USE OF ARTIFICIAL NEURAL NETWORK IN PREDICTING THE WEAR RESISTANCE OF SOME IRON-BASED POWDER METALLURGY MATERIALS SUBJECTED TO A THERMOCHEMICAL TREATMENT

2019 ◽  
Author(s):  
Mihaela Marin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wear Resistance ◽  
Powder Metallurgy ◽  
Thermochemical Treatment ◽  
Iron Based ◽  
Artificial Neural

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

2018 ◽  
Vol 18 (2) ◽  
pp. 111-115
Author(s):  
Hassan Abdoos ◽  
Ahmad Tayebi ◽  
Meysam Bayat
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Wear Resistance ◽  
Wear Behavior ◽  
Affecting Factors ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Comparison Of The Results

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.

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