Development of a surface roughness prediction system for machining of hot chromium steel (AISI H11) based on artificial neural network

Rajesh Rai, Arun Kumar, Shrikantha S. Rao, Shriram

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

An attempt have been made to apply the principles of artificial neural networks (ANN) towards developing a prediction model for surface roughness during the machining of high chromium steel through face milling process. Now a days, hot chromium steel is prominently used in die and mould industry as well as in press tools, helicopter rotor blades, etc. Initially, Taguchi design of experiments was applied while conducting the experiments to reduce the time and cost of experiment. Multilayer perceptron (MLP) network using Feed Forward Error Back propagation was chosen as the neural network architecture to describe the process model. The experiments were conducted on a C.N.C milling machine using carbide cutters. Pearson correlation coefficient was also calculated to analyze the correlation between the system inputs and selected system output i.e. surface roughness. The results of ANN modeling were substantiated by testing and validation of the resulting surface roughness values and the results have been encouraging. The outputs of Pearson correlation coefficient also showed a strong correlation between the feed per tooth and surface roughness, followed by cutting speed.

Original languageEnglish
Pages (from-to)53-59
Number of pages7
JournalJournal of Engineering and Applied Sciences
Volume5
Issue number11
Publication statusPublished - 01-01-2010

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Chromium
Machining
Surface roughness
Neural networks
Steel
Helicopter rotors
Milling machines
Milling (machining)
Experiments
Multilayer neural networks
Network architecture
Backpropagation
Design of experiments
Turbomachine blades
Carbides
Testing
Costs
Industry

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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Development of a surface roughness prediction system for machining of hot chromium steel (AISI H11) based on artificial neural network. / Rai, Rajesh; Kumar, Arun; Rao, Shrikantha S.; Shriram.

In: Journal of Engineering and Applied Sciences, Vol. 5, No. 11, 01.01.2010, p. 53-59.

Research output: Contribution to journalArticle

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