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Artificial neural network approach to predict ion nitrided case depth and surface hardness of AISI 4340 steel

  • Sule Y. Sirin and Melih İnal
Published/Copyright: May 28, 2019
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Materials Testing
From the journal Materials Testing Volume 61 Issue 6

Abstract

Since surface hardness affects wear resistance and case depth affects fatigue strength, the optimum value of both is extremely important with respect to the area of use. The aim of this study was to investigate the possibility of predicting case depth and surface hardness in ion nitrided AISI 4340 steels as a function of process time and temperature by using artificial neural networks and to obtain useful case depth and surface hardness data from an artificial neural networks model. Two projections were created for ion nitrided case depth and surface hardness, both depending on process time and temperature, and the conclusion was reached that the experimental data provides sufficient predictability regarding the artificial neural networks model. In the multilayer perceptron artificial neural networks architecture designed, two outputs (case depth and surface hardness) were determined in the same network according to the inputs, thus providing the integrity of the system characterization. The system was created by means of a Matlab simulink graphical user interface, which determined the artificial neural networks outputs according to the specified input with the purpose of visualizing the process. Different input values could be entered for visually determining the output values of the process.

*Correspondence Address, Prof. Dr. Melih İnal, Department of Informatics, Kocaeli University, Umuttepe Campus, 41380 Kocaeli, Turkey, E-mail: ,

Assist. Prof. Dr. Sule Yildiz Sirin, born in 1970, graduated from the Department of Mechanical Engineering at Yıldız Technical University, Istanbul, Turkey in 1992. She received her MSc and PhD degrees in the Mechanical Engineering Department of Kocaeli University, in 1996 and 2004, respectively. She is presently employed as Assistant Professor at the Vocational School of Asım Kocabıyık, Kocaeli University, Kocaeli, Turkey. Her research areas are the mechanical properties of materials, welding and surface engineering.

Prof. Dr. Melih İnal, born in 1971, graduated from the Electronics and Computer Education Department of Marmara University, Istanbul, Turkey in 1993. He received his MSc degree at the Electronics and Computer Education Department of Kocaeli University, Turkey in 1996. He received his PhD degree in Electrical Education from that university in 2001. He is presently Professor in the Department of Informatics at Kocaeli University Kocaeli, Turkey. His research areas are pattern recognition, soft computing, machine learning and information systems.

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Published Online: 2019-05-28
Published in Print: 2019-06-01

© 2019, Carl Hanser Verlag, München

Articles in the same Issue

  1. Inhalt/Contents
  2. Contents
  3. Fachbeiträge/Technical Contributions
  4. Investigation of phase transformations in mill scales for the purification process
  5. Exposure response function for a quantitative prediction of weathering caused aging of polyethylene
  6. Mechanical properties characterization of resistance spot welded DP1000 steel under uniaxial tensile tests
  7. Corrosion failure analysis of a perforated F32 reactor
  8. Prediction of mechanical properties of Al6061 metal matrix composites reinforced with zircon sand and boron carbide
  9. Tribological behavior of a hydrostatically extruded ultra-fine grained Ti-13Nb-13Zr alloy
  10. Characterization of boronized AISI 1050 steel and optimization of process parameters
  11. Experimental investigation on mechanical properties of AA7075-AlN composites
  12. Modeling of machining parameters for MRR and TWR in EDM characteristics on Al/10 wt.-% TiB2 composites
  13. Artificial neural network approach to predict ion nitrided case depth and surface hardness of AISI 4340 steel
  14. Theoretical and experimental investigation of stress distribution in a crane hook
  15. Mechanical properties, degradation and flue gas analysis of basalt and glass fiber reinforced recycled polypropylene
  16. Corrosion behavior of a precipitation hardened Ni–Cr–Co–Mo alloy under partial slagging coal gasification conditions
  17. Effect of different loading systems on acousto-ultrasonic characteristics of concrete under axial compression
https://doi.org/10.3139/120.111356

Articles in the same Issue

  1. Inhalt/Contents
  2. Contents
  3. Fachbeiträge/Technical Contributions
  4. Investigation of phase transformations in mill scales for the purification process
  5. Exposure response function for a quantitative prediction of weathering caused aging of polyethylene
  6. Mechanical properties characterization of resistance spot welded DP1000 steel under uniaxial tensile tests
  7. Corrosion failure analysis of a perforated F32 reactor
  8. Prediction of mechanical properties of Al6061 metal matrix composites reinforced with zircon sand and boron carbide
  9. Tribological behavior of a hydrostatically extruded ultra-fine grained Ti-13Nb-13Zr alloy
  10. Characterization of boronized AISI 1050 steel and optimization of process parameters
  11. Experimental investigation on mechanical properties of AA7075-AlN composites
  12. Modeling of machining parameters for MRR and TWR in EDM characteristics on Al/10 wt.-% TiB2 composites
  13. Artificial neural network approach to predict ion nitrided case depth and surface hardness of AISI 4340 steel
  14. Theoretical and experimental investigation of stress distribution in a crane hook
  15. Mechanical properties, degradation and flue gas analysis of basalt and glass fiber reinforced recycled polypropylene
  16. Corrosion behavior of a precipitation hardened Ni–Cr–Co–Mo alloy under partial slagging coal gasification conditions
  17. Effect of different loading systems on acousto-ultrasonic characteristics of concrete under axial compression
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