Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling

By Raphael Corne, Chandra Nath, Mohamed El Mansori, T. Kurfess. 2017

141 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/dd30e668b468348d8311631ee5bdd247131b02a0

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, background): “(2017) proposed to predict tool wear and breakdowns taking into account changes in spindle power and cutting forces during superalloy Inconel 625 drilling (Corne et al., 2017).”