material using machine learning enhanced by virtual sampling methodsCitations 0 · OpenAlex
- 2026 Prediction of quality class in face milling of S355J2 steel using meta model based on cutting temperature and surface roughness dataCitations 0 · OpenAlex
- 2026 Prediction of quality class in face milling of S355J2 steel using meta model based on cutting temperature and surface roughness dataCitations 0 · OpenAlex
- 2026 Machine learning estimation of surface roughness and carbon emissions in Inconel 718 turning process using power analyzer-based signalsCitations 0 · OpenAlex