Hybrid Modelling and Optimization of the Oblique Cutting of AISI 1045 Steel

Abstract

Optimization is a very important issue in mechanical industry, especially in machining processes, where different aspects must be considered. Thus, selecting the most proper cutting conditions plays a key role for obtaining efficient and competitive products. This article proposes a hybrid approach for modelling and optimizing the oblique turning processes. Analytical modelling and statistical regressions are combined for predicting the values of the most important parameters involved in the oblique cutting process. The predictions of the model were validated by using experimental data, showing coincidence for a 95%-confidence level. Then, an a posteriori multi-objective optimization is carried out by using a genetic algorithm. Two important and conflicting objectives are simultaneously considered: unit cutting time and tool wear rate, which describe the productivity and tool waste, respectively. The outcome of the optimization process is a set of non-dominated solutions, which are optimal in the wide sense that no other solution in the search space can improve one objective without worsen the other one. Finally, the decision-maker chooses the most convenient solution depending on the actual workshop conditions.