A review of optimization techniques in metal cutting processes

By Indrajit Mukherjee, P. Ray. Computers & industrial engineering, 2006

624 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/8a430888858097d21bcde9769ab65c21005c9a5c

How researchers see this paper

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

In Industrial and Manufacturing Engineering

1. Safian Sharif
2. Azlan Mohd Zain
3. Habibollah Haron
4. Norfadzlan Yusup
5. Siti Zaiton Mohd Hashim

In Mechanical Engineering

1. Safian Sharif
2. Azlan Mohd Zain
3. Habibollah Haron
4. Norfadzlan Yusup
5. Siti Zaiton Mohd Hashim

Citations in the corpus

• Prediction of surface roughness in the end milling machining using Artificial Neural Network (2010, methodology): “Although the Statistical Regression technique may work well for modelling, this technique may not describe precisely the underlying nonlinear complex relationship between machining parameters and performance measures (Mukherjee & Ray, 2006).”
• Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011) (2012, methodology): “…techniques such as GA, SA and ACO for optimization process parameters have been applied in the traditional machining due to likely to deal with highly nonlinear, multidimensional and ill-behaved complex engineering problem (Chandrasekaran, Muralidhar, Krishna, & Dixit, 2010; Mukherjee & Ray, 2006).”