Title: The use of artificial neural network for the prediction of wear loss of aluminium-magnesium alloys
Authors: T. Hariprasad; D. Shivalingappa; A. Nagaraj; Geetha Manivasagam
Addresses: Department of Mechanical Engineering, Adhiyamaan College of Engineeing, Dr. M.G.R. Nagar, Hosur, Krishnagiri District, Tamil Nadu, 635 109, India ' Department of Mechanical Engineering, Adhiyamaan College of Engineeing, Dr. M.G.R. Nagar, Hosur, Krishnagiri District, Tamil Nadu, 635 109, India ' Department of Mechanical Engineering, Podhigai College of Engineering and Technology, State Highway 18, Adiyur, Tirupattur, 635601 Tamil Nadu, India ' School of Mechanical and Building Sciences, VIT University, Vellore Campus, Vellore – 632 014, Tamilnadu, India
Abstract: This paper reports on the effectiveness of a back-propagation artificial neural network model that predicts the wear loss of Al-Mg alloys samples. Artificial neural networks (ANNs) have the capacity to eliminate the need for expensive and difficult experimental investigation in testing and manufacturing processes. This paper shows that ANN can be employed for optimising the process parameters of aluminium alloys. The ANN predictions show very good agreement with experimental values with correlation coefficient of 0.823, thus ANN can be considered an excellent tool for modelling complex processes that have many variables.
Keywords: wear loss; artificial neural networks; ANNs; process parameters; aluminium alloys; magnesium; modelling.
International Journal of Computer Aided Engineering and Technology, 2015 Vol.7 No.1, pp.72 - 80
Published online: 04 Dec 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article