Email this article A NOVEL APPROACH TO PREDICTING THE CUTTING FORCE IN TURNING USING DIMENSIONAL ANALYSIS
Abstract
Cutting forces are a critical indicator of the machining process, and their modeling is important for a variety of reasons, including tool life assessment, chatter prediction, tool condition monitoring, assessment of machining strategies and machining process optimization and control. This paper presents a new principle of modeling the main cutting force using dimensional analysis (DA). Dry longitudinal single-pass turning of two different steels (20MnCrS5 and S235JRG2) with two different cutting inserts, was considered. Taguchi's 33×21 design with 6 trials was applied to arrange seven parameters: depth of cut, feed rate, cutting speed, feed velocity, rake angle, cutting edge angle, and workpiece material parameter, i.e., tensile strength. The obtained results, including additional validation tests, showed a very good prediction capacity of the DA-based model in estimating the cutting force during the turning process. The analysis includes the influence of parameters on the cutting force as well as examination of 3D surface diagrams and correlation coefficients. The chip slenderness ratio proved to be the most important dimensionless group for cutting force prediction. By performing additional experimental trials, the correction coefficient for the tool nose radius was estimated and extended models were developed. The well-known Victor-Kienzle model can be used to predict the cutting force if the exact values of mc and kc1.1 coefficients. The proposed DA-based models proved to be valid for predicting all three cutting force components with high accuracy.
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