Optimizing machining economics models of turning operations using the scatter search approach

Improving performance of metal-cutting operations can lead to considerable savings. In the area of machining optimization, the determination of optimal machining parameters is a substantial problem. The complexity of machining economics problems presents difficulties for some optimization techniques. Scatter search is one of the optimization techniques recently developed in the area of metaheuristics. It is a population-based methodology that shares features with evolutionary methods. It has proved highly successful when solving a diverse array of complex optimization problems. The paper focuses on the application of scatter search to resolve the machining economics models of turning operations. By using several turning models, the computational investigation of scatter search was done by comparison with other optimization methods. The experimental results show the effectiveness of scatter search in the machining economics problems.