Ann Analysis of Wear Behaviour of Plasma Sprayed Iron Aluminide Coating


  • Rojaleena Das National Institute of Technology, Rourkela
  • Anupama Sahu National Institute of Technology, Rourkela
  • Chaithanya M National Institute of Technology, Rourkela
  • Mishra S.C National Institute of Technology, Rourkela
  • Alok Satapathy National Institute of Technology, Rourkela
  • Ananthapadmanabhan P.V Laser & Plasma Technology Division, B.A.R.C., Mumbai
  • Sreekumar K.P Laser & Plasma Technology Division, B.A.R.C., Mumbai


Neural Network, Solid particle erosion, Iron aluminide coating, Plasma Spraying


Intermetallic compounds find extensive use in high temperature structural applications. The Fe3Al based intermetallic alloys offer unique benefits of excellent oxidation and sulfidation resistance at a potential cost lower than many stainless steels. Plasma spraying is considered as a non-linear problem with respect to its variables: either materials or operating conditions. To obtain functional coating exhibiting selected in-service properties, combinations of processing parameters have to be planned. These combinations differ by their influence on the coating properties and characteristics. To control the spraying process, one must recognize the parameter interdependencies, correlations and individual effects on coating characteristics. This paper proposes a mathematical technique based on neural computations to study the effects of process variables on the wear behavior of iron-aluminide coatings made by plasma spraying. ANNs are excellent tools for complex processes that have many variables and complex interactions. The analysis is based on an Artificial Neural Network (ANN) taking into account training and test procedure to predict the dependence of erosion wear behavior on angle of impact and velocity of erodent. This technique helps in saving time and resources for experimental trials.


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J. Z. Chen, H. Herman and S. Safai, Evaluation of NiAl and NiAl-B Deposited by Vacuum Plasma Spray, J. Thermal Spray Technology, 2, 357 (1993)

C. T. Liu and V. K. Sikka, Nickel Aluminides for Structural Uses, J. of Metals, 38, 13(1986)

Robert B.Hiemann, Plasma-Spray Coating-Principles and Applications, VCH Publishers Inc., New York, NY, USA, (1996)

José Roberto Tavares Branco, Robert Gansert, Sanjay Sampath Christopher C. Berndt, Herbert Herman, 2004, Solid Particle Erosion of Plasma

Toosi, M., and Zhu, M., 1995, An overview of acoustic emission and neural networks technology and their applications in manufacturing process control. Journal of Industrial Technology, 11(4), 22-27.

Hubick, K., 1992, ANNs thinking for industry. Process & Control Engineering, 15(11),36-38.

Hoskins, J. C., and Himmelblau, D. M., 1992, Process control via artificial neural networks and reinforcement learning. Computers & Chemical Engineering, 16(4), 241-251.

Coit, D. W., and Smith, A. E., 1995, Using designed experiments to produce robust neural network models of manufacturing processes. 4th Industrial Engineering Research Conference Proceedings, 229-238

Fan, H. T., and Wu, S. M., 1995, Case studies on modeling manufacturing processes using artificial neural networks. Journal of Engineering for Industry, 117, 412-417.

S.Rajasekaran , G. A. Vijayalakshmi Pai ,2003,--Neural Networks, Fuzzy Logic And Genetic Algorithm-Synthesis & Applications-Prentice Hall of India Pvt.Ltd. ,New Delhi

Z. Zhang, K. Friedrich, Artificial neural network applied to polymer composites: a review, Comp. Sci. Technol., in press.

V. Rao and H. Rao , 2000, ‘C++ Neural Networks and Fuzzy Systems’ BPB Publications




How to Cite

R. D., “Ann Analysis of Wear Behaviour of Plasma Sprayed Iron Aluminide Coating ”, JME, vol. 3, no. 2, pp. 78–81, Jun. 2008.




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