An investigation of tool-wear monitoring in a high-speed machining process

By R. Haber, J. Jiménez, C. Peres, J. R. Alique. 2004

147 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Mechanical Engineering, Medical Laboratory Technology

View Paper: https://www.semanticscholar.org/paper/1d2b12c5da29c62d31567f1d80939118668b5d9e

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. J. V. Abellán-Nebot
2. A. Pramanik
3. Fernando Romero Subirón
4. Guy Littlefair

In Mechanical Engineering

1. J. V. Abellán-Nebot
2. Subramaniam Shankar
3. N. R. Sakthivel
4. A. Pramanik
5. T. Mohanraj
6. R. Rajasekar
7. Fernando Romero Subirón
8. Guy Littlefair

In Medical Laboratory Technology

1. J. V. Abellán-Nebot
2. Fernando Romero Subirón

Citations in the corpus

• Tool condition monitoring techniques in milling process — a review (2020, background): “[49] Haber RE, Jiménez JE, Peres CR, Alique JR.”
• A review of machining monitoring systems based on artificial intelligence process models (2010, methodology): “For example, Haber [ 10 ] reported that acoustic emission (AE) sensors attached in the soft vice jaws were more sensitive than those attached in the spindle, whereas Lan [11] reported a higher sensitivity of AE sensors in the spindle quill.”