Application of AE and cutting force signals in tool condition monitoring in micro-milling

By Krzysztof Jemielniak, Pedro José Arrazola. 2008

171 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/9178a659eb425d7e35013ca3fa24cd2e23e8447d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Garret E. O'Donnell
2. David A. Dornfeld
3. Roberto Teti
4. Noureddine Zerhouni
5. Said Rechak
6. Tarak Benkedjouh
7. Kamal Medjaher
8. Krzysztof Jemielniak

In Electrical and Electronic Engineering

1. Noureddine Zerhouni
2. Kamal Medjaher
3. David A. Dornfeld
4. Garret E. O'Donnell
5. Roberto Teti
6. Said Rechak
7. Tarak Benkedjouh
8. Krzysztof Jemielniak

In Mechanical Engineering

1. Noureddine Zerhouni
2. Kamal Medjaher
3. Garret E. O'Donnell
4. David A. Dornfeld
5. Roberto Teti
6. Said Rechak
7. Tarak Benkedjouh
8. Krzysztof Jemielniak

Citations in the corpus

• Tool condition monitoring techniques in milling process — a review (2020, result): “Finally, it was concluded that better results were attained using cutting force and AE signals, which were in micro-milling process [52].”
• Health assessment and life prediction of cutting tools based on support vector regression (2013, methodology): “In the same framework, Jemielniak and Arrazola (2008) proposed a contribution where acoustic emission and cutting force signals were used for TCM in micro-milling of cold-work tool steel.”
• Advanced monitoring of machining operations (2010, methodology): “Later, the method was improved by substituting the polynomial approximation by lowpass filtering of the time series representing the SF [130].”
• Tool wear predictability estimation in milling based on multi-sensorial data (2016, unknown): “mechanical phenomena has been documented in [16–18].”