Data-driven simulations of flank wear of coated cutting tools in hard turning
Keywords:carbide tools, online monitoring, data-driven modeling, finish turning
Insurance of surface quality and dimensional tolerances in finish 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 metrics.