A review of machining monitoring systems based on artificial intelligence process models

By J. V. Abellán-Nebot, Fernando Romero Subirón. 2010

412 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/30c16b878a05f403f449cce0607381f17c490b3a

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. A. Ozbayoglu
2. Krzysztof Jemielniak
3. Tomasz Urbanski
4. Joanna Kossakowska
5. Sebastian Bombiński
6. Longbing Cao
7. Weiwei Jiang
8. Omer Berat Sezer

In Industrial and Manufacturing Engineering

1. Krzysztof Jemielniak
2. Tomasz Urbanski
3. Joanna Kossakowska
4. Sebastian Bombiński

In Mechanical Engineering

1. A. Ozbayoglu
2. Krzysztof Jemielniak
3. Tomasz Urbanski
4. Joanna Kossakowska
5. Sebastian Bombiński
6. R. Paurobally
7. Arjan Siddhpura

Citations in the corpus

• Tool condition monitoring based on numerous signal features (2011, methodology): “This can be achieved by various means such as statistical methods, autoregressive modeling, pattern recognition, expert systems, and others [1, 5 , 6]. The neural network approach has recently been the most intensively studied method for feature fusion [5].”
• Review of tool condition monitoring in machining and opportunities for deep learning (2020, background): “Hence, by measuring the cutting force, the tool wear or breakage can be monitored [32].”