<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Faculty of Material Engineering and Metallurgy

Powder Metallurgy Progress

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

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

Data publikacji: 19 gru 2018

Zakres stron: 111 - 115

DOI: https://doi.org/10.1515/pmp-2018-0012

Słowa kluczowe
Wear Behavior, Powder Metallurgy Steels, Artificial Neural Network

© 2018 Hassan Abdoos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Data publikacji: 19 gru 2018

Zakres stron: 111 - 115

DOI: https://doi.org/10.1515/pmp-2018-0012

Słowa kluczoweWear Behavior, Powder Metallurgy Steels, Artificial Neural Network

© 2018 Hassan Abdoos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Wear Behavior, Powder Metallurgy Steels, Artificial Neural Network