Prediction of Wear Behavior in Porous Sintered Steels: Artificial Neural Network Approach

Open access


Due to the increasing usage of powder metallurgy (PM), there is a demand to evaluate and improve the mechanical properties of PM parts. One of the most important mechanical properties is wear behavior, especially in parts that are in contact with each other. Therefore, the choice of materials and select manufacturing parameters are very important to achieve proper wear behavior. So, prediction of wear resistance is important in PM parts. In this paper, we try to investigate and predict the wear resistance (volume loss) of PM porous steels according to the affecting factors such as: density, force and sliding distance by artificial neural network (ANN). ANN training was done by a multilayer perceptron procedure. The comparison of the results estimated by the ANN with the experimental data shows their proper matching. This issue confirms the efficiency of using method for prediction of wear resistance in PM steel parts.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] Khorsand H. Abdoos H.: The mechanical behavior and fatigue of porous sintered steel K. N. Toosi university of Technology press Tehran Iran 2015

  • [2] Khorsand H. Abdoos H. Amirjan M.: Metallurgical Engineering Journal vol. 43 2011 p. 3

  • [3] Aliabadi A. Heydarzadeh Sohi M. Ghambari M. Sheikhi Moghadam K.: Advance Material vol. 7 2017 no. 28 p. 27

  • [4] Sahri SM. Ghayour H. Amini K. Naseri M. Morteza H. Rastegari H. Golparvar M. Javaheri V.: The effect of quench and temper treatment on microstructure characteristics and wear properties of a medium carbon-high chrome wear resistant steel Steel symposium Yazd Iran 2015

  • [5] Pourasiyayi H. Pourasiyayi H. Saghafiyan H.: Advance Process in Material Science Quarterly vol. 6 2012 no. 2 p. 71

  • [6] Fallahdoost H. Khorsand H. Eslami-Farsani R. Ganjeh E.: Materials &Design vol. 57 2014 p. 60

  • [7] Asrardel M.: Prediction of Combustion Dynamics in An Experimental Turbulent Swirl Stabilized Combustor with Secondary Fuel Injection. M.Sc. Thesis. Tehran : University of Tehran 2015

  • [8] Matlab R2016a/Help / tangsig

Journal information
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 209 209 3
PDF Downloads 118 118 3