Performance evaluation and hybrid deep recurrent neural network-based prediction of SS304 turning characteristics using nanoparticles added water emulsified MQL-Reference-Cited by-同舟云学术

Performance evaluation and hybrid deep recurrent neural network-based prediction of SS304 turning characteristics using nanoparticles added water emulsified MQL

Published:2023-04-11 Issue:8 Volume:13 Page:7349-7373
ISSN:2190-6815
Container-title:Biomass Conversion and Biorefinery
language:en
Short-container-title:Biomass Conv. Bioref.

Author:

Ponnusamy Prasanth,Tamilperuvalathan Sekar

Publisher

Springer Science and Business Media LLC

Subject

Renewable Energy, Sustainability and the Environment

Link

https://link.springer.com/content/pdf/10.1007/s13399-023-04106-y.pdf

Reference68 articles.

1. Aslan A (2020) Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of AISI 5140: a comprehensive study. Measurement 163:107959

2. Özbek O, Saruhan H (2020) The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2. J Mater Res Technol 9(3):2762–2772

3. Neşeli S, Yaldız S, Türkeş E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44(3):580–587

4. Rao KV, Vidhu KP, Kumar TA, Rao NN, PBGSN M, Balaji M (2016) An artificial neural network approach to investigate surface roughness and vibration of workpiece in boring of AISI1040 steels. Int J Adv Manuf Technol 83(5):919–927

5. Elsheikh AH, Guo J, Huang Y, Ji J, Lee KM (2018) Temperature field sensing of a thin-wall component during machining: Numerical and experimental investigations. Int J Heat Mass Transf 126:935–945

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