Modelling cutting instability in rough turning 34CrNiMo6 steel

Juho Ratava, Mika Lohtander and Juha Varis

International Journal of Operational Research, 2016, vol. 25, issue 4, 518-531

Abstract: To maximise rough turning efficiency, using robust constant parameters or constant measured parameter adaptive control is not enough, but true adaptive control is needed. In order to safely optimise volume removal rate, it is necessary to model the cutting instability appearing at high levels of feed rate. This allows the prediction of the phenomenon and thus use of maximal cutting values while maintaining safe and controlled operation at all times by applying adaptive control. In this paper, various models are studied based on cutting parameters, sensor data and a combination of both. The capabilities of the models to classify cutting samples captured from the machining process are then examined and a model suitable for cutting condition prediction is recommended.

Keywords: rough turning; cutting instability; statistical modelling; adaptive control; 34CrNiMo6 steel; volume removal rate; cutting parameters; sensor data; depth of cut; feed rate; cutting speed. (search for similar items in EconPapers)
Date: 2016
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