6533b7d4fe1ef96bd1261f82
RESEARCH PRODUCT
Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks
Liborio CavaleriPanagiotis G. AsterisAthanasia D. SkentouMaria G. DouvikaNikolaos M. VaxevanidisP. Psyllakisubject
Artificial neural networkComputer science0211 other engineering and technologiesMechanical engineering02 engineering and technologyengineering.materiallcsh:Technologylcsh:ChemistrySoft computing technique0202 electrical engineering electronic engineering information engineeringGeneral Materials Sciencesoft computing techniquesInstrumentationlcsh:QH301-705.5021101 geological & geomatics engineeringFluid Flow and Transfer ProcessesArtificial neural networklcsh:TProcess Chemistry and Technologyartificial intelligence techniquesGeneral EngineeringArtificial intelligence techniqueTribologyTribological performancelcsh:QC1-999Computer Science Applicationslcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Tool steelengineering020201 artificial intelligence & image processinglcsh:Engineering (General). Civil engineering (General)artificial neural networkslcsh:PhysicsTribometerHardening (computing)description
The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use. Furthermore, the achieved optimum ANN model seemed to be a simple and helpful design/educational tool, which could assist both in educational seminars, as well as in the interpretation of the surface treatment effects on the tribological performance of tool steels.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-07-11 |