Argatov, Ivan I.Chai, Young S.2021-03-292021-03-292021-04-011350-6501https://depositonce.tu-berlin.de/handle/11303/12897http://dx.doi.org/10.14279/depositonce-11698This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.A widely used type of artificial neural networks, called multilayer perceptron, is applied for data-driven modeling of the wear coefficient in sliding wear under constant testing conditions. The integral and differential forms of wear equation are utilized for designing an artificial neural network-based model for the wear rate. The developed artificial neural network modeling framework can be utilized in studies of wearing-in period and the so-called true wear coefficient. Examples of the use of the developed approach are given based on the experimental data published recently.en620 Ingenieurwissenschaften und zugeordnete Tätigkeitenwear coefficientsliding wearartificial neural networkspecific wear ratealuminum alloy matrix compositesArticle2021-03-202041-305X