Machine learning for monitoring hobbing tool health in CNC hobbing machine

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of...

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Bibliographic Details
Main Authors: Nagesh Tambake, Bhagyesh Deshmukh, Sujit Pardeshi, Haitham A. Mahmoud, Robert Cep, Sachin Salunkhe, Emad Abouel Nasr
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Materials
Subjects:
machine learning approach
condition monitoring
hobbing cutter
failure mode effect analysis (FMEA)
hyperparameter optimization
CNC hobbing machine
Online Access:https://www.frontiersin.org/articles/10.3389/fmats.2024.1377941/full

Internet

https://www.frontiersin.org/articles/10.3389/fmats.2024.1377941/full

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