The purpose of this paper is to realize an effective hybrid modeling (empirical-geometric) in order to describe the real behavior of the average roughness variation of the workpiece surface in turning with an elementary operation of superfinishing, using different analytic methodologies. The previous works are limited to describe the roughness for the usual elementary operations, citing the roughing and the semi-finishing, while this analysis builds technical rails for the industrialists in order to well conduct the operation of superfinishing in turning, by choosing the cutting parameters from the proposed model.
A statistical analysis of the average roughness measurements capability study, by the statistical process control method SPC and the ANN artificial neuron network, Levenberg–Marquardt's methods modified Monte Carlo SRM response surface.
The objective of this work was to describe the average roughness generated by the penetration of the cutting tool into a part in superfinishing turning. First, the authors used artificial colony analysis to determine optimal cutting conditions in order to have an average roughness lower than 0.8 µm. The cutting conditions selected: (1) the feed rate f ϵ [0.05; 0.2] mm/rev; (2) the pass depth ap ϵ [0.25; 1] mm; (3) the corner radius re = 0.2 mm and (4) cutting speed Vc ϵ [75; 100] m/min.
This work consists to realize an effective hybrid modeling (empirical-geometric) in order to describe the real behavior of the average roughness variation of the workpiece surface in turning with an elementary operation of superfinishing, using different analytic methodologies. The previous works are limited to describe the roughness for the usual elementary operations, citing the roughing and the semi-finishing, while this analysis builds technical rails for the industrialists in order to well conduct the operation of superfinishing in turning, by choosing the cutting parameters from the proposed model.
