Skip to content
DraconiticResearch
Search Sources API
203,948 studies31 sourcesUpdated 34 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

Integrated experimental and machine learning analysis of nano-MQL assisted turning of AA7075-T6

Related

  1. Machine Learning-Based Prediction of Bioprinting Quality Using Integrated Experimental and Computational Data
    Khulood Abu Maria, Sara Abu Tarboosh, Adi El-Dalahmeh +1
    Bioengineering (Basel, Switzerland)Peer-reviewedCC BYFull textEurope PMC
  2. Machine-learning-assisted target prioritization and experimental validation of the neoeriocitrin–mTOR axis in impaired osteogenic differentiation
    Yuhao Wang, Jianguang Luo
    Frontiers in PharmacologyPeer-reviewedCC BY 4.0Pharmacology & toxicologyFrontiersCrossref
  3. Machine learning-assisted design and explainable optimization of CdSnP2-based integrated solar-photodetector devices
    Md. Alamin Hossain Pappu, Kazi Abrar Shafin, Mainul Hossain +1
    arXivPreprintarXiv licenceFull textOptics & photonicsarXiv
  4. Artificial Intelligence-Assisted Engineering of Nanocomposite Corrosion Protection Materials: A Critical Review of Machine Learning, Molecular Simulation, and Experimental Verification
    Kufre Essien, Akanimo N. Ekanem, Aniedi E. Nyong +4
    Materials and DevicesPeer-reviewedCC BY-NC-NDFull textPhysical chemistryDOAJ
  5. Machine Learning-Assisted Optimization of Surfactin Titer Produced by Bacillus subtilis MTCC 2423 from Minimum Fermentation Data with Experimental Validation
    Ashwini Padole, Riju De, Vivek Rangarajan +1
    ACS OmegaPeer-reviewedCC BY 4.0Full textGeneral chemistryCrossref
BibTeX
@article{binali2026integrated,
  title = {{Integrated experimental and machine learning analysis of nano-MQL assisted turning of AA7075-T6}},
  author = {Rüstem Binali and Elif Ayrancı and Mustafa Kuntoğlu and Tayfun Çetin and Abdullah Aslan and Abdullah Selim Parlakyigit and Bahar Sayın Kul},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-73437-3},
  url = {https://www.nature.com/articles/s41598-026-73437-3},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Integrated experimental and machine learning analysis of nano-MQL assisted turning of AA7075-T6
AU  - Rüstem Binali
AU  - Elif Ayrancı
AU  - Mustafa Kuntoğlu
AU  - Tayfun Çetin
AU  - Abdullah Aslan
AU  - Abdullah Selim Parlakyigit
AU  - Bahar Sayın Kul
PY  - 2026
DA  - 2026/10/26
JO  - Scientific Reports
DO  - 10.1038/s41598-026-73437-3
UR  - https://www.nature.com/articles/s41598-026-73437-3
SN  - 2045-2322
ER  -