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Data-driven simulations of flank wear of coated cutting tools in hard turning
(Kaunas Univ Technol, 2015) Cakan, A.; Evrendilek, F.; Ozkaner, V.
Insurance 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.