Modeling the surface roughness and cutting force for turning

By W. Lin, B. Lee, C. L. Wu. 2001

221 citations recorded by the source corpus.

Subfields: Computational Mechanics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/bb3d2d65fcea9e462b5a64f664191a0c5f383c8f

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. Safian Sharif
2. Y. Şahin
3. M. Muralidhar
4. Chimata Murali Krishna
5. M. Chandrasekaran
6. Uday S. Dixit
7. A. Motorcu
8. E. Turkes

In Mechanical Engineering

1. Y. Şahin
2. Safian Sharif
3. M. Muralidhar
4. Chimata Murali Krishna
5. M. Chandrasekaran
6. Uday S. Dixit
7. A. Motorcu
8. E. Turkes

Citations in the corpus

• Surface roughness model in machining hardened steel with cubic boron nitride cutting tool (2008, methodology): “In metal cutting process, extensive attention has been given to machining of hard and hardened steels in an experimental way and theoretical method [10–35].”
• Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel (2004, methodology): “[8] used an abductive network to construct a prediction model for surface roughness and cutting force.”
• Optimization of tool geometry parameters for turning operations based on the response surface methodology (2011, methodology): “[23] developed an abductive network model to estimate the surface roughness and cutting forces.”
• Application of soft computing techniques in machining performance prediction and optimization: a literature review (2010, background): “[50] predicted surface roughness and cutting force using abductive neural network during turning of high carbon steel with carbide inserts.”