Affiliation:
1. University of Matanzas, Cuba
2. Pontifical Catholic University of Chile, Chile & University of Matanzas, Cuba
3. Fusecon NV, Suriname
Abstract
This chapter 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. Three conflicting objectives, which represent the three pillars of the sustainability as defined in the triple bottom line, are simultaneously considered: the carbon dioxide emissions, the cost, and the cutting time. The outcome of the optimization process is a set of non-nominate solutions, which are optimal in the wide sense that no other solution in the search space can improve one objective without worsening the other one. Finally, the decision-maker chooses the most convenient solution depending on the actual workshop conditions.