Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network

By Chung-Chen Tsao, Hong Hocheng. 2008

225 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/85c16994c3b76fbb2fcbcaaa8f6bd00030eb9767

How researchers see this paper

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

In Biomedical Engineering

1. Tom Sunny
2. J. Babu
3. M. Muralidhar
4. Chimata Murali Krishna
5. N. A. Paul
6. J. Paulo Davim
7. Adem Çiçek
8. T. Kıvak

In Electrical and Electronic Engineering

1. Adem Çiçek
2. Tom Sunny
3. J. Babu
4. M. Muralidhar
5. Chimata Murali Krishna
6. N. A. Paul
7. J. Paulo Davim
8. T. Kıvak

In Mechanical Engineering

1. Adem Çiçek
2. Tom Sunny
3. J. Babu
4. M. Muralidhar
5. Chimata Murali Krishna
6. N. A. Paul
7. J. Paulo Davim
8. T. Kıvak

Citations in the corpus

• Application of soft computing techniques in machining performance prediction and optimization: a literature review (2010, methodology): “A number of authors have used radial basis function networks and observed their superiority in terms of network training [25, 26, 118, 134].”
• Taguchi method based optimisation of drilling parameters in drilling of AISI 316 steel with PVD monolayer and multilayer coated HSS drills (2012, background): “Tsao and Hocheng [18] undertook the prediction and evaluation of the thrust force and surface roughness in drilling of a composite material.”
• Assessment of delamination in composite materials: A review (2016, background): “Rejection rates of up to 60% are reported in case of aircraft assembly work as a result of such defects.(5) Therefore, delamination can critically downgrade the performance of the finished part.”