Çakan, AhmetEvrendilek, FatihÖzkaner, Vedat2021-06-232021-06-2320151392-1207https://doi.org/10.5755/j01.mech.21.6.12199https://hdl.handle.net/20.500.12491/8488Insurance of surface quality and dimensional tolerances in finish hard turning necessitates the development of accurate predictive models. This study aimed at modeling flank wear of multilayer-coated carbide inserts in finish dry hard turning of AISI 4340 and AISI 52100 hardened steels based on 28 artificial neural networks (ANNs) and the best-fit multiple non-linear regression (MNLR) model. Online-monitored flank wear of multilayer-coated carbide inserts was modeled as a function of the three cutting speeds of 70, 98 and 142 m min(-1), and the two workpieces under the constant feed rate and cutting depth of 0.027 mm min(-1) and 0.2 mm, respectively. Out of the 28 ANNs, 18 ANNs appeared to be capable of better predictions for tool flank wear than the best-fit MNLR model. Probabilistic neural network (PNN) outperformed all the remaining models based on all the training, cross-validation and testing dataset-related performance metrics.eninfo:eu-repo/semantics/openAccessCarbide ToolsOnline MonitoringData-Driven ModelingFinish TurningArticle10.5755/j01.mech.21.6.1219964864922-s2.0-84955083347Q4WOS:000369210700009Q4