Modelling and optimization of turning processes for slender parts

Abstract

In this paper, an abductive network is adopted in order to construct a prediction model for surface roughness and error-of-roundness in the turning operation of slender parts. The abductive network is composed of a number of functional nodes. These functional nodes are self-organized to form an optimal network architecture by using a predicted square error (PSE) criterion. Once the process parameters (workpiece length L, spindle speed n, feed rate f and depth of cut t) are given, the surface roughness and error-of-roundness can be predicted by this developed network. To verify the feasibility of the abductive network, regression analysis has been adopted to develop a second prediction model for surface roughness and error-of-roundness. Comparison of the two models indicates that the prediction model developed by the abductive network is more accurate than regression analysis. It can be found that the use of the abductive network for surface roughness and error-of-roundness is feasible. A simulated annealing optimization algorithm with a performance index is then applied to the developed network for searching the optimal process parameters. The optimal cutting condition can be obtained with the object of maximizing the metal removal rate and minimizing the surface roughness and error-of-roundness to the lowest/smallest extent permissible.