Surface roughness prediction based on cutting parameters and tool vibrations in turning operations

By O. Abouelatta, J. Madl. 2001

384 citations recorded by the source corpus.

Subfields: Computational Mechanics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/a4183ec1819d75cad499d1169d3aca62c5c670d7

How researchers see this paper

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

In Computational Mechanics

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

In Mechanical Engineering

1. J. V. Abellán-Nebot
2. A. Noorul Haq
3. R. Jeyapaul
4. Fernando Romero Subirón
5. P. Marimuthu
6. P. Venkata Ramaiah
7. G. Rajyalakshmi

Citations in the corpus

• A review of machining monitoring systems based on artificial intelligence process models (2010, background): “Abouelatta [ 27 ] studied the influence of single harmonics and the power spectral density (PSD) for surface roughness prediction, and several empirical models based on multivariate regression were developed.”
• Tool-Wear Monitoring Based on Continuous Hidden Markov Models (2005, neutral): “Similar proposals for tool-wear monitoring can be found in [2,15,1,8,14].”
• Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method (2008, background): “Thus in material removal processes, improper selection of cutting conditions will result in rough surfaces [ 6 ].”