@ARTICLE{Hawryluk_M._Application_2020,
 author={Hawryluk, M. and Mrzygłód, Barbara and Gronostajski, Z. and Głowacki, M. and Olejarczyk-Wożeńska, Izabela},
 volume={vol. 65},
 number={No 1},
 journal={Archives of Metallurgy and Materials},
 pages={193-200},
 howpublished={online},
 year={2020},
 publisher={Institute of Metallurgy and Materials Science of Polish Academy of Sciences},
 publisher={Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences},
 abstract={This article discusses the results of studies using the developed artificial neural networks in the analysis of the occurrence of the four main mechanisms destroying the selected forging tools subjected to five different surface treatment variants (nitrided layer, pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN). Knowledge of the forging tool durability, needed in the process of artificial neural network training, was included in the set of training data (about 800 records) derived from long-term comprehensive research carried out under industrial conditions. Based on this set, neural networks with different architectures were developed and the results concerning the intensity of the occurrence of thermal-mechanical fatigue, abrasive wear, mechanical fatigue and plastic deformation were generated for each type of the applied treatment relative to the number of forgings, pressure, friction path and temperature.},
 type={Article},
 title={Application of Artificial Neural Networks in the Analysis of Mechanisms Destroying Forging Tools},
 URL={http://journals.pan.pl/Content/114521/PDF/AMM-2020-1-24-Mrzyglod.pdf},
 doi={10.24425/amm.2019.131114},
 keywords={decision support system, durability of forging tools, artificial neural network, loss of material, wear},
}