Tool life predictions in milling using spindle power with the neural network technique

By Cyril Drouillet, Jaydeep Karandikar, Chandra Nath, Anne-Claire Journeaux, Mohamed El Mansori, T. Kurfess. 2016

209 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/6e856bf9c713f4896a4a3b43e6e2b79a398f8782

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Munish Kumar Gupta
2. Mustafa Kuntoğlu
3. D. Pimenov
4. Andrés Bustillo
5. Vishal S. Sharma
6. Szymon Wojciechowski

In Mechanical Engineering

1. D. Pimenov
2. Munish Kumar Gupta
3. Mustafa Kuntoğlu
4. Andrés Bustillo
5. Vishal S. Sharma
6. Szymon Wojciechowski

Citations in the corpus

• Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, methodology): “(2016) the remaining useful life (RUL) prediction of milling cutters was implemented using NN based on the power value of the machine spindle motor for the end milling operation (Drouillet et al., 2016).”