A hybrid analytical-neural network approach to the determination of optimal cutting conditions

In the contribution, a new hybrid optimization technique for complex optimization of cutting parameters is proposed. The developed approach is based on the maximum production rate criterion and incorporates 10 technological constraints. It describes the multi-objective technique of optimization of cutting conditions by means of the artificial neural network (ANN) and OPTIS routine by taking into consideration the technological, economic and organizational limitations. The analytical module OPTIS selects the optimum cutting conditions from commercial databases with respect to minimum machining costs. By selection of optimum cutting conditions, it is possible to reach a favourable ratio between the low machining costs and high productivity taking into account the given limitation of the cutting process. To reach higher precision of the predicted results, a hybrid optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. Experimental results show that the proposed optimization algorithm for solving the nonlinear-constrained programming problems (NCP) is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems. To demonstrate the procedure and performance of the proposed approach, an illustrative example is discussed in detail.