Tool wear prediction using long short-term memory variants and hybrid feature selection techniques-Reference-Cited by-同舟云学术

Tool wear prediction using long short-term memory variants and hybrid feature selection techniques

Published:2022-07-21 Issue:9-10 Volume:121 Page:6611-6633
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Sayyad Sameer,Kumar Satish,Bongale Arunkumar,Kotecha Ketan,Selvachandran Ganeshsree^ORCID,Suganthan Ponnuthurai Nagaratnam

Funder

Ministry of Higher Education, Malaysia

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-022-09784-y.pdf

Reference52 articles.

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4. Rizal M, Ghani JA, Nuawi MZ, Hassan C, Haron C (2014) A review of sensor system and application in milling process for tool condition monitoring. Res J Appl Sci Eng Technol 7(10):2083–2097

5. Sayyad S, Kumar S, Bongale A, Kamat P, Patil S, Kotecha K (2021) Data-driven remaining useful life estimation for milling process: sensors, algorithms, datasets, and future directions. IEEE Access 9:110255–110286

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