Predicting surface roughness in machining: a review

By Panorios Benardos, George-Christopher Vosniakos. 2003

1,113 citations recorded by the source corpus.

Subfields: Computational Mechanics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/8238bb5c24e597a14a3ea6bd50cacd97c5c3a49e

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In Computational Mechanics

In Mechanical Engineering

Citations in the corpus

Grey–Taguchi method to optimize the milling parameters of aluminum alloy (2009, methodology): “A general overview of the different approaches that are used for predicting surface roughness and certain remarks concerning each approach can be found in [ 14 ].”
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].”
Laser beam machining—A review (2008, background): “ANN models are able to learn, adapt to changes and mimic human thought process with little human interactions [155].”
Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation (2009, background): “1 Fishbone diagram showing parameters affecting surface roughness [1] Int J Adv Manuf Technol (2009) 40:1166–1180 1167”
Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011) (2012, background): “In the review paper by Benardos and Vosnaikos (2003), the authors provided an evaluation based on machining theory, experimental investigation, design of experiments (DOE) and artificial intelligence (AI) techniques in optimizing machining process parameters.”
Surface roughness variation of thin wall milling, related to modal interactions (2008, methodology): “The bibliography shows many methodologies and practices employed on this topic [17].”
Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks (2005, background): “Recently, Benardos and Vosniakos [32] made an extensive literature review on predicting surface roughness in machining and confirmed the effectiveness of neural network approaches.”
Prediction of surface roughness in the end milling machining using Artificial Neural Network (2010, methodology): “Modelling techniques for the prediction of Ra can be classified into three groups which are experimental models, analytical models and Artificial Intelligence (AI)-based models (Benardos & Vosnaikos, 2003).”