scholarly journals Probing the subsurface lattice rotation dynamics in bronze after sliding wear

2019 ◽  
Vol 172 ◽  
pp. 6-11 ◽  
Author(s):  
W. Cai ◽  
P. Bellon ◽  
A.J. Beaudoin
Keyword(s):  
Sliding Wear ◽  
Lattice Rotation ◽  
Rotation Dynamics

Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network

2020 ◽  
Vol 14 (2) ◽  
pp. 6789-6800
Author(s):  
Vishal Jagota ◽  
Rajesh Kumar Sharma
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Sliding Wear ◽  
Austenitizing Temperature ◽  
Tempering Temperature ◽  
Die Steel ◽  
Artificial Neural ◽  
Tempering Time

Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.

AISI 4140 X H13 STEEL SLIDING WEAR BEHAVIOR

2019 ◽  
Author(s):  
Eric Espíndola ◽  
Mateus José Araújo de Souza ◽  
BEATRIZ SEABRA MELO ◽  
Vinicius Silva dos Reis ◽  
Clóvis Santana ◽  
...  
Keyword(s):  
Sliding Wear ◽  
Wear Behavior ◽  
H13 Steel ◽  
Aisi 4140

METHOD FOR LUBRICATED SLIDING WEAR PARTICLE CHARACTERIZATION

2019 ◽  
Author(s):  
Alessandro Augusto Olimpio Ferreira Vittorino ◽  
Túlio Alves Rodrigues ◽  
Marco Aurélio Freitas Santos Júnior ◽  
Washington Martins da Silva Jr.
Keyword(s):  
Sliding Wear ◽  
Wear Particle ◽  
Particle Characterization ◽  
Lubricated Sliding

Dry sliding wear behavior of aluminum graphene nanoplatelet (GNP) composites

2016 ◽  
Vol 58 (7-8) ◽  
pp. 640-643 ◽  
Author(s):  
Ilyas Istif ◽  
Mehmet Tunc Tuncel
Keyword(s):  
Sliding Wear ◽  
Wear Behavior ◽  
Dry Sliding Wear ◽  
Dry Sliding ◽  
Graphene Nanoplatelet
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