Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments

By Panorios Benardos, George-Christopher Vosniakos. 2002

503 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/c6f11baa71ef719a654badfefc0a48f50bbc1f69

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. Ș. Karabulut
2. J. V. Abellán-Nebot
3. Fernando Romero Subirón
4. P. Sahoo
5. Bharat Chandra Routara
6. Asish Bandyopadhyay
7. Mehdi Khashei
8. Mehdi Bijari

In Electrical and Electronic Engineering

1. Ș. Karabulut
2. Mehdi Khashei
3. Mehdi Bijari
4. J. V. Abellán-Nebot
5. Fernando Romero Subirón
6. G. R. Ardali
7. P. Sahoo
8. Bharat Chandra Routara

In Mechanical Engineering

1. Ș. Karabulut
2. Mehdi Khashei
3. Mehdi Bijari
4. J. V. Abellán-Nebot
5. Fernando Romero Subirón
6. P. Sahoo
7. Bharat Chandra Routara
8. Asish Bandyopadhyay

Citations in the corpus

• A review of machining monitoring systems based on artificial intelligence process models (2010, methodology): “The table shows research works based on full factorial designs [29, 54, 62, 68, 69], Taguchi’s orthogonal arrays [ 8 , 70] and response surface designs [71].”
• Predicting surface roughness in machining: a review (2003, methodology): “ANN modeling was also used along with designed experiments by Benardos and Vosniakos [47] in face milling.”
• Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, background): “Benardos and Vosniakos (2002) proposed a NN modeling approach for face milling operations to predict surface roughness (Ra) (Benardos & Vosniakos, 2002).”
• The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks (2009, methodology): “[29] the authors use a neural network-based approach to predict surface roughness in CNC face milling.”
• Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method (2015, methodology): “Benardos and Vosniakos [6] suggested an ANN modeling approach for the prediction of surface roughness for a Taguchi design of experiments method (TM).”
• Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation (2009, methodology): “Benardos and Vosniakos [9] used Taguchi design to consider prediction of Ra in CNC face milling of Al alloy.”
• Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2009, methodology): “These approaches can be generally categorized as follows: (i) empirical or statistical methods that are used to study the effect of an ANN’s internal parameters and to choose appropriate values for them based on the model performance [3,38].”