Modern engineering problems are often composed by a large number of variables that must be chosen simultaneously for better design performance. The optimization of phenomenological model is an impossible task in terms of computational time. To improve this disadvantage, the Response Surface Methodology (RSM), defined as a collection of mathematical and statistical methods that are used to develop, to improve, or to optimize a product or process, is configured as important alternative to model real process. In the literature, different approaches based on optimization methods have been proposed to design system engineering. In this context, the Differential Evolution algorithm (DE) is a stochastic optimization method that is based on vector operations to improve a candidate solution with regard to a given measure of quality. For illustration purposes, in the present contribution the DE is applied to optimize multiple correlated responses in a turning process. As a case study, the turning process of the AISI 52100 hardened steel is examined considering three input factors: cutting speed, feed rate and depth of cut. The outputs considered were: the mixed ceramic tool life, processing cost per piece, cutting time, the total turning cycle time, surface roughness and the material removing rate. The optimization of cutting speed, feed rate and depth of cut indicate the better configuration of process to minimize the cost.
Optimal control of fed-batch fermentation involving multiple feeds using Differential Evolution
