Application of machine learning based algorithm to predict performance of turning Al–SiC-MWCNT using cryogenically treated textured insert

This work intends to improve dry machining of Al–SiC-MWCNT (Aluminum–Silicon Carbide-Multi-Walled Carbon Nanotube) composites with cryogenically treated textured cutting tool inserts. The study aims to optimize machining parameters such feed rate, cutting speed, depth of cut, and nanoparticle concen...

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Veröffentlicht in:	Hybrid Advances Jg. 10; S. 100432
Hauptverfasser:	Saikrupa, Ch, Reddy, G ChandraMohan, Venkatesh, Sriram
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2025 Elsevier
Schlagworte:	Cryogenically treated tool Machine learning based algorithm Power consumption prediction Surface roughness Turning: MMC prediction Turning: MMC Surface roughness Power consumption Machine learning based algorithm Cryogenically treated tool
ISSN:	2773-207X, 2773-207X
Online-Zugang:	Volltext
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