ANN-Based Wear Performance Prediction for Plasma Nitrided Ti6Al4V Alloy

Fatih Kahraman; Süleyman Karadeniz; Hülya Durmuş

doi:10.3139/120.110289

Article

ANN-Based Wear Performance Prediction for Plasma Nitrided Ti6Al4V Alloy

Fatih Kahraman , Süleyman Karadeniz and Hülya Durmuş

Published/Copyright: May 26, 2013

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From the journal Materials Testing Volume 54 Issue 1

Abstract

Surface modification of a Ti6Al4V titanium alloy was made by the plasma nitriding process. Plasma nitriding was performed in a constant gas mixture of 20% H₂–80% N₂ at temperatures between 700 and 1000° C and process times between 2 and 15 h. Samples nitrided at different treatment times and temperatures were subjected to the dry sliding wear test using the pin-on-disc set up under 80N normal load with rotational speed of counter face disc of 0.8 m/s at room conditions. An artificial neural network (ANN) model of was developed for prediction of wear performance of the plasma nitrided Ti6Al4V alloy. The inputs of the ANN model were processing times and temperatures, diffusion layer thickness, Ti₂N thickness, TiN thickness and hardness. The output of the ANN model was wear loss. The model is based on the multilayer backpropagation neural technique. The ANN was trained with a comprehensive dataset collected from experimental conditions and results of authors. The model can be used for the prediction of wear properties of Ti6Al4V alloys nitrided at different parameters. The ANN model demonstrated the best statistical performance with the experimental results.

Kurzfassung

Mittels Plasmanitrieren wurde die Oberfläche einer Ti6Al4V Legierung modifiziert. Der Plasmanitrierprozess wurde unter einem konstanten Gastrom mit 20% H₂ und 80 % N₂ bei 700 bis 1000° C über zwei bis 15 Stunden ausgeführt. Die bei den verschiedenen Temperaturen und Zeiten nitrierten Proben wurden im Trockenreibversuch im Stift-Scheibe-Versuch bei einer Normalkraft von 80 N und einer Rotationsgeschwindigkeit der Scheibe von 0,8 m/s bei Raumtemperatur untersucht. Um das Verschleißverhalten der plasmanitrierten Ti6Al4V Legierung vorhersagen zu können, wurde ein Artificial Neural Network (ANN) Model entwickelt. Die Eingangsgrößen für das ANN waren die Prozesszeiten und -temperaturen, die Dicke der Diffusionsschichten, der Ti₂N und der TiN sowie deren Härte. Die Ausgangsgröße war der Abrieb. Das Model basierte auf einem mehrschichtigen rückwärtspropagierenden ANN. Das ANN wurde mit einem umfangreichen Datenset trainiert, die aus den Ergebnissen der experimentellen Arbeiten der Autoren gewonnen wurden. Das Modell kann nunmehr für die Vorhersage der Verschleißeigenschaften von Ti6Al4V-Legierungen verwendet werden, die mit verschiedenen Parametern nitriert wurden. Das Modell wies die beste statistische Performanz im Vergleich zu den experimentellen Untersuchungen auf.

Fatih Kahraman, born in Kars, Turkey, 1979, graduated from Pamukkale University in 1998 with a degree in Mechanical Engineering. He completed his Master of Science in Mechanical Engineering from the Dokuz Eylül University in 2002. During that time, Mr. Kahraman has published articles. Then, in 2008, he completed his PhD at the Dokuz Eylül University.

Süleyman Karadeniz, born in Isparta, Turkey, 1946, graduated from İstanbul Technical University in 1969 with a degree in Electrical Engineering. He completed his Master of Science in Mechanical Engineering from the Braunschweig Technical University in 1976. Then, in 1981, he completed his PhD at the Hannover University.

Hülya Durmuş was born in Manisa, Turkey, 1977, and graduated from Pamukkale University in 1998 with a degree in Mechanical Engineering. She completed her Master of Science in Mechanical Engineering at the the Celal Bayar University in 2000. During that time, Mrs. Durmuş has published articles. Then, in 2006, she completed her PhD from Celal Bayar University.

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Published Online: 2013-05-26

Published in Print: 2012-01-01

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https://doi.org/10.3139/120.110289