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

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M. Marin;F. B. Marin;C. Gurau;G. Gurau
10.5593/sgem2019/2.1/S07.090
1314-2704
978-619-7408-79-9
English
19
2.1
Informatics
In this paper, the artificial neural network (ANN) technique is used to predict the wear resistance e under dry sliding conditions of some iron-based powder metallurgy materials subjected to a thermochemical treatment. The analyzed specimens were obtained using the conventional powder metallurgy route: mixing the raw powders with 1% zinc stearate, cold compacting at a pressure of 400 MPa and sintering at a temperature of 1150°C for 60 minutes. Following the sintering step, the specimens were subjected to carburizing in fluidized bed in a laboratory furnace at 930°C for 30 and 60 minutes. The compacts disc dimensions obtained are O8x6 mm. Density, porosity, particle size and carburizing time were used as the inputs parameters. Wear rate and hardness were used as the outputs parameters.The wear tests results were compared, analysed and predicted by applying the ANN technique. It is observed that the ANN predicted values are in good agreement with the experimental values obtained. The results show that ANNs can be use as an alternative technique to evaluate the wear rate and harndess values of the materials and also can reduce the large experimental work. The applications of proposed model not only reduce the overall experimental test but also reduce the cost by optimizing the process parameters.
conference
https://www.sgem.org/index.php/jresearch-article?citekey=Marin20197689696
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19th International Multidisciplinary Scientific GeoConference SGEM 2019
19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference-SGEM
Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
689-696
30 June - 6 July, 2019
website
cdrom
5412
artificial neural network; prection; wear resistance; powder metallurgy.